Faculty Mechanical, Maritime

FACULTY MECHANICAL, MARITIME AND MATERIALS ENGINEERING Delft University of Technology Department Marine and Transport Technology Mekelweg 2 2628 CD Delft the Netherlands Phone +31 (0)15-2782889 Fax +31 (0)15-2781397 www.mtt.tudelft.nl

Specialization: Transport Engineering and Logistics

Report number: 2015.TEL.7918

Title: Designing and Modelling of Large Scale GLARE Production System from Lean Perspective

Author: K.B.Chhadva

Assignment: Master Thesis

Confidential: Yes (Permanent)

Professor (TU Delft): Prof.dr.ir. G. Lodewijks

Supervisor 1 (TU Delft): Dr. W.W.A. Beelaerts van Blokland

Supervisor 2 (TU Delft): Dr. Michel Oey

Supervisor (Company): Ir. Harry Gharbharan (Fokker Aerostructures, Papendrecht)

Date: July 8, 2015

This report consists of 146 pages and 7 appendixes. It may only be reproduced literally and as a whole for commercial purposes only with written authorization of Delft University of Technology. Requests for consult are only taken into consideration under the condition that the applicant denies all legal rights on liabilities concerning the contents of the advice.

Preface

This thesis is the result of Master of Science graduate research performed at Fokker Aerostructures in Papendrecht, The Netherlands. It is performed as a part of the graduation program for study of Transport Engineering & Logistics (TEL) at the Delft University of Technology. The assignment was to design the production system for production of GLARE panels of A320 neo family aircraft.

First of all I would like to thank Dr. Wouter Beelaerts van Blokland, Dr. Michel Oey, Ir. Harry Gharbharan and Ir. Leo Muijs for their help and guidance during this research. Also, I would like to thank Prof. G. Lodewijks for participation in the graduation committee. Furthermore, I would like to thank Erik van Meer, Aart Kraaij, Leo Meijers, Marko Bosman, Pim Tamis, Faisal Rajabali and Sander Sinkgraven of Fokker Aerostructures for their support during this research.

Delft, June 2015

Kaushal Chhadva

i Executive Summary

Problem Definition

Fokker Aerostructures is a well-known company involved in the design, development and manufacturing of lightweight structures and modules for the aerospace and defense industry. One of the products of the Fokker Aerostructures is GLARE, a fiber metal laminate made up of glass fibers and aluminum. Currently, Fokker Aerostructures produces GLARE panels for the Airbus A380 aircraft. The current production rate is of 30 shipsets with total 660 panels per year. It is expected that there will be a soaring demand for the GLARE panels, once Airbus start using it for their new A320 and A321 neo aircraft. Currently Airbus and Fokker are working on project called “GLARE Automation” to check the feasibility of use of GLARE for A320 and A321 neo aircraft. The Airbus and Fokker expects the future demand of GLARE would be of 300 shipsets per year. As the current production system is not capable enough to cope with such high rate production demands. The challenge of Fokker Aerostructures is to design a new large scale production system for the production of GLARE panels. Besides adjusting the production from small scale production to large scale production, Fokker Aerostructures is aiming to decrease the recurrent costs of GLARE production.

Objective and scope

The objective of this research is to design and model a lean production system for the GLARE panels which would be capable enough to cope up with the high demand rate emerging in future. Along with satisfying high demand the production system also needs to be automated to lower the operation costs and recurrent costs. For new production system it is made sure that the waiting time & Inventory between work stations is minimized by well-organized production process sequences and balanced production line. Moreover, the research provides information on number of work process station required for different processes, number of tools, location of buffer stations in future production system, conceptual production layout, etc. The main scope of research is limited to Work Process 3 (Layup and curing processes) and Work Process 4 (post curing processes).

Analysis and solutions

The starting step of research was to do literature research on GLARE development, benefit & application, production system and GLARE Automation project requirements of Fokker. Then the current production system of GLARE panel was analyzed by using tools like value stream mapping and spaghetti diagram. This gave more insight to constrained work stations, Inventory buffer stations & waste processes. The research shows that most constrained work stations are Layup and painting process stations. Moreover, these processes are manual and time consuming. If those processes are not modified than future production system would need 30 layup stations and 12 paint stations (based on simulation results), resulting to 290 FTE. Therefore, for the high volume future production system this two processes needs to be modified.

Moreover, lean wastes like over production, waiting times, inventory, transportation and defects exist in the current system. In current A380 system waiting times accounts to 62% of the total lead time, so the panel which had 5 days of processing time has 13 days of lead time. The main reason for waiting time is overproduction at the layup stations, batching for the autoclaves, unbalanced and push system based production line with buffer in between every processes. The defect rate in current system is 3.2%, out of which 2.3% defects are created due to the use of wrong process and mistakes at the layup process station. Thus along with layup and paint process modifications, above discussed lean wastes also needs to be minimized.

ii Based on current system analysis, four main modifications are suggested for the future production system: Layup process modifications - For reducing non-value adding activities like tool cleaning alternatives like automated laser cleaning & degreasing system needs to be used, for eliminating the use of plastic foils, semi-permanent or permanent mold release agents can be used. For main activities like the layup of aluminum sheets and prepreg, automated pick and place robots and tape laying end effectors needs to be used. The aluminum pick and place system is currently in experimentation at the Fokker test facility, results of the pick and place experiments are used in this research. For stringer layup, 2 concepts are considered: Stringer positioning head concept (Jig) and pick and place of individual stringers. Its speeds were obtained from PAG, which has similar automated cells. From the analysis results, it became clear that both concepts need same number of process stations, so it is recommended to use pick and place stringer system as it is well developed system, whereas stringer head concept would need further research. For doublers layup it is proposed to do it manually, as 13 out of 30 panel’s needs doublers and process is less time consuming and less complex. To reduce the time on main production line, doublers should be prepared in advance on feeding line. For the prepared doublers average layup time/panels would be 1.3 hours. By all this modifications average cycle time of layup of a panel can be reduced from 27.8 to 10.1 Hours.

Paint process modifications - It is also suggested to change the painting process from manual process to separate semi-automated processes arranged on continuous line, as it will help in reducing the non-value adding activities and reduce waiting times and lead times. The method to automate sanding and painting operations were researched. For reducing the drying time force drying by ovens needs to be used. Force drying can be done by convection oven or infrared oven, this research proves that infrared oven would be more advantageous. By these modifications, average painting process times can be reduced from 12.2 hours to 5.4 hours and total drying time can be reduced from 40 hours to 1.2 hours, thus lead time reduction of 45.6 hours. Autoclave modifications - To reduce the waiting times of the panel before and after the autoclave process, autoclave batching process needs to be changed. For that it is proposed to use smaller autoclaves, as that will reduce batching size. Also the arrangement of panels in the autoclave: stacked arrangement and panels placed next to each other were considered for analysis; it turned out that the stacked arrangement will lead to more autoclave space utilization and less investment costs. The number of autoclave and their size influence of queuing pattern are analyzed by simulation model (results are shown in section 6). Last modification is to use Flexible workers (dynamic allocation of workers) instead of fixed workers to balance the production line and these decreases the waiting times of panels and also increases the utilization of workers.

The semi-automated production line is designed based on above discussed modifications, by arranging the processes in proper sequence. The numbers of work stations are calculated for the processes using the discrete event simulation model. Then in order to achieve continuous smooth flow on the production line and to reduce the unnecessary waiting times, production line balancing is done in simulation model by the using alternative like flexible workers and autoclave batching modifications. Based on the results of line balancing, locations of push pull decoupling point in the production system is decided. The future production system is represented by future stream map (shown in Fig 134). Finally, before finalizing the layout its flexibility to product mix variations, demand variations and product type variation is checked by modelling these scenarios. Based on above considerations and factory design requirements like resource arrangement types, space requirements for transport movements, space for workstations, space for buffers & extra space for flexibility; conceptual production system layout is designed (shown in Fig 135). Then to check the benefits of the modifications, KPIs are calculated and compared with basecase scenario (based on A380 system). The detailed comparison of KPIs is explained in section 6.3; it shows promising results with high continuous flow (83.5%), reduced inventory (from 117 to 46 panels), factory space reduction (14%) and recurrent costs reduction of A320 shipsets by 38.3% and for A321 shipsets by 35.6%.

iii Explanation of terms used in report

GLARE - Glass Laminate Aluminum Reinforced Epoxy. Fuselage panels produced by Fokker for the Airbus A380 are made of this material. It consists of layers of aluminum and prepreg sheets.

Panel - The fuselage of aircraft is made of fuselage panels (panels). Fokker produces 22 different panels for the Airbus A380. The fuselage panels made by Fokker are made up of GLARE material.

Aluminum sheet – GLARE is made up of 0.3 to 0.4 mm thin aluminum sheets. In the aluminum sheet production unit, one sheet is actually a nested sheet consisting of multiple sheets. These sheets are milled out of the nested sheet by a milling machine before they are kitted and sent to layup.

Prepreg - Prepreg is a sheet of glass fibers that are impregnated with adhesive. In the autoclave process, the impregnate melts and sticks to the glass fibers and aluminum sheets.

Shipset - All the panels produced for one aircraft are called a shipset.

Stringer - A stringer is a thin strip of aluminum. The stringers are attached to the panel. Later in the assembly of the fuselage the stringers are attached to the airplane frame. Stringers direct the forces on the panels to the frame of the fuselage.

Doubler – A doubler is a small thin GLARE section. Doublers are used as stiffeners at high stress areas by using extra layers of GLARE panel. High stress areas are usually around door locations.

iv Table of Contents

1. Introduction ...... 1 1.1 Fokker Technologies ...... 1 1.2 GLARE ...... 2 1.2.1 GLARE Development ...... 2 1.2.2 GLARE Benefits ...... 3 1.2.3 GLARE Application ...... 3 1.2.4 Glare Production Processes ...... 4 1.3 Airbus A320 Family ...... 10 1.4 GLARE Automation Project ...... 12 1.5 Challenge for Fokker Aerostructure ...... 15 1.6 Research Objective ...... 15 1.7 Scope of Research...... 16 1.8 Research Question ...... 16 1.9 Research Approach ...... 16 1.10 Report structure ...... 17 2. Literature Survey & Methodology ...... 18 2.1 Lean Manufacturing ...... 18 2.1.1 Introduction ...... 18 2.1.2 Lean principles ...... 19 2.1.3 Lean Wastes ...... 19 2.1.4 Lean Tools ...... 20 2.2 Value Stream Mapping ...... 22 2.3 Takt Time ...... 24 2.4 Push – Pull System & Decoupling Point ...... 25 2.4.1 Push System ...... 25 2.4.2 Pull System ...... 25 2.4.3 Buffer Storage & Decoupling Point ...... 26 2.5 Theory of Constraints ...... 27 2.6 Production Layouts ...... 28 2.7 Analytic Hierarchy Process method (AHP) ...... 30

v 2.8 Simulation Model...... 32 2.8.1 Objective of Model ...... 32 2.8.2 Modelling Software ...... 32 2.9 Research Methodology ...... 34 2.9.1 Advantage of Integrated Approach ...... 36 3. Current Production Process Analyses ...... 37 3.1 Current Production Processes ...... 37 3.2 Results of VSM ...... 42 3.2.1 Takt Time ...... 43 3.2.2 Lean Wastes in current system ...... 46 3.3 Current Production system Layout ...... 48 3.4 Current Production system and Future Production system (similarity & difference) ...... 49 Conclusion from Current Production system analysis ...... 50 4. Future Production System ...... 51 4.1 Future System Requirements ...... 51 4.2 Layup process modifications ...... 53 4.2.1 Process Design ...... 54 4.2.2 Material handling on the assembly line ...... 62 4.2.3 Work in Process management ...... 62 4.2.4 Feeding production line ...... 62 4.2.5 Layup line Balancing and line layout ...... 71 4.2.6 Maintenance Policy ...... 73 4.3 Paint process modifications ...... 74 4.3.1 Process Modifications ...... 75 4.4 Autoclave Capacity Modifications ...... 82 4.5 Flexible workers for line balance ...... 86 Sub-Conclusion ...... 87 5. Modelling ...... 88 5.1 Input and output variables of the model ...... 88 5.2 Key Performance Indicators ...... 89 5.3 Working of the Simulation model ...... 92 5.4 Validation and Verification of the model ...... 92

vi 6. Results ...... 93 6.1 Alternative 1: Basecase based on A380 GLARE Technology ...... 94 6.1.1 Production with 5 days per week ...... 96 6.2 Alternative 2: Lean Future Production system ...... 102 A) Layup process modification ...... 105 B) Paint process modification ...... 108 C) Autoclave Size modification ...... 110 D) Flexible workers for work balance ...... 112 6.2.1 Production with 5 days per week ...... 114 6.3 Lean Key Performance Indicator Comparison ...... 120 6.4 Effect of Variations ...... 127 7. Production system layout ...... 130 8. Conclusion ...... 136 8.1 Conclusion ...... 136 8.2 Recommendations for further research ...... 141 8.3 Reflection on the research project execution ...... 142 Bibliography ...... 143

Appendix 1: Current production processes of GLARE ...... 166 Appendix 2: Value Stream Mapping activities details ...... 148 Appendix 3: Doublers Combination ...... 150 Appendix 4: AHP Model ...... 154 Appendix 5: Simulation Model ...... 156 Appendix 6: Cost Model ...... 158 Appendix 7: Simulation results ...... 160

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1. Introduction

1.1 Fokker Technologies

Fokker Technologies is a leading global aerospace specialist that designs, develops and manufactures highly engineered aircraft systems and provides through-life aircraft fleet support services. Their main advantage is there capability to approach aircraft systems design from the perspective of an aircraft manufacturer and understanding the aircraft and its operation as a whole. Headquartered in Papendrecht the Netherlands, Fokker Technologies has facilities in the Netherlands, Romania, Turkey, Canada, Mexico, USA, Singapore and China. In 2013 the group achieved revenue of €762 million with 4688 employees.

Fokker Technologies has following four business units:

 Fokker Aerostructures: light-weight aero structures i.e. tails, wing components, fuselage panels  Fokker Elmo: electrical wiring and interconnect systems  Fokker Landing Gear: landing gear  Fokker Services: aircraft and parts availability services

Fokker Aerostructure is one of the four business units of Fokker technologies. They are recognized, first-class specialist in the design, development and manufacturing of lightweight structures, modules and landing gear for the aerospace and defense industry. They are forefront in providing innovative solutions to major aircraft manufacturers such as Airbus, Boeing, Lockheed martin, Gulfstream and Dassault using advanced technology and materials like GLARE and thermoplastic composites.

The Aerostructure parts manufactured by Fokker have a recognized position in flaps and fuselage parts of Airbus A380, A340 & Boeing 747 & 748. For Dassault and Gulfstream business jets, Fokker manufactures tail sections & wing movables. Fokker Technologies is a partner in NH Industries, the consortium that designs, develops and produces the NH90 helicopter. They manufacture the tail structure, doors, landing gear and the intermediate gearbox of NH90 helicopter. Fokker also manufactures special products like launching units, mobile systems and shelters for Dutch Space, Royal Dutch Army and Lockheed Martin.

Commercial aircraft Business Jets Defense Helicopters Special Products

Large commercial aircraft Business Jet Defense Helicopter Special Products

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1.2 GLARE

GLARE is a "Glass Laminate Aluminum Reinforced Epoxy" composed of several very thin layers of metal (usually aluminum) combined with the layers of glass-fiber called "pre-preg" and bonded together with a matrix such as epoxy.

1.2.1 GLARE Development

Fiber Metal Laminates were developed at Delft University of Technology in The Netherlands by prof. L. B. Vogelesang and his team, as a family of hybrid materials that consist of bonded thin metal sheets and fibers embedded in adhesive, like rubber toughened epoxy (shown in Fig 1 & 2). The variant with glass fibers is called ‘GLARE’. It has excellent fatigue, impact and damage tolerance characteristics combined with low density. It represents the best of both worlds, i.e. composite and metallic materials. The metal sheets have a high bearing strength and impact resistance, while the fibers/epoxy layer act as a barrier against corrosion of the inner metal sheets and prevent rapid fatigue crack growth. The laminate has an inherent high burn-through resistance as well as good damping and insulation properties. The material can be produced as semi-finished sheet material. Like monolithic aluminum, this sheet can be machined and formed into products (Gunnink, Vlot, Vries, & Hoeven, 2000).

Fig. 1 Fiber Metal Laminates (AGY Materials, 2006) Fig. 2 GLARE Composition

GLARE consists of plies aluminum sheets interspersed with layers pre-impregnated glass fibers, as illustrated in Fig 2. These glass fibers layers supplied in a pre-impregnated layer, is called prepreg. The fibers in a prepreg are impregnated with a toughened epoxy adhesive system. After lay-up of the laminate in a mold, the whole laminate is cured in an autoclave under controlled temperature and pressure conditions. This process yields the ready-to-use FML sheet.

In August 1997 a large project was initiated in The Netherlands by Structural Laminate Industries, called the GLARE Technology Development. The aim of this project was to commercialize GLARE and develop it from laboratory material towards a large-scale application in aircraft design, production and airline operation. Fokker Aerostructures produces the test panels for this project on an industrial base. In 1999 the project was successfully finished, with Fokker Aerostructures being able to produce GLARE panels for the aviation industry. GLARE materials are currently commercially available in six different standard grades, an overview is provided in Table 1. The number of prepreg layers per fiber layer and the orientation of the fibers define the type of GLARE. The thickness of the aluminum layers varies between 0.2 - 0.5 mm (Vlot & Gunnink, 2001).

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Table 1 GLARE Types (Vlot & Gunnink, 2001)

Grade Sub Metal Sheet Thickness Prepreg Characteristics [mm] & Alloy orientation GLARE 1 -- 0.3 - 0.4 & 7475-T761 0/0 Fatigue, strength, yield stress GLARE 2 GLARE 2A 0.2 - 0.5 & 2024-T3 0/0 Fatigue, strength GLARE 2B 0.2 - 0.5 & 2024-T3 90/90 Fatigue, strength GLARE 3 -- 0.2 - 0.5 & 2024-T3 0/90 Fatigue, Impact GLARE 4 GLARE 4A 0.2 - 0.5 & 2024-T3 0/90/0 Fatigue, strength in 0° direction GLARE 4B 0.2 - 0.5 & 2024-T3 90/0/90 Fatigue, strength in 90° direction GLARE 5 -- 0.2 - 0.5 & 2024-T3 0/90/90/0 Impact GLARE 6 GLARE 6A 0.2 - 0.5 & 2024-T3 +45/-45 Shear, off-axis properties GLARE 6B 0.2 - 0.5 & 2024-T3 -45/+45 Shear, off-axis properties

1.2.2 GLARE Benefits

A GLARE offers numerous benefits over aluminum, are discussed below (AGY Materials, 2006):

 GLARE offers significant weight savings (15-30 percent) when compared to conventional aluminum alloys.  Corrosion resistance is enhanced as the laminate’s structure acts as a barrier to the penetration of moisture.  Outstanding fatigue resistance and impact properties, Crack growth rates 10 to 100 times slower than aluminum.  Impressive mechanical properties.  Fire resistance and lightning strike resistance.

1.2.3 GLARE Application

Aircraft manufacturers, institutes and universities are continuing investigation of GLARE, Some applications are already realized. Due to its excellent impact performance GLARE is considered by several aircraft manufacturers and airlines for floor applications and for impact sensitive areas, such as; the cockpit crown, forward bulkheads and the leading edge of aircraft. The flame- resistant capability of GLARE makes it suitable for flame sensitive areas such as; fire walls, cargo-liners, etc. This characteristic is especially attractive for fuselage design because of the considerable increase in safety and evacuation time. The fatigue and damage tolerance aspects of GLARE provide significant weight as well as cost savings. An application of GLARE in fuselage skins has shown weight savings of 15 to 30 % (Asundi & Choi, 1997). The combination of all above mentioned properties make GLARE a strong candidate material for fuselage skin structures of new generation aircraft.

The first civil application of GLARE was in a bulk cargo floor of the Boeing 777 and the bulkhead of the Bombardier Learjet 125. Despite these applications on small scale it was not that popular until 2001. The crucial development of GLARE took place in 2001 when Airbus selected GLARE for its high capacity aircraft, the A380. Fuselage panels for the skin of the A380 are made out of GLARE. The use of Glare results in a weight saving of around 800 kg. The fact that GLARE can be repaired in the same way as standard aluminum is very helpful for the application. Due to its high impact resistance, particularly against bird strike, the entire front end of the A380 tail is also composed of GLARE. This can be seen in Fig 3, where a material overview of the skin of the Airbus A380 is provided.

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Fig. 3 GLARE in A380 (Rajabali, 2014)

The fuselage GLARE panels for the skin of the A380 are produced by Fokker Aerostructures in Papendrecht. Fokker Aerostructures produces 30 shipsets (660 Panels) of Airbus A380 per year, which is 2.5 shipsets per month and about 2 weeks to produce 1 shipset. A shipset is complete set of panels for one aircraft. For A380, 27 panels are there in 1 ship set, out of that 22 panels are manufactured by Fokker and other 5 by PAG (Rajabali, 2014). An example of an Airbus A380 GLARE panel is provided in Fig 3. Fokker Aerostructures produces besides the panels, also the butt straps and the front ends of the tail. Butt straps are kind of backing strips used to connect two GLARE panels. The production of 30 shipsets per year sums up to more than 11 thousand square meter GLARE per year. The produced A380 GLARE panels are between 4 and 11 m long and 3 to 4 m wide. The aluminum sheets are between 1 to 11 meters long and up to 1.5 meters wide, with a thickness of 0.3 or 0.4 millimeters. The current production system details are summarized in Table 2.

Table 2 Current Production Summary

Yearly Production Airbus A380 Shipsets 30 Panels 660 Area 11070 m2 Aluminum sheets 23000

1.2.4 Glare Production Processes

GLARE consists of three basic materials: aluminum, pre-impregnated glass fibers (prepreg) and adhesive. The aluminum coils are supplied by a supplier from Austria and prepreg and adhesive are supplied by Cytec Industries Inc. located in UK. Before staring GLARE production these materials are converted to proper sizes at sheet production and prepreg cutting units. After aluminum sheets, prepreg and adhesive are in their proper form, lamination of the panel takes place. The aluminum and prepreg are laminated in a mold, which is called “lay-up” process of the GLARE panel. This section will describe the detailed production processes of sheet production, prepreg and adhesive cutting & GLARE production.

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Aluminum sheet production

The whole process of aluminum sheet production is summarized in Fig.4:

Fig. 4 Aluminum sheet Production Processes

The aluminum sheets are cut from a large aluminum coil and milled into the right shape at milling process line. Aluminum coils arrives from suppliers and are stored in storage area then coils are loaded to rotational units which are loaded into decoiling machine (Fig. 5). Then the sheets are cut into required lengths and stored on the alternate milling table. After milling the sheets on 2-D milling machine, sheets are manually inspected and marked. Then to avoid any damage, sheets are again rolled up at coiling machine (Fig. 6). The coiled sheets are then stored in the transportation unit (Fig 7).

Fig. 5 Decoiling & cutting of sheets Fig. 6 Rolling up of sheets Fig. 7 Storage & Transport Unit

Transport units are then moved to chemical treatment area where sheets are decoiled and put up in frame (Fig 8 & 9). Frame is then guided by overhead crane through chemical baths (Fig 10). In the chemical treatment line, the sheets are pickled to roughen there surface and anodized to create a corrosion-resistant layer. Then these treated sheets are rinsed and dried. At the end of chemical line, frame along with the sheets are removed from overhead crane and transported to priming area. Then primer is applied on the sheets by automated spraying robots and then sheets are cured at controlled temperature. Primer is applied to obtain better bonding between sheet and prepreg.

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Fig. 8 Sheet decoiling Fig. 9 Framed sheet Fig. 10 Chemical treatment line

Primed sheet are then unloaded from frame, inspected for prime thickness and again rolled back to prevent any damage (Fig 11 & 12). As the primer is susceptible to UV light, the sheets are then packed in to black bags. To track those bags bar codes are applied on them. Bar codes contain information like the size of the sheet, panel and shipset for which it was fabricated, etc. Then those bags are loaded to transporting units for storage in buffer area (Fig 13). The buffer has storage capacity of 3500 bags. It acts as a decoupling point between sheet production processes and GLARE layup processes. As per demand at layup sheets are then transported to GLARE layup area from buffer area.

Fig. 11 Prime thickness inspection Fig. 12 Sheets rolled back Fig. 13 Sheets stored in buffer area

Prepreg and Adhesive Cutting

The pre impregnated glass fibers and adhesives are supplied by Cytec Industries Inc. These materials are supplied in form of rolls of 200 meter. Before using these materials for layup process they are cut in the needed size and direction by prepreg and adhesive cutting machine. As these materials are affected by heat, they are stored in refrigerated buffer area at -30˚ C before and after the cutting process. The process is summarized in Fig. 14.

Fig. 14 Prepreg & Adhesive cutting

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GLARE production

The aluminum sheets and the prepreg are laminated in a mold manually; this is called the “layup” process (Fig 15). The edges of the aluminum sheets are provided with adhesives in order to prevent porosity and voids in the GLARE panel (Fig.16). The position and direction of the sheets and prepreg layers, which depends on the panel configuration, are marked with a Laser Projection System (Fig 17). The number of layers of sheet and prepreg varies with the type of panel. Different Molds are used for layup process, depending on the dimension and curvature of panel. When the layup is complete, it is packed in a breathable foil (which allows air to flow through) and autoclave foil (which seals the mold). After that tooling’s like thermocouples and vacuum hoses are connected.

Fig. 15 Sheet layup Fig. 16 Adhesive on sheet edges Fig. 17 Laser projection system

The panel ready for curing is then transported to autoclave area. Then mold is loaded on to autoclave stands by side loading forklifts (Fig 18). Each stand can accommodate multiple molds based on their dimensions. The stands loaded with molds are then put in autoclave at high pressure (up to 11 bars) and high temperature (up to 200˚ C) so that prepreg and aluminum sheets get bonded. After that curing process takes place, where temperature is gradually reduced. Current production system has 3 autoclaves with different dimensions. The largest autoclave is of 5m diameter and 17m in length. After the autoclave cycle, the GLARE panel and mold are brought at debagging station, where pressure tooling and foils are removed and then panel is visually inspected. The GLARE panel is then removed from the mold and mounted to transporting frame, which is hoisted by overhead monorail from horizontal to vertical direction. The monorail then moves the panel to further processes like NDI & machining.

Then the panel is inspected for porosity, voids and other defects with a non-destructive inspection tool: C-scan (Fig. 20). It checks the panel with an Ultrasonic scanning with sound wave guiding water jet for any defects. If the results of scan are satisfactory, then panels are transported for machining to 5 axis CNC machine for milling and drilling. To accommodate panels with different dimensions and curvature, the machining bed of the milling machine is kept flexible. The vacuum cups of machining bed keeps the panel on fix position during machining. Here, panels are milled to their exact dimensions, windows or door openings are cut out and holes are drilled for tack rivets (shown in Fig 19).

The panels are then moved to paint box where they are sanded and then primer and top coat is applied. After the paint gets dried, panels are moved to shipment area for shipment preparation. Then they are fitted upright in a container, which is loaded to low loading trailer. This trailer takes the containers to North Germany where PAG assembles fuselage sections which are then transported to the Toulouse, south of France for final assembly of A380.

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Fig. 18 Autoclave

Fig. 19 Five Axis CNC milling & drilling Fig. 20 C Scan inspecting GLARE panel

The whole process of GLARE production is summarized in Fig.21. The process starts after production of aluminum sheets and prepreg and adhesive cutting as explained before in this section. The layup process and bagging process are carried out in layup process area of factory. Then the panels & molds are transported to autoclave area. Once autoclave is finished, panels are moved to inspection and machining area. After machining operations panels are moved to the paint shop and finally to shipment area. In Fig.21 boxes are shown in different colors to show the different processing areas.

Fig. 21 Overview of GLARE production processes

For 6 GLARE panels (skin) the process is same as described above. But for other 16 panels stiffeners are added to the panels. As a result of that some of the above processes need to be repeated. Stringers and Doublers are applied to the panels as stiffeners.

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Stringers are thin strips of material which are attached to the panel in longitudinal direction of the aircraft. In the current production of A380, 3 of the 22 GLARE panels produced are with stringers. Doublers are used as stiffeners at high stress areas by using extra layers of GLARE panel. High stress areas are usually around door locations. An example of a stringers and a doubler panel is given in Fig 22 & Fig 23 respectively.

Fig. 22 GLARE panel with stringers Fig. 23 GLARE panel with doublers

Once the machining of GLARE panels is complete, the decision whether it is a panel with doubler or stringers is made. If doublers or stringers need to be applied, then panel goes back to layup process where stringers or doublers are attached to panel and whole process is repeated again. Otherwise, it goes to paint shop for sanding and painting. Fig 24 explains the production process for panels with doublers or stringers.

Fig. 24 Overview of GLARE panels with doublers / stringers

The stringers used are supplied by Premium Aerotech Gmbh (PAG) and are chemical treated in the same way as the aluminum sheets. After the chemical treatment process they are also applied with primer to ensure adequate bonding between the stringer and the prepreg of the GLARE panel to which it is attached. The final pretreated stringers are stored on racks for the lay-up process.

In Appendix 1, the above mentioned production processes are explained with more details.

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1.3 Airbus A320 Family

The A320 single-aisle jetliner family (composed of the A318, A319, A320 and A321) is the world’s bestselling single-aisle aircraft family. It is used in a full range of services from very short haul airline routes to intercontinental segments, on operations from challenging in-city airports to high-altitude airfields and on an Antarctic ice runway.

The A320 Family is airbus concept of aircraft commonality. Within the A320 Family all aircraft types are based on the same fuselage. Furthermore, all four types have the same flight deck and are basically equipped with the same mechanical and electrical components. This brings advantages to both, the manufacturer and the customers. Most parts and components are interchangeable between all four aircraft types offering the possibility to be procured at a lower price because higher quantities allow for the usage of more advanced machines that will produce the components in less time. The reduction of lead times also help to react in unpredicted situations such as maintenance or lack of spare parts (Airbus, 2015).

Pilots with a rating for the A320 Family aircraft types can fly them all because they all share the same flight deck and almost the same operational procedures. This single type rating solution offers the chance to easily replan pilot assignments. More importantly, airlines can easily use a different A320 Family aircraft on a certain flight according to the demand which improves efficiency and lowers operational costs. Thus airlines will benefit from the fact that Airbus designed all its flight decks in a common way giving the pilots the chance for transition from one type to another in less time for less money (Airbus, 2015).

The most popular aircraft in family is the A320, which accommodates 150 passengers in a typical two class arrangement, and up to 180 with high-density seating. The stretched fuselage is A321 version seats 185 passengers in the two-class layout, and up to 220 for a high-density cabin. The shorter fuselage is A319 has a 124-passenger capacity in the two-class configuration, and up to 156 in high density, while the smallest is A318 with capacity of 107 passengers in the two-class cabin and 132 with high density seating.

Fig. 25 A320 Family (Factbook, 2014)

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In next 20 years airbus is expecting higher demand of single aisle aircraft due to their operational flexibilities, suitability on various routes and increase in regional air traffic. Within this segment the evolution of aircraft in terms of size continues. The largest demand is in segments of 150 and 200 seat category; this demand is met by aircraft such as the A320 today and is forecasted to take 46% of all Single-Aisle demand. Most number of orders for this type of aircraft are by low cost regional air carriers (Airbus, 2014).

Fig. 26 Single Aisle Aircraft Forecast (Airbus, 2014)

To ensure its competitive edge in global airline market, Airbus continues to invest in improvements in development of the A320 family. Airbus wants to achieve better engine efficiency, less fuel consumption, higher payload capacity and use of composite materials for weight saving. Composite material would provide high structural reliability, easy maintainability, increase structural life and also save weight. For this purpose airbus launched A320 Neo (new engine option) family replacing old A320 family. In addition to the new engines, the modernization program also improvements like Aerodynamic refinements, large curved winglets, weight savings, a new cabin with larger luggage spaces, and an improved air purification system (Airbus, 2011).

These improvements in combination are predicted to result in 15% less fuel consumption per aircraft, 8% lower operating costs, less noise production and reduction of nitrogen oxide (NOx) emissions by at least 10% as compared to the old A320 family (Airbus, 2011). Airbus also wants to increase range of flying for this single aisle aircraft. A321 Neo variant with flying range of 7200 Km was launched in January 2015, making it longest of any single-aisle aircraft, making it ideally suited on transatlantic routes. The deliveries for this will begin from 2019.

For A320 neo family aircraft GLARE is considered an attractive option as it will help in weight reduction and also increase fuel efficiency. But its production costs needs to be brought down while also increasing the production capacity. For this purpose GLARE Automation project is launched by Fokker Aerostructures and Airbus.

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1.4 GLARE Automation Project

For A320 Neo Family, Airbus wants to replace the use of aluminum alloy 2024. For that purpose Airbus Research and Technology has started a project to investigate the application of Fiber metal laminates in A320 Neo family. GLARE is considered an attractive option if its costs can be brought down and significant weight savings can be achieved. Fig 27 shows the current technology cost comparison for fuselages (Tamis, 2014).

Fig. 27 Technology costs comparison for fuselages (Airbus)

Airbus has sought cooperation with Premium Aerotec Germany (PAG) and Fokker Aerostructures in Research and Technology (R&T) project. Therefore, Fokker Aerostructures together with Airbus is investigating the possibilities of a GLARE fuselage design for single aisle A320 neo family. This project is called the GLARE Automation project. The aim of this project is to achieve higher production rate, cost savings as compared to current A380 GLARE costs and weight saving as compared to current A320 fuselage structure. Currently only A320 and A321 aircraft are considered for this project.

The weight saving in structural weight will actually result into further reduction in aircraft weight and better fuel efficiency due to “snow ball effect”. Less structural weight means less lift force needed by airplane, resulting to reduction in wing size. Reduction in wing size results to less drag forces, so less thrust is needed for takeoff. Thus aircraft will require engines of less power which will result to better fuel efficiency and less weight (TU Delft OpenCourseWare, 2014).

The explanation for goal for achieving reduction on costs compared to the current Airbus A380 GLARE production is provided in Fig 28. It shows different phases of achieving the goal. The R1 cost reference is for the currently available 2024 aluminum alloy based design of Airbus A320 family. Reference R2 is theoretical and has a similar penalization as R1, but the panels are made of GLARE. For cost estimations, this design should be considered to be made by using the current A380 production processes and provides the baseline for cost reduction investigations.

The R3 reference also makes use of GLARE panels for fuselage but now with new panel sizes. This will provide an indication about the cost influence of new panel sizes. The reference V1, considers the production of GLARE fuselages with new panel sizes, weight optimized shell design with automated manufacturing. This is the target design for the new single aisle aircraft types of the A320 Neo family. V1 should be 50% lower compared to the recurring costs of reference R2.

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Hidden, due to confidential data

Fig. 28 Reference for cost calculation (Tamis, 2014)

The reduction of the recurring cost can be achieved by the possible cost savings in procurement, manufacturing and engineering, penalization architecture and assembly. Main consideration for this project is that the costs of a GLARE panel should be significantly reduced. The project should result to profitable business case for Fokker Aerostructures. The planning for achieving the goal is to have new green field facility for A320 Neo family GLARE production, incorporating the A380 manufacturing experience. The new production system should be designed to achieve single shot bonding of GLARE. The project would also judge the feasibility of results for improvements in the existing A380 production system.

The project has been divided into 4 work process groups. The group work process 1 looks for cost modelling and project management. The group work process 2 focuses on aluminum sheet production unit. While the work process 3 concentrates on layup & curing processes and work process 4 will look for solutions for post cure processes like C scan, painting and finishing machining, etc.

The planning is to start high-rate GLARE production at Fokker Aerostructures in 2020. The Research and Test phase of the project has started in the beginning of 2014 and will be finished in 2018. After the R&T phase, the design and industrialization phase will start in 2018. This project considers that from 2020, GLARE production rate would be 600 shipsets per year. The work share would be equally divided between PAG and Fokker. This project only considers the production of A320 and A321 neo aircraft GLARE panels. It has been assumed that 50% of production would be for A320 and 50% for A321.

The aircraft in the A320 neo family, the A320neo and A321neo, consists of four main sections (Fig 29): section 13/14, 15/21, 16/17 and 18. The dimensions of the sections and panels are shown in Table 3 & 4.

Hidden, due to confidential data

Fig. 29 A320 Panel sections (Rajabali, 2014)

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Table 3 A320 Neo Panel Details (Fokker Aerostructures, 2014)

A 320 neo Section Width Section Width Section Width Section Width 13/14 (mm) 15/21 (mm) 16/17 (mm) 18 (mm) Panel 1 4100 Panel 1 3450 Panel 1 4100 Panel 1 3600 Panel 2 2100 Panel 2 2100 Panel 2 2100 Panel 2 2100 Hidden, due to confidential data Panel 3 4100 Panel 3 3450 Panel 3 2100 Panel 3 3600 Panel 4 3320 Panel 4 5450 Panel 4 2900 Length =5988 mm Length = 6100 mm Length = 9188 mm Length = 3360 mm

Table 4 A321 Neo Panel Details (Fokker Aerostructures, 2014)

A 321 neo Section Width Section Width Section Width Section Width 13/14 (mm) 15/21 (mm) 16/17 (mm) 18 (mm) Panel 1 4100 Panel 1Hidden, 345 due0 to confidentialPanel 1 data4 100 Panel 1 3600 Panel 2 2100 Panel 2 2100 Panel 2 2100 Panel 2 2100 Panel 3 4100 Panel 3 3450 Panel 3 4100 Panel 3 3600 Panel 4 3320 Panel 4 3360 Panel 4 4000 Length = 10254 mm Length = 6100 mm Length = 11835 mm Length = 3360 mm

The A320neo family has already won over 3,721 orders from 70 air carriers; it represents a 60 percent market share compared to its direct competitor Boeing (Airbus, 2015). The order details for 2 variants are shown in Fig 28. It clearly shows that 76 % orders of total 3721 orders are for A320 variant. The A320neo popularity has been clearly recognized by operators worldwide. Airbus has estimated that the future production for this program will be 600 aircraft per year. From the recent order trends and considering the benefits that A321neo will offer, airbus and Fokker are expecting that volume of A321 orders will increase in future. So for GLARE Automation project the basecase is based on 50:50 ratio of A320 and A321. According to many CEOs of airline operating companies A321 neo is better choice compared to A320 as by having 10 to 15% extra cost, seats can be increases by 60 and this increases the load factor. Due to this reason A321 is now gaining more popularity (Forbes Business, 2014).

Orders till April 2015 80%

70% 60% 50% 76% 40% 30% 20%

OrderRecieved(%) 10% 24% 0% A320 Neo A321 Neo A320 Neo Family

Fig. 30 A320 Family Order Details (Airbus, 2014)

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Based on Airbus expectation of producing 600 aircraft a year, the future demand estimated for Fokker Aerostructures for the Airbus A320 neo program will be 300 shipsets of GLARE panel per year: 150 shipsets for the Airbus A320 and 150 shipsets for the Airbus A321. The other 300 shipsets will be produced by Premium Aerotech Gmbh (PAG).

The design of the new production system will be based on the expected 300 shipsets per year.

Table 5 Future Production of GLARE for A320 Family

Yearly Production Airbus A320 Family Shipsets 300 Panels 4500 Area 105000 m2 Aluminum sheets 41550

1.5 Challenge for Fokker Aerostructure

The future demand of GLARE production will be 300 shipsets per year, while the current production facility is designed for 30 shipsets per year. So the production needs to be increased tremendously from small-scale production to large scale production for A320 Neo Family Aircraft. Table 6 shows the increase in production scale in terms of shipsets and panels.

Table 6 Production Scale Comparison

Current Production Future Production Shipsets / Year 30 300 Panels / Year 660 4500 Shipsets / Month 2.5 25 Panels / Month 55 375 Shipsets / Day 0.125 1.25 Panels / Day 2.75 18.75

As the current production system is not capable enough to cope with such high rate production demands. The challenge of Fokker Aerostructures is to design a new large-scale production system for the production of GLARE panels. Besides adjusting the production from small scale production to large scale production, Fokker Aerostructures also has to decrease the recurrent costs of GLARE production. Thus, company is aiming to automate the production and logistics processes for its new production system. 1.6 Research Objective

The aim of this research is to design and model a lean future production system for the GLARE panels which would be capable enough to cope up with the high demand rate of GLARE panels emerging in future. The future production system needs to be designed with automated production and logistics processes. In new system it should be made sure that the waiting time & Inventory between work stations is minimized by well-organized production processes and balanced production line.

The research also investigates the effect of modifications in future production system on recurring costs of panel production. Moreover the research provides information on number of work process station required for different processes, number of tools, location of buffer stations in future production system. While designing the future system parameters like defects, variation in demand, order patterns and size of GLARE panels would be considered.

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1.7 Scope of Research

The main scope of research is limited to Work Process 3 (Layup and curing processes) and Work Process 4 (post curing processes). This research also covers the integration part of WP 2 (sheet metal) to WP 3. So, the transportation system of sheet delivery is covered in this research. However, the WP 2 processes are left outside the scope of this research. The outputs from previous research of WP 2 by Piet de Vries are considered after consulting with Fokker experts. 1.8 Research Question

Based on the background of the problem and the research objective derived in the earlier paragraphs, the following research question has been formulated for this research:

“What are the design considerations for production system layout of A320 Neo family aircraft GLARE panels based on lean manufacturing principles?”

The main problem is divided into several sub-questions in order to find a clear solution to the research question. The sub-questions are formulated as follows:

1) What process modifications need to be made in the GLARE production process for the future production system?

2) What type of production line should be implemented? And how many workstations would be needed for the production line, considering flexibility and demand variation?

3) Where should be the push – pull decoupling points in the production system?

4) What should be the main KPIs for the lean production system? And what is the effect of modifications on to those KPIs?

1.9 Research Approach

The first step was to define the problem statement, research objectives and research question. The Second step of research was to do literature research on GLARE development, benefit & application, GLARE panels existing production system and GLARE Automation project requirements of Fokker. Based on the literature research and research done at the factory by time measurements, data collection & interview of experts, the current production system is analyzed by using tools like value stream mapping and spaghetti diagram. This gave more insight to overloaded work stations, inventory buffer stations & waste processes. Then basecase production system is modelled for A320 and A321 aircraft, the basecase is based on consideration that the production is with A380 technology same as current processes.

Then for constrained resources, resources with high non-value adding activities and for activities with Lean waste alternatives are researched to reduce those waste activities. The automation possibilities are researched for reducing the waste activities and also to reduce the recurring costs. Then different ways of arranging the processes on the production line were researched for GLARE production system to reduce the inventory and waiting times. Decoupling point between the feeding line and production line are determined.

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For determining the decoupling points, dynamic capacity allocation, queuing pattern etc. dynamic simulation model is made to check the effects of the system dynamics on the queuing, so buffers can be planned accordingly. Then different alternatives are checked by varying the demand mix of A320 and A321, demand variation and product variations.

Then based on the results of the model, KPIs are calculated and compared with basecase. Finally, recommendations are proposed after considering the results of the model and KPIs. Based on the results Future stream mapping and conceptual factory layout are designed, which represents the future production system.

1.10 Report structure

This report consists of 8 sections. Section 2 will discuss about the Literature survey & methodology used for this research. The analysis of the current production system is provided in Section 3. In Section 4, the future production system and process modifications are discussed. Next, the simulation model developed for analysis is explained in Section 5. After that simulation results of alternatives and scenarios are presented in Section 6. And the future stream mapping & conceptual lean production layout are shown in Section 7. Finally conclusions, recommendations for further research and reflection are provided in Section 8.

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2. Literature Survey & Methodology

This Section will give insight to the literature survey & methodology which are used to analyze the current GLARE production system and to design the future production system. Section 2.1 will give an overview of the Lean manufacturing. Section 2.2 will show theories on value stream mapping. The Takt time theory is provided in section 2.3. From section 2.4 to 2.7 theories used for designing future system are explained. The section 2.8 will elaborate on why modelling and which modelling method & modelling package has been chosen for this research. Section 2.9 contains the research methodology used in this research. 2.1 Lean Manufacturing

2.1.1 Introduction

The term lean manufacturing was first used by John Krafcik of the MIT International Motor Vehicle program to describe a manufacturing system that operates with least excess resources. Womack and Jones in 1996 describe lean as the ability to do "more and more with less and less". The main idea of Lean is to maximize customer value by minimizing waste. It simply means creating more value for customers with less resource. A lean organization understands customer value and concentrates on key processes to continuously increase it. The ultimate goal of lean is to provide perfect value to the customer through an efficient process that has zero waste (Womack & Jones, 2003).

In the early 1900 Henry Ford “married consistently interchangeable parts with standard work and moved conveyance to create what he called flow production.” Ford lined up production steps in process sequence wherever possible using special-purpose machines and go/no-go gauges to fabricate and assemble the components going into the vehicle. This was a truly revolutionary different from the shop practices of the American System that consisted of general-purpose machines grouped by process. Then soon after World War 2 the Toyota Production System (TPS) introduced lean manufacturing concepts into the manufacturing industry (Lean Enterprise Institute (A), 2015).

The TPS is an integrated socio-technical system that comprises its management philosophy and practices. The TPS organizes manufacturing and logistics for the automobile manufacturer, including interaction with suppliers and customers. The TPS is also called Just in Time, because it described how material should be processed and moved in order to arrive "Just in Time" for the next operation. TPS shifted the focus of the manufacturing engineer from individual workstations and their utilization, to the overall flow of the product through the total process. Toyota concluded that by selecting appropriate size machines as per the actual volume needed, by lining the machines up in process sequence, pioneering quick setups and having each process pull the product from previous step, it would be possible to obtain low cost, high variety, high quality, and very rapid throughput times to respond to changing customer desires (Lean Enterprise Institute (A), 2015).

The keystone of lean manufacturing is its focus on the customer. As per it the value of the product can only be defined by the customer. The value of the product comes from what the customer wants, where the customer wants it and when the customer wants it. Besides creating value for the customer, lean manufacturing is also focused on production speed. Accelerating the production speed minimizes the cost of any process by eliminating the waste in either manufacturing or service. This could be achieved by identifying and removing inefficiencies like non-value added activities, cost or unnecessary waiting times within a process, caused by

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defects, excess production and other processes (Womack & Jones, 2003) (Murman, Allen, & Bozdogan, 2002).

2.1.2 Lean principles

Waste elimination is guided by five basic principles (Lean Enterprise Institute (B), 2015):

1. Specify value from the viewpoint of the customer perspective. 2. Identify all the steps in the value stream for each product family and eliminate those steps that do not create value. 3. Make the value-creating steps in sequence so the product will flow smoothly toward the customer. 4. As flow is introduced, let customers pull value from the next upstream activity. 5. After value is identified, value streams are recognized, waste processes are removed, and pull system is introduced, begin the process again and continue it until a state of perfection is reached in which perfect value is created with no waste.

1. Identify 2. Value Stream Value Map

3. Create 5. Achieve Smooth Perfection Flow

4. Pull

Fig. 31 Lean Principles

2.1.3 Lean Wastes

One of the main aims of lean manufacturing is to avoid waste within the production system. In the lean manufacturing wastes are referred to as Muda, a Japanese word for waste. Waste is defined as “anything other than the minimum amount of equipment, materials, parts, space, and worker’s time, which are absolutely essential to add value to the product” (Womack & Jones, 2003). Toyota’s Taiichi Ohno identified seven forms of waste. He formulated his Muda list for manufacturing; however his theory also applies equally to product development and order processing and other activities of any business.

For example, one of the original wastes is excess inventory. Company developing software will not have physical inventory, but the waste of uncompleted or waiting projects can be counted as inventory. Irrespective of the type of industry, the 7 Wastes provide a guiding set of principles to help identify and reduce non-value adding activities. In the principal of the 7 Wastes, there is the thinking that extreme use of resources, idle resources, unnecessary movement of resources and defective resources are all wastes and needs to be eliminated.

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The seven forms of waste that prevent the ability to add value are (Womack & Jones, 2003) (Santos, Wysk, & Torres, 2006):

1. Transportation: Unnecessary transport and handling of products. Transport does not make any transformation (value addition) to the product that the customer is paying for. Also every time the product is transported it consumes time and also increases damage possibility. 2. Inventory: Unnecessary raw material, work-in-progress or finished goods represents an inventory. It holds the capital of the company and influences cash flow. 3. Motion: An unnecessary movement of people, machines or tooling that does not add value to the product from customer viewpoint. 4. Waiting: Time the product is not transported or in between work stations the product is waiting. Higher amount of inventory results to long waiting time and is non-value activity. 5. Over-processing: Doing more work on product than required by the consumer. It’s adding steps to the production process that do not add value to customer. 6. Over-production: Producing more products than actual demand by the customer at that particular time. 7. Defects: In case of defects, rework needs to be performed in order to solve the problem. These results in higher costs of manufacturing which the customer is not paying for, it does not add value to the product.

2.1.4 Lean Tools

Based on the five lean principles and seven wastes, several tools have been developed to apply these steps in a real manufacturing environment for finding and eliminating wastes: (Ross, 2015) (Abdulmaleka & Rajgopal, 2006):

1. Value stream mapping: Value stream mapping is a method of making a ‘one page picture’ of all the processes occurring in a company. It maps the process starting from receiving a customer order up to the point of receiving the product by the customer, containing both value adding as well as waste processes. The aim is to get a clear view of the material and information flow involved when processing an order. 2. 5S: Seiri, Seiton, Seiso, Seiketsu, Shitsuke. The English synonyms as sort, stabilize, shine, standardize and sustain. 5S is a tool that focusses on work place organization and standardized work procedures. 3. Kanban: The idea of having a Kanban is to create a visual display (signboard) that allows operators to determine when to restock supplies, and change over the machines. The visual signs also make it possible for management to see in a glance in what stage the process is. 4. Just-in-time (JIT): A system where a customer initiates demand and then it’s transmitted backwards from the final assembly all the way back to raw material, thus ‘pulling’ all requirements just when they are required. 5. Poka Yoke: It is the Japanese word for ‘mistake proofing’. It can be any instrument in a lean manufacturing process that helps an operator to avoid mistakes. The aim is to avoid product defects by focusing on the part of the process were a problem has occurred. This way mistakes can be quickly eliminated before they become defects. Poka Yoke can be implemented either as preventive mechanisms or as warning mechanism. Poka Yoke in this case is a quality assurance technique that ensures quality. 6. Kaizen: Kaizen Events are dedicated activities where a team attempts to identify and implement a significant perfection in a process. The events are limited in scope and intended to create significant change and improvement in shorter time.

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7. Heijunka: A production smoothing technique utilized by the Toyota Production System so that load leveling is achieved by volume or product mix. This method is used in combination with set up reduction method so that smaller quantities of items can be produced without costly changeover costs or lost capacity. 8. Lean Supermarket: A lean supermarket is an inventory storage system designed to store components when continuous flow is not possible. The supermarket controls inventory levels and replenishment. Whenever one-piece flow cannot be achieved, a Lean supermarket is often employed as a way of managing buffer inventory and allows employees to have easy access to the parts they need. 9. Standardized Work: Standardized Work is a technique where process actions are documented so that an ideal standard work process is developed. This standardized work process is useful in improving consistency and overall performance. 10. Takt Time: Takt Time is a measure of the maximum available time to meet customer demand. It is measured as the available production time divided by the rate of customer demand. 11. Total Productive Maintenance: Total Productive Maintenance is a system for predicting the maintenance needs of equipment so that machine breakdowns are minimized. This methodology uses statistics and standardized work processes within the maintenance function. Another component of this technique is that machine operators are trained for many of the day-to-day maintenance jobs. 12. Single Minute Exchange of Dies (SMED): SMED is an approach to that attempt to minimize setup times. The goal of SMED is a 1-minute change over. Although the name focuses on die changes, the goal and focus on short changeovers can be applied to other machine also. 13. Six Sigma: Six Sigma is a quality improvement strategy focused on removing inconsistency from a process. Although originally developed for manufacturing processes, the Six Sigma methodology has been successfully applied to a wide range of processes. As a tool for process improvement and reduction of defects, Six Sigma compliments Lean and is a component in many Lean programs. 14. 5 Why’s: The 5 Why’s process is used to reveal the root cause of a problem or defect. These techniques depend on asking why something occurred, and then asking why this cause occurred. The process is repeated until the root cause is found. 15. 2-Bin System: A 2-bin system is an inventory replenishment system. It can be considered a particular form of a Kanban. In a 2-bin system, inventory is kept in two bins. As the first bin, the “working bin,” is emptied, a replenishment quantity is ordered from the supplying work center. During the replenishment period, material is used from the second bin which typically contains enough to satisfy demand during the lead time plus and also has some safety stock. In this way, there is always a bin of parts available at the work center to be processed.

The tools used in this research are value stream mapping, Takt time, lean supermarket and just in time pull system. These tools are explained in detail in following section.

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2.2 Value Stream Mapping

A production system is usually highly complex with different products being produced sequentially or parallel. To implement the lean principles, value stream mapping can be used to group a wide variety of products into a single “value stream”. The process of value stream mapping is to identify the current value stream of a product and to use this current state as a basis for predicting the future value stream.

Lean Thinking define the value stream as: “The set of all the specific actions required to bring a specific product through the three critical management tasks of any business: the problem- solving task running from concept through detailed design and engineering to production launch, the information management task running from order-taking through detailed scheduling to delivery, and the physical transformation task proceeding from raw materials to a finished production in the hands of the customer” (Womack & Jones, 2003).

The mapping of these activities of value stream is called value stream mapping. In one popular work on value stream mapping, learning to see by Rother and Shook, value stream mapping is defined as: “Value stream mapping is the simple process of directly observing the flows of information and materials as they now occurs, summarizing them visually, and then envisioning a future state with much better performance” (Rother & Shook, 2003).

It is difficult to illustrate a typical value stream map, as there have been different methods and steps proposed to actually create the map and practitioners use different sets of icons. However, the most common method for value stream mapping is that developed by Rother and Shook in their work “Learning to see”. In this report the mapping techniques, method and icons used would be from this famous work. A value stream is the collection of all actions, value added and non-value added avoidable and unavoidable processes needed to produce a certain part or product to meet the customer’s needs.

Based on value stream mapping production operations could be grouping into the following three types of activities (Al-Rufaifi & Al-Khafaji, 2012) (Rother & Shook, 2003): a. Value-Added Activities (VA): These are the activities that transform the materials into the exact product as per the customer requirement. It includes activities that directly add value to the product from customer perspective. b. Non-Value Added Activities (NVA): These are activities, which are not required for transforming the materials into the product as per the customer need. Anything, which is a non- value added may be defined as a waste. It adds unnecessary time, effort or cost so considered as a non-value added. Another way of looking at waste is that, any material or activity for which the customer is not willing to pay. The business value adding activities that are not directly adding value to product, but are needed for production are also considered non-value adding activities.

These are activities that do not add value from the perspective of the customer but are necessary to produce the product unless the existing supply or production process is changed. This kind of waste may be eliminated in the long run but is unlikely to be removed in the near term. As part of continuous improvement process, these activities should be reduced and eliminated.

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Value-stream mapping is done in four steps as follows (Rother & Shook, 2003):

1. Selecting a product family

The first step in value stream mapping is selecting a product family. This means selecting the process flow of the product that will be investigated. Production systems are in general complex systems with a wide variety of different products. So it is necessary to map the processing steps of material and information for one product family from door to door in the plant. A product family is a group of products that pass through similar processing steps or over common equipment lines.

2. Current state map

The current production process steps and the parameters of every process step are determined and plotted with specific symbols. The outbound and inbound shipping are drawn together with their frequencies. The inventory locations and levels in time units of demand (days, hours, minutes or seconds) are plotted before and after each process step. The next step is to add the useful information about the product and information flow: pull, push, supermarket, safety stock and electronic or manual information flow.

3. Future State Map

The purpose of the future state map is to improve the value stream by eliminate the sources of waste in the current state map. The production can be done at Takt time to prevent inventory and over production. Distributing the production of different products evenly over time at the pacemaker process and leveling the production mix. Continuous flow can be established between work stations to reduce handlings and non-productive waiting times. If continuous flow is not possible than supermarket system can be established. The purpose of placing a supermarket pull system between processes is to have a means of giving accurate production instruction to the upstream process without predicting the downstream demand. Fig 32 shows the supermarket pull system with Kanban. After implementing all above mentioned changes the future value stream map is drawn.

Fig. 32 Supermarket pull system (Rother & Shook, 2003)

4. Achieving the future state

To achieve the future state as drawn in the future state map, the value stream map should be divided into segments or loops. A loop covers all the activities which belong together. There are several loop types. The pacemaker loop encompasses the flow of material and information

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between the customer and the pacemaker process. The supplier loop covers all the activities from product order to the supplier to raw material product deliver. Additional loops are the loops upstream of the pacemaker loop; these loops are usually divided per process step. As soon as the loops are defined, identify the objectives and goals for each loop as developed in the future state map. All these objectives and goals can be combined in a value stream plan. This plan shows exactly what is planned by when, the measurable goals and clear checkpoints with deadlines.

It should be clear that value stream improvement is also management’s responsibility. So specific time need to be dedicated for learning and improvement on actual work floors with workers. Moreover after achieving the goals, constant reviewing is also important.

Disadvantage of Value Stream Mapping

The main disadvantage of VSM is that it concentrates on individual processes and is unable to discover the interactions between the processes. Production processes are influenced by either logistical complexity or technological complexity which is not taken into account in VSM. Moreover, it is also more deterministic since it does not take into account fluctuations in demand or process inconsistency. So, VSM lacks the ability to analytically predict the effects of variations on the future performance of a production system (Erikshammar & Weizhuo Lu, 2013) (Marvel & Standridge, 2009). To effectively predict the future system behavior simulation is necessary along with VSM. 2.3 Takt Time

To achieve a customer driven value stream it is important to design the production system to be consistent with the pace at which the customer is demanding a part or product. This pace is often referred to as the “Takt time”, which is originally a German word for cadence or pace. Takt time lean control principles have mainly been applied in high-volume flow environments in which jobs move through the production system in one direction along a limited number of identifiable routings (Fekete & Hulvej, 2013).

For manufacturing industries like Fokker Aerostructures, Takt Time is the pace of production (e.g. producing one GLARE panel every 30 minutes) that aligns service demand with customer demand. In other words, it is how fast you need to provide service in order to fill your customer orders. Takt Time is calculated as (Vorne, 2009):

Takt Time = Planned Service Time / Customer Demand

Takt Time is a key concept in lean manufacturing. It is the heartbeat of a lean organization matching actual service demand to customer demand. It is not a goal to be surpassed, but rather a target for which to aim:  Serving faster than Takt Time results in overutilization of resources and overproduction, the most fundamental form of waste.  Serving slower than Takt Time results in bottlenecks – and customer orders that may not be completed on time.

However, it should be noted that for manufacturing industries where demand is changing every few days managing Takt time can be challenging issue. For this dynamic demand extra resources and flexible manning is needed. When customer demand is high, Takt Time is low, and more resources are utilized. When customer demand is low, Takt Time is high, and fewer resources are utilized. This frees up resources to work on other processes or on improvement initiatives. Second solution for the dynamics of demand is to have the buffer in between process stations.

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2.4 Push – Pull System & Decoupling Point

In production systems push or pull systems are used to schedule the processes based on the customer orders or product demand at downstream processes. But in some production line, it is advantageous to have both pull and push systems separated by decoupling point (Lyonnet & Toscano, 2014). Decoupling point is generally a buffer station with fixed inventory storage capacity.

2.4.1 Push System

A push system releases and schedules materials, items, or services into the process as expected customer orders are processed and materials become available. Push is generally anticipatory and often based on projected need (Wang J. X., 2011) (Rother & Shook, 2003).

Generally in the production systems there is a planning department that feeds the manufacturing floor with information about what to build and when to build it. It is a list of which product (customer) should be first, which product (customer) should be second, and so forth. This list is published as per schedule. However, the list rarely takes into account the reality of the factory floor, for example, machine breakdowns, lost parts, and absenteeism. It is assumed that all products take processing time as planned, with no defective parts ever built. In reality this never happens, then time and inventory buffers are built to compensate. In push system though the downstream operation has stopped, the upstream operation keeps on making product and putting it in buffer.

2.4.2 Pull System

A pull system controls the flow of work through an organization by only releasing materials, items, or services into the process as the customer demands or downstream demand only when they are needed. The planning department that feeds the manufacturing floor with information about what to build but when to build is decided by downstream processes. This system creates only the parts requested from a downstream workstation. If there is no signal to build, the operator stops building. It is based on Lean principle “What the customer wants, when the customer wants it” (Wang J. X., 2011) (Rother & Shook, 2003).

Appropriate use of a pull system is seen as key in achieving just in time (JIT) services. It is one of the three elements of just-in-time, along with Takt time and continuous flow (using Kanban). The pull system allows the production of what is needed, based on a signal of what has just been sold or consumed. The downstream process takes the product it needs by pulling it from the upstream producing processes. This customer pull is a signal to the producer that the service is provided or product is consumed. The pull system links accurate information with the process to minimize overproduction.

Main advantages of Pull system over push system are:

 Shorter lead times  Lower Inventory Costs  Less material handlings  Lower space requirements  Shorter time to detect errors

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2.4.3 Buffer Storage & Decoupling Point

It is general belief that inventory is like having insurance. Inventory is kept in buffers to protect against things that can go wrong like: Equipment break downs, defected parts, suppliers unreliably, discount on large orders, etc. Inventory is kept so that even if these things go wrong, the process can keep going. Also decoupling point is needed between production lines and feeding lines if just in time is not possible due to process time gap.

Thus, the balance needs to be established between buffer storage and customer service level. The result of this balancing creates the decoupling point. The decoupling point separates the part of the production system which is oriented towards activities for customer orders (Pull) from the part of the system based on forecasting and planning (push).

Fig. 33 Decoupling Point

The decoupling point is important for a number of reasons (Donk, 2000):

 It separates the order determined activities from the forecast driven activities. This is not only important for the distinction of different types of activities, but also for the related information flows and the way by which goods flow is planned and controlled.

 It is the main stock point from which deliveries to downstream processes are made and the quantity of stock should be adequate to satisfy demand for a certain period.

 The upstream activities can be optimized, as they are based on forecasts and are more or less independent from irregular demands of downstream processes.

In order to implement both forecast-driven and customer-driven production operations within the same production system, a buffer or decoupling point must be included. When customization is involved in a multi-stage production process, it is vital to decide on the positioning of the decoupling point. The choice of the position is influenced to a great extent by factors like production capacity, customer order lead-time, inventory holding cost and customization done (Wang & Rapp, 2004).

When supermarket system (with Kanban) is used for pushing and pulling products from the buffer, Replenishment of inventory in buffer is based on actual consumption and not based on future prediction. When the buffer is full the upstream processes will stop production. But when downstream processes are well aligned with upstream processes then inventory can be replenished based on sequential pull system (pre-planned production) instead of supermarket pull system (Kanban). In sequential pull system, planning department schedules upstream processes. This is useful when the response time to replenish the inventory is high, due to higher processing times at upstream processes or at feeding production lines.

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2.5 Theory of Constraints

The theory of constraints is a vital tool for improving process flows. It helps in understanding and managing bottlenecks processes to create an efficient process flow. It is a scientific approach for improvement; it assumes complex system like manufacturing processes consists of several activities which are linked to each other and any of these activities can constraint the whole process. Thus to increase the throughput of the system, focus must be on identifying and improving such constrained processes.

The focusing process in TOC that helps in managing the transformation is explained in the book “The Goal” by (Goldratt & Cox, 2004):

4. Elevate 1. Identify Bottleneck constrains

3. 2. Exploit Subordinate Bottleck Processes

1) For identifying bottlenecks processes in manufacturing factory, simplest technique is to look for inventory accumulation, processes with longest cycle times and starved equipment’s.

2) To increase the throughput, exploit the bottleneck processes. It should be made sure that these processes are continuously working during shift hours. The maintenance activities of these processes should be done in off shift times (on weekends).

3) The main objective of subordinating and synchronizing processes is to have constant availability of material at bottleneck processes. So, the upstream processes need to be synchronized with the bottleneck processes.

4) To elevate the constrained processes modify equipment design, upgrade equipment components or buy new equipment’s.

After the constrained processes are eliminated process is repeated to look for new constrains, it is a continuous improvement process. In this research TOC will be used for finding bottleneck processes in the existing production system.

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2.6 Production Layouts

The production system layout design is one of the most important phases in any facility planning and material handling project related to new product manufacturing. It depicts the well-planned process of integrating equipment, material, and manpower for processing the product through the plant in an efficient manner. This means that the material moves from receiving (as raw materials and parts) to shipping (as finished product) in the shortest time and with the least amount of handling. This is important, since the more time the material spends in the plant, the more costs it collects in terms of inventory, obsolescence, overhead, and labor charges. The layout is the beginning of a footprint of the actual plant arrangement to obtain the most efficient flow of products. Production layout is also the basis for determining the cost of the facility in the case of new construction.

The main principles of Production Layout are given below (R.Panneerselvam, 2012):

 Principle of minimum movement – Decrease the unnecessary movement of products and people  Principle of Flow – Resources should be arranged as per production sequence so there is continuous flow of material.  Principle of Space – Adequate space should be available for the resources and it should be efficiently used.  Principle of Flexibility – While designing it should be made sure that production system layout is flexible. Flexibility is needed for the accidental breakdowns and maintenance of equipment’s.  Principle of Interdependence – All dependent operations should be located close to each other.  Principle of overall integration – All the resources and services should be integrated in the single operating layout unit.

The important elements that are used in analysis of layout and production layout requirements are given below (Seth & Crowson, 2012):

 Flow pattern of products: Production layout is an arrangement of machines, departments, and services best suited to the physical dimensions of the plant. It is a carefully thought-out plan for installing equipment as the product smoothly follows the process as determined by the process flow chart. Fig 34 shows some examples of flow patterns.

Fig. 34 Example of flow patterns

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 Number of workstations: The production rate desired in a system is a function of the total output required from the facility. Suppose that the production schedule is set for 1 shipsets units per day. In order to determine the floor space requirement, one must know whether the plant is scheduled for one shift or two shifts. Further, decision needs to be made about the capacity of the equipment through which the panels will be processed. If a machine’s capacity is 1 panel per hour, then only 8 panels can be produced in a given shift. There is a choice of either scheduling a second shift or installing a second, similar machine to obtain the same output. In this research due to high production rate 3 shifts is the only option, but decision needs to be made weather to operate the production system for 5 days or 7 days. Based on that the number of workstations is determined after determining the cycle time and capacities.

 Batch Processing or Continuous Flow: Based on needed production rate and flow pattern, decision needs to be made on continuous or batch production. In many cases the production flow uses resources that are common to different production lines and processing is time consuming then they are mostly operated in batches to have higher utilization rates. For example in GLARE production autoclaves are operated in batch mode to have higher utilization as current production rate is less and are shared resource between different production systems.

 Material Handling in Layout: In a manufacturing plant, the overall process is broken into various small operations connected by numerous transportation and staging steps. Materials handling should be considered an integral part of the total manufacturing operation. The material inside the plant is in one of two states: (1) Stored and ready for its next move (2) Getting Transported. The goal here is to keep the cost of these functions to a minimum, since neither of these add value to the product but is an essential part of the total product cost.

Arrangement of Resources

One important consideration in production layout is how the resources should be arranged in the layout for the production lines. For the production system with multiple production lines (production line for different products or feeder lines), three different layouts arrangements of resources are possible.

1) Product Layout – If all the processing equipment and machines are arranged according to the sequence of operations of the product, the layout is called product type of layout. In this type of layout, only one product of one type is made on one production line. Resources and material handling equipment’s for each product are dedicated for particular product on the production line. The output of one machine is input to next machine in sequence. 2) Process Layout – In this type of layout machines of similar type are grouped together in one zone. The machines and not arranged according to the sequence of operations but are arranged according to the type of the operations. Sequence and equipment’s to use are not fixed from beforehand. This layout is commonly suitable when sequence is of non-repetitive type. Job shop is example of process layout. 3) Hybrid Layout – In order to increase the flexibility, above mentioned layouts types are combined. In this layout products are made on dedicated resources arranged on production line like product layout, but similar resources of different production line are arranged in one zone. Here also the production sequence is fixed from beforehand. For example: All the autoclaves of panel production line and doubler production line are arranged in one zone.

The type of layout to use depends on the type of production line and product types.

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2.7 Analytic Hierarchy Process method (AHP)

The Analytic Hierarchy Process method (AHP) was developed by Thomas Saaty in the beginning of 1980 and it represents a tool in the decision making analysis. It is designed to assist the planners in resolving complex decision making problems where a number of planners participate, and a number of criteria are selected and evaluated.

AHP is a widely used multi-criteria decision making tool. Unlike the conventional methods, AHP uses pair-wise comparisons which allow verbal judgments and enhances the precision of the results. The pair-wise comparisons are used to derive accurate ratio and scale priorities. AHP provides a proven, effective means to deal with complex decision making and can assist in identifying and weighing criteria, analyzing the data collected and expediting the decision- making process. AHP helps capture both subjective and objective evaluation measures, providing a useful mechanism for checking the consistency of the evaluations thus reducing bias in decision making (Dalalahp & AL-Oqla, 2010).

When making complex decisions involving multiple criteria, the first step is to select the main goal, second step is to select the main criteria’s and third step is to select the alternatives. Fig 35 shows the problem structure of the AHP method, where A represents the criteria and B represents the alternatives.

Fig. 35 AHP problem structuring (Zoran, Saša, & Dragi, 2011)

In next phase of AHP decision maker assigns relative weight to pairs of attributes of one hierarchy level, for example all the criteria’s are assigned relative weight against each other by pairwise comparison. Then all the alternatives are assigned relative weight against each other by pairwise comparison for each of the criteria’s selected.

Determining the relative weight is the third phase of the AHP method application. Pairwise matrix, by pairs, transfers into problems of own values determination in order to get the normalized and single eigen vectors, as well as the weight of all attribute on each hierarchy level (A1, A2…An) with a weight vector t= (t1, t2,…tn). In fourth phase of the AHP method and it involves the establishment of the composite normalized vector. After the vector of criteria is established, the next round involves the determination of alternative importance in the model, within each criterion. In the end, the total problem combination is carried out in the following way: the weight of each criterion is multiplied by the weight of the reviewed criterion for each alternative, and these values are then summarized for each alternative separately. The result is the weight of the reviewed alternative within the model. The weight of all of the rest alternatives is calculated in the same way. After that, the final ranking of alternatives in the model is determined (Zoran, Saša, & Dragi, 2011).

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The most common nine-point scale is used, presented in Table 7:

Table 7 Point Scale for ratings (Saaty, 2008)

Scale Ranking 1 Equally important 3 Moderately important 5 Strictly more important 7 Very strict, proven importance 9 Extreme importance 2,4,6,8 Mid-values

Validation Check

Considering that the alternative comparison is based on a subjective estimation by the decision maker, it is necessary that it is constantly monitored in order to have the required accuracy. The inconsistency validation check is useful for identifying possible errors in judgments data entry as well as actual inconsistencies in the judgments themselves. Inconsistency measures the logical inconsistency of the expert judgments. For example, if “X” is more important than “Y” and “Y” is more important than “Z” and then saying that “Z’ is more important than “X” means it’s not consistent.

The AHP method ensures that the evaluation consistency is monitored constantly in the alternative pairwise comparison procedure. The consistency index:

The consistency ratio is C.R. = C.I/R.I., Where R.I. is the random consistency index determined from Saaty table based on the size of the matrix (n), Table 8 represents it :

Table 8 Random consistency index values R.I. (Saaty, 2008)

λmax is the matrix Eigen value, whereas n is the matrix size.

If C.R. < 0.10, the calculation of relative criteria importance of AHP model is considered acceptable and validated. In the opposite case, the decision maker has to prove the reasons for unacceptably high evaluation inconsistency.

In this research AHP is used for decision making of the transport system for sheet delivery system. The expert selection, criteria selection and alternatives choices are explained in detail in section 4 of the report. The AHP model is made in Microsoft excel for decision making based on the above mentioned theory with validation checks.

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2.8 Simulation Model

“Simulation is the method of developing and experimenting with computer-based models of operations, such as production processes, to analyze and evaluate the behaviors of a system” (Kellner, Madachy, & Raffo, 1998). Here, a model represents the operations of a system over time, where it receives a set of inputs and generates the required outputs. It allows the user to dynamically interact, update variables and measure the effect of changes. Thus, new alternatives can be evaluated before implementation which results in validation of proposed modifications. 2.8.1 Objective of Model

Model will be used as a tool for evaluating the modifications for future state production system. Hence it should be able to measure the production system parameters so that they can be compared with existing system. It should be able to measure parameters like throughput time, waiting time, Inventory, No of servers needed and server utilization rate. The model should take into consideration panel sizes of GLARE panels, defects, material consumption, etc.

2.8.2 Modelling Software

There are many possible alternatives to develop a model using tools like Microsoft excel, discrete event simulation software, continuous time simulation software, etc. The selection of proper tool is very essential for the research as results depends on it.

Microsoft excel model evaluates the system and processes in terms of value at a single value in time. This is sufficient for approximate rough estimates. But for dynamic systems which involves number of resources, waiting queues, utilization rates, transportation movements between resources, stochastic timings, etc. Microsoft excel model is not suitable. Moreover, in such complex systems involving lot of processes, inventory buffers, higher numbers of objects, etc. modelling in excel using macros is too complicated and time consuming. The visualization is also important in production system model of new factories as it gives more insight of the new system, which is not possible in excel.

Discrete-event simulation is a category of modeling that is used for systems that are complex, dynamic, and stochastic. System complexity depends not only on the number of elements that need to be considered in a system, but on the degree of dependency among the elements. In production systems like that of Fokker there are many elements that are highly interconnected and thus have dependencies. A dynamic system is one where the states change over time, thereby complicating the analysis. Stochastic systems contain elements of uncertainty, again complicating the analysis. For example, the time it takes to repair a machine on breakdown or processing time of manual processes are variable; that is, the time varies each time the task is performed and that variability is expressed as a distribution function (McDowell, Greenwood, & Hill, 2009). Fig 36 provides a high-level representation of a simulation model.

The processes in the GLARE productions involve probability in defects, stochastic in break downs, processing time, etc. To evaluate the key performance indicators of the processes accurately, model is needed. Discrete event simulation allows the user to model the system behavior over time and evaluate the effect of various scenarios on the performance of the system. The disadvantage of DES is that it requires in depth knowledge of the programming. However the new software packages like Flexsim, Simio, etc. allows users to create simulation models without coding using a smart object based process modeling approach.

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Fig. 36 Representation of a simulation mode (McDowell, Greenwood, & Hill, 2009)

Simio Simulation Package

For this research Simio simulation package is used. The main reason for using Simio LLC modelling package is because it provides an "object-oriented" approach in which the system is described by "intelligent objects" which represent physical components such as forklifts, AGVs, conveyors, work stations, workers, etc. these objects helps in modelling the production system. This intelligent object contains the functions like processing times & capacity, transport capacities, storage buffers, cost functions, working schedules, etc. So there is no need of writing the detail codes, it reduced the complexity and also saves time. Moreover new functions or algorithms if needed can easily be created and integrated with inbuilt modules using process triggers. If extra intelligent objects are needed, then they can also be created by using module commands. Its seamless interface with MS Excel enhances the ability to import the data that is needed to run the model and export results from the model to excel for further analyses.

The 2D and 3D graphics development can also be done very easily without any codes. It provides the option of directly importing the objects and drawings from the google sketch up and AutoCAD. Also for future use by other users it is very convenient as no in depth knowledge of programming is needed and also the Interface is very user friendly.

Simio simulation package application in manufacturing area includes: designing green field production plant, process improvement using lean manufacturing, production planning/scheduling, etc. It has been used by companies like:

 Nissan Motor Co Ltd for its assembly line of Barcelona plant  Lockheed Martin uses it for decision support system in production of F-35 fighter jet  Vancouver airport for Modeling Passenger and Baggage Flow  BAE Systems for Forecasting production resources

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2.9 Research Methodology

The literature review reveals that lean manufacturing, theory of constraints and discrete event simulation theories are distinct. Their combined used in research is still very limited. Moreover, an application of these theories in GLARE production system (aerospace supplier) is new. From the literature review it’s clear that a traditional lean method does not validate the proposed changes before implementation, relying instead on a series of iterations to modify the system until performance is satisfactory. This approach cannot be used for the aerospace companies where implementation of unsuitable solutions is very risky. Thus, an enhanced lean process that includes future state validation before implementation should be used. Also the focus of lean is on reducing the waste activities in the processes, but does not focus on increasing throughput of processes. So, theory of constraints needs to be used along with lean to find and remove bottleneck processes to increase the throughput of the system.

The approach for lean transformation has been proposed by various authors. One famous example is: The approach proposed by Feld, it has streamlined road map to lean manufacturing implementation. This approach identified four phases in implementing a lean manufacturing program (Feld, 2000).

Fig. 37 Traditional Lean Approach

As with Feld’s approach, the initial phase is an assessment of the organization’s capabilities, goals set and it then progresses from an analysis of the current state gap directly to future state design and implementation. This methodology did not specifically consider validating the future state prior to implementation.

The methodology used in this research is integrating lean method, theory of constraints and process simulation and it is adopted from the methodology proposed by (Marvel & Standridge, 2009) which was used for automobile tier 1 and tier 2 suppliers. It presents a systematic description on how this combined approach can identify and remove gaps in the system and also reduce the risks by simulating and visualizing the future changes before implementation. An integrated application would help to predict the outcomes of dynamic situations that could not be addressed by value stream mapping alone.

Fig. 38 Integrated Approach (Marvel & Standridge, 2009).

The Future State Validation step has been added before implementation in this approach. The step is performed by using discrete event computer simulation as previously discussed. The validation step help in ensuring that the future state design effectively addresses the current state gap.

In this approach after the initial step of mapping the current state, the workflow would be divided. Discrete event simulation and value stream mapping would be carried out in parallel (shown in Fig 39). To build a model, the model conceptualization step summaries the essential

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features of a production system and also shows the logical relationships of the components in the model. Data is collected by observations, interviews, workshops or documentation. The model developed is verified by tracing entities to determine if the model’s relationships are correct and accurate enough. In parallel to modelling, gaps in existing system is identified by using lean manufacturing tools and theory of constraints. Then conceptual solutions for future design are proposed.

Before experimenting with model for future design solution validation is important. The performance of the simulation model is visualized together with experts, in order to validate the model. Then if problems are identified during validation the process returns to the steps concept model and data collection. The Future state design and DES workflows is merged at the experimental design step integrating the static VSM and dynamic DES, to see expected results about proposed changes. DES is used to evaluate and validate the expected outcomes of the different modifications by measuring the behavior of system in term of KPIs. If the outcomes are desired, then future VSM is proposed in order to increase future system performance and throughput.

Fig. 39 Research Methodology

[Adopted from (Marvel & Standridge, 2009)]

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2.9.1 Advantage of Integrated Approach

The traditional lean theory analyzes the production system by using Value stream mapping, which is the static picture of the system. But, changes in the system over time are not represented. Lean tools lack the ability to analytically determine the effects of changes made to a single component on other components or overall system performance. This deficiency makes validation of system changes before implementation almost difficult if not impossible. Moreover, theory of constraints integration with lean help in bottleneck analysis. A simulation model of the production system enhances the planned future system in many ways (Abdulmaleka & Rajgopal, 2006), (Marvel & Standridge, 2009):

 The model can be analyzed using computer based experiments to assess system performance under a variety of scenarios.  The dimension of time can be included in the model so that dynamic changes in system behavior can be represented and assessed.  The behavior of individual entities such as parts, inventory levels, and material handling devices can be observed and inferences concerning system behavior made.  The interaction effects among components can be implicitly or explicitly included in a simulation model.  The dynamic representation of processes enriches understanding and is not taken lightly. So a consensus on improvement possibilities can be achieved when bottlenecks become visible.

Table 9 shows the principles of waste (muda) and how simulation is able to support that:

Table 9 Lean and DES Comparison

No Wastes Discrete Event Simulation 1 Transportation: Unnecessary movement Modelling of process flow and measuring of resources and products transportation times and distances 2 Inventory: Components in process Modelling of Buffers 3 Motion: Extra movement of people or Modelling of the interconnection between equipment that is not necessary for the resources and the processes and process measuring distance travelled by people 4 Waiting: Waiting for next process step Modelling of queues that evolve as a result of variability in interconnected processes 5 Overproduction: Production ahead of Modelling of the variability between demand demand and production 6 Over processing: Doing more work on Modelling the process flow and product than required by the consumer measuring utilization of resources and activities 7 Defects: Waste products produced due Modeling the stochastic of defected to mistakes made by resources products production

To summarize it, an enhanced lean process adding the idea of validating the future state using simulation modelling and experimentation before implementation is used. This ensures that future state system effectively addresses the current state and future state gap. Issues that are often overlooked in traditional lean assessments such as component interaction, random variability, and time dependencies are considered in this research by using a discrete event simulation model.

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3. Current Production Process Analyses

This section elaborate on the methodology described in section 2, making use of the theories explained there. Section 3.1 will show the analysis of the current production system and Section 3.2 and 3.3 will show the result of analysis. Section 3.4 shows the gaps between current production system and expected future production system. 3.1 Current Production Processes

The Value stream mapping (as explained in section 2.2) is used to analyze the current state production of GLARE production and assembly processes. Moreover, to gain more insight in the exact process steps of the production of the GLARE more into detail, flowchart of the processes from receiving of the aluminum coil to delivery of the final GLARE panels is given in Appendix 1.

In the given production system, the number of shipsets needed by customer (PAG) is the most important parameter. Based on this demand the production processes are planned by the planning department. The main entities that are analyzed in this research are GLARE panels. The current system analysis is based on the production processes of GLARE panels of A380 aircraft. In existing system total 22 panels are produced. The panels are of 3 types: skin panels, skin panels with doublers and skin panels with stringers. The processing steps are different for 3 types of panels. So, the panel’s needs to be divided into different product family based on the similarity of their production processes. Then for each of the product family’s different value stream maps is plotted to get detail overview about the value added and non-value added times. Table 10 gives the overview of different product families based on process sequence.

Table 10 Product Family

Product Lay-up & 2nd Lay-up 2nd 2nd Bond Rework Autoclave Debagging C-scan Machining C-scan Paint Family Bagging & Bagging Autoclave Debagging testing Lugs 1. Skin X X X X X X X Panels 2. Stringer X X X X X X X X X X X

Panels 3. Doubler X X X X X X X X X X X

Panels

After the products are divided into product families, next step is to plot the current value stream maps. For the current value stream mapping all the activities in the production process are analyzed. The details of all the activities taking place for each of the production process are explained in appendix 2. Then these activities are sorted to value added or non-value added activities, considering customers viewpoint of value. Fig 40, 41, 42 shows the value stream map of above mentioned product families.

Based on the dimensions of the panels the processing time of panels varies. So for the current system analysis average timings are used for all 3 product families.

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Fig. 40 Product Family 1: Skin Panels VSM

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Fig. 41 Product Family 2: Stringer Panels VSM

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Fig. 42 Product Family 3: Doubler Panels VSM

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The modelling of an A380 GLARE factory’s value stream was based on five elements, which are:

1) ‘Production Process’ describes the production activities within the factory. 2) ‘Business Process’ describes order processing tasks including production planning and production control. 3) ‘Information Flow’ for the transmission of data and documents between individual processes and between processes and production control. 4) The ‘Customer’ reflects the customer demand which needs to be met by the production, thus modelling the system load. 5) The ‘Supplier’ represents the production’s supply with raw materials and parts.

The symbols used are from the book “Learning to see” by (Rother & Shook, 2003). In the given system, the demand rate (number of shipsets) is most important parameter. The customer demand will trigger the planning department, which will trigger production processes. Then the product will flow through all the process steps, with a certain speed. Ideally, if all processes take equal amount of time to complete then product can be directly processes on next station. But, as there is difference in processing times of workstations, products queues up between processes, creating work in process inventory. This is point where dynamic modelling comes becomes effective. However, in order to create a computer model of a process, it is important that researcher understands the whole production system. The detailed flowchart and VSM shows the details of production system. By means of research and multiple days on floor in factory, knowledge is collected; this is translated to modelling software.

The data used for aluminum sheet production is from previous research of Piet de Vries. As the main point of interest in this research is GLARE panel production processes, sheet production would not be discussed in detailed.

The data used for processing timings of GLARE production was obtained from Fokker’s industrial engineer Erik Van Meer. The data was then verified by following the production process of GLARE panels. Then value added and non-value added activities were evaluated based on the information gathered from multiple days of work in A380 GLARE production factory. The data about the waiting times of panels in buffer was extracted from GLARE production dashboard system. When the workers start the work at any process station they make the entry in GLARE dashboard system about the panel number, date and time of start of work on that panel. The data of past 6 months was used for calculating the average waiting time of panels between process stations.

For Value stream mapping every activity is evaluated from customer viewpoint, thus it can be categorized into customer value added activity or non-value added activity. The Non-value added activity can be of two type’s activities that add Business Value (Business Value Added) and ‘Waste’. Business value added activities includes the activities which are helpful for production but does not directly add value for the customer. Waste activities are lean wastes and need to be eliminated.

Thus, VSM of current system helps to identify lean waste in the current system, based on that analysis is done to find the main reasons behind this wastes and how to avoid that for future. The results are discussed in section 3.2 of the report.

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3.2 Results of VSM

Current System VA NVA CT Waiting time Total lead time 350

300

250 200

150 Time Time (Hours) 100 50 0 Product Family 1 Product Family 2 Product Family 3

Fig. 43 Current system analysis results

The Fig 43 shows the value added, non-value added activity timings, cycle times, waiting times and total lead time for three different types of panels. The skin panels have lead time of 10.4 days, but have processing time of only 28.1 Hours. The stringer panels have lead time of 13.5 days, but their processing time is of 5 days. Similarly, doubler panels have lead time of 10.1 days and their processing time is of 2 days. Thus, the existing production system in not lean.

Value added activities in GLARE production

The activities that add value are the actual layup process of aluminum, prepreg & adhesives, curing process during in the autoclave, inspection process as customer wants defect free product, machining process, painting and finishing processes.

Non-Value added activities in GLARE production

The activities that do not add value but are required for the production and are unavoidable (business value added) includes activities like rolling & unrolling of aluminum sheets, mold degreasing, laser projection system calibration, attaching pressure tooling’s, covering the mold by foils during bagging, removing the foils during debagging, loading the molds to autoclave carriers, initializing C Scan machine & adjusting frame, secondary activities during machining like defaming & framing at milling table, drilling holes and putting nails for doublers positioning and repetition of all activities again during 2nd bonding cycle. The information flow activities between processes and production control are also considered business value added activities. The waste activities includes unnecessary transport movements due to non-continuous factory layout, loading and unloading of product at buffer points in between process stations, producing excess sheets than actually required and waiting of panels in buffer stations

The detailed description of value and non-value added activities are given in appendix 2.

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3.2.1 Takt Time

For manufacturing industries like Fokker Aerostructures, Takt Time is the pace of production (e.g. producing one panel every 4 hours) that aligns production demand with customer demand. In other words, it is how fast company can produce panels in order to satisfy customer orders. However, it should be noted that for industries like Fokker where demand is changing every few months and processes are complex due to product variations and large difference in cycle times of processes, managing the Takt time can be challenging issue. For this dynamic demand extra resources and flexible manning is needed. When customer demand is high, Takt Time is low, and more resources are utilized. When customer demand is low, Takt Time is high, and fewer resources are utilized. This frees up resources to work on other processes or on improvement initiatives.

Second solution to the dynamics of demand is the planned buffer in process. However, the inventory to keep in buffer should be minimized. Thus, the Takt time approach can be used with fixed times and times can be revised after every week based on the demand planning. The company operational timings are mentioned in the table below. The company operates throughout 24 hours; employees are arranged in 3 shifts, each of 8 hours. The net work hour per shift are 7, due to breaks in between and shift changing time loss.

Table 11 Working Hours

Shift Type Working Net Working Net Working Net Working Hours/Day Hours/Day Hours/ Week Hours/Year 3 shifts, 24 21 105 5040 5 days per week

The current demand of GLARE panels is 30 shipsets, with each shipsets having 22 panels. So, in total 660 Panels are produced in 1 year. But due to Second bonding cycle, 480 of 660 panels needs be processed 2nd time. Moreover same resources are also used for making 390 doublers which are similar to skin panels. Thus the processes actually have to produce 1530 panels. So the Takt time calculation would be based on demand of 1530 panels. But some resources like lugs cutting are done only once for every panel and resources like paint box are used 2 times for all the panels. Thus, Takt time calculation is different for different resources. Table 12 shows the Takt times for all the processes.

Table 12 Takt Time

Process Takt Time (Hours) 1) Layup & Bagging 3.3 2) Autoclave 3.3 3) Debagging 3.3 4) C Scan 3.5 5) Machining 4.8 6) Finishing & Painting 7.6 7) Rework Lugs 7.6

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Comparison of Cycle times to Takt time

1st Bonding Cycle Product Family 1 (Skin Panels) Takt Time 9.0 8.0 7.0 6.0 5.0 4.0

Time (Hours) 3.0 2.0 1.0 0.0

Fig. 44 Takt time comparison (skin panels)

1st Bonding Cycle Product Family 2 (Stringer Panels) 2nd Bonding Cycle Takt Time 25.0

20.0

15.0

Time(Hours) 10.0

5.0

0.0

Fig. 45 Takt time comparison (stringer panels)

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1st Bonding Cycle Product Family 3 (Doubler panels) 2nd Bonding Cycle Takt Time 9.0 8.0

7.0

6.0 5.0

4.0 Time Time (Hours) 3.0 2.0 1.0 0.0

Fig. 46 Takt time comparison (doubler panels)

The cycle time used here is process cycle time and it specifies the amount of time the unit is being worked on at specific production step. The cycle time’s show in Fig 44, 45 & 46 are cycle time per resource. Ideally these times should be synchronized to Takt time. But in the current system some of the times are very low as compared to the Takt time, resulting to unnecessary waiting times due to overproduction. For resource like paint box cycle times are high (For stringer panels) as compared to Takt time which also results to unnecessary waiting due to unavailability of server.

The difference cycle times of different process stations indicate the imbalance between processes. The production lead times are very high compared to processing times, due to waiting times which needs to be reduced by introducing pull system between the workstations.

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3.2.2 Lean Wastes in current system

1) Inventory of Aluminum sheets (raw material for GLARE production)

Inventory of raw materials like aluminum coils, prepreg, adhesives & stringers is there as they are delivered by suppliers from different countries only few times a week. The inventory of aluminum sheets between workstations is due to the difference in processing times of workstations. Moreover, currently 1 routing machine is used for processing of aluminum sheets, as the processing of individual sheet takes lot of time. So group of 8 similar sheets are routed together though only few sheets are actually needed thus creating extra inventory. To store this extra sheets the storage place called “promeg” is used. It contains inventory of 2100 processed sheets. The inventory of sheets is also kept for minimizing the response time in case of defected sheets. In A380 GLARE the aluminum sheets used are having lot of variations in dimensions and shape, if sheets used are more standardized than it can reduce the needed inventory. The sheets are pulled to layup process from the decoupling point “promeg” based on demand.

2 & 3) Overproduction, push system & Waiting times in buffer

The layup process in the current production is the most time consuming process. This process is done manually by workers. The current system has 5 layup stations and there are 2 workers every shift for each layup station. Irrespective of demand by the downstream processes, layup stations are operated continuously. Therefor the layup station is overproducing than the actual demand. These panels are then pushed to autoclave storage area. After layup process the panels wait in the autoclave storage area.

The autoclaves are operated in batches and all the panels with similar curing curve are processed simultaneously. The main reason for that are high costs of the autoclave. Once the panels are out of autoclave they need to be debagged at debagging station. So from the Autoclave, panels are pushed to debagging point. As there is one debagging station, panel’s needs to be stored in racks until debagging station is empty. Moreover, debagging point gets the list from production planning department about the priority panels and they are processed first. Other panels which are overproduced (produced ahead of schedule) at layup station wait in buffer storage racks. Thus, this results to unnecessary work in process inventory. Many times if all the transportation frames are occupied, debagging station cannot process new panels. As from debagging point panels are separated from molds and loaded to vertical frames (shown in Fig 22). Currently 23 vertical frames are used for carrying panels between process stations. Thus this also causes unnecessary waiting of panels. The main reason for shortage of frames is defected panels, which needs to be stored on frames in buffer until they are approved by airbus.

After debagging panels are pushed to storage buffer of c scan, based on the priority list from planning department c scan operator pulls the panels from storage buffer. After processing it on c scan panels are pushed to buffer again. Then the machining operator pulls the panel based on the priority list. After machining the panels are again pushed to buffer storage. The skin panels are then pulled by painter as per priority list and processed at paint box. While the doubler and stringer panels go back to layup for 2nd bonding cycle, after the layup station is available. The whole production cycle is repeated again. Finally after painting and finishing process panels are pulled for lugs cutting and transport preparation as per final dispatch list.

Thus the main factors that contribute to work in process inventory between process stations are overproduction and unbalanced production line (difference between cycle times of various processes) Moreover, sometimes unexpected machine failures also contribute to the work in process inventory & waiting times.

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4) Defects

In the current production system 660 GLARE panels are made every year. Out of 660 panels made during 20 March 2014 to 20 March 2015, 21 panels were scrapped. The panels are scrapped after C scan process, by that time most of the value gets already been added to the product. Thus, 3.2% of scrap panels is very high defect rate for panels worth thousands of euros. Fig 47 shows the reasons of the scrap panels.

Scraped Panels

15 10

5 Noof Defects 0 Incorrect Jig or tool defect Machine / Wrong process Incomplete parts specification Installation failure applied Reasons of Defects

Fig. 47 Reasons for scrap and repair panels

The main reasons of defects are as follows:

1) Incomplete/Incorrect specification: The sheets are inaccurately placed or holes are drilled at wrong place due to wrong specifications drawings.

2) Jig/tool defects: Improper cleaned tools results in dents on the surface of the panels. Also if pressure is kept high on mold during bagging it results to scrap panels.

3) Machine failure/mistake: The scrap pulled by vacuum accidentally delaminates/scratches the side of panels; holes are out of tolerance, etc.

4) Wrong process applied: When workers do not follow the work specifications, mistakes like inadequate tool cleaning, prepreg getting folded, wrong positioning of sheets, left over materials like adhesives cover, cutter, etc. is made at the layup station.

5) Incomplete parts: Some of the prepreg or aluminum sheets parts were missing in the panel. This occurs if workers at the layup station do not check the bill of material. From Fig 47 it is clear that the main reasons for Scrap Panels the defects and repair are wrong application of process and mistakes by the workers at the layup station as a result, those panels have delamination 2 1 (Voids between layers), inclusions or dents. Other reasons are mistakes in bagging process and 18 accidents in automated milling machine. Fig 48 shows the comparison of defect at various workstations for scraped panels.

Layup Bagging Milling

Fig. 48 No of defects at workstations

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3.3 Current Production system Layout

The current production layout is product type layout, where all the resources are arranged in the production process sequence. It involves lot of material handling steps and higher transport movement between workstations. For seeing the material handling movements in current layout spaghetti diagram is used. It is the simplest tool to visualize the transportation movements. When the transportation paths are seen, it is often easy to spot the opportunities to reduce these wastes. Fig 49 shows the flow of raw materials, GLARE panels and empty molds. The current layout has non-continuous flow of materials due to presence of buffer storages. All the buffer storages are shown by red.

Fig. 49 Spaghetti Diagram (Material Flow in Current Layout)

Transportation (Lean waste)

There is lot of waste transportation movement as panels are picked up from buffers, processed and then put back in buffer. Along with waste transportation times, it also has waste activities (transactions) for loading and unloading at buffer storages. Moreover this buffer storage also occupies factory space. The main storage buffer for work in process panels are before the autoclave and c scan. Fig 49 shows the movement of uncured panels and molds from layup station to buffer before autoclave. Then cured panels are stored in buffer until debagging point is empty. At debagging point molds get empty and after cleaning they are put back in storage racks. The panels from debagging point are transported in vertical frames hoisted by overhead cranes. The common buffer is used for C scan and machining. Moreover the paint box is located on the other side of machining workstations, so panels are transported back to paint box.

Thus, the non-continuous layout and presence of buffer storages causes lot of waste transportation movement.

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3.4 Current Production system and Future Production system (similarity & difference)

This research focuses on production system of GLARE panels for A320 & A321 Aircraft. The production process technique of producing GLARE would still remain the same as it is for A380 panels. The main difference is that the single shot bonding would be used for A320 & A321 panels. The single shot bonding is still in research phase and it will be used for A380 panels later this year. The single shot bonding would eliminate processes of the second time layup, autoclave, debagging and c scan.

The production rate of the future production system would be very high as compared to current production system. So the new Takt time is very less as compared to current production system Takt time. This research is based on assumption, that new production system should be capable of producing 300 shipsets. This assumption is used by Fokker for GLARE automation project. The working hours would be same as that off current situation. Based on 3shifts and 5days/week shift type, 5040 net hours are available to meet the demand. The production system would produce 150 shipsets of A320 and 150 shipsets of A321. Each shipsets has 15 panels, so production system would produce 4500 panels. Table 13 shows the Takt time for all the processes.

Table 13 Takt Time comparison

Processes New Takt Old Takt Time (min) Time (min) 1) Layup & Bagging 67.20 198.00 2) Autoclave 67.20 198.00 3) Debagging 67.20 198.00 4) C Scan 67.20 198.00 5) Machining 67.20 288.00 6) Finishing & Painting 67.20 456.00 7) Rework Lugs 67.20 456.00

The Takt time needed for the new production is very low as compared to current system. For achieving this required pace of production there are two methods: 1) Buy new similar resources or 2) Modify the processes to reduce the cycle time and eliminate waste activities. The main focus in this research would be to design the new production system with low cycle times synchronous to Takt time and eliminate the waste activities.

If new similar resources as current system are used for production of A320 family aircraft panels, then the amount of layup and paint box workstations needed would be 30 and 12 respectively. This will need large number of workers and high operation costs, which Fokker wants to avoid for future production system. This alternative is explained in detail in section 6.1 of this report.

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Conclusion from Current Production system analysis

 The Layup process & Painting and Finishing processes are the most constrained processes, for the future production system this processes needs to be modified to meet higher pace of production.  The most non-value adding time is spent into layup process as the process is manual and it involves lot of non-value adding activities like tool cleaning and degreasing, laser calibration, placing of foils, bagging, etc. This needs to be minimized in future system.  The maximum defects in the panels are resulting from the errors in the layup process, this need to be minimized in the new production system.  The overproduction and push system is causing lot of work in process inventory. For new system pull system needs to be implemented by having one piece flow, it will reduce the work in process inventory and the production lead times.  The current layout is not continuous and it is resulting to unnecessary transport movement. The new production system layout should be continuous with well-planned resource allocation to minimize transport movements.  The production line is unbalanced resulting to high amount of work at certain work stations and less work at other stations. This leads to waiting of panels and underutilized workers.

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4. Future Production System

This section will give overview about the future production system requirements and modifications which needs to be done in the existing processes to achieve the higher pace of production. Section 4.1 will explain the production system requirements, section 4.2 will explain layup process modifications, 4.3 explain paint process changes, Autoclave modifications are explained in section 4.4 and section 4.5 explains use of flexible workers for line balancing. 4.1 Future System Requirements

The challenge for this master thesis is to develop a future production system that is able to meet customer demand, following the process requirement of Fokker. The demand is generated by the customer airbus and based on this demand Fokker would plan the production of panels. But before staring the production of panels at the layup station, components needed for it like aluminum sheets, doublers, prepreg, adhesives & stringers should be prepared and delivered to layup station. The requirements of the production systems are obtained from meetings and brainstorming sessions with Fokker experts Leo Muijs, Leo Meijers, Harry Gharbharan and GLARE process specialist Faisal Rajabali. The requirements of production systems are:

Production rate requirement:

1) Every year 300 shipsets should be produced, with each shipset having 15 panels. The assumption to be used for demand of A320 and A321 is 50:50. So, 150 shipsets of A320 and 150 shipsets of A321 needs to be produced.

2) The production of Doublers for the panels should be done in advance before starting layup, for 15 panels of A320 total 56 doublers and for 15 panels of A321 total 50 doublers are needed. Appendix 3 contains details on doublers.

Process requirement:

1) The first step for GLARE panel production is lay up, but before starting the lay up required amount of sheets should be made available at lay up station. A right sheet should be at the layup station at the right time. The right time is the time when the lay-up robot wants to pick up the sheet for the lamination of the panel. This requirement can be applied in two ways: Continuous delivery of a single sheet at the right time or Batch delivery of sheets for one panel in a kit before start time of the lay-up process.

2) The Buffers and inventories of sheets need to be eliminated, so pull system of production for sheets needs would be used. Sheets should be made on demand for the lay up station.

3) The doublers need to be made in advance before starting the panel production. The doublers production needs to be optimized by proper combination.

4) Stringer would be directly delivered by PAG.

5) Before starting the layup the clean tools should be available at the lay up station.

6) The layup of aluminum sheets, prepreg & adhesives needs to be automated for future system.

7) The panels dimension and properties like double curved or single curved should be taken into account for future system.

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8) The pre compacting is necessary after layup before starting doublers and stringers installation on panels.

9) The doubler layup can be manual or automatic. The prepared doublers should be available at the doublers lay up station. The preparation includes Alodine and BR127 application before putting the doublers on the panel.

10) The stringer layup needs to be automated as it is highly time consuming process.

11) The bagging of the panel after the layup can be manual process as it is complex process and needs human inspection.

12) The autoclave process of curing the panel should be same as current production system. Only size of the autoclave can be changed.

13) The Debagging process can be manual in the future production system, as it is very less complex and does not consume much time.

14) The C scan would be done at higher speed by use of phased array. The speed of scanning increases by 50% due to phased array.

15) The milling process would be similar to current production, only alternative milling bed would be used to save installation times.

16) The sanding process is necessary before painting; it needs to be automated for future system. The process should remain the same, but it should only be automated.

17) The type of primer and topcoat would remain same for future system, the process of applying the paint needs to be automated.

18) The primer and topcoat needs to be dried before starting the next process, the drying can be by convection or radiation type heating.

Doublers Combination Requirement:

1) The single curved and double curved doublers need to be made separately. Only single curved doublers can be stacked on top of each other, while double curved doublers cannot be stacked.

2) The maximum thickness of stacked doublers is 15 mm.

3) Doublers of different GLARE type, thickness and different no of aluminum layers should be made separately.

This research follows the above requirements during selecting the concepts for the process modifications. All the modifications selected follow those requirements and were verified with Fokker experts.

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Future system modifications

4.2 Layup process modifications

To achieve required Takt time the processes needs to be modified by automating time consuming activities. Because of automation of certain processes the manual activities of the processes needs to be rearranged at different stations for workers safety. Thus, the production line sequence should be reorganized. Here, the layup process can be considered as assembly of various parts coming together to make the panel. The automated and manual assembly activities can be separated to different stations and panel can be transported during the assembly. This concept is well proven for mass production in automobile industry where car are assembled in stages at different work stations. Moreover this concept is based on lean assembly line principles of achieving continuous flow to reduce work in process inventories and waiting times (Baudin, 2002).

Lean semi-automated assembly flow line for Layup

A semi-automated flow line consists of several machines or workstations which are linked together by work handling devices that transfer parts between the stations. The transfer of work parts occurs automatically and the workstations carry out their specialized functions automatically or manually. Currently the GLARE production involves manual production processes at workstations and also the work handling is also done by manually driven vehicles. But to achieve the higher pace of production these processes needs to be automated.

The designing of a semi-automated assembly line is complex job, for simplification it can be subdivided into various components. Then those components can be merged together to complete the design. The assembly line design methodology include following components (Chow, 1990):

1) Process design

2) Material Handling

3) Work in process management

4) Parts procurement and feeding

5) Line layout & Balancing

6) Maintenance policy

The first thing to consider during designing the GLARE Layup assembly line is process design, it is the most important step in designing as other parameters would be designed based on the selected processes. For example process timings affect the line balancing and part feeding rates. Material handling is also important area that needs special attention. Though it does not add value to the product but it facilitates the processes. Material Handling involves movement of tools and panels from one station to another. Any delay in that can affect the performance of whole line. Work in process management deals with the in process inventory and its buffers. Part procurement and feeding would include feeding of parts like aluminum, prepreg, doublers and stringers to the process stations. Any delay in feeding can stop the whole assembly line. Line layout and balancing includes the orienting the work stations in proper sequence and proper numbers. Maintenance policy consideration is also important as the both workstations and transport equipment’s are subjected to failures. Any unplanned failure can stop the complete assembly line.

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4.2.1 Process Design

As per the analysis results from previous section, the most time consuming process in GLARE production is layup process. It involves lot of handcrafting of prepreg, adhesives and aluminum to achieve the complex GLARE product configurations. It also involves secondary activities like cleaning and degreasing of molds, bagging of panels and material preparation. It also involves layup of doublers and stringers on the panels. All those activities are carried out manually at one stationary layup station as shown in Fig 50.

Fig. 50 Current Layup process

For the future semi-automated production system it is necessary that those activities are separated and done at individual workstations. The workstations would be designed such that their cycle times would be synchronous to Takt times. Each of the workstation would be dedicated to one particular tasks and the arrangement of tooling would be accordingly done. The secondary processing activities should be done at feeding stations beforehand, so that cycle times of main production workstations are less. Fig 51 shows the new arrangement of workstations for layup. The automation possibilities of the shown workstation are discussed later in this section.

Fig. 51 New arrangement of workstations for Layup

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Process modifications

1) Mold Degreasing and Cleaning

According to the production planning the dedicated mold for each of the panels would be transported from the mold storage to the cleaning workstation. Mold needs to be degreased and cleaned before starting the layup; otherwise any impurities can result into defected panel. The particles on the mold surface results into dents on the panel. This is also one of the main reasons for defects in the current production system. In the current system molds are degreased manually by workers. It takes on average 1.5 Hour to clean the mold of bigger panels which has length of 11 m and width of 3 m. The shipsets of A320 and A321 also includes panels of 11.8 m length and 5.4 m width. So the timings would be even more if it is done manually. Moreover, this is non-value adding activity and it needs to be minimized. So, it is suggested to automate the process.

The automated cleaning of mold by using state of the art laser cleaning technology is possible. This system already exists for mold cleaning in automobile industries, composites manufacturing & casting mold cleaning where molds made up of invar, aluminum or high speed steel are cleaned by lasers. The laser system distributes thousands of focused laser pulses per second onto a contamination layer deposited on molds. The powerful single laser beam pulses are linearly deflected and placed adjacent to each other. Most of the laser energy is being absorbed by the surface layer and is directly transformed into thermal energy. This energy vaporizes existing contaminations and removes them effectively from the molds.

Due to their high reflection factor, metallic surfaces are especially suitable for laser cleaning. The substrate is not mechanically or thermally stained by the cleaning process. An integrated suction system provides continuous vacuum at the laser target interface and immediately captures the vaporized contaminants. They are pulled through a hose to a fume extractor which removes particle by HEPA (High-efficiency particulate arrestance) & carbon filter. This also makes the system useful for usage in clean room environment.

According to Fokker experts this system can be effective, as it will reduce cleaning times. Moreover, airbus also uses such system for production of A350 molds.

Fig. 52 Laser Cleaning System (Source: Niemeier Enterprises LLC & Clean laser GmbH)

The benefits of using Laser systems are:

 Faster Cleaning of molds (22m2/hour), thus non-value added time reduction  Laser mold cleaning is an ecofriendly process that quickly and safely removes difficult residues without producing chemical or abrasive waste.  Extends the service life of molds, as no wear is generated.

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2) Layup of Aluminum and Prepreg

The most important step of GLARE production is Layup of aluminum, prepreg and adhesives on the mold. The process needs very high accuracy, an error in layup results to defected panel. Currently, the process is done manually by workers and is very time consuming. The lay up time of panel with length 10.1 m and width 3.1 m length takes on average 33 hours. In A380 production only 3 panels are of that big size, but in A320 & A321 production 12 panels are of that size. Though this is value adding time, but still it needs to be minimized in order to meet the required pace of production system.

In current production system, before starting the actual layup process, some secondary activities need to be completed like starting the laser projection system and calibrating it with the mold position. It helps in accurately positioning of sheet and prepreg. This take on average 1 hour. After that release foil needs to be applied to mold to prevent sticking of aluminum with the mold. For layup folded sheets packed in polythene are delivered from the buffer, they are unpacked and unrolled at the tables located at lay up station. These sheets are inspected by workers for any irregularities like dents. If any dents are there then workers order new sheet and wait till they are delivered. Adhesives and prepreg are also unpacked and unrolled on table at lay up station. These are all non-value adding activities and needs to be eliminated from the process. The value adding activities are the layup of aluminum sheets, putting the adhesives on splices and putting the prepreg layers. This process is repeated until the desired configuration of panel is achieved.

Fig. 53 Lay Up Process activities

Fig. 54 Lay Up activities

In future production system non-value adding activities like applying release foil can be removed. The release foils would not be needed if the molds are coated with permanent release coatings of materials like Teflon, and Polytetrafluoroethylene (PTFE). There are also semi- permanent mold release agents which do not need coatings but can be sprayed after certain autoclave cycles. However, the disadvantage of semi-permanent mold release agents is that it requires 15 min of drying time after spraying before starting the layup (Amber Composites, 2015).

In lean production systems feeders lines supply defect free products to the main line. Also the quality control and assembly preparation of those products is done at the feeding lines and not at the assembly line. By doing this the secondary operations are separated from the main assembly line, decreasing the cycle times of workstations at the assembly line. The more details about this would be discussed in feeder line section in the report.

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The laser projection system and its calibration are needed to guide the people to place the sheets accurately. This system would not be needed when the process will be automated in future production system. The robots in automated production system can work according to defined coordinate system and place the sheets accurately on the molds. The layup of aluminum can be done by robots equipped with pick and place end effectors. The High speed pick and place robots take product from one location and place it at required location with pin point accuracy. By using custom made end effectors different materials type can be picked and placed by robots. The most common types of end effectors for pick and place are vacuum based.

Currently, Fokker is investigating the use of pick and place robots for layup of sheets. The custom made end effector for aluminum sheet layup has already been developed. For experimentation, Glare panel of 1.5 x 1 meters was placed up automatically by using the pick and place robot at Fokker current production facility. Fig 55 shows the experimental setup of robot with custom designed end effector.

Fig. 55 Pick and Place experiment at Fokker test facility

Times taken by robots for various movements and activities during experiment are given in the Table 14:

Table 14 Aluminum sheet pick and place timings

No Activity Time (in seconds) 1 Picking Al sheet 15 2 Full movement 10 3 Placing Al sheet 15 4 Change tool 15 5 Empty movement 10

The robots speeds for picking the sheet is slow, as the sheet is only 0.3 mm thick and picking at high speed can cause waviness in the sheet. After picking, the movement of robot to mold is at higher speed. Curvature of panel is achieved by adjusting the end effector. Then the sheets are placed on the desired location and empty end effector moves to its reference position. The timings shown in the above table are for sheets of smaller dimensions. But according to Fokker experts this timings would be higher for larger sheets as the complexity increases in handling. The exact correct timings would be known once the demonstration of layup is done at end of TRL 3 of GLARE automation project. Based on Fokker experts opinion it is decided to take higher timings (double of experiment timings) and to do sensitivity analysis to check the effect of change in timings. The Section 6 shows the timings of layup of all 30 panels based on layup speeds.

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One robot can pick up the sheet of maximum dimension of approx. 6 m with the designed end effector. So for the longer panels the 2 robots would be needed to pick up the sheets of larger length. FT GmbH)

For the Prepreg layup the pick and place robot with different end effector will be used. The prepreg sheets needs to precut before layup. In current production facility the cutting station is separate from layup station. However for the new production facility it would be better to have the cutting machine in combination with a robot pick and place cell that automatically cuts the prepreg material and rolls them as per desired length and width. During the experiment the prepreg layup by pick and place robots was demonstrated but for larger length this is not that effective as prepreg is not rigid and develops waviness as shown in Fig 56.

Fig. 56 Prepreg by pick and place Fig. 57 Conceptual end effector of prepreg layup (Source: AFPT)

Thus the end effector as shown in Fig 57 needs to be used for lay up of prepreg. The precut prepreg can be stored on the rollers and then in can be layup on the mold, the consolidation unit will put pressure on to it and tape guidance unit will take the carrier tape from the prepreg. The speed of prepreg layup by tape laying end effector is 50 mm/sec for 230 mm wide prepreg. The speed of adhesive tape lay up would be 100 mm/sec.

Thus, one robot can do all three activities by changing the end effectors after every operation. Moreover the robot also needs to move in the parallel direction of mold, which was not considered in the experiment. But according to Fokker experts it is possible to automate the lay up the process. The layup speed of aluminum is as discussed above, 100 mm/s for adhesives layup and 50 mm/s for prepreg layup are possible to achieve. In this research the new processing times for lay up station are calculated based on these speeds of the robots. For calculating the layup process times the model is developed in the Microsoft excel based on the bill of material of the panels and the process speeds of the robot. The process times calculated from this model are used for the simulation model in Simio simulation tool.

Thus after the process modification of layup, the new process would only include the value adding activities of layup of aluminum, adhesives & Prepreg on the main assembly line, while the preparation activities would be on feeding line. This will help in reducing the main cycle time of the panels at the assembly line.

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3) Doubler layup

The doubler and stringer layup station should be separate from the lay up station in the new production system. The doubler and stringers would be put on top of the skin panel made at the layup station. The doubler would be made on the separate feeder line using the same process as that of GLARE panels. In the current production system the preparation activities of doublers like cleaning, applying Alodine and BR127 on edges, drying & adhesives application are done during layup of the doublers. Fig 58 shows the doubler preparation activities before doubler layup. Blue boxes represent preparation activities, while orange shows main activity.

Fig. 58 Doubler Layup activities

For future system it is proposed that these activities can be done at the feeder line and directly prepared doubler should be delivered to main assembly line. This saves the time of main assembly line and thus the lead time can be reduced. Moreover, when prepreg pieces are cut manually at the layup station from the prepreg sheets lot of waste is generated due to different shapes of doublers. For the future system when the prepreg cutting is done automatically before doubler layup cell than optimization of prepreg sheets usage can be achieved. Nesting of prepreg for various doublers can be done from the one prepreg sheet.

The doubler layup on the skin panels can be done automatically by having the pick and place robots, same as that used for aluminum sheets. But it can be done manually as the process is simple and less time consuming. It can be done on the same workstation where vacuum packing (pre compacting) is removed manually. Only 5 panels of A320 and 8 panels of A321 would have doublers. The number of workstations needed for this and its effect on the assembly line are known from the simulation runs. The time calculations for manual doublers layup are based on the current A380 doublers layup process. From the simulation result it was found that 4 workers can do the doublers layup manually. Also in the future system for this manual task Takt time screens can be placed at the workstations, so workers can work at uniform rate and do not affect the production line downstream processes.

4) Vacuum Packing (Pre Compacting) and bagging

After the layup of aluminum, adhesives and prepreg at the layup station by robots, the air in between the layers needs to pulled out before putting the stringers and doublers. Because the air in between layers will affect the accuracy of doublers positioning. Thus pre compacting is necessary after the layup. In pre compacting the vacuum foil would be applied over the panel and vacuum would be created over the panel, according to GLARE process specialist Faisal Rajabali this state needs to be maintained for 15 minutes. After that foil can be removed and doublers can be assembled on the panel. This process can easily be done manually as it involves less complexity and is not time consuming.

After the stringer layup panels needs to be bagged before putting it in autoclave. The process for future system would remain same as that in current production system (explained in section 1). Though this is non-value adding activity but it is necessary for production. This can be done manually as it involves less complexity and is not time consuming. The timings for the bagging are calculated based on the A380 process parameters as the process will not change.

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5) Stringers Layup Concept

In current production of A380 panels, 3 panels require stringers and the actual lay up time of stringer on average is 14 hours for each panel. The placing of the stringers requires very high accuracy. For future production of A320 & A321 all the 30 panels require stringers. To reduce the cycle timings process needs to be automated. If the process is done manually it will need around 12 layup stations, the calculations are explained in detail section 6.1 of this report. There are 2 possible ways in which stringer layup can be automated, are discussed below in more detail.

Concept 1: Stringer Positioning Head (Jig)

The stringer lay up can be done automatically by using the pick and place robots. In one panel around 12 stringers are assembled. Instead of assembling each stringer individually, a stringer head (Jig) can be developed which can have all the stringers prepositioned on to it before the assembly manually. The robots can first put the adhesives on the panel by using automatic tape laying end effector as discussed for the adhesive layup above. After that robot can change the end effector and position the stringer head on to the panel accurately.

After positioning the stringers on to panel they need to be fixed to their positions, otherwise during bagging they can move from their desired positions. To prevent stringers from moving, local heating needs to be provided for short interval of time by an end effector with heating system inbuilt into it. After that end effector can retrieve back to its reference position to change the tool. Moreover by the time stringers are assembled on to the first panel, the second head can already be prepared with stringers by workers and robot can immediately start assembly operations for second panel. Fig 59 shows the sketch of stringer head jig, which should have same curvature as that of panels and the stringer pitch would be as per design of the panel.

Fig. 59 Stringer Head Jig

Advantage of this concept:

1) Less processing time per panel, thus it will reduce lead time of main layup assembly line 2) Accuracy of stringer positioning would be higher than manual process

Disadvantage of this concept:

1) Research needs to be done for developing the stringer head, which will take time and investment

Design Challenge:

1) Pitch of the stringers would not be same for every panel, so the Jig should be such that the pitch can be adjusted as per panel type. Otherwise, multiple Jigs would be needed.

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Concept 2: Pick and Place individual stringers

The stringers can be picked and place individually on to the panel by the help of 2 robots. Here first robots have to put the adhesives on the panel and then stringers can be put on the panel. The accuracy of +/- 0.3 mm can be achieved by having the 3d laser tracking system which can guide the robots. This system is already in used at PAG factory at Nordenham, Germany for stringer layup for A350 Aircraft (FFT, 2013).

Fig. 60 Stringer Pick and Place (Source: FFT)

Advantage of this concept

1) Already proven and developed technology 2) Less processing times

Disadvantage of this concept

1) Individual stringer positioning takes more time due to more robot movements, thus processing time would be higher than stringer head positioning concept

Times taken by robots for various movements are given in Table 15; the timings for stringer pick and place were obtained from the FFT, who developed this concept for the PAG.

Table 15 Stringer Pick and Place Timings

Robot Process Time Speed (mm/s) (sec) Pick stringer 10 Position stringer 10 Place stringer 28.5 Full movement of Robot 30 Empty movement 10 Adhesive placement 100 Tool change 30

The Microsoft excel model is used to calculated the process times of the stringer layup based on the bill of material of stringers and the robots speeds as shown in Table 15. The detailed timings are shown in section 6 for all the panels of A320 and A321. These timings are used as input for the Simio simulation model.

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4.2.2 Material handling on the assembly line

The transport between the workstations is possible by vehicles like AGVs, Forklifts, monorails, etc. The transportation of molds between workstation is outside the scope of this research. It would be covered by my Transport Engineering & Logistics colleague Bart Rinsma. In this research it is assumed that transportation would be done by certain type of AGVs, capable of travelling at particular speed and would be custom designed to carry the molds. 4.2.3 Work in Process management

The work in process management deals with managing the inventory on the assembly line. The difference in cycle times of the process stations can lead to temporary buffers on the assembly line. But as per lean principles it needs to be avoided by line balancing and proper production scheduling. The buffer stations inventory and locations is determined by the discrete event simulation model in Simio. Section 6 will explain in detail the queuing pattern and in process inventory. Based on that results the space allocation for buffers is done while designing the production system layout.

4.2.4 Feeding production line

A feeder line is an assembly line in a factory where people and equipment produce parts for the main line. Using feeder lines can increase efficiency and output. In GLARE future production system main feeding lines are of aluminum sheets and doublers. These products would be assembled at the main Panel production line. All the processing on the doublers and aluminum sheets can be done on pre-assembly or feeding lines. Fig 61 shows the feeding lines and main assembly line as per fishbone principle of moving line. The main advantage of this line is the parallelization of work reduces the lead time on the main assembly line (Altfeld, 2010).

Fig. 61 Fishbone Principle of Moving line (Altfeld, 2010)

1) Doubler feeder line

The doublers feeder line would have same production process sequence as that of actual panel’s upto milling. The doublers need to be made in advance before starting the assembly of the main panels. Ideally the Takt time of doubler feeding line should be:

Table 16 Takt time

Shipsets/day Doublers/ Total doublers Net Available Takt time shipset needed everyday Time/ Day 1.2 (18 panels) 53 63 21 Hours 20 Minutes

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Combination of Doublers

The doubler required in one particular panel can have different GLARE type, different thickness & different length and widths. To minimize the number of separate panels needed for doublers similar type of doublers can be combined together in one panel. This is necessary as 53 individual doublers production for shipset would need separate molds, separate processing at all the workstations and separate frames for transportation. Moreover it will increase the material handling activities. Thus, similar doublers need to be combined into one doubler panel. The factors to take into consideration while combining the doublers into one panel are:

(1) GLARE Type (2) Thickness (3) Single curved or double curved

(4) Splices (5) Prepreg direction

The doublers are combined as per the process requirements of doublers production. The combination is of 3 different types based on these requirements:

1) Double Curved doublers in one Interface

The double curvature doublers cannot be combined into one panel and they cannot be stacked onto top of other panels. So they should be made separately into smaller molds. The molds need to be designed as per the curvature of the doublers, which is determined by the curvature of the panels. But this will result to 36 individual DC (double curved) doublers molds. To reduce the logistics of the individual molds this molds can be combined to one interface, which would be the combination of all small molds. Fig 62 shows the example of such interface of DC doubler molds.

Fig. 62 DC Mold Interface

While combining the molds into interface, the maximum dimension of the interface were constrained to 10 meter in length and 2.7 meter in width, so that the molds can cured into the Large sized autoclaves. In total 5 such interfaces would be needed for DC doublers of A320 and A321.

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2) Single Curved Doublers without splices combined

The doublers with width less than or equal to 1200 mm would be made without splices. These doublers can be combined into one panel provided if they have same GLARE type and same number of aluminum and prepreg layers. If they do not have same GLARE type and same number of layers then different thin GLARE panels of doublers can be stacked on top of each other, after putting thin separating materials between layers. But to avoid the impression of one layer onto other all the stacked layers should be of same dimensions. By this combination 40 doublers would be made for A320 and A321. The final shape of doublers would be cutout by milling machine from this combined doubler panels. The maximum thickness for stacking doublers panels on top of each other is 15 mm. Fig 63 shows the example of combination of SC doublers.

Fig. 63 SC doublers without splices

In the above figure the combined panels of the SC doubler is shown, the doubler are shown by brown colour, the orange colour is the scrap generated after cutting the doublers at the milling. The main reason for the scrap is the uniform size of needed panels when stacked. For example: one layer of combined doublers would be of dimensions 1200 * 500 mm and 2nd layer would be of 1150 * 500 mm, but it will be made of 1200 *500 mm due to process requirement, resulting to scrap of 0.025 m2 material. The doublers of different GLARE types are stacked on top of each other; in Fig 63 different colour represents that.

3) Single Curved Doublers with Splices

Fig. 64 Spliced Doublers Combined

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The spliced doublers (width greater than 1200 mm) cannot be combined or stacked on top of each other as the impression of splices develops on the other layers. So they need to be made next to each other on one mold. Fig 64 shows the example of combination of spliced SC doublers.

Based on the above mentioned methods 106 doublers are combined into 13 panels and mold interfaces. The list of doublers and its combination is attached in appendix 3.

Advantages of Doubler Combination:

1) Less number of molds needed 2) Less tool handling movements 3) Process time savings for bagging and debagging, as instead of 106 times, process would be done only 13 times 4) Less Autoclave cycles 5) Less number of C scan frames and standardized frame pitch would be needed

Disadvantage of Doubler Combination:

1) Due to combination of doublers into 1 panel, advanced made doublers needs to be stored. So, storage (decoupling point) would be needed.

Doublers Production Process

The doublers productions processes include the layup process (similar to main panels as explained before), Autoclave process, debagging, c scan and machining. After that doublers would be prepared for the actual layup by applying Alodine and primer. The process is shown in Fig 65, blue box shows the main production activities and orange box shows the assembly preparation activities.

Fig. 65 Doubler Production Process

The doublers would be made on the separate resources on the feeder line. The layup cell of the doubler would need only 1 robot as the sheets needed for doublers are smaller than 6 meter in length. The bagging and debagging of the doubler panels can be done on dedicated stations. The frame used for C scan for doublers would be separate with different pitch to fix smaller panels. At the debagging station doublers would be framed into C scan frames. After C scan the doubler are machined on the dedicated smaller size milling station.

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After the production of doublers to protect them from corrosion alodine needs to be applied. For 24 Hours the alodine needs to be kept before assembling it to panel. Then the doublers are stored for 24 Hours, after that BR 127 is applied and dried for 30 minutes. This is not lean as it is waiting inventory but due to process requirement it cannot be avoided. Then the doublers are delivered for the assembly to main panels.

The doublers are specific for particular panels and cannot be interchanged. So they would be feeded to the assembly line according to the panel production sequence. According to Johansson (1991) material feeding principle for assembly lines, kitting should be used in this case as the materials are sorted by assembly objects (panels) and assembly stations are not fixed from before.

Fig. 66 Material Feeding principle (Johansson (1991))

When doublers are produced in lot together for different shipsets in advance it can be stored by making kits. The Kitting can be done at storage points before storing the doublers. All the doublers of same panel can be kitted in one container. This container should have all the information about the panels and doublers on it in form of stickers and barcodes. When demand arises at the assembly line this can be easily searched and delivered to the assembly station.

Advantages of Kitting:

 Reduces mistakes of incomplete assembly at the assembly line  Less searching time and order pickup times  Reduces chances of wrong picking  Better inventory management

Disadvantages of kitting:

 Extra persons are needed for doing the kitting  Extra kitting space is needed

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2) Aluminum Sheets feeding line

The aluminum sheets production line has already been covered in the previous research of Piet de Vries, in this research that would not be analyzed in detail. Only the proposed modification will be used in model. The feeding of aluminum sheets from sheet processing area to the layup station would be covered in this research. The current aluminum production system is already explained in section 1.2.4 of this report.

The modifications suggested for future production system in previous thesis work of Piet Vries was to reduce the response time of sheet delivery by shifting the milling machine to the end of aluminum sheet production line. This is already included in the basecase of Fokker. So, in this research the new production line with milling at end would be taken into consideration. The main point of interest in this research is to check on how these sheets should be feeded to the layup stations (continuously or in kits) and where should be the decoupling point. The future production line of aluminum sheets is shown in Fig 67.

Fig. 67 Aluminum sheet production line

The blue boxes in the Fig 67 shows the main production activities, while orange box shows the assembly preparation for main assembly line like inspection of sheets, positioning it in sequence as needed for assembly & kitting (if needed).

Sheet Delivery to Layup stations

For A320 panels 371 sheets and for A321 panels 379 sheets of different dimensions are required at the layup stations. This sheets needs to be delivered at right time to right layup station. According to Johansson (1991) material feeding principle for assembly lines, sheets can be kitted or delivered continuously to the layup station. The transport system to be used for delivery of sheets needs to be selected. For selecting the transport system, the inputs from the Fokker experts are taken into account.

The Analytic Hierarchy Process method (AHP) is used for decision making of the transportation system. It is a mathematical method, commonly used for multi criteria decision making, where the most suitable alternative is selected out of a group of available alternatives on the basis of a defined number of decision making criteria’s. This method is particularly suitable for use in cases like this when there is not enough information available on the reviewed alternatives in the decision making. The transportation system and equipment’s needs to be customized to the specific requirements for 0.3 mm thin different dimensioned aluminum sheets. So, the views of the experts for selecting the transport system are very vital.

The 5 experts from Fokker were consulted for transport system selection and there inputs were used for AHP criteria selection and AHP analysis. All the 5 experts are involved in the GLARE Automation project. Following were the experts:

1) Ir. Pim Tamis 2) Ir. Harry Gharbharan 3) Ir. Leo Muijs 4) Ir. Leo Meijers 5) Ir. Marko Bosman

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AHP Analysis model

Fig. 68 AHP Model Problem Structure

The main criteria’s based on which transport system would be selected includes: Reliability of transport system, Investment costs and operation costs (including workers costs), Required Factory space, Automation possibility, Maintenance needed by the transport system, Flexibility, Technical Adaptability (Implementation of Equipment for this specific case), Logistics parameters like speed and response time as per demand at the layup station and ability to deliver sheets in kits or continuously (sheet by sheet).

The transport alternatives considered for the sheet delivery are conveyors, automated guided vehicle based kit delivery, manual carts for kit delivery & overhead cranes. This equipment’s are pairwise compared to each of the criteria’s discussed above. Fig 68 shows the problem structure of AHP model, the transport alternatives are given weightage for each of the criteria’s by pairwise comparison against other alternatives and the criteria’s weightage and their contribution to the final goal is obtained by pairwise comparison with other criteria’s as discussed in section 3.7. The points scale used for ratings in same as discussed in section 3.7. The AHP model is made in Microsoft excel to do the matrix computation and validation checks. The model provides the final results of best possible transport system based on the input ratings from experts. Only the results of the model are discussed here and the model is shown in Appendix 4.able 17 Criteria Importance

Fig 69 shows the average of the criteria weights. The weightage differs between experts as they had different views about the needed system. All the 5 experts selected have equal decision making power, so the arithmetic mean of the above ratings will be used in this research.

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Average Criteria Weights Continous Delivery 1.54% Logistics Parameters 16.46% Technical Adaptability 19.10% Flexibility 5.92% Maintenance 4.33% Automation 5.76% Factory Space 3.86% Operation Cost 10.36% Investment Cost 10.95% Reliablity 21.73%

0.00% 5.00% 10.00% 15.00% 20.00% 25.00%

Fig. 69 Resultant Value of Criteria Importance

The average of Experts ratings for each of the alternatives for particular criteria is shown in Fig 70. It shows how the alternative contributes to criteria’s. It shows that high reliability can be obtained by manuals karts and AGVs. An investment cost of manual karts is very less, so it’s most advantageous, while AGVs are expensive so they have very less weightage. Operation Costs of manual karts would be high due to need of workers for driving carts, so it has lowest weightage. Overhead Cranes are most advantageous for factory space, as they do not need any floor space and can travel above the equipment’s.

For automation AGVs are most advantageous while manual carts have very less weightage. Manual carts needs very less maintenance, so they are most advantageous. For achieving the flexibility AGVs and manual carts are most advantageous and conveyor has very less weightage. Technical Adaptability of manual carts is highest while for logistics parameters (like speed & response time) AGVs are most advantageous. For continuous delivery conveyor are the most advantageous as kitting can be eliminated, while other alternatives require kitting of sheets before delivery to layup station.

Transport System Comparison 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10

0.00

Reliablity

Flexibility

Logistics

Technical

Parameters

Automation

Adaptability

Maintenance

FactorySpace

OperationCost

Investment Cost Continous Delivery 1 2 3 4 5 6 7 8 9 10 Conveyor AGVs Manual Carts Overhead Cranes

Fig. 70 Transport Alternatives Comparison

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The final results are obtained in the model by multiplying the criteria weightage (Fig 69) with transport alternative weightage for those criteria (Fig 70) and then taking the sum of weightage of for those particular alternatives.

The criteria’s like reliability, logistics parameters, technical adaptability and Cost parameters (Investment & Operational) has high weightage as compared to other criteria’s. So, these are the most important in final decision and equipment which has better ratings for those criteria has highest chances of selection. Manual Carts and AGVs are two best transport alternatives as per the results of the AHP model. Fig 71 shows the final result of the AHP model.

Final Result 0.350 0.300 0.250 0.200 0.150 0.325 0.265 0.100 0.207 0.202 0.050 0.000 Conveyor AGVs Manual Carts Overhead Cranes

Fig. 71 Best Alternative Selection

Based on the AHP result, kit delivery system was simulated in the simulation model and the number of transport resources needed was found. From the model result it is found that only 1 kit delivery vehicle for sheet transportation is needed. Thus, one forklift or pulling truck and one worker would be needed for manual carts system.

The results were discussed with the Fokker experts and a manual cart in their view seems promising alternative. It would not cost high investment and operation costs would be also low as only 1 worker is needed. Moreover, as the kitting is automated worker only has to deliver the formed kits to the layup station, so there would be negligible chance of mistakes. Thus, the kitting system with manual kit delivery is selected for aluminum sheet transportation.

Moreover, 2nd alternative can be to use the AGVs of the tool handling, for the transportation of kits. For the use of AGVs, at kitting station and at kit cart location at the layup station, loading/unloading platforms would be needed.

Thus, from the analysis it is clear that kitting would be needed after the milling, and for kit delivery system manual carts and AGVs are two best alternatives. However, in this research the manual kit delivery system is considered, as it is the best alternative. The kitting can be done after the milling and cleaning of sheets. Then kits can be stored in lean supermarket (decoupling point) and delivered to layout station on demand. Here, the decoupling point would be needed so that the sheets can be delivered to layup stations on time. If decoupling point is not kept than the stations needs to wait until kit is ready. Based on simulation results, decoupling point of maximum 5 kits storage would be needed, on average 3 kits would be in decoupling point. The waiting pattern of kits in decoupling point is shown in Appendix 7.

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4.2.5 Layup line Balancing and line layout

The workstation shown in Fig 51 would has different processing times for all 30 panels, so initial minimum number of works stations needed for each of the processing activities is decided by Line balancing which acts as a input for dynamic Simio model for 1st iteration. Then the number of workstations are changed by seeing the utilization rates, queuing of panels and waiting times to balance the line, so that the production system has continuous one piece flow (Lean principle).

The 1st step in line balancing is to identify all the processes of the assembly line and the sequence of the processes. In this case the processes are tool cleaning, layup of aluminum sheets and prepreg, pre compacting, doublers layup, stringers layup and bagging. The sequence of the processes is shown in Fig 72. As per the process requirements this sequence cannot be changed. The 2nd step in line balancing is to draw the precedence diagram based from the results of the 1st step.

Fig. 72 Line Balance Precedence Diagram

The Precedence diagram shows the activities of the Layup assembly line and also their sequence. The tool cleaned by automated laser systems would be transported to layup, where robots will do the layup of aluminum and prepreg. Then before doing the doubler or stringer layup, laminate needs to be pre compacted by providing vacuum, this would be done manually shown as activity C. Then the panels which require doublers have doublers layup activity as shown as activity D. Both activities of pre compacting and doubler layup can be done on the same process stations as both activities are manual. Then the panels would be transported to stringer layup station shown (D to E). The panels which do not require doublers would directly go from pre compacting to stringer layup station (C to E). Then after the stringer layup autoclave bagging would be done, shown by activity F.

Next step is to calculate the cycle times of the activities shown above. As discussed before the timings of tool cleaning, layup and stringer layup are calculated in Microsoft Excel model based on Bill of Material and robot speeds. The times for pre compacting is fixed 30 minutes for all the panels. Timings for doubler layup and bagging are calculated in model based on the parameters extrapolated from A380 panel’s data. Then 4th step is to calculate theoretically minimum number of work stations require to do the activities shown in Fig 72.

Minimum No of workstation =

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The sum of cycle time is calculated in excel model, based on 50:50 ratio of A320 and A321 for now, later in scenario effect of different demand rates will be checked. The demand is taken as baseline of Fokker of 150 shipsets of A320 and 150 shipsets of A321. The net available production time per year is taken as 5040 hours based on 240 working days a year ( 48 weeks and 5 working days/week) and 21 effective hours per day(due to exclusion of break timings for every shift).

Minimum No of tool cleaning stations = = 1.1 Workstations = 2 Workstations

Minimum No of Layup stations = = 4.3 Workstations = 5 Workstations

Minimum No of Stringer stations = = 1.8 Workstations = 2 Workstations

The pre compacting can be done just after the layup station, outside the layup cells; it should be made sure that the transportation system used should not affect the accuracy of placed sheets. For every layup station would have one pre compact positions outside its robotic cells. Similarly for bagging the every stringer position would have one bagging position outside its robotic cell.

Also the doublers production should be done in clean room, it will only require layup and Bagging workstations.

Minimum No doublers production stations= = 1.4 Workstations = 2

Workstations

Both the layup stations would have bagging positions outside the automated cells for autoclave bagging. The tools for doublers would be cleaned at the workstations where the tools of panels are cleaned.

Next step in Line balancing is to reorganize the tasks on assembly line by dividing the work equally between underutilized and over utilized workstations. But in this case it is not possible due to process requirements. The tasks of the workstations cannot be reorganized, only the number of workstations can be changed. So the above calculated number of workstations would be changed by seeing their utilization rates, queuing of panels between stations and waiting times to balance the line. Moreover, the flexible workers can be used between manual process stations to balance the line; this concept is explained later in this section. Balancing is done by discrete event simulation model in Simio. It would be discussed in more detail in section 6. The results of this calculation would be input for the Simio model.

Line Layout

The assembly line layout needs to have smooth flow of material. For the assembly line of the layup the straight line type layout is recommended as then it merges well with the overall production system layout. The processes would be semi-automated with automated transportation. The manual processes are bagging and pre compacting and would be done on fixed positions, so workers do not have to move with the line and are separate from automated process stations. Only movement would be of the tools with panels on them and that would be done by AGVs (assumption as discussed before). Final line layout is shown in Fig 135 in the overall production system layout.

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4.2.6 Maintenance Policy

In designing the production line it is important to consider the maintenance policy, as the number of work stations needed and the sizes of the buffer are affected by that. There is no data available on breakdowns for those equipment’s. So after consultation with Fokker experts, it is decided that system should be designed to maximum level of 90% unitization. If the utilization exceeds 90% then extra equipment should be used. Moreover, it should be noted that Takt time calculation, theoretical calculation and simulation results are based on assumption of 5 working days every week. This allows the service department to do plan maintenance (Preventive maintenance) on weekends. The preventive planned maintenance will reduce the chances of accidental breakdowns and increase the effective utilization rate.

To check the effect of breakdowns on the buffer size, estimated data of Fokker engineers and operators is used in the model. This is based on their past experiences of the equipment’s. The breakdown does not have much influence on the production rate, but emergency panel storage buffers are needed to store panels. So, based on simulation results emergency buffers are kept wherever needed in conceptual production layout (shown in Fig 135).

Thus, the process of the layup needs to be modified by using automated solutions to reduce the non-value adding times and cycle times. Those automated and manual solution are arranged on the production line in a way that continuous flow is created, it has following advantages and disadvantage:

Advantages of Layup flow line production

 It allows Semi-automation of production line, as manual workstations can be separated from robotic cells  Feeding of parts is required to dedicated stations only. For example, stringers need to be feed only to stringer layup stations instead of all layup station.  Less work variation will result into better quality, as the worker will only do dedicated work so they would achieve expertise into that.  More safety (as manual and automated cells are separate)  Higher Robots Utilization as automated cells are separate than manual workstations  Cycle time reduction on main assembly line as assembly preparation is done on feeding production lines ( For example: Prepared doublers would be feeded to doubler stations)  People working on a feeder line will conduct quality checks and make sure that raw material of satisfactory quality is delivered to main layup line.

Disadvantages of Layup flow line production

 More handling of molds results to more transport movement, it cannot be avoided as manual and automated process stations needs to be separated.  Worker can get bored of monotonous job; however by using the concept of Flexible worker (explained later) this will be avoided.

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4.3 Paint process modifications

The current paint process is also very time consuming as it is completely manual process. If the same process is used for future system, it will require 12 Paint boxes and 24 workers to do the job. So the process needs to be modified by eliminating or reducing non-value adding activities. Also automation needs to be used to reduce the number of employees, which will decrease the operation costs.

The current painting process is done in 2 stages. The 1st stage includes sanding of panels and sanding and priming of the splices. Then panels needs to be transported to platforms at bagging stations for sealing the stringers. Afterwards the primer of splices and stringer sealant needs to be dried for 24 Hours, so panels are stored in buffer. Then panels are reloaded into paint box for 2nd stage of painting process. The 2nd stage of painting includes sanding of splices, cleaning the panel with solvent and spraying multiple layers of primer. If the panel requires top coat (top layer of paint) then panel is dried inside paint box for 30-40 minutes at 70 degree Celsius. As the oven is integrated into the paint box, during drying of panel it cannot be used.

As only one paint box is used for sanding, painting and drying it results to non-value adding activities like changeover of tools, preparation of paint pumps, moving panel up and down in the pain box to have appropriate reach and transporting panels in and out of the paint box. Moreover drying time is also part of the lead time; completely made panel worth thousands euros is waiting in buffer. For large scale production of future system it will lead to around 40 waiting panels in storage buffer (from simulation results). Also the cycle time of manually sanding and painting are high for panels with stringers, as stringers increases the complexity of work. For A320 & A321 all the panels are with stringers. Fig 73 shows the painting processes, orange blocks shows 1st stage and green blocks shows the 2nd stage processes of painting.

Fig. 73 Current Painting processes

For the future production system it is proposed to have paint process which has separate sanding, drying and painting processes. The process will still require some manual activities for inspection and activities which are very less time consuming and simple. The most time consuming activities like sanding and painting would be automated. In the new process sealing of the stringers can be arranged in line with finishing and painting so that panels do not need to be transported back to bagging platforms. After the sealant application panels can be dried inside the oven so that panels do not have to wait for 24 Hours.

For that infrared ovens or convection ovens can be used. According to process manual sealant can be dried at temperature less than 50 degree Celsius. The oven can also be used for drying

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primer and top coat. After the painting the quality checks like paint thickness measurement, visual inspection, etc. can be the part of the process and done at the last position of the painting line, so panels do not need to be transported back to some other position for quality control. Fig 74 shows the future paint shop line processes. The blue boxes signify the manual activities and green indicates automated processes.

Fig. 74 Future Paint shop line process

4.3.1 Process Modifications

In order to achieve the required pace of production sanding and painting needs to be automated and drying by convection or radiation heating is needed, so in this section the equipment’s which can be used for that purpose will be discussed. While selecting the equipment’s it is made sure that process is not changed, but only automated by using robots with suitable end effector.

1) Automated Sanding of panels

The sanding of panels is necessary before the painting, as sanding creates the rough surface to which paint will adhere. The sanding for A380 panels is done by workers using orbital sanders with different grits and sanding papers (shown in Fig 75).

Fig. 75 Sanding Equipment’s

With the orbital sanders the workers can sand 1 m2 of panel in 3.5 minutes. For sanding the stringers the orbital sanders are used for its outside surface. The inside end of stringers are sanded by the sanding papers. As the process is manual the area of sanding stroke or speed of sanding is limited by the workers movement speeds and diameter of sanding grit.

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For future production system the robots can be used for sanding, the end effectors of robots can have sanding tools similar to orbital sander. The faster and uniform movement of robots will reduce the process times of sanding. The robots can be programmed with the 3D contour of the panel, based on that robots can sand the panel with higher accuracy. Moreover, with automation the depth of sanding passes and number of sanding passes can be changed easily. The automatic sanders have already been in use for wet sanding in automobile industry and dry sanding in wood industry. Fig. 76 Stringer sanding

Fig 77 shows the actual sanding robot and conceptual sanding robot developed by automation company Push Corp Inc.

Fig. 77 Automatic Sanding (Source: Push Corp Inc.) Robots can decrease the sanding times as they have faster movement. Moreover robots can also carry multiple sanding tools; the end effector of the robot can be developed in such a way that it can accommodate higher number of orbital sanders onto it. So, in one sanding stroke the sanding area would be increased. For example, when 8 sanding tools are used in one end effector, sanding area covered would be 8 times in one stroke. So, that will decrease the sanding time to 1/8th of original time.

Fig. 78 Sanding End Effector with multiple tools used in wood sanding (Source: ABB Ltd)

The sanding disk diameter and amount of sanders in one end effector can be determined based on the pitch distance between the stringers. The sanding is done with certain force, so for force detection the technologies like force sensing can be used. The company named “Fanuc Robotics” has already developed force sensing technology for sanding robots. Based on this technology sanding robots have been developed by Robotics integrator “Aerobotix Inc.” For U.S defense jet

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program of F-35. The demonstrations of sanding for other aircraft like F/A-18, F-16, F-22 and B- 2 have also been done (Waurzyniak, 2013). The robots can be mounted on platform which moves on rail so it can have reach across the full length of the panel.

The sanding parameters like sanding force, sanding depth and no of sanding passes needed can only be determined after performing the sanding tests with robots on GLARE test panels. These parameters are not of much interest in this research as the main aim of this research was to show that automation feasibility for sanding and to measure the effect of automation of the painting process and on the KPIs of the production system.

Thus, automated sanding robots with custom designed end effectors can be used for sanding the GLARE panels and outside of the stringers sides. For sanding inside of the stringer, it can be done in manual way by using sanding papers. This manual activity can be done before sealing the stringers at the manual process station as shown in Fig 74. The cleaning of panel after sanding can also be done manually at the same process station.

2) Automated Painting of panels

The painting of GLARE panels for A380 is done manually by spray guns, spray pumps and by paint brushes. The spray guns are generally used by workers for spraying primer and top coat on the panel. The spray pumps are only used by experienced paint workers as that speed of painting is difficult to manage. The inside end of stringers is painted by paint brushes as it is difficult to reach there by spray guns. Fig 79 shows the painting equipment’s used for A380 panels. The speed and accuracy of spraying depends much on the experience of the painter. On average 5 to 6 minutes are needed for 1 m2 of painting.

Fig. 79 Painting Equipment’s

In the current painting process, the paint thickness varies on the panel as the workers movement is not constant while spraying. At some places high amount of primer is sprayed and some place have less amounts, thus thickness varies. This not only leads to waste of costly primers and paints but it adds to the weight of the panel.

For future production system to achieve higher pace of production painting can be automated by using automatic spraying robots. The automated spraying robots have been in use from years in automobile industry. The painting automation is also gaining importance in aviation industry due to increase in demand of aircraft. In 2013, Boeing automated the spraying of wings of Boeing 777 aircraft by ABB paint robots. The process time of painting was reduced from 4 hours to only 25 minutes; moreover it also resulted to weight savings of 27 Kg per pair of wings. The work of 35 to 40 painters is now done by 2 robots (Boeing, 2015).

For GLARE panels automated spray painting system can be used, panels remain stationary at one position and robots can be mounted on platform with rail so it can travel across the length of the panels. Though 2 robots can be used, 1 on either side of the panel so that both side of

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panels can be painted simultaneously, but due to high speeds 1 robot per paint box is enough to complete the panel. The paint head of spray robot can swivel to reach any positions. So that inside of the stringers can also be sprayed easily by the robot.

The automated robots with spraying end effector are made by companies like ABB, Kuka, Fanuc, Kawasaki, etc. Fig 80 shows the ABB spraying robots which is used in many automobile companies and in Boeing for 777 aircraft wing painting. The ABB IRB 5400 spraying robot comes with wrist technology that allows up to +/-140 degree wrist pitch. So inside side of the stringers can be reached by the robot easily. Moreover this version of robot can be easily integrated with flexible rail system of up to 15 meters (standardly used). The maximum moving speed is about 1.5 m/s. Also the work envelope of the robot is enough to cover the height of the panel without moving it (ABB, 2015).

Fig. 80 Automated Painting Robot & its application (Source: ABB Ltd)

The automatic spraying by robots has many advantages like:

 Faster robot movements can reduce painting process times  Uniform paint thickness can be achieved resulting to weight savings  Better surface finish can be achieved  Multiple paint layers can be simultaneously applied  Workers are not exposed to harmful and toxic chemicals of paint  Eliminates dependency on workers  Less wastes of paint and primers

Disadvantage of using the robots:

 Higher Investments costs

However, the operations costs are reduced by large extent due to automation, so higher investment costs is justified. This is quantified by using the KPI called “recurrent costs” in section 6 of the report.

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3) Drying ovens

Drying is the removal of a solvent from a primer or topcoat. The drying ovens are needed for drying the stringer sealants, primer and top coat. Drying requires heat transfer and mass transfer. Heat transfer heats the solvent while mass transfer removes the solvent from the product. Higher temperatures and higher mass transfer means faster drying. The natural drying time of sealants is 24 hours while for primer it can be up to few days, based on the primer type used. High drying time will lead to very high built up of inventory of panels. So for reducing the drying time forced drying by ovens needs to be used for future production system. The forced drying can be based on convection heating or it can be based on radiation heating. The current oven which is integrated with paint box is based on convection based heating. For future production system infrared radiation based drying is also an alternative to consider. In this section both type of ovens are explained and compared.

Convection drying ovens

Convection dryers and utilize heated air to process a GLARE panels for drying. Convection heating offers consistent, even heating throughout the dryer or oven. Air can be heated by using electrical heaters. Drying ovens use fan pressure, as well as natural convective force, to circulate heated air within an insulated enclosure. The amount of air circulated in the oven is related to the oven volume and the supply and return duct design rate. The time-temperature curve or "oven profile" is determined based on the process requirements of GLARE panels. The curing curve for polysulfide and polythioether sealant is shown in Fig 81. The curing times are reduced by half for every 7°C increase in temperature and doubled for every 7°C decrease in temperature. An accelerated cure of the applied sealant can be accomplished by the use of heating lamps or a convection oven. However, the temperature shall not exceed 50°C (Fokker FP 5020).

Curing Time Vs Temperature 60

C C ° 40 30 20 Temperature 10 0 0 5 10 15 20 25 30 Time (Hours)

Fig. 81 Stringer sealant drying times

In current production system panels after sealant application are dried at ambient temperature for 24 Hours. For future production system it needs to be dried at 49 degree for 2 hours in convection oven, so the inventory of panels will be less and also the lead time would be reduced by 22 hours.

The drying time of the used primer (ROF 2001) in convection oven is 45 minutes at 70°C after 10 minutes flash off at ambient temperature. The drying time of the other primer (37035A) is

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30 minutes after 30 minutes flash off at ambient temperature. However, for future system primer (37035A) needs to be eliminated from the process as it has harmful chemical contents like chromium. The European Union program “Reach” will prohibit industries from using that primer in future. So in this research the only primer (ROF 2001) will be considered (Fokker, FP 1180). The drying time for the paint topcoats in convection dryers is 45 minutes at 70°C after 10 minutes flash off at ambient temperature (Fokker, FP 1181).

Infrared drying ovens

Infrared heating is the transfer of energy via electromagnetic radiation between a radiation source and a panel. Metallic materials absorb heat in the infrared wave band. Radiant heating often is already used for drying textiles, ceramics, paint on metal, hardening powder paint and heat treatment of plastics. The advantage of Infrared heating in a paint drying is the transfer of heat energy directly to the part rather than air. In general, this allows it to heat parts 3 to 4 times quicker than convection ovens (HERR Industrial Solutions, 2013).

Medium-wavelength electric infrared heating is especially well suited for the curing and drying of primer and topcoat coatings, because these wavelengths correspond well with the absorption bands for water, which coatings contain. Companies like Volkswagen, Honda, Renault, Mercedes-Benz, BMW, etc. are using it for drying car paints (IRT System, 2014).

Fig. 82 Infrared Curing of Paint (Source: Infrarr S.N.C && IRT System)

The infrared radiations are emitted by the lamps as shown in Fig 82. The object absorbs the radiations emitted. The temperature is measured by the pyrometers and distance between object and emitter is measured by the distance sensor. Based on the set temperature profile and pyrometers reading the lamps get switched on/off automatically to control the heat. The Infrared emitter moves on the overhead rail to cover the length of the object. The angle of the emitter can also be adjusted, so that the heat is projected on to the surface in the line of sight. This allows complex shapes or curved shapes to be cured by the infrared dryers.

The similar system can be used for curing the panels. The curing of primer and top coat can be done in very less time with infrared drying as compared to convection oven. The panel can be positioned stationary in the oven, while the infrared emitters can move around it on overhead rails as shown in Fig 82.

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The temperature and heat emitted can be controlled by automated temperature control system as discussed above. The angle of emitter needs to be adjusted according to the panel curvature and stringer angle. Then based on the time temperature profile emitter can be moved across the length of the panel. However, while determining the time temperature profile it should be noted that panels would also be heated with conduction heat transfer. The panels are metallic objects and aluminum is good conductor of heat so the heat will also be transmitted by conduction, once it gets heated by radiation. Fig 83 shows the concept of curing the panels by infrared radiation.

Fig. 83 Infrared Curing for panels

Infrared Emitter works on electricity so need of natural gas or fuel for heating air is eliminated. It also does not require air movement or circulation. However, the angles of emitter and speed of emitter movement needs to be tested before implementation. As per the Fokker process manual and manuals of the supplier and manufacturer of water based primers (Akzo Nobel), it is possible to dry them by infrared curing. So this research will take drying into account and prove its benefit by use of the lean KPIs (discussed in section 5 and 6).

The comparison of the convection and infrared oven is given in Table 18.

Table 18 Comparison between Convection and Infrared oven

Convection Oven Infrared Oven 1. Distributes heat evenly in the dryer 1. Only Heat the coating surface 2. No line of sight adjustment 2. Line of sight adjustment required for requirements for stringer shapes stringer shapes 3. Curing times are more 3. Fast Curing times 4. Temperature will remain uniform 4. Temperature needs to be maintained by control system 5. Proven & in use technology for GLARE 5. New Technology for GLARE panel of A380

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4.4 Autoclave Capacity Modifications

In current production system Autoclaves are operated for batch of the panels, so the number of panels are collected before starting the autoclave and then when complete autoclave capacity gets filled, curing in autoclaves starts. So the panels have high waiting time before the autoclaves. This waiting time also increases the lead time of the production of panels. Moreover, to store the panels with molds the buffer space is needed in the factory.

After the curing in the autoclave the batch of panels comes out at the same time, but next process station debagging is not capable of handling that amount of panels at same time so panels are stored again in the buffer racks. This also leads to high waiting time and work in process inventory. The capacity of debagging process is not a problem, but the peak demand arising due to batching of panels in autoclave lead to waiting and work in process inventory. Thus, large batching size before the autoclaves is disrupting the continues flow of the production system. It needs to be modified for the future production system. Fig 84 shows one of the storage buffer rack located near the autoclaves in the current production factory.

Fig. 84 Panels waiting in Storage Buffer

To have the continuous flow of panel, autoclaves process can be modified in two ways:

1) Continuous autoclave process: In the continuous autoclave process, autoclave can have three different segments for heating, curing and cooling. The product moves from one segment to other resulting into one continuous flow line. This will result to continuous flow. This idea is theoretically possible, but hard to use it in practice as such technology does not exist yet.

Fig. 85 Current Autoclave & Continuous Autoclave (Source: Thesis work of R. Van Osnabrugge)

The Fokker experts think that the continuous autoclave is not an option to consider for GLARE Automation project. Moreover, the autoclave is a large pressure vessel and the change in pressure between different segments can damage the panels. Furthermore, this technology in autoclaves does not exist and will need lot of research before implementation. So the continuous autoclave is not an option to consider for the future A320 production system in this research.

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2) Capacity modifications:

In this modification process of the autoclave can be kept same as current autoclaves, but the size of the autoclaves can be modified, to change the batching amount of panels or batching can be completely eliminated. The change in capacity of autoclaves will affect the queuing of panels before and after the autoclave process, parameters like waiting times and work in process inventory is checked in simulation model. So the capacity and size of the autoclaves can be decided in such a way that waiting of panels and inventory is minimized.

The change in the capacity of the autoclaves will also affect the number of autoclaves. And the number of autoclaves will affect the investment costs. The size of the autoclaves also affects the costs of the autoclaves. So, to check the effect of capacity modifications on the autoclaves the investment costs are calculated by the formula:

Investment Cost of Autoclaves (€) 548697 * 1.7 = + Where D is diameter of Autoclave and L is length of Autoclave, D & L are in meters

The formula mentioned above gives accurate costs of the autoclave, it has been used for cost calculation by Fokker engineers for autoclaves of Hoogeveen Factory. From the formula it’s clear that dimension of autoclave plays a big role in the cost of the autoclave. The costs are proportional to square of diameter and direct to length of autoclave. The factor that makes difference in having higher number of small autoclaves and less number of big autoclaves is the fixed cost of 548697 euros. To reduce the waiting times and inventory, capacity of autoclaves needs to be decreased which will reduce the amount of batch size of panels. Its effect on the number of autoclaves needed is known by simulation model in Simio. Different scenarios with different capacities were experimented in model; the results are discussed in section 6.

From the formula it’s clear that the autoclaves cost is proportional to the volume of autoclave, but the decision also needs to be made about the dimensions of autoclave. If the panels are put in autoclave in a way that two panels are on top of each other than diameter of autoclave does not increases by large proportion. But when the panels are put next to each other the length of the autoclaves gets double. Fig 86 shows two ways arrangement of panels in autoclave.

Fig. 86 Panel arrangement in Autoclaves

When panels are put next to each other and only 1 layer of panel is there in autoclave, it will lead to large waste of empty space and waste of energy due to cylindrical shape of the autoclave. But, when panels are stacked in carrier and 2 panels are on top of each other than more autoclave space is utilized. Fig 87 shows the space utilization of autoclaves with 1 & 2 panels.

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Fig. 87 Autoclave space utilization

If panels are stacked on top of each other than the diameter of autoclave needed would be higher than width of panel. The diameter of the autoclave for larger panels with width 5.5 meter and length of 12 meter would be 6.7 meter instead of 6.1 meter. The allowance of 300 mm needs to be taken for the side. The diameter of autoclave for medium sized panels with width 4.1 meter and length of 6 meter would be 5.5 meter instead of 4.7 meter and the diameter of autoclave for small sized panels with width 3.6 meter and length of 3.3 meter would be 4.9 meter instead of 4.1 meter. The diameters were calculated in tool called Geogebra.

The height of mold is assumed as 1 meter and distance between 2 molds when stacked was assumed as 0.5 meter. Assumptions are based on current A380 mold sizes. Fig 88 shows the calculation in Geogebra tool. In the Fig 88, AB & CD represents the width of the panel and GE shows the radius of needed autoclave.

A B C

Fig. 88 Autoclave diameter calculation (A – Big panels, B – Medium panels, C- Small panels)

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By increasing the diameter by 800 mm two panels can be stacked on top of each other, instead of making the length of autoclave double. The costs of autoclave when panels are stacked and when panels are placed next to each other is given in Table 19. The costs were calculated by using the formula shown above with the calculated diameters and length of the panels.

Table 19 Costs of Autoclave

Costs of Autoclave ( In million euros) Panel Arrangement Small Medium Large Autoclave Autoclave Autoclave Panels placed next to each other 3.30 3.82 9.92 Panels Stacked on top of each other 2.62 2.91 6.35

From Table 19 it can be clearly observed that autoclaves with stacked panels would be cheaper than long autoclaves where panels are placed next to each other. So, it is recommended to use the autoclaves with the stacking type panel arrangement. In this research this panel arrangement would be used for cost & capacity calculation. The exact number of autoclaves needed with this arrangement are calculated by the simulation model and explained in section 6. The waiting times and queuing pattern after changing the capacities of autoclave are also presented in section 6. To summarize it the batching size of panels needs to be reduced by making the autoclaves smaller so that waiting of panels is reduced. And the panels need to arrange in stacking way to reduce the investment costs of autoclaves.

For the autoclave process, mold containing panels need to be loaded in the autoclave carrier. The side loaders or forklifts are needed for loading the heavy molds. The transport of the molds at the layup assembly line is going to be automated by the use of AGVs. So, then use of side loader and autoclave carrier should be avoided, as it would be extra investment. Instead the AGVs selected should have the feature to load the molds directly in the autoclave. These types of “lift AGVs” are already used by companies like Lockheed Martin, Siemens and Bombardier.

Fig. 89 Lift AGVs with Loading & Unloading feature (Source: Kuka Robotics, 2015)

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4.5 Flexible workers for line balance

When the line balancing between process stations is not possible by work task rearrangement due to process constraints, production line balancing can be done by using higher capacity of process stations. For the higher capacities of manual processes, extra workers are needed. This increases the production costs and also results to underutilized workers. To tackle these problem Flexible workers can be used for line balancing.

Not assigned to a specific operation or a process station, a Flexible worker function is to meet the on-demand labor requirement at any station of the production line. This need arises if a production system processes has large cycle time difference and it results to queuing, or process station is exceeding Takt time requirement. Flexible workers can be involved in various other works at the production facility such as maintenance, etc. when they are not required for work on production line.

Number of Flexible workers at a production line is determined during balancing of the line considering the workload at the line. Instead of keeping a fixed number of operators at stations for manual processes and in order to eliminate idle operators at the stations, Flexible workers are a better option in terms of efficiency.

In this research the production line balancing is done in discrete event simulation model, the processes and the amount of flexible workers to use are determined by simulation. Based on simulation results it is clear that line balancing needs to be done. So, it is decided to balance the manual processes of layup and bagging by one type of Flexible workers, doublers framing and debagging by second type and deburring & finishing process by third type of workers. Three different types are selected as the skills required are different for those processes, for example doubler layup and bagging requires high skills whereas debagging does not require high skills.

Instead of assigning the fixed process capacity, dynamic allocation of workers is done as per demand. This reduces the waiting times of panels, waiting inventory and also increases the utilization of workers. Thus making the production system more lean. The results are discussed in section 6.

The disadvantage of using Flexible workers is that the workers need the knowledge of multiple processes. Also the movement of workers between the process station increases, so the work fatigue needs to be reduced by having the process stations close to each other or by providing them some transport medium like personal transporter. However, based on the simulation results, it is known that the workers of group 3 has to travel maximum distances per day along the line, on average a worker has to travel 345 meter in one shift.

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Sub-Conclusion

This section showed the analysis for future production system, where multiple solutions were studied, such as Lean assembly flow line for Layup, painting process modifications, autoclave capacity modifications and flexible workers for line balance.

The new production system with continuous line is designed based on the lean principle of achieving the continuous flow. Methods for reducing non-value adding activities like tool cleaning by laser system, eliminating the use of plastic foils by use of mold release agents and shifting the assembly preparation activities to feeder line are proposed. The concept of automation for layup of sheets (currently in research at Fokker) and automated layup of stringer (concept from PAG) are used for the new system, the process timings were calculated from the test results of pick and place tests. The model is made in Microsoft excel which is based on Bill of Material and pick and place timings to calculate the process timings. Then line balancing is done based on the process times to calculate theoretically number of process stations. Finally from discrete event simulation the number of process stations and number of buffer positions needed on line are calculated. Based on that new line layout is proposed (shown in Fig 135). For doublers feeding line the combination production of doublers by combining doubler type is proposed, this reduces the number of tools and also reduces material handling. For selecting the transport system of feeding lines AHP analysis was done.

It is also proposed to change the painting process from manual process to semi-automated processes arranged on continuous line, as it will help in reducing the non-value adding activities and reduce waiting times and lead times. It was proposed to separate sanding, painting and drying operations and to do them at separate workstations. The method to automate sanding and painting operations were suggested; automation will reduce the processing times and also number of needed employees. Two drying oven types: convection oven and Infrared oven were discussed and compared. The use of drying ovens would reduce the drying time from hours to minutes, leading to reduction in lead time.

To reduce the waiting times of the panel before and after the autoclave process, autoclave batching process needs to be changed. For that it is proposed to use smaller autoclaves, as that will reduce batching size of panel. Also the arrangement of panels in the autoclave: stacked arrangement and panels placed next to each other were considered for analysis; it turned out that the stacked arrangement will lead to more autoclave space utilization and less investment costs. The number of autoclave and their size influence of queuing pattern are analyzed by simulation model (results are shown in section 6). Last modification is to use Flexible workers instead of fixed workers to balance the production line and this decrease the waiting times and inventory and also increases the utilization of workers.

These proposed modifications are decreasing the non-value adding activities and waiting times of panels, thus decreasing the lead time and work in process inventory. This will result to more lean production system. Moreover, by automation not only processing times are reduced, it also helps in reducing the number of needed employees (FTE) thus decreases the recurring costs of production.

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5. Modelling

To measure the performance of a future production system a discrete event model is created in Simio and cost calculation model is created in Microsoft excel, for which input and output variables used will be explained in section 5.1. The key performance indicators are explained in section 5.2. The model working is explained in section 5.3 and the verification and validation of the model is explained in section 5.4.

5.1 Input and output variables of the model

5.1.1 Input Variables

The input variables are the variable that the user of the model decides to update during experiments, such as the number of required products (demand) or the number of available resources. The interface of the model and input sheet in Microsoft excel is used for giving the input to the model. The input variables are as follows:

 Panel Information  Processing times of 30 panel type for all the processes  Initial number of resources  Production sequence  Material consumption by the panels  Vehicle speeds and capacity  Probability of defected panel  Available working hours per year  Material rates  Investment Costs  Labor rates (per hour tariff)

The processing times of all the panels for all the processes are calculated by the Microsoft excel model which is linked to the Bill of Material of the panels. The Robots speeds of pick and place are the input to the sheet for calculating the processing times of the layup timings and stringer placement timings. The timings for doubler layup, bagging process and debagging process is extrapolated from the data collected of A380 panels, as this processes are not modified. However, the timings are different as the panels are of different dimensions.

The autoclave process timings are decided after consulting GLARE process expert. Three different autoclaves curing cycles are used in the model based on the dimension of the panel. The smaller panels can be cured in 3 hours, medium sized panels in 4.5 hours and big panels in 6 hours. The timings for c scan and mailing are also calculated based on parameters from the machine speeds, but with new dimensions of the panel. The complete list of parameters and process timings is provided in section 6.2.

The initial numbers of resources to be used for the model are determined by taking the sum of process time and dividing it by available working hours as shown in line balance calculation. This helps in reducing the number of iterations to achieve the best steady state model. However, if the utilization rate of the resources is very high above 90 then new resources are added, as there should be scope of maintenance and breakdowns. An illustration of this process is shown in Figure 90.

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It should be noted that the number of transport equipment is only used as an input for aluminum sheet delivery to the layup station. But for the transportation of molds at the layup assembly line model assumes that enough resources are always available. This is left out of the scope as Bart Rinsma; my TEL colleague is working on the tool management and tool handling part. So, this research model will only cover transportation of aluminum sheet kits.

Fig. 90 Iterative process for determining number of resources

5.1.2 Output Variables

The output variables are the resulting variables obtained from the simulation model

 Number of good produced panels & defected panels  Final number of resources needed  Lead time of the product  Utilization rates of the servers  Waiting queues and Waiting times  Buffer Inventory  Work in process inventory  Number of transport equipment’s needed  States of the transport Equipment’s (Idle & Busy)

These output variables would be used for calculating the key performance indicators (KPIs) or some output variables itself are key performance indicators. 5.2 Key Performance Indicators

A Key Performance Indicator (KPI) is a metric used to evaluate the success of a certain project or research study. For this research quantitative KPIs are selected after understanding the process of GLARE production and based on the objective of this research. The following KPIs are selected to measure the performance of the production system:-

 Value added times: The value added times are the times which adds value to the panel from customer perspective.  Non-Value added times: The non-value added times are times which do not directly add value to the product but are necessary for the production of the panel.  Value added work (VAW): The indicator (VAW) is determined by dividing the value added work time spent by the total time spent. In the ideal Lean process the ratio would be 100. It will give indications for the future continuous improvement possibilities.

VAW = ⁄ )*100

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 FLOW: The Flow is determined by the sum of the process times of the processes within a value stream map divided by the lead-time of the product, times the one hundred percent. The closer this ratio gets to one hundred percent, the better value flows through the processes. Inconsistency or nonexistence of flow is an indicator for unnecessary waiting time, one form of lean waste. It gives indication of Pull system in the production system.

FLOW = ( ⁄ )*100

 Scraped panel: Quality plays an important role in the process performance; it is of importance to have an adequate indicator for it. The scraped panel is the indicator that gives the indication of defects due to process errors during production.

 Queuing Parameters

Waiting times: The waiting times will give an indication of the period a panel is waiting for the next process. For example “How long a panel waits before autoclave?” If the product is not able to flow to the next step, it will have to wait between processes and an inventory is created. According to lean it is one form of waste as inventory is waiting in between processes. The goal is to have a system which is have the lowest waiting time ideally zero. The KPI gives indication of the bottleneck processes. Also, the lead time of the products is dependent on the waiting times; the more the waiting time, longer is lead time.

Buffer levels: The level of inventory will be extracted from the model, both the maximum value and its changing value over time. The total number of panels in the buffer will give information about the required size for the buffer areas in the layout.

 Number of Servers: The number of servers needed for achieving the higher production rate. Based on the processing time and process constraints model will give indication of the final number of servers needed. The number of resources will determine the space needed in the layout.

 Inventory of panels: The total number of panels in the production system will be the total inventory. It is the sum of work in process inventory and waiting inventory. It should be minimum as the inventory consumes the cash of the company in form of raw material, process costs, etc.

In order to show the effects of lean within a production system, it is necessary to set indicators that can be used to differentiate between the situations before lean is introduced and after its introduction. The situations can be compared by using quantifiable indicators like Turnover per Capita, Number of Production employee, R&D Budget, etc. (Beelaerts van Blokland, 2008). Thus, the KPIs like turnover/production employee, turnover per/square meter of factory space, etc. relates lean improvements to company’s business performances.

 T/Ep: The T/EP indicator (turnover per production employee) provides insight into the degree of turnover, compared to the number of the production employees. The number of production employees will decrease by automation causing the indicator to increase. If the indicator increases, this would mean that modifications have led to an improvement.

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 T/TS: The turnover per total surface indicates the connection between the turnover and the used factory surface for the production system. An improvement of this indicator can be caused by a constant or increased turnover at an unchanged or reduced surface area. With this measure, the efficiency of the space using can be examined. In this research the turnover will remain constant as demand is fixed, but factory surface will change with implementation of lean in production system.

 Buffer space & Total factory surface area: The buffer space is the space needed in the factory to store the inventory. By introducing lean in the production system and establishing push system the buffer space can be reduced. So, the comparison of buffer space and total factory area before and after lean implementation will show the effect of lean introduction on factory layout.

Recurring Costs: The recurring costs of panel of the production system can be determined from three cost items:

Material usage: The used aluminum material for the sheets and the bond primer which is sprayed onto the sheets, costs of prepreg and adhesives, cost of primer and the cost of the paint of the panel.

Process Tariff/ Hour: The second recurring cost item is the process tariff/hour. It includes the labor cost which depends highly on the extent to which the production system is automated. It also includes the overhead costs and operational costs of the resources. The Fokker uses 76.6 euros for 1 process hour for recurrent costs analysis.

Depreciation of the fixed assets: This cost item can be determined from the initial investment costs, the useful life of the assets and the residual value of the initial investment after the useful life, its formula is:

[ ] [ ] Depreciation = (

With these three cost items, the recurring costs of the production of GLARE panels can be calculated. The recurring costs can be expressed in €/panel. If the yearly recurring costs of the production are divided by the yearly production of panels. The calculation of the recurring costs per panel can be calculated as per formula:

Recurring costs ( =(

For calculating the recurring costs the cost model is made in Microsoft excel. Input variables like material costs, material consumption per panel, process hours, Process tariff/hour, Investment costs of equipment’s, Useful life of equipment’s and their residual value are used by the model. The final output of the model is the recurrent costs per panel.

Recurring costs of the panel production before and after the process modifications are compared in the research to show the benefits.

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5.3 Working of the Simulation model

In the simulation model the panels are modelled as an entity, which is created on order generation and destroyed on expedition from the factory. In total 30 panels and 13 doubler panels are modelled. The processing times of the panels vary with the dimensions, so modelling individual panels give more accurate results. The material consumed in the making the panel like aluminum sheets and doublers are also made in the model and then destroyed on production of panel. The machines are modelled as servers in simulation model with specific processing time for each panel. Every server is modelled with constrained processing capacity and input buffer and output buffers.

This input buffer represents the queues of the actual system. The transportation is done by the Simio objects called a vehicle which has specific capacity, travelling speed, loading times and unloading times. The interface of simulation model is shown in Appendix 5. As mentioned in section 2 the interface of the model is very simple in Simio and does not require any detailed coding. The modification needed can be directly made by clicking on the object and modifying its properties. The screen shot of the interface is shown in Appendix 5. The results and graphs are directly loaded on the interface. The charts of resource states and queuing pattern give better insight to the performance of the future production system.

Run length

Run length of model used is of 1 Year. For the experiment 10 replications of simulation runs were done. For the manual processes triangular distribution is used for processing times. So the difference in processing time is checked for replications, it’s of few minutes between extreme readings. This difference is negligible as compared to overall lead time of panels which is in hours. 5.4 Validation and Verification of the model

After modelling the system in Simio, model has to be verified, debugged and validated. Verification of the model means whether the model is correct, which is done by checking the model logic and structure. Validation means whether it is the correct model, which could be done comparing modelled results with the actual results obtained from the field or by getting it checked by experts or by similar model.

For validation of the model the results were compared with the simulation model made by Fokker’s Industrial Engineer Erik Van Meer in incontrol simulation. Currently as Fokker uses different input parameters so for validation those input parameters were used. The final results like number of servers for all the processes and number of panels made in same simulation run length were checked. The resulting output variables of the model were also validated by going through these results with Fokker GLARE process experts. The Fokker experts, with whom the results are shared, are: Leo Meijers, Leo Muijs, Erik van Meer, Pim Tamis & Harry Gharbharan.

To verify the model, status label (counters) were used with every servers. Based on input variables, the numbers of entities processed by server were cross checked from these status labels for various time intervals, this method is called balance check method. Model was also verified by tracing entities from source to sink.

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6. Results

This section shows the result of the simulation model and excel model of the cost calculation. The performances of the designed future production systems are provided in this section. The performance can be measured by KPIs as discussed in section 5. In this section 2 alternatives of production system are presented and KPIs comparison is done. After that for new production system experimentation is done to check its flexibility. The following alternatives and improvements results are presented in this section:

1. Base case (Production with A380 GLARE production processes and technology)

2. Lean Future Production System with modifications:

 2a. Layup process modification  2b. Paint process modification  2c. Autoclave capacity modification  2d. Flexible workers

The first alternative is base case, if the A320 and A321 panels are produced with A380 GLARE production processes and the same technology. The demand of the A320 and A321 is expected to be 10 times to that of A380, so higher number of resources would be needed. But as the panel sizes and thickness of A320 and A321 are smaller there would not be linear relationship between demand and amount of resources. So from the A380 production process data, processing times of A320 and A321 panels is extrapolated considering new panels size, thickness and amount of material going into the panel. The base case production system in discrete event simulation model then provides output like waiting times, number of stations, etc. which are needed for KPIs calculation.

The 2nd alternative is the lean production system with improvements in layup process, painting process and autoclaves as discussed in previous section. The improvements are modeled in simulation to check its effect on the productions system KPIs. The inputs for the new production system model are based on the data from suppliers, pick and place experiments and from parameters extrapolated from A380 production system.

For both the alternatives, simulation runs are done considering ideal production system and production system with defects. This research is based on production with 5 days and 15 shifts per week assumption, to check the systems behavior with 7 days and 21 shifts per week production simulation runs were done. The results of that scenario are shown in Appendix 7.

To check the flexibility of the new suggested production system with modifications following experimentation are done:

1) Product mix variation of A320 and A321

2) Product demand variations of A320 and A321

3) Product Variety of A320 and A321 (If 600 half fuselages are made, only rear or front parts)

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6.1 Alternative 1: Basecase based on A380 GLARE Technology

Table 20 Cycle time for all the panels (Time in Hours)

Panel Al sheets Layup Autoclave Debagging Cscan Machining painting Rework lugs Doublers A320panel1 8.0 30.3 4.5 1.9 5.3 3.1 15.3 2.1 0.0 A320panel2 4.0 14.4 4.5 1.4 3.3 1.9 8.8 2.1 0.0 A320panel3 7.0 27.2 4.5 1.9 5.3 2.9 14.6 2.1 0.0 A320panel4 7.0 28.8 4.5 1.9 4.6 2.2 15.3 2.1 0.0 A320panel5 18.0 34.6 4.5 1.7 4.7 3.1 12.4 2.1 15.0 A320panel6 8.0 16.7 4.5 1.5 3.3 1.9 9.5 2.1 0.0 A320panel7 18.0 34.6 4.5 1.7 4.7 3.1 12.4 2.1 15.0 A320panel8 10.0 32.8 6.0 2.6 6.6 3.6 15.3 2.7 0.0 A320panel9 6.0 21.0 6.0 1.9 4.0 2.1 9.5 2.7 0.0 A320panel10 4.0 12.5 6.0 1.5 4.0 3.5 5.8 2.7 0.0 A320panel11 25.0 63.9 6.0 3.8 8.4 3.0 25.6 2.7 8.0 A320panel12 6.0 29.3 3.0 1.4 3.9 2.0 16.8 1.6 9.0 A320panel13 2.0 14.2 3.0 1.1 2.8 1.7 9.5 1.6 0.0 A320panel14 6.0 29.3 3.0 1.4 3.9 2.0 16.8 1.6 9.0 A320panel15 3.0 19.1 3.0 1.3 3.3 2.0 13.2 1.6 0.0 A321panel1 13.0 34.4 6.0 2.7 7.1 3.8 14.6 2.9 6.0 A321panel2 5.0 18.7 6.0 1.9 7.1 2.2 8.8 2.9 0.0 A321panel3 13.0 33.9 6.0 2.7 7.1 3.8 14.6 2.9 4.0 A321panel4 13.0 35.9 6.0 2.7 6.0 2.5 14.6 2.9 2.0 A321panel5 13.0 29.4 4.5 1.7 4.7 2.8 11.7 2.1 0.0 A321panel6 7.0 16.1 4.5 1.4 3.4 1.9 8.8 2.1 0.0 A321panel7 14.0 29.4 4.5 1.7 4.7 2.8 11.7 2.1 0.0 A321panel8 16.0 43.2 6.0 3.0 7.8 4.4 14.6 3.2 8.0 A321panel9 6.0 24.3 6.0 2.1 4.6 2.3 8.8 3.2 0.0 A321panel10 15.0 41.0 6.0 3.0 7.8 4.4 14.6 3.2 4.0 A321panel11 14.0 28.4 6.0 3.0 6.6 2.4 14.6 3.2 4.0 A321panel12 5.0 29.3 3.0 1.4 4.0 2.0 16.8 1.6 3.0 A321panel13 2.0 14.2 3.0 1.1 2.9 1.7 9.5 1.6 0.0 A321panel14 5.0 29.3 3.0 1.4 4.0 2.0 16.8 1.6 3.0 A321panel15 4.0 19.2 3.0 1.3 3.5 2.0 13.2 1.6 0.0 Average 27.9 4.7 1.9 5.0 2.6 13.2 2.3 Table 20 shows the cycle times of all the panels for all the processes. As discussed before those times are extrapolated from A380 production process times. The second row of the table indicated the aluminum sheets consumed for the panels. The last row indicated the doublers consumed by the panels.

These timings are used in the simulation model for determining the queuing pattern at servers, buffer levels and the number of process stations needed. The modelling of every panel with different timings would give more accurate results. Only for representation in the report; value added times and non-value added time of the processes, average timings (shown in last row) would be used. The average of timings would not affect the robustness of system, as for the design of system every panel’s cycle timings are considered.

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Processes VA NVA

25.0

20.0

15.0

10.0

5.0 Process Timings (Hours) 0.0

Fig. 91 Value added activities (VA) and Non-value added activities (NVA) timings for each process

Total VA & NVA 45.00 40.00 35.00 30.00 25.00 20.00

Time(Hours) 15.00 10.00 5.00 0.00 Total VA Total NVA Process Activities

Fig. 92 Total Cycle time (Value added and Non-Value added activities)

The value added activities (VA) and non-value added activity (NVA) time of the processes are shown in Fig 91. The layup & bagging process and Finishing & painting have most non-value adding processes. Those activities need to be eliminated or reduced by automation. The ways to reduce them and eliminate them has been mentioned in Section 4. The non-value added activities also contain business value adding activities that are necessary to make the panels, but does not add value from customer perspective. So, those activities need to be reduced.

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6.1.1 Production with 5 days per week

When the panels of A320 and A321 are made with A380 GLARE production technology and factory operates for 5 days (15 shifts a week), then number of process station needed are shown in Fig 93. It shows the two results, one for Ideal production system with no defects and second is for production system with defects, the probability of defect used in simulation was based on defects data (shown in section 4) obtained from A380 production system. The number of resources does not increase by higher amount, their utilization rate increases (shown in Fig 94). Moreover the processes after the C scan and machining are not affected by defected panels, as those finishing processes does not result to scraped panels. Also the defects in panels are determined at C scan, so the panels are scrapped after C scan and are not processed at downstream processes of painting and finishing.

Number of Process Stations Ideal Case Case with defects 35

25 20 15

10 No ofStations No 5 0

Fig. 93 No of Process stations

Ideal Case Utilization of Process stations Case with defects 100

90 80 70 60 50 40 30 20 10 Utilization Utilization (%)Rate 0

Fig. 94 Utilization of Process Stations

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In simulation model all the panels were modelled, but for representation panels are shown in group based on the sizes. Fig 95 shows the 3 group of panels with their processing time, waiting time and lead time. Two different waiting times are used: one is ideal waiting time when panels are not in process and are waiting as process stations are not free. Second is in process waiting time when panels are waiting as part of process itself (For example: During primer or paint drying). The maximum waiting time is due to batching of Autoclave, as panels have to wait before and after the autoclave. Then panels also have to wait in buffers before processes like C scan, machining, painting and lugs cutting. Fig 97 shows the amount of waiting panels (queuing pattern before servers). The in process waiting time is result due to sealant drying & topcoat drying. The drying times are same for all panels irrespective of their sizes. The higher lead times results to high work in process Inventory. This also means higher number of molds and transport frames would be needed.

Small Panels Waiting time & Lead time Medium Panels Large Panels 140

120

100

60 Time(Hours) 40

0 Processing times Idle Waiting times Inprocess waiting times Lead times

Fig. 95 Waiting time & Lead time of Panels

Inventory of Panels 140

120

100

60 No ofPanels No

0 In process Inventory Waiting Inventory Total Inventory

Fig. 96 Inventory of Panels

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Queuing of Panels at the process stations

Fig. 97 Queuing at process stations

Fig. 98 Panels waiting for drying

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From the simulation results of queuing at the process stations, the amount of buffer stations needed to store the panels were determined (shown in Fig 99), the amount buffer storage are selected based on the peaks occurring at the process stations (from Fig 97 and 98).

Based on these results the buffer storage space estimation is done for the production system layout. The main reason for these buffers is processing time difference between the panels for the processes. The buffer storages before and after the autoclave would require space for big molds with panels (horizontal position), whereas other buffers would be for panels in frames (vertical position).

Buffer Storage 45

40 35 30 25 20 15 10 Noof positions 5 0

Fig. 99 Buffer Storages Production Costs:

The recurring cost of GLARE panel production is determined according to the method as described in section 5.2. The input variables to determine the production costs are shown in Table 21. The input values of required material, material rates, labor rates, etc. are determined from data of Fokker Aerostructures (A320-A321 Baseline 15-1-2015).

Table 21 Input Parameters for recurrent cost calculation

€ 7.85 per m2 € 5.51 per m2

€ 5.50 per m2 Confidential data € 1.76 per m2 € 2.44 per m2 € 5.03 per m2 € 14.82 per m2 € 16.00 per m2 € 76.6 per Hour 1630 Hours

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Recurring Costs:

The recurring costs can be determined from three costs: Material costs, process costs and depreciation costs. The material costs, the process costs (inclusive of labor rates, overhead costs, etc.) and the depreciation on the investment are listed in respectively Table 22, Table 23 and Table 24. The detailed list of the Investment costs is provided in Appendix 6. The recurrent costs are calculated per panel for A320 and A321.

Table 22 Material Costs

m2/shipset Cost (€)

438 7008

4491 66556

1430 11222

Hidden, due to confidential data 1101 6066

231 1276

6326 11150

1231 3003

615 3098

109378

7292

Table 23 Process Costs per year

Process FTE Labour Total Cost (€) Hours/year Decoiling 8 11520 882432 Chemical Treatment line 11 17280 1323648 Primer & Curing 8 11520 882432 Deframing 8 11520 882432 Milling 4 5760 441216 Kit Delivery 4 5760 441216 Layup 205 334080 25590528 Autoclave 8 11520 882432 Debagging & Doublers Framing 8 11520 882432 C Scan 8 11520 882432 Confidential data Milling 4 5760 441216 Deburring 8 11520 882432 Painting & Finishing 85 138240 10589184 Rework Lugs 11 17280 1323648 Doublers Alodine and Kitting 4 5760 441216 Total 384 610560 € 46,768,896

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Table 24 Depreciation per Shipset

Depreciation on Investment Investment in Fixed Assets € 167,539,700 Asset lifetime for use 5000 shipsets Hidden, due to confidential data Residual Value (%) 10 Residual Value € 16,753,970 Depreciation per shipset (Average of A320 and A321) € 30157

Based on the material costs, process costs and depreciation costs, the recurrent costs for A320 and A321 shipsets are calculated (shown in Table 25). The material costs and process costs are the major contributor for the recurrent cost.

Table 25 Recurrent Costs

Cost Item A320 A321 Material Costs per Shipset € 77238 € 109378 Process Costs per Shipset € 146543 € 165250 Hidden, due to confidential data Depreciation Costs per Shipset € 28348 € 31967 Total Cost per Shipset € 252128 € 306595 Total Cost per panel € 16809 € 20440

Similar analysis was done for recurrent cost calculation with 7 days and 21 shifts (shown in appendix 7). The comparison in costs is given in Table 26.

Table 26 Cost Comparison

5 Days/week 7 Days/week 5 Days/week 7 Days/week A320 A320 A321 A321 Total Cost per Shipset Hidden,€ 252128 due to confidential€ 265035 data € 306595 € 321150 Total Cost per panel € 16809 € 17669 € 20440 € 21410

The recurrent cost of panels increases by 5.1 % for A320 panels and by 4.7% for A321 panels for production with 7 days. This is due to increase in the operation costs as working hour increases so more FTE are needed. For example: For operating 4 autoclaves two operators are needed every shift with 5 days production, resulting to 8 FTE (due to 1630 Hours). Also for scenario with 7 days production 4 autoclaves are needed with two operators per shift, resulting to 12 FTE; however the utilization of FTE would decrease.

Thus, in this research production with 5 days shift (Scenario A) is considered as base case for comparison for future lean production system. Along with recurrent cost reduction for future system, the waiting time, inventory & lead time also needs to reduce. From the analysis it is clear that layup process, painting process are the most time consuming process and needs high amount of FTE, the operation cost is high due to these processes. So, these processes are modified for the future system by use of automation. Higher amount of waiting time and inventory are resulting due to batching of panels for autoclaves and paint drying. That is modified by changing the size of the autoclaves and by using the paint drying systems. These modifications are discussed in section 4.

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6.2 Alternative 2: Lean Future production system

The results of the modifications done for the future system as explained in section 4 are explained first then different scenarios will be experimented. The modifications were designing of assembly layup line with automation, paint process modifications and Autoclave modifications and flexible workers.

For the layup of aluminum and prepreg, robot speeds which were used for process time calculations are shown in Table 27. The speeds are based on pick and place test results of experiment carried out at Fokker test facility. It should be noted that original test results were modified after consultation with Fokker experts as the complexity in real layup would be higher than test setup due to dimension of the sheets. Moreover sensitivity analysis was also done with the speeds to check its effect on the needed number of layup stations. The result of sensitivity analysis is shown in Table 28. It shows that when movement time increases by 80 percent, then the number of stations needed increase by 1. So, the pick and place sheet speed is very less sensitive parameter. Prepreg and adhesive tape lying speed is more sensitive parameter, its value are obtained from the supplier. Thus, after confirmation with Fokker experts, this time are finalized and are used in this research.

Table 27 Speeds used for simulation Table 28 Sensitivity Analysis result

The timings for doubler layup process are based on parameters of A380 process, as it will not change. The timings for stringer layup concept are based on timings obtained from PAG, Germany which has similar setup for A350 stringer placement. The pick and place concept timings are shown in Table 29. If the stringer head concept is implemented then the timings would change, the parameters for stringer head concept are shown in Table 30.

Table 29 Stringer Pick and Place Concept Table 30 Stringer Head Concept

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The tools needs to be cleaned by laser tool cleaning system and supplied to layup station; the speed of cleaning taken is 22m2/Hour. In this research it’s assumed that each panel has its dedicated mold. Dedicated molds are needed as dimensions and curvature differs for every panel. The transportation of molds is done by AGVs with loading and unloading features, the time has been assumed as 5 minutes for transporting, loading and unloading the molds at the process stations. The Autoclaves curing time used in this research are as follows:

1) Small size panels of Section 18 of A320 & A321: Curing cycle of 3 Hours

2) Medium Size panels of Section 13/15 of A320 & Section 15 of A321: Curing cycle of 4.5 Hours

3) Large Size panels of Section 17 of A320 & Section 13/17 of A321: Curing cycle of 6 Hours

This curing cycle time were decided after consultation with Fokker engineer Erik Van Meer and same timings are used by Fokker for GLARE automation project calculations. The capacity of autoclaves and arrangement of panel in autoclaves are decided based on simulation results and analysis shown in section 4.

For the process of debagging the timings are calculated based on A380 process, where around 0.50 minute/m2 is for removing the bagging material and 30 minutes are needed for framing the panel. The C scan process will be modified in future system, Fokker has already finalized phased array of C scanning method, and so new process is taken into account in this research. The scanning times of C scan are reduced by 50% by use of phased array C scan technology.

The machining process will also be modified for the future system; the Milling machine used will be with 2 milling beds, so the panel installation can be carried out in advance when the other is being milled. By doing this machine installation time of 1 hour can be saved. The Lean tool of SMED (single minute exchange of die) inspires this modification, according to that all external operations needs to be completed before stopping the machine. This will increase the productivity of machine. The milling speed and drilling speed of machine are taken as 20 seconds/meter and 12 seconds per hole respectively. These speeds are based on current M Torres milling machine.

The deburring of panels after milling will be done manually as current A380 panels, so parameters of 2 minute/meter is taken based on the A380 process. It is proposed to modify painting process as shown in section 5, with the automated robots for sanding and painting. The speeds of process are assumed as follows:

1) Sanding – 0.51 min/m2 (with 8 sanding disks end effector)

2) Painting – 0.75 min/m2 for first concentrated layer and 0.5 min/m2 for finishing layer

3) Drying times of 2 hours for sealant and 45 minutes for paint and primer are from Fokker process manual, these timings are with convection heaters. The heating with infrared is 3 to 4 times faster than convection heating, so times are at least 3 times less.

All the parameters described above were verified with Fokker experts.

Variation in timings for manual processes

As the manual processes are done by workers, so the speed will not remain constant. So to check the effect of variation simulation runs were also done with modelling the manual process time with triangular distribution. The minimum and maximum were taken as -15% and +15% of the average processing time. This has negligible influence on the KPIs as the manual processes takes very less time as compared to overall lead time of panel production.

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Table 31 Cycle time of all processes for all panels for future production system (Time in Hours)

GLARE Layup Pre Stringer Autoclave Debagging Cscan Milling Deburring Sanding Stringers Paint Rework Panels Station compact Station Curing Station Scanning Station Station Station sealant Spraying lugs A320panel1 3.7 0.5 2.0 4.5 0.7 3.0 0.7 1.2 0.6 1.9 1.0 2.1 A320panel2 2.3 0.5 1.2 4.5 0.6 2.1 0.2 0.5 0.3 1.1 0.6 2.1 A320panel3 3.2 0.5 1.9 4.5 0.7 3.0 0.6 1.1 0.6 1.8 1.2 2.1 A320panel4 3.7 0.5 2.1 4.5 0.7 2.6 0.3 0.7 0.5 2.0 1.0 2.1 A320panel5 6.3 5.3 1.7 4.5 0.7 2.7 0.7 1.2 0.5 1.6 1.1 2.1 A320panel6 2.5 0.5 1.3 4.5 0.6 2.1 0.2 0.6 0.3 1.3 0.6 2.1 A320panel7 6.3 5.3 1.7 4.5 0.7 2.7 0.7 1.2 0.5 1.6 1.1 2.1 A320panel8 4.7 0.5 2.9 6.0 0.8 3.8 0.8 1.6 0.9 3.0 1.9 2.7 A320panel9 2.8 0.5 1.8 6.0 0.7 2.4 0.2 0.8 0.4 1.9 1.0 2.7 A320panel10 3.2 0.5 1.1 6.0 0.7 2.4 0.8 1.4 0.4 1.1 1.0 2.7 A320panel11 14.1 3.5 4.9 6.0 0.9 4.7 0.5 1.3 1.1 5.2 2.5 2.7 A320panel12 3.7 3.7 1.5 3.0 0.6 2.2 0.3 0.6 0.3 1.1 0.6 1.6 A320panel13 2.1 0.5 0.8 3.0 0.6 1.7 0.2 0.4 0.2 0.6 0.4 1.6 A320panel14 3.8 3.7 1.5 3.0 0.6 2.2 0.3 0.6 0.3 1.1 0.6 1.6 A320panel15 2.3 0.5 1.2 3.0 0.6 2.0 0.3 0.5 0.2 0.9 0.5 1.6 A321panel1 6.6 3.7 3.0 6.0 0.9 4.1 0.9 1.7 1.0 3.2 2.1 2.9 A321panel2 3.6 0.5 1.8 6.0 0.7 4.1 0.2 0.8 0.5 2.0 1.1 2.9 A321panel3 6.6 3.2 3.0 6.0 0.9 4.1 0.9 1.7 1.0 3.2 2.1 2.9 A321panel4 6.7 1.9 3.0 6.0 0.8 3.5 0.3 1.0 0.8 3.3 1.7 2.9 A321panel5 6.2 0.5 1.5 4.5 0.7 2.7 0.6 1.0 0.5 1.5 1.1 2.1 A321panel6 2.0 0.5 1.2 4.5 0.6 2.1 0.2 0.5 0.3 1.1 0.6 2.1 A321panel7 6.2 0.5 1.5 4.5 0.7 2.7 0.6 1.0 0.5 1.5 1.1 2.1 A321panel8 8.8 4.3 3.4 6.0 0.9 4.4 1.1 2.0 1.1 3.7 2.4 3.2 A321panel9 3.3 0.5 2.0 6.0 0.7 2.8 0.2 0.9 0.6 2.3 1.2 3.2 A321panel10 8.1 3.2 3.4 6.0 0.9 4.4 1.1 2.0 1.1 3.7 2.4 3.2 A321panel11 9.3 2.5 3.4 6.0 0.8 3.8 0.2 1.0 0.9 3.8 2.0 3.2 A321panel12 3.7 3.7 1.5 3.0 0.6 2.3 0.3 0.6 0.3 1.1 0.6 1.6 A321panel13 2.1 0.5 0.8 3.0 0.6 1.8 0.2 0.4 0.2 0.6 0.4 1.6 A321panel14 3.8 3.7 1.5 3.0 0.6 2.3 0.3 0.6 0.3 1.1 0.6 1.6 A321panel15 2.3 0.5 1.2 3.0 0.6 2.0 0.3 0.5 0.2 0.9 0.5 1.6 The above cycle timings for the processes are calculated based on the parameters explained at the beginning of this section. While calculating this process times Bill of material (WS2015-0001 V2) is taken into consideration. The Microsoft excel model has been made linking bill of material and process parameters, to give the above process times.

These timings are used in the simulation model for determining the queuing pattern at servers, buffer levels and the number of process stations needed. The modelling of every panel with different timings would give more accurate results. Only for determining the value added times and non-value added time of the processes, average timings (shown in last row) would be used. The average of timings would not affect the robustness of system, as for the design of system every panel’s cycle timings are considered.

For the doubler feeding line, the doubler combination based on method explained in section 5 is used. The combination is given in appendix 3. The parameter stays the same for the doubler feeding line processes, only after milling process; Alodine would be applied on doublers and then would be stored for 24 Hours.

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A) Layup process modification

The main modification of layup process as explained in section 4 is to have layup assembly line with processes separated at different process station. The non-value adding activity like tool cleaning would be reduced by laser cleaning system. The automated layup process times, doubler layup and automated stringer layup times are shown in Table 27 & 29. Based on those timings the simulation experiment was done, the number of servers needed for those processes are as follows:

No of Process Station for layup line 7 6 5 4 3

No ofstation No 2 1 0

Fig. 100 No of process stations needed for Layup Assembly line

The layup station needed for aluminum & prepreg layup is 6, this is 1 more than theoretical calculation as layup cell size constraints were used in model. For larger panels layup station with 2 robots needs to be used while smaller panels can be processed on layup stations with 1 robot. So due to this constraint 1 extra process station is needed. The utilization rate of big layup cells is around 76 % and for small layup cells is around 65%. This also gives time for maintenance & breakdowns. This ratio are based on 50/50 demand of A320 and A321, if in future the demand of A320 increases then small layup cells would be utilized more and big layup cells would be utilized higher if the demand of A321 increases. This is explained in detail in the next scenarios.

Pre compacting and doubler layup is done manually; it needs 6 positions outside the layup cells. However all this 6 positions are not continuously used. This position also acts as a buffer when workers are doing the job on other positions. In between 6 positions for pre compacting and doubler layup, 5 workers are needed. The utilization rate of doubler layup station and Workers is shown in Figure 101. It shows pre compacting is used for 40.7%, while for 1.04% process station is blocked (panel is unattended) as workers are not free and are working on some other doubler layup position.

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Fig. 101 Utilization Rates

For stringer positioning stringer pick and place or stringer head concept can be used. Based on the results for both concepts, 2 process stations are needed. This is due to the fact that the most time of stringer positioning process is due to adhesive layup and heating to stick the stringers. This process remains same for both the concept. So, both concepts need 2 process stations. Therefore, it is recommended to use stringer pick and place concept as it is already developed and does not need further research.

Based on the timings shown in Table 31, the utilization rate of stringer positioning server is 91%. It is also resulting to waiting time of 45 minutes and queuing on the line as shown in Fig 102. These parameters are based on 50/50 ratio of A320/A321, if the ratio of A321 increases in future to than utilization increases to more than 95%. So, the breakdown or maintenance scope is not left, thus it’s better to use 3 stations. When 3 stations are used waiting is eliminated on the layup assembly line. The utilization rate with 3 stations is 61%, moreover this makes the system more flexible to handle any product mix of A320 and A321.

Fig. 102 Panels Queuing at Stringer station (with 2 stations)

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Doubler feeding line

The doubler would be produced on the feeding line and supplied to main assembly line. The doublers are combined into panels as per the combination method as explained in section 4. The production process needed is similar to panel production, only the molds used for layup and the frames used for C scan would be different. The advantage of having the dedicated doubler line is that, the resources can be designed as per the needs of the doubler panels. For example, the size of autoclave can be decided based on the doubler panel molds sizes, layup cell size and end effector dimensions, etc. Moreover it also simplifies the production planning as it is separate production line which is based on demand of main assembly line. The number of stations needed to make the doublers is as follow:

No of Process Station for Doubler Production

1 No ofStations No 0

Fig. 103 Doubler production line process stations

The doublers are produced in advance, so the main assembly line always gets the required amount of doubler feeded. But the inventory of doublers also needs to be minimized. In simulation model both the production line (main assembly and feeding line) were linked to each other. The panels in model can only start the layup if the doublers for it ready. Once the panel goes for layup, doublers are prepared for the assembly and delivered to doubler layup station when needed. But as the doublers of different panels are combined into one doubler panel during production, it is necessary to have at least all doublers of 1 shipset of A320 & A321. The production and consumption of doublers on assembly line is shown in Fig 104. It shows that at least doublers of 1 shipsets of A320 and A321 are always there in stock. So, storage is a decoupling point between two lines.

Fig. 104 Doubler Production & Consumption

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B) Paint process modifications

This section will explain the simulation results of paint process modifications. The current manual paint process is time consuming and higher number of workers. So, the automation possibilities were recommended in section 4. Moreover, the process is also redesigned from one paint box for all activities to continuous production line with different process stations for different activities. Along with automation, drying options were also recommended to reduce the lead times and Inventory.

Based on the new process time calculated from parameters discussed earlier, the numbers of process stations needed on production line are as follows:

No of Process Station

4 3 2

1 No ofStations No 0

Fig. 105 Number of process stations for painting and finishing processes

For the process of sanding 2 process station would be required with one sanding robot (8 orbital sanding end effector). For manual processes like applying stringer sealant, sanding inside the stringers and splice correction (only in case of defects), 4 process stations are needed. Then the panels with stringer sealant would be dried in oven. Then panels would be painted with primer layer, but just before that panel needs to be cleaned with chemical solvent. After cleaning with solvent in 15 minutes primer needs to be applied. So, cleaning & inspection can be done at position just outside the automated paint room. The workers can be shared resource (Flexible workers) between cleaning stations and manual process station, quality control and deburring stations as it will increase the utilization of workers and also increase the processing capacity of manual processes.

For primer painting and topcoat painting two process stations are required, each with one robot. However, if one paint shop would be equipped with 2 robots then one process station would be needed. But from redundancy perspective it is better to use two separate process stations with 1 robot. For A320 and A321, the airbus has requirement that the panels of the cargo compartment of aircraft also needs to be painted with corrosion resistant top coat, so 7 panels of the shipset would be painted with 2nd topcoat, for that 1 position is required with 1 robot. Then finally the quality control tests like paint thickness measurement, inspection before expedition, etc. can be carried out at the last position of production line.

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For drying ovens there are 2 alternatives: convection oven and Infrared oven. The drying ovens would be needed for stringer sealant drying, primer drying and topcoat drying. The positions needed for drying by the use of ovens are shown in Fig 106 and Fig 107.

Convection drying oven:

Fig. 106 Drying positions in use with convection drying

Infrared drying oven:

Fig. 107 Drying positions in use with infrared drying

For stringer drying in convection oven 3 drying positions would be needed, while with infrared oven 2 drying positions are needed. For primer and topcoat drying by convection will require 4 drying positions (though at times peak of 6 occurs), with infrared drying 2 positions are required. Moreover, the drying time with infrared oven is faster than convection oven, so it also reduces the lead time of the panel.

Thus, it is recommended to use infrared type of ovens for drying the sealants, primer and top coat. But before implementation time temperature profile, emitter speed movement, intensity of light, etc. should be calculated and tested on demo panels.

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C) Autoclave Size Modification

As discussed in section 4 to reduce waiting time of panels, batching of panels before autoclave needs to be modified. Different scenarios with different batching were experimented in the model and based on the batching size of panels, autoclave sizes and their costs were calculated by formula defined in section 4. Following are its results:

Table 32 Autoclave Modification Scenario 1

Scenario 1 No of Before Autoclave After Autoclave Total Costs No Batching Autoclave Waiting times Waiting times of Autoclaves (min) (min) Big Autoclave 3 100.8 Medium Autoclave 2 92.4 14.8 21.87 Million Euros Small Autoclave 1 128.4

Table 33 Autoclave Modification Scenario 2

Scenario 2 No of Before Autoclave After Autoclave Total Costs Batching of 2 Autoclave Waiting times Waiting times of Autoclaves (min) (min) Big Autoclave 2 113.4 Medium Autoclave 1 220.2 43.2 17.56 Million Euros Small Autoclave 1 81.5

Table 34 Autoclave Modification Scenario 3

Scenario 3 No of Before Autoclave After Autoclave Total Costs Batching of 4 Autoclave Waiting times Waiting times of Autoclaves (min) (min) Big Autoclave 1 324.6 Medium Autoclave 1 319.8 99.5 19.61 Million Euros Small Autoclave 1 160.8

Table 35 Autoclave Modification Combination Scenario

Combination No of Before Autoclave After Autoclave Total Costs Batching of Autoclave Waiting times Waiting times of Autoclaves (2,2,1) (min) (min) Big Autoclave 2 113.4 Medium Autoclave 1 220.2 29 17.28 Million Euros Small Autoclave 1 128.4

Table 32 shows the result with no batching, one panel are processed in the autoclave. It has the low waiting times, but needs high amount of autoclaves which results to high investment costs. Table 33 shows the results with batching of 2 panels; it has higher waiting times than no batching case, but has less investment costs. The result of 4 batching panels is shown in Table 34, it has very high waiting times and also high investment costs. Only 1 autoclave of 3 types is needed when batching of 4 panels is done. The high investment costs are due to increase in length of autoclaves.

Table 35 shows the combination of all the scenarios. The waiting times and investment costs can be reduced by batching of 2 panels for large and medium size autoclaves and using no batching for small size autoclave. This combination shows the least investment costs and less

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waiting times. Here experimentation was only done with 1, 2 and 4 capacity panels because batching of 3 panels would not completely fill the autoclave and batching of more than 4 would only increase the waiting times and investment costs. The queuing pattern obtained for all above scenarios is shown in Fig 108.

Scenario 1: Batching of 4 Panels Scenario 2: Batching of 2 Panels

Scenario 3: Batching of 1 Panel Combination of Scenario

Fig. 108 Queuing variation for different Batching combinations

From the above figure its clear that batching of panel results into queing for downstream process, high peaks are generated when autoclave curing is completed. Highest queing occurs for autoclave batching of 4 panels and minimum for batching of 1 panel and for combinaion scenario. Thus, by reducing the batching, waiting times (lean wastes) can be reduced.

The batching combination shown in Table 35 (combination scenario) is recommended and would be used further in this research.

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D) Flexible workers for work balance

As discussed in section 4, the production line balancing is done by using the Flexible workers. The benefits of using the Flexible workers and processes where workers can be used are discussed below:

Flexible Workers 1:

The Flexible workers need to be used for pre compacting, doubler layup and bagging process. The utilization of pre compacting and bagging process station is only for 50 percent of time, so when workers are used based on fixed capacity per process, it leads to underutilized workers. By using the Flexible workers the utilization rate increases to 86%. And the capacity to handle the peak arising (due to difference in cycle times) at the doubler layup is also increased, resulting to smooth flow of panels on the production line. Moreover as all the three processes are in the same work area of clean room, so workers movement is not a problem. In between 6 pre compacting & doubler layup station and 4 bagging stations, total 5 workers would be needed every shift.

Flexible Workers 2:

The Flexible workers can be used for doublers framing and debagging processes. As both this processes are only used for 30% of the time, so it will lead to underutilized workers. So the use of Flexible workers increases their utilization. Moreover, due to extra capacity availability at debagging station, the waiting of panels after autoclave is almost eliminated. Fig 109 shows the peaks at the debagging process; it’s very much less than shown in Fig 108. Thus, by autoclave size modification and dynamic capacity at debagging station the waiting at debagging has been almost eliminated. This will save the factory space needed to store the layup tools. Moreover, empty tools would be available for next layup process in short time as they no longer wait in buffers.

Fig. 109 Panels waiting for debagging process

Flexible Workers 3:

The Flexible workers can be used for deburring, stringer sealant, cleaning, paint quality control and for rework lugs cutting processes If the work is not balanced between this processes than waiting of panels occurs at manual process station (average waiting time of 145 minutes) and workers at cleaning and quality control are utilized for 50% of time. When the Flexible workers are used than waiting is at manual process station is reduced to 6 minutes and workers are utilized for 72% of the time. In between 4 deburring positions, 4 sealant positions, 1 cleaning position, 2 quality control position and 4 rework lugs positions, total 7 workers would be needed every shift with 5 days of production per week.

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Value added and Non-Value added activities for future production system

Processes VA 6.00 NVA

5.00

4.00

3.00

Time(Hours) 2.00

1.00

0.00

Fig. 110 Value added (VA) and Non value added (NVA) activites for the processes

For the future system the non-value adding activities are reduced by modifying the process. Fig 110 shows the Value added and Non-value added activity timings for all the process. The processes like tool cleaning, bagging, debagging, stringer sealant are completely non-value adding (business value adding activities). These activities are not eliminated but their time is reduced for the future system by using systems like laser cleaning system for tool cleaning. The non-value adding time for machining process is reduced by using milling machine with changeover beds that reduces installation times. By separating the sanding and painting processes at different stations the activities like paint & primer preparation times, sanding preparation times, panels in and out transportation times, flash off time between primer and paint, etc. is eliminated. The total value added (VA) and non-value added time (NVA) for future production system is shown in Fig 111.

Total VA & NVA Activites 25

10 Time(Hours)

0 Total VA Total NVA

Fig. 111 Total processing time (VA and NVA)

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6.2.1 Production with 5 days per week

When the panels of A320 and A321 are made with modified future production system with new technology and factory operates for 5 days (15 shifts a week), then number of process station needed are shown in Fig 112. It shows the two results, one for Ideal production system with no defects and second is for production system with defects, the probability of defect used in simulation was based on defects data obtained from A380 production system. The defects caused at the layup station by workers due to wrong process application are eliminated. The defects will be reduced from 3.2% to 0.9% by automating the layup. The number of resources does not increase for the case with defect, their utilization rate increases (shown in Fig 113). Moreover the processes after the C scan and machining are not affected by defected panels, as those finishing processes does not result to scraped panels. Also the defects in panels are determined at C scan, so the panels are scrapped after C scan and are not processed at downstream processes.

Ideal Case No of Process stations Case with Defects 7

6 5 4 3 2 No ofstations No 1 0

Fig. 112 No of Process stations

Utilization of stations Ideal Case Case with Defects 90 80

70 60 50 40 30 Utilization Utilization % 20 10 0

Fig. 113 Utilization rate

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Fig 114 shows the 3 group of panels with their processing time, waiting time and lead time. Two different waiting times are used: one is ideal waiting time when panels are not in process and are waiting as process stations are not free. Second is in process waiting time when panels are waiting as part of process itself (For example: During primer or paint drying).

The waiting time is reduced by the suggested modifications, but it still exists in small amount. The maximum waiting time is due to waiting between C scan and debagging. Fig 116 shows the amount of waiting panels (queuing pattern before servers). The in process waiting time is also reduced by use of infrared dryers. The lead time is reduced due to less processing time and less waiting time. The inventory is also less as lead times are reduced. This also means less number of molds and transport frames would be needed.

Small Panels Waiting time & Lead time Medium Panels Large Panels 45 40 35

30 25 20

Time(Hours) 15 10 5 0 Processing times Idle Waiting times Inprocess waiting times Lead times

Fig. 114 Waiting time and lead time of panels

Inventory of Panels 50 45

35 30 25

No ofPanels No 20 15 10 5 0 In process Inventory Waiting Inventory Total Inventory

Fig. 115 Inventory of Panels

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Queuing pattern

The queuing of panels is reduced by the suggested modification of different size autoclave and using dynamic capacity by Flexible workers. The queuing pattern for period of 1 week (5 working days) is shown in Fig 116 for the different processes.

Fig. 116 Queuing at different processes

The waiting of panels for debagging and machining is eliminated (except few peaks), while for other processes like sanding, deburring and sealant stations it’s very less (average 1 panel in buffer). That panel can wait at the extra position kept at the deburring, until sanding station is empty. The waiting between C scan and debagging cannot be eliminated, unless extra equipment is used. The waiting time is on average 1.2 hours, but extra equipment will cost around 1.5 million euro extra. Moreover before C scan panels will be placed in vertical frames and these frames can already be installed at the C scan station (secondary position), so once machine is free it can directly start scanning. This will save the time for installation and also no extra buffer space will be required.

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From the simulation results of queuing at the process stations, the amount of buffer stations needed to store the panels were determined (shown in Fig 117, the amount buffer storage are selected based on the peaks occurring at the process stations (from Fig 116). Based on these results the buffer storage space would be kept in the factory layout. The main reason for these buffers is processing time difference between the panels for the processes. The amount of buffers needed has been reduced considerably by modification like line balancing by Flexible workers and autoclave modifications. The buffer storages before and after the autoclave would require space for big molds with panels (horizontal position), whereas other buffers would be for panels in frames (vertical position). No storage is required for drying of panels as they are dried in infrared ovens.

For the buffers after the C scan, the panels would be stored at the buffer positions kept at the process stations. In this research it is assumed that transport on the production line is done by electrified monorail system. The monorail system for frame transportation is already used in moving line production for automobiles.

Storage Buffer 6 5

4 3 2

1 No ofpositions No 0

Fig. 117 Storage buffers for panel

Production Costs:

The recurring cost of GLARE panel production is determined according to the method as described in section 5.2. The input variables to determine the production costs were discussed in alternative 1 (base case scenario, shown in Table 21). The material costs would also be same as base case scenario. The process costs and depreciation rates changes as number of equipment’s and full time employees (FTE) are different for new production system. The detailed list of the Investment costs is provided in Appendix 6. Table 36 shows the process costs for future production system with semi automation production system. Its shows the large decrease in number of FTE and also less process costs, due to introduction of automation for processes like layup and painting & finishing.

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Table 36 Process costs

Hidden, duedue to confidentialconfidentconficonfideconfidentialdentialntialial datadata

Table 37 Depreciation per shipset

Depreciation on Investment Investment in Fixed Assets € 167582200 Asset lifetime for use 5000 Residual Value (%) 10 Residual alue € 16758220 Depreciation/shipset € 30165

Based on the material costs, process costs and depreciation costs, the recurrent costs for A320 and A321 panel are calculated (shown in Table 38). The material costs and process costs are the major contributor for the recurrent cost.

Table 38 Recurrent costs with 5 days per week production

Cost Item A320 A321 Material Costs per Shipset € 77238 € 109378 Process Costs per Shipset € 49769 € 56123 Depreciation Costs per Shipset € 28355 € 31975 Total Cost per Shipset € 155362 € 197476 Average Cost per panel € 10357 € 13165

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The recurrent costs with 7 days/week production was calculated (shown in Appendix 7), the results of recurrent costs are shown in Table 39, where recurrent costs are compared.

Table 39 Cost Comparison

5Days/week 7Days/week 5Days/week 7Days/week A320 A320 A321 A321 Total Cost per Shipset € 155362 € 167516 € 197475 € 211181 Average Cost per panel € 10357 € 11168 € 13165 € 14079

The recurrent cost of panels increases by 7.8% for A320 panels and by 6.9% for A321 panels for scenario 2 (production with 7 days). The operation increases by 29% due to 6 extra shifts. As working hour are increased so more employees (FTE) are needed. For example: For operating 4 autoclaves two operators are needed every shift with 5 days production, resulting to 8 FTE (due to 1630 Hours). Also for scenario with 7 days production 4 autoclaves are needed with two operators per shift, resulting to 12 FTE; however the utilization of FTE would decrease.

It is recommended to use 5 days (15 shifts) production, as the recurrent cost is less with 5 days per week production. This will also have spare 2 days per week for use in case of emergency like uncertain failure of equipment for large duration. The 2 days can also be used for preventive maintenance, which will reduce the accidental breakdowns. Moreover, if system is designed with 7 days per week production to satisfy current demand, than it would not have any spare capacity to satisfy any sudden increase in demand. However, it should be noted that these results are based on Investment cost data available till May, 2015. If in future Investment cost increases then situation might change in favor of 7 days production. Because increase in Investment costs will increase the depreciation per shipset costs.

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6.3 Lean Key Performance Indicator Comparison

In order to show the effects of lean for the future production system, it is necessary to compare indicators that are used to differentiate between the situations before modifications are introduced within the production system and after the modifications introduction. In this research the alternative 1 (Based on A380 system) is situation without modifications implementation and alternative 2 (Future production system) is with proposed modifications. The KPIs selected are explained in section 5; they were quantified by using the excel process time model & simulation model.

KPI 1: Value added times and Non-Value added times VA NVA 50

20 Time(Hours) 10

0 A320 based on A380 Future System

Fig. 118 Value added and Non value added times

The value added time and non-value added time for all the activities are shown in Fig 118. It shows that timings of value added and non-value added activities are reduced for the future system due to modifications. The value added times are reduced due to the automation of processes like layup and painting. The non-value adding activities are reduced due to use of laser tool cleaning system, use of extra milling beds to save installation time, C scan installation in advance and by separating painting, sanding and drying processes.

As per lean principles non-value adding activities should be reduced as they do not add any value to the product from customer perspective. To evaluate the production system in terms of value added and non-value added works, the ratio of value adding activity timings to total activity timings, KPI (VAW) is used in this research, for ideal lean system it should be 100%. Fig 119 shows the VAW for both production systems, it shows that the VAW increases due to the modifications. It increases from 69.70 to 77.78%. This ratio indicates the scope of continuous improvement. Here, after modifications the value of VAW is not 100% due to activities like rework lugs, bagging, debagging & stringer sealant and splice correction. In future improvement projects, Fokker should try to reduce or eliminate those activities by modifying the processes.

The non-value adding activity of rework lugs is needed to cut the transport lugs, which are kept on panel for holding the panels in the transport frame. If the frames used are developed with other panel holding mechanism, than it can be eliminated. The stringer sealant and splice correction activity is needed if the adhesive has not been cured properly and air bubbles or other defects are present on panel. So, methods to do it right at first time should be investigated. The bagging and debagging processes are needed for the autoclave, but methods to reduce the timings should be researched or “out of autoclave curing” needs to be used in future (Composite Manufacturing, 2014).

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KPI 2: VAW (Value added work)

80%

75%

70%

65% VAW VAW (%) 60%

55%

50% A320 based on A380 Future System

Fig. 119 Value added work

The lean waste of waiting between processes is reduced by modifications like reduced batching of autoclaves, use of Flexible workers for painting line and drying of primer, topcoat and sealant by use of infrared ovens. For ideal lean production system, it should be completely balanced with Flow of 100%. Fig 120 shows that Flow of panels increases from 53.6 % to 83.5 % for the future production system.

The Flow is not 100% due to processes like batching of 2 panels is still there for autoclaves of large and medium size panels and waiting before C scan. For creating ideal lean production system with 100% Flow, 12 autoclave with single capacity (no batching) should be used and two extra C scan would be needed. This will increase the investment by approx. 18 million euros. That much high investment is not desirable for eliminating few hours of waiting.

KPI 3: Flow 90.00 80.00 70.00

60.00

50.00

Flow Flow (%) 40.00 30.00 20.00 10.00 0.00 A320 production based on A380 Future System

Fig. 120 Flow of panels

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The ideal waiting time (which results due to waiting between processes), in process waiting time (due to drying of paint, primer and sealant) and total waiting time comparison for both production systems is shown in Fig 121.

KPI 4 : Waiting time A320 based on A380 Future System 60.0

50.0

40.0

30.0

Time(Hours) 20.0

10.0

0.0 Ideal Waiting time Inprocess Waiting time Total Waiting time

Fig. 121 Waiting time

By reduction of waiting time and process times, inventory of panels (lean waste) is also reduced. In process inventory indicates the inventory which are at process stations, it is reduced as the number of process stations are reduced for future system due to automation. The waiting inventory indicates the inventory waiting between process stations and in drying buffers, it is reduced for process stations due to less waiting times. This also reduces the number of layup molds needed from 60 to 30 and transport frames for transporting panels from 85 to 32. The comparison of inventory of panels is shown in Fig 122.

KPI 5 : Inventory A320 based on A380 Future System 140

120

100

60 No ofPanels No 40

0 In process Inventory (WIP) Waiting Inventory Total Inventory

Fig. 122 Inventory of Panels

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Due to manufacturing errors caused during production, panels needs to be scrapped. Based on the data obtained from A380 panel production, the probability of defected panels for the processes was used in the simulation model. For the future system probability of defects resulting due to wrong process application at layup process station are removed from the model. The number of panels scrapped during production of 300 shipsets is shown in Fig 123. The number of scrapped panels is reduced from 144 to 41, due to modifications for the future system.

KPI 6 : Scrapped panels 160 140 120 100 80 60 Scrappedpanels 40 20 0 A320 production based on A380 Future System

Fig. 123 Defected Panels

The required factory surface area comparison for the new green field factory for basecase and future production system is shown in Fig 124. The factory space required is reduced by 14% as the less process stations and less buffer space is needed. The buffer space needed is reduced by 16% as waiting inventory is reduced. The storage buffer space required for future system is reduced as less number of molds and transport frames are needed due to less work in process inventory.

KPI 7: Factory Space A320 based on A380 Future System 80000 70000

60000 50000 40000 30000

SurfaceArea (m2) 20000 10000 0 Total Factory Space Buffer Space

Fig. 124 Factory space

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Hidden, due to confidential data

Fig. 125 Recurrent Cost

The recurrent cost of the A320 and A321 shipset for basecase and future production system are shown in Fig 125. The recurrent costs are reduced for A320 shipsets by 38.3% and for A321 shipsets by 35.6%. This is due to reduction in operation costs, as with semi-automated production system less number of Full time Employees (FTE) are needed. The modification of layup process and painting process automation reduces the process costs. Also the use of Flexible worker reduces the number of workers, as the utilization of workers is higher and no fixed FTEs are needed for less utilized process stations like cleaning, quality control, etc.

The aim of Fokker’s GLARE automation project is to achieve 50% cost reduction by reducing operation costs, material procurement costs, modifying panel design and by modifying assembly of panels. This research shows that reduction upto 38.3% for A320 and 35.6% for A321 can be obtained in theHidden, costs by due having to confidential the semi-automated data production process for layup and painting and other modifications as discussed in section 4 of this report. The detailed cost breakdown for the future production system is shown in Fig 126.

Fig. 126 Cost Analysis

The material costs contribute highest to the recurrent costs, so Fokker should try to procure the materials at reduce price, as it would be in higher lots. However, this is more of a procurement issue. Table 40 shows the effect of reduction of cost price of material on the recurrent costs.. The 15% material price reduction will reduce the recurrent costs of basecase system by 4.6% for A320 & 5.3% for A321, while for future system it is reduced by 7.5% for A320 and 8.3% for A321. The reason why the reduction has less result in basecase system is that the material costs has relatively less effect on the recurring costs in it as compared to the future production systems. From the results it’s evident that material price has more influence on A321 costs than A320 costs as material needed for A321 is more than A320.

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Table 40 The effect of material cost price reduction on the recurrent costs

Hidden, due to confidential data

The process costs also contributes about 30% to recurrent costs, this is dependent on the wages of FTE. The wages are influenced by the factors like factory location, government policies, availability of labour, etc. For example the minimum wage for workers in Netherlands is 1500 euros, in Hungary it is 800 euros and in India it is 170 euros. The wages used in this research are based on data of PapendrechHidden,t factory;due to confidential however the data final wages would be based on the location of green field factory. Table 41 shows the effect of process costs on the recurrent costs. The 15% process costs reduction will reduce the recurrent costs of basecase system by 8.7% for A320 & 8.1% for A321, while for future system it is reduced by 4.8 % for A320 and 4.3 % for A321. The process cost is less sensitive for the future system as it is semi-automated and requires less number of FTE.

Table 41 The effect of process cost reduction on the recurrent costs

Production System A320 system based on Future System A380 A320 A321 A320 A321 FTEs 384 137 Process costs (100%) Hidden,146543 due to confidential165250 data 49769 56123 Process costs (85%) 124561 140463 42304 47704 Old Recurrent Cost of shipset 252128 306595 155362 197475 New Recurrent Cost of shipset 230147 281807 147896 189057 Reduction 8.7% 8.1% 4.8% 4.3%

Similar to material costs and process costs sensitivity analysis was done for depreciation costs of fixed assets. It showed that by reducing the investment by 15%, the recurrent cost reduction of about 1.5 % is obtained for basecase system and 2.6 % is obtained for future system. It became clear that lowering the investment has the least impact on the recurrent costs, compared to the material costs and process costs. So based on this analysis it can be concluded that in order to reduce the recurrent costs even further Fokker should aim at reducing the procurement price of the material and process costs.

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Hidden, due to confidential data

Fig. 127 Turnover per Production employee The indicator turnover per production employee provides insight into the degree of turnover, compared to the number the production employees. Here, the number of production employee’s decreases causing the indicator to increase. This means that process modifications has led to an improvement of the production process. The indicator increase is due to reduction in FTEs from 384 to 137. Along with automation of processes, concept of using Flexible worker also plays important part in reducing the FTEs; as it eliminates the use of workers for less utilized processes like cleaning, quality control, etc.

Hidden, due to confidential data

Fig. 128 Turnover per square meter of factory area The turnover per total surface indicates the link between the turnover and the used surface in a company. An improvement of this indicator is caused by a constant turnover but reduced surface area. This measure shows the efficiency of use of space, it has increases due to process modifications as less buffer spaces are needed (shown in KPI 7). Moreover due to reduction in inventory the storage buffers space needed for frames and molds has reduced. Thus, the space utilization has increased.

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6.4 Effect of Variations

1) Product mix variation

The design of production system and analysis in this research is based on Fokker basecase of A320 and A321 ratio of 50:50 and total 300 shipsets. The Fokker’s basecase is based on the Airbus prediction of future demand. If the demand of A320 or A321 changes in the future then the production system should be able to respond. Different combination of A320 & A321 were simulated to check the effect on production system, it’s shown in Fig 129.

A320 – A321 Product Mix Variations 100-0 75-25 50-50 25-75 0-100 10 9 8 7 6 5 4 3

No of Stationsof No 2 1 0

Fig. 129 No of work stations for different Product mix variations

The design of production system in this research is based on 50:50 product mix of A320 & A321, which is shown in green in Fig 129. The production system can respond to product mix of 25:75, however for aluminum sheet feeding line extra milling station is needed. If the product mix becomes 0:100, when only A321 would be made then the number of stations needed on doubler feeding line increases as A321 has more doublers. Fig 129 shows the increase in number of resources, it shows 1 extra doubler layup station, 1 extra C scan and 1 extra doubler milling station would be needed. Moreover, the layup station for panels remains the same but one small layup cell needs to be converted for larger panels by extra robot installation.

Till know airbus has got 3721 pending orders for A320 neo and A321 neo. The majority of orders are for the A320 (76%), while only 24% are for A321. Thus, this scenario of 100% A321 seems impossible from current orders of next 6 years. Thus, the production system is flexible enough to respond to 25:75 ratio of product mix, while the current order trend is of 76:24. If in future order trend changes than extra Order (Till April 2015) investment would be needed. 100%

50%

0% PendingOrders (%) A320 A321

Fig. 130 Order trends (Airbus data of orders)

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2) Product demand variations

The analysis in this research is based on the demand of 300 shipsets (150 of A320 and 150 of A321). If the product mix remains constant (50:50) and demand increases in future than production system should be able to respond. The effect of increase in demand was simulated to check the effect on production system; the results are shown in Fig 131.

Demand Variation 300 325 350 375 400 425 450 12 11 10

9 8 7 6 5

4 No of of Stations No 3 2 1 0

Fig. 131 Demand Variations

The process stations like tool cleaning, layup, pre compacting & doubler layup, autoclave, C scan and machining are sensitive to demand. Other stations are not affected as they are just working positions for workers, utilization of workers & number of workers needed increases. Fig 131 shows the increase in the number of station with increase in demand. If the demand increases to 325 shipsets than extra C scan would be needed. Also for the aluminum feeding line extra milling station would be needed. If demand increases further to 350 shipsets than extra doubler layup station would be needed. Also the panel milling station (M Torres) needed increases. The current production system can be designed based on 300 shipsets demand but for future demand extra space needs to be kept in the final layout. Based on this analysis extra space has been kept in final layout for stations like layout, C scan, aluminum milling, panel milling & tool cleaning.

The number of Flexible workers (group 1) for layup clean room increases by 4 when demand exceeds 350 shipsets. The number of Flexible workers (group 2) for debagging and doublers framing does not increases, only their utilization increases. The number of Flexible workers (group 3) for painting and finishing line increases by 4 when demand exceeds 400 shipsets.

The numbers of resources shown in Fig 129, 131, 132 are total resources (both of main production line and doubler feeding line).

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3) Product type variations

The analysis in this research is based on the demand of complete 300 shipsets (all panels of fuselage). But as explained in the first section the overall demand is of 600 shipsets, out of which 50% would be produced by Fokker and other 50% by PAG. If both companies make 300 complete shipsets than they both will need infrastructure of making complete fuselage of aircraft. Instead of making 300 complete fuselage panels, both companies can make 600 half shipsets (600 front section of fuselage or 600 rear section of fuselage). Thus this will decrease the product variety from 30 panels to (14 front section or 16 rear section panels). The product type variation was simulated to check the effect on production system; the results are shown in Fig 132.

Section 14-15 (Front of Fuselage) Product Variation Section 17-18 (Rear of Fuselage) 9 8

7 6 5 4 3

No of Stationsof No 2 1 0

Fig. 132 Product variation

The panel division of fuselage is not symmetric, the panels varies in sizes and curvature (SC or DC). This affects the processing times of workstations and hence the number of work stations. The number of stations needed for front section is less as it includes only 4 large size panels and other 10 are medium size panels. While the rear section of fuselage has 8 large and 8 small size panels. The detailed sizes of the sections of fuselage have been explained in introduction section 1 of this report. The product type variation does not have high effect on the number of station, but it has effect on the sizes of the equipment’s.

If only front section is made than 2 medium size autoclaves would be needed and 1 large size autoclave would be needed for panels and 1 for doubler. The dimension of the large autoclave needed would be 11 m in length and 5 m in diameter, instead of 12 m in length and 6.7 m in diameter resulting to investment saving of 2.6 million euros. The total cost of autoclave needed for front section of fuselage panels is 12.8 million euros, while the total cost of autoclave needed for rear section of fuselage panels is 17.4 million euros. While the cost of autoclave when both companies make 300 shipsets is 20.3 million euros. Moreover, instead of 30 different types of layup molds, only 14 front section or 16 rear sections layup molds would be needed.

However, before making final decision, Fokker should investigate recurrent price and selling price for individual panel on more detailed level, to predict which section production would be more profitable.

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7. Production system layout

Main aim of this research was to develop lean production system arranged in well-organized continuous layout. The method of developing lean production system has been discussed in section 4. In this section final layout of production system will be discussed based on the simulation results of the number of workstations and storage buffers.

The production lay-out designed in this research is a conceptual lay-out for the future production system. The goal of the production lay-out is to visualize how the lean future production system should be: it shows the number of workstations, processes in particular sequences, buffer spaces and transportation space needed, etc. it will also give initial estimate of the needed area and the investment costs. It should be noted that this layout is not the final floor plan, but it presents the inputs for the final floor plan like area needed for workstations, space needed between workstations, space for transportation, sequence of arranging workstations, etc. The lean production lay-out is based on the VSM of the future production system. The details like decoupling points and lean supermarket are shown in the future production system VSM, based on the simulation results (explained in section 6). The VSM of future production system is shown in Fig 134.

For the feeding line aluminum coils and stringers supplied from suppliers needs to be stored (assumption based on current system, nothing yet known about the suppliers). So there would be decoupling point at the upstream end of the production system. Aluminum coils would be decoiled automatically and put on a table and cut to dimension and chemically treated. Then sheets and stringers go through processes like primer painting and curing. Then stringer traverses are deframed, stringers are placed directly on a stringer kit and transported to stringer kit storage supermarket (decoupling point). Then on demand at the stringer layup station the stringer kits are delivered to stations. Here, the inventory can be replenished based on sequential pull system (pre-planned production), where planning department plans the production on stringer feeding line based on the planning & stringer consumption of main panel production line. Similarly the aluminum sheets will be deframed, rolled on table, milled and kitted in the kitting boxes, a robotic system with vacuum naps developed for horizontal sheets will pick up the horizontal sheet and place it in the kit carts. Then the kit cart would be stored in the lean supermarket (decoupling point), and delivered to layup stations on demand. Here also the inventory can be replenished based on sequential pull system (pre-planned production), where planning department plans the production on feeding line based on the planning of main production line. Also for emergency situations like sheet defect at the layup station, extra buffer would be needed before the milling process station. This would help in reducing the response time to maximum of 16 minutes (based on milling process time for longest sheet). The decoupling point is needed in order to feed the main production line on time, without any delay.

For the doublers feeding line the doublers are made and stored in lean supermarket (decoupling point). Then when panel starts at the layup station, doubler of those panels should be prepared and kitted, kits needs to be delivered to doubler layup stations. Here, just in time delivery between two lines is not possible and decoupling point is needed. The main reason is that due to combination of doublers of different panels into 1 doubler panel for production benefits, the production scheduling of both lines cannot be coordinated. For example: one doubler panel contains doublers of panel 1, panel 5 & panel 7, but as per main line planning, panel 7 would be made after 12 hours then the doublers of that panel needs to be stored. In order to have production scheduling flexibility on main line, it is better to keep doublers of 1 shipsets of A320 and A321 in decoupling point. The queuing pattern of kits waiting in decoupling points is shown in Appendix 7. Based on those results, space is kept in the production system layout.

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Future stream mapping

Fig. 133 Future value stream map

Fig. 134 Future system Value stream mapping

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Once the panels are made at the layup station, they are pulled by the downstream process of doubler layup and pre compacting. Then panels are pulled by the stringer layup station as soon as they are empty. So, there is no waiting in between those processes. Then the panels are pushed to the autoclave buffer area, where panels (medium & large) are batched and cured into an autoclave, as soon as autoclave cycle is completed the panels are pushed to the debagging station.

Due to cycle time differences between panels and processes, queuing occur between C scan and debagging. So panels wait at the debagging station until C scan position is available. The C scan pulls the panels. For 3 C scans, 6 panel positions are kept so that the panel can be installed in advance before the C scan is available. It saves the process time. After C scan all the downstream processes are balanced, so panels are pulled by all the downstream processes. No storage buffers are needed between stations (only extra positions for flexible workers). Thus, the push pull decoupling point is between debagging and C scan.

But as the cycle time difference is there between all the processes there is waiting of average 9 minutes between machining and deburring, waiting of 6 minutes between deburring and sanding, 5 minutes between sanding and sealant application. So for that much time the panels can be stored at the extra positions kept for flexible workers. Those extra positions for workers are kept at the deburring station and manual process station. These positions also allow flexible workers to work on the panels without any transport movements. After workers have completed their work, Panels can wait at those positions until downstream process pulls it. Thus instead of push system, pull system is created. So, there would be no need of buffers before every processes and also no need of production control panel priority lists as current system. The overall flow of 83.6% is obtained. This is less than 100% (ideal lean system) due to waiting before the autoclave & waiting after the debagging (in decoupling point) and due to drying time (in infrared ovens).

Arrangement of resources

The system contains three productions line: 1) Main panel production line 2) Doubler feeding line and 3) Aluminum sheet & Stringer feeding line. The scope of this research was limited to main panel production line and doubler feeding line. However, the transportation of aluminum sheet system was included as it affects the production layout. To have the complete view of the production layout, aluminum and stringer line is also included in the layout, based on the results of the previous research of Pier De Vries and Fokker engineer’s calculations.

As discussed in the section 2, the production layout can be based on the three type of layout: 1) Product layout (where resources are dedicated and are arranged in different line) 2) Process layout (where resources are shared and are arranged in zones) and 3) Hybrid layout (where resources are dedicated, but arranged in common zones). As per the process requirements of GLARE manufacturing the sequence of processes is fixed, so all the panel follows a pre-arranged route through a series of processes. It represents the product layout type of arrangement. Both main panel production line and doubler production line resources need to be arranged by product layout arrangement. Also the resources needs to be dedicated for doubler production and needs to be of different sizes than panels production line, specifically designed based on the sizes of the doublers. For example, the milling machine needed for doubler 5 meter in length and 3 meter in width, whereas milling machine needed for panels would be 12 meter in length and 6 meter in width. The resources of both lines can be arranged on separate lines or in combined zones (where similar resources are grouped together) resulting to hybrid layout.

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Hybrid type layout would advantageous for arrangement of resources due to following reasons:

 All similar set of equipment needs to be in one zone of the production layout, this helps in reducing installation costs. For example: The layup process needs clean room environment, so in one layup clean room zone both doublers and panels layup can be carried out. The water connections, filtering units can be shared installation between C scans.  The machine operators can be shared resources, resulting in reduction in FTEs. For example: The C scan process needs one operator for 4 C scans, when all C scan are in one zone the operator can monitor C scan of both production line.  Material Handling can be done by shared. For example: If AGVs are used for panel layup mold transportation, then same AGVs can also be used for doubler layup mold transportation.

So, process stations can be dedicated, but they can share the infrastructure and resources.

The Spaghetti Diagram for the future production layout with all the transportation movements and the dimensions of the process stations has been shown in Fig 135. The final layout is designed after considering the Principle of minimum movement, Principle of Flow, Principle of Space, Principle of Flexibility, Principle of Interdependence and Principle of overall integration as discussed in section 2. The numbers of workstations shown are for 5 days and 15 shifts per week production (based on simulation results).

The space factors taken into account while designing the production layout are:

Space requirement for process stations

The space required for the process station is calculated based on the maximum size of the panels. The maximum size of the panel is 12 meter in length and 5.4 meter in width in Horizontal position when it is on layup molds. The maximum height would be 5.4 meter and maximum length would 12 meter when panels are in transport frames (vertical position), the width of frame is around 15 centimeters. For process like layup, pre compacting, bagging and debagging process stations sizes are based on horizontal position of panels and extra space is added on side for movement of the workers. The size of autoclaves is decided based on the analysis discussed in section 4 of the report.

The size of the C scan and milling stations is allocated based on the vertical position of the panels, with extra space on sides for movement of the equipment. The C scan and milling station cells are kept 30 meter in length, so one cell has two positions. Panels can be installed on empty position while machine is busy on other position; this saves the installation time for the actual process. For the processes like deburring and sealant application the space allocation has been kept, considering 1.5 meter on both sides of panels for movement of workers. The automated painting, drying and sanding cells are kept 6 meter in width after considering the work envelopes and space requirement of the robots.

Space requirement for transport movements

In this research it has been assumed that molds would be transported by transport equipment’s like AGVs. The molds would be kept on platform at process stations and AGVs would drive underneath it to load or unload them. The design of platforms, type of AGVs, degree of motion for AGVs, turning radius calculation, etc. are outside the scope of this research. Those aspects are covered in the research work of my colleague Bart Rinsma. The space for AGV movement has been shown in the Fig 135. The space allocation has been made based on assumption that AGVs would have possibility of straight X-Y direction movement (so no turning would be needed). These AGVs are called omnimove, and are used by companies like Airbus for

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transportation of molds and by Siemens for transporting large coaches of trains (Kuka omnimove, Industrial Robotics, 2015).

The transportation of vertical frames would be done by electrified monorail system. The monorail would have frames attached to it, and panels would be fixed in those frames. The running rails are either attached to the production hall ceiling or suspended on a pillar mounted steel structure. The rail passes through top of the process stations, panels are hanging in frames for the processing.

Space for Flexibility

The extra space for flexibility has been kept between processes of the painting and finishing line. The space is shown in Fig 135 with light green lines; this space has been kept so that the panels can be processed at any of the positions of the process stations. This space allows the motion in perpendicular direction of the monorail. The flexibility is needed in case of failures of certain process station. For example: if one sanding station is out of service, all the panels from deburring station can be transported to other sanding station for processes. Thus between all process stations 15 meter of extra space is kept for flexibility. The largest panel size is of 12 meters, so extra 3 m has been kept for side tolerance.

Space for Buffer storages

Based on the dimension and the number of tools, empty tool storage space has been kept. The assumption is the storage would be upto 3 racks high. Similarly, for the empty frames the storage has been kept based on the dimensions of frames. It has been assumed that all the frames would be of one size. Assumption is based on A380 production system, which has 23 frames all of 1 size. For the defected panels, buffer storage has been kept near the C scan so the panels can be stored there. The non-conformance report of defects needs to be approved by airbus, this takes few days of time and so then panels can be stored in the buffers until final decision.

Based on the simulation results of queuing and flexible workers, extra positions has been kept at debagging, deburring and at manual process stations. These positions can also be used by workers for working on the panel, thus extra transport movement is avoided. At the end of the production line buffer storage is kept to store the completed panels in their cassettes. At this movement it is not known that how frequently the panels would be transported to airbus, so the space kept is based on the assumption that it can store two weeks of production, about 15 shipsets.

For accidental breakdowns emergency buffers are needed. The buffers are kept in layout before autoclave, C scan and near the painting and finishing processes. The space allocation is based on the simulation results of accidental breakdowns. These buffers only need to be used in case of equipment breakdowns.

Space for future expansion

The current predicted demand is of 300 shipsets, if in future demand increases than for that extra space would be needed near the process stations, which are sensitive to change in demand. The amount of increase in process stations with increase in demand scenario has been discussed in section 6 of this report. According to the results, the extra stations would be needed for processes like layup, autoclave, C scan, milling (sheets), milling (panels) and tool cleaning station. The empty space has been kept at those processes for future expansion.

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Spaghetti Diagram for the Production system layout

Fig. 135 Spaghetti Diagram for the conceptual future production layout

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8. Conclusion

This section concludes the report. It will state the final conclusions of this research project in section 8.1. Furthermore, it will provide recommendations for future research in section 8.2 and reflection on project execution in section 8.3. 8.1 Conclusions

This research answers the following main research question:

“What are the design considerations for production system layout of A320 Neo family aircraft GLARE panels based on lean manufacturing principles?”

And to answer this research question, the following set of sub questions has been formulated:

1) What process modifications need to be made in the GLARE production process for future production system?

Before sub question 1 could be answered, the current state process of A380 panel production had to be analyzed. This is done by using lean tool called “Value stream mapping”, where each of the production process is analyzed by analyzing its activities. For each of the processes, activity mapping was done; detailed list of activities is attached in Appendix 2. Those activities are divided into value added activities or non-value added activities. The value stream map gives the indications of the lean wastes in the production system. The value stream map of the current system shows that the process like layup and painting has very high non-value adding activities and are the most time consuming processes. The main non-value adding activities are mold degreasing, laser projection system calibration, attaching pressure tooling’s, covering the mold by foils during bagging, removing the foils during debagging, paint and topcoat preparation activities, paint and primer drying activities. The current paint process is operated in 2 cycles, due to primer and sealant drying time of 24 Hours. The main reason is unavailability of separate drying equipment. Moreover, the processes are manual and time consuming. If those processes are not modified than future production system would need 30 layup stations and 12 paint stations (based on simulation results), resulting to 290 FTEs. Therefore, for the high volume future production system this two processes needs to be modified. Along with elimination of non-value adding activities, their cycle times needs to be reduced by automating the processes.

Moreover, lean wastes like over production, waiting times, inventory, transportation and defects exist in the current system. In current A380 system waiting times accounts to 62% of the total lead time, so the panel which had 5 days of processing time has 13 days of lead time. The main reason for waiting time is overproduction at the layup stations, batching for the autoclaves, unbalanced and push system based production line with buffer in between every processes. The defect rate in current system is 3.2%, out of which 2.3% defects are created due to the use of wrong process and mistakes at the layup process station. The transportation movement was analyzed by Spaghetti Diagram (section 3.3); it shows that the non-continuous production layout and presence of in between buffers causes lot of waste transportation movement.

Thus along with layup and paint process modifications, above discussed lean wastes also needs to be minimized.

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Based on the analysis of the current system three main modifications are suggested:

Layup Process Modifications: For reducing non-value adding activities like tool cleaning Fokker can use automated laser cleaning & degreasing system, for eliminating the use of plastic foils semi-permanent or permanent mold release agents should be used. For value adding activities like the layup of aluminum sheets and prepreg, automated pick and place robots and tape laying end effectors needs to be used. The aluminum pick and place system is currently in experimentation at the Fokker test facility, results of the pick and place experiments are used in this research. During the experiment the prepreg layup was done by pick and place robots it was not that successful as prepreg is not as rigid as aluminum and develops waviness. So, in this research automated tape laying end effector, which is proven technology for composites is considered. Its speed values are decided based on the supplier’s information.

For stringer layup, 2 concepts were considered: Stringer positioning head concept (Jig) and pick and place of individual stringers. Its speeds were obtained from PAG, which has similar automated cells. From the analysis results, it became clear that both concepts need same number of process stations, so it is recommended to use pick and place stringer system as it is well developed system, whereas stringer head concept would need further research. For doublers layup it is proposed to do it manually, as 13 out of 30 panel’s needs doublers and process is less time consuming and less complex. To reduce the time on main production line, doublers should be prepared in advance on feeding line. For the prepared doublers average layup time/panels would be 1.3 hours. By this modifications average cycle time of layup of a panel can be reduced from 27.8 to 10.1 Hours.

Paint Process Modification: For the future production system it is proposed to have paint box which has separate sanding, drying and painting processes. The most time consuming processes like sanding and painting needs to be automated. While the activities which are very less time consuming and simple like inspection and sealant filling can be done manually. These activities are arranged on production line in proper sequence, automated and manual activities are separated to different stations. For sanding it is suggested to develop end effector with multiple orbital sanding disks on it, this will reduce the sanding process time. For painting primer and topcoat automated spray robots needs to be used, one example of robot ABB IRB 5400 is provided in the analysis, it has wrist technology which has pitch of +/- 140 degree wrist movement. This makes it suitable for painting under the stringers. In order to reduce drying time and lead time two types of drying ovens: convection oven and Infrared oven were studied and compared. Based on analysis results, it is recommended to use to Infrared ovens, as the number of equipment’s needed are less and it also has faster drying. By these modifications on average painting process times can be reduced from 12.2 hours to 5.4 hours and total drying time can be reduced from 40 hours to 1.2 hours, thus lead time reduction of 45.6 Hours.

Autoclave Modification: To reduce the waiting times of the panel before and after the autoclave process, autoclave batching process needs to be changed. For that it is proposed to use smaller autoclaves, and reduce batching quantity of panel. From the waiting time and investment cost comparison, it was found that batching should not be used for small panels, while batching of 2 panels should be done for medium and large panels. Also for the arrangement of panels in the autoclave: stacked arrangement and panels placed next to each other were considered for analysis; it was found that the stacked arrangement will lead to more autoclave space utilization and less investment costs. The number of autoclave and their size influence on queuing pattern were analyzed by simulation model (results are shown in section 6). Based on the results it is concluded to use 1 small autoclave (without batching), 1 medium autoclave (with batching of 2) and 2 large autoclave (with batching of 2), with panel arrangement of stacking type.

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These proposed modifications are decreasing the non-value adding activities and waiting times of panels, thus decreasing the lead time and work in process inventory. This results to more lean production system. Moreover, by automation not only processing times are reduced, but it also helps in reducing the number of needed employees thus decreases the recurring costs of production.

2) What type of production line should be implemented? And how many workstations would be needed for the production line, considering flexibility and demand variation?

For the future production system semi-automated production line would be needed. As discussed in sub question 1 most of processes are automated, but processes like debagging, bagging, doubler layup, sealant application, cleaning and rework lugs would be still done manually. So those processes are arranged at the separate manual process stations on the production line.

The production process starts at the layup process (explained in section 4.1), where automated tool cleaning, automated aluminum and prepreg layup, manual doubler layup, automated stringer layup and manual bagging processes are carried out at separate stations (in mentioned sequence). Here, the layup molds need to be moved from one workstation to other by help of transport equipment like AGVs. This would be done on the main production line; simultaneously the materials like aluminum sheets, doublers and stringers needs to be prepared on the feeding line and provided to the main production line workstations. The feeding lines and production line works on the different Takt time and the resources are also separate. After layup tools are moved to autoclaves then to manual debagging from where panel’s needs to put in vertical frame attached to automated monorails. The monorail system transports the panels to automated C scan and automated milling station.

In the later part of semi-automated production line are painting & finishing processes, (explained in section 4.2), where processes are arranged in sequence of: automated sanding, manual process station for sealant and other manual activities, drying of sealant in oven, manual solvent applying, automated primer, drying primer in oven, automated topcoat applying & drying topcoat in oven. The panels are transported on the line by monorail system. At the end of the line after quality control station, panels would be deframed and prepared for the transport. The empty frames are then transported back to the empty frame storage area. The complete production line movement (as per process sequence) is demonstrated in Fig 135.

Based on this processes and the process parameters (given in section 6.2), excel model is made to calculate the processing times, the model was made in excel linked to the Bill of material. This acts as an input for the discrete event simulation model. From the model outputs like number of workstations, utilization rates, queuing parameters, etc. are obtained. To check the production system flexibility and demand variations following scenarios were experimented.

 Product Mix variations: In this scenario production system was experimented with different product mix of A320 and A321. The number of workstations needed for all the processes is shown in Fig 129.  Product Demand variations: In this scenario production system was tested with different product demands (change in Takt time). The number of workstations needed for all the processes is shown in Fig 131.  Product type variations: In this scenario, it was tested that if in future only half fuselages with double demand (600 shipsets) then how would production system behave. The number of workstations needed for processes with product type variations is shown in Fig 132. This scenario affects the sizes of the equipment’s like autoclave. In order to make it more flexible, extra spaces should be kept in layout, so in future production system can make complete fuselages.

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From the result of the analysis, it can be concluded that production system is flexible enough to respond to 25:75 ratio of A320 and A321. Moreover, the current order trend of next 6 year production orders is in other direction, 76:24 ratio of A320 and A321. But if in future the ratio becomes 0:100, then one extra process station would be needed for processes like layup, milling and C scan on doubler feeding line.

The process stations like tool cleaning, layup, pre compacting & doubler layup, autoclave, C scan and machining are sensitive to demand variation. The current production system has been designed based on 300 shipsets demand, for future demand extra space has been kept near the sensitive processes in the production system layout.

3) Where should be the push – pull decoupling points in the production system?

The lean production lay-out is based on the VSM of the future production system. The details like decoupling points or lean supermarket are shown in the future production system VSM (Fig 134), based on the simulation results (explained in section 6). The aluminum sheets would be produced on the aluminum feeding line by push system, and then the sheets would be put in kit carts. Then the kit cart would be stored in the lean supermarket (decoupling point), and delivered to layup stations on demand. Here, the inventory can be replenished based on sequential pull system (pre-planned production), where planning department plans the production on feeding line based on the planning of main production line. For the raw materials like aluminum sheets, prepreg and stringers, it is not known yet from where they would be delivered and how frequently they would be delivered, so if they are delivered like for the existing A380 system then decoupling point would be needed at the upstream end of the processes. This Decoupling point will protect against external supply chain volatility, but would result to in process inventory.

For the doublers feeding line the doublers are made and stored in lean supermarket (decoupling point). Then when panel starts at the layup station, doubler of that panels are prepared and kitted, kits are delivered to doubler layup stations. Thus, just in time delivery between two lines is not possible, decoupling point is needed. The main reason is that due to combination of doublers of different panels into 1doubler panel for production benefits, the production scheduling of both lines cannot be synchronized. Similar to aluminum sheets, stringers are chemically treated, painted and dried after that they are deframed and kitted manually in kits. Those kits are stored in lean supermarket (decoupling point). Then on demand at the stringer layup station the stringer kits are delivered to stations. Here, the inventory can be replenished based on sequential pull system.

For the panels the layup processes are based on pull system, where every process station pulls from upstream process. Only the autoclave is based on push system, panels are pushed to debagging. Due to cycle time difference between debagging and C scan process, queuing occurs at the debagging. Then C scan pulls the panels from debagging station, until then they are waiting. Further all downstream processes are based on pull system. Thus, push – pull decoupling is between debagging and C scan. Moreover as the production is based on advance orders, there would no need of push pull decoupling point at the downstream end of the production system. However, storage buffer should be kept at downstream end (for reasons like transport problem, problem at customer factory, etc.)

4) What should be the main KPIs for the lean production system? And what is the effect of modifications on to those KPIs?

In order to show the effects of process modifications within a production system, it is necessary to set key performance indicators that can be used to differentiate between the situations before and after the modifications. In this research the alternative 1 (Based on A380 system) is situation without modifications implementation and alternative 2 (Future production system) is

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with proposed modifications. The KPIs selected are explained in section 5; they were quantified by using the excel process time model & simulation model.

The following table gives the KPIs and their values before and after the modifications. The simulation models of both the situations were made to quantify the KPIs.

KPIs Basecase Future system (Alternative 1) (Alternative 2) 1 Value added activity (Hours) 39.49 19.92 Non Value added activity (Hours) 17.17 5.69 2 Value added work 69.70% 77.78% 3 Flow 53.67% 83.57% 4 Waiting times (Hours) 52.4 5.0 5 Total Inventory ( No of panels) 117 46 6 Scraped panels (from 4500 produced) 7 Turnover/production employee (euros) 8 Turnover/ Total surface area (euros)

9 Total Factory Space (square meter) Total Buffer space (square meter) Hidden, due to confidential data 10 Recurrent costs per shipset: A320 (euros) A321 (euros)

With all sub-questions answered, it becomes possible to answer the main research question. For this research, the following research question is formulated:

“What are the design considerations for production system layout of A320 Neo family aircraft GLARE panels based on lean manufacturing principles?”

In order to design the lean production layout for future system, first main consideration is to have the lean production system with balanced processes. So, the current production system is analyzed by lean manufacturing to identify and remove the waste processes. The processes are analyzed by Value stream mapping and ways for improvement are suggested (explained in sub question 1). Then after considering new production processes, the production line is designed by arranging the processes in proper sequences, while arranging the sequences it’s made sure that automated and manual activities are separated to different work stations (explained in sub question 2).

After considering process parameters, demand rate and different no of shifts, numbers of workstations for all the processes are determined from the developed discrete event simulation model. Then in order to achieve continuous smooth flow on the production line and to reduce the unnecessary waiting times, production line balancing is done in simulation model by the using alternative like flexible workers and autoclave batching modifications. Based on the results of line balancing, locations of push pull decoupling point in the production system is decided. Finally, before finalizing the layout its flexibility to product mix variations, demand variations and product type variation is checked by modelling these scenarios. Based on above considerations and other design requirements like resource arrangement types, space requirements for transport movements, space for workstations, space for buffers & extra space for flexibility; production system layout is designed (shown in Fig 135). Then to check the financial benefits of the modifications, investment costs and recurrent costs are calculated and are compared with basecase scenario. The benefits of the modifications are explained with help of KPIs (explained in sub question 4); it shows promising results with high continuous flow (83.5%), reduced inventory (from 117 to 46 panels), factory space reduction (14%) and recurrent costs reduction of A320 shipsets by 38.3% and for A321 shipsets by 35.6%.

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8.2 Recommendations for further research

The recommendations are given to further investigate and improve the production process of GLARE panels. In order to achieve the high volume production and to decrease the recurrent costs, the process needs to be modified by large extent. This research showed the main processes which will require modifications and also ways to achieve it were discussed.

This research showed the benefit of automating the layup process by pick and place robots and tape laying end effectors. Moreover, in order to reduce the non-value adding activities solutions like laser tool cleaning systems and permanent or semi-permanent mold release agents are suggested. However, before implementation testing’s would be needed. The doubler combinations are suggested to decrease the number of tools, transport movements and process time savings, but before finalizing the combination exact shape of doublers and prepreg directions needs to be considered in future. These design details are not available till know, so all doublers are assumed to be of rectangular shapes. In order to further decrease the tool size for interface molds (for DC doublers), research needs to be done for finding ways to stack DC doublers.

For the paint process modifications methods like automated painting and sanding are advised. For automated sanding the end effector needs to be designed with multiple orbital disks. The diameter of orbital disks, pitch between them, curvature of end effector and force application needs to be further analyzed more in detail before implementation. In order to reduce the drying time the infrared drying oven is proposed, but before implementation more details like time temperature profile based on emitter intensity and speed needs to be studied, solutions like thermal FEA model can be used for further research of the different time temperature profiles.

The research also shows the advantage of reducing the batching of autoclaves and having the stacking configurations for arranging the panel in autoclave. It also showed the solutions like lift AGVs which can be used for stacking the panels. But before implementation it will require more detail designing like type of lifting mechanism to use and ways to stack in autoclave. In order to balance the production line and to decrease the waiting times the alternative of flexible worker is suggested. The research showed its benefits that it will increase the utilization of workers and also decrease the waiting times of panels, but before implementation workers would require training and skills for all the process on that part of production line.

For the cost calculations, developed model in Microsoft excel was on a level which was sufficient to answer the questions of this research. Its main aim in this research was to calculate the recurrent costs, in future other inputs like non-recurring costs, bank interest rates on investments, etc. also needs to be taken into account before finalizing the selling price. Moreover all the cost calculations in this research were for complete shipsets. In future the cost calculation needs to be done for individual panels. The input variables for the model were based on the quotes of supplier and estimates of Fokker engineers during the period of this research, this keeps changing with time. Therefore the input variables of the model should be updated regularly during the research phase of this project.

As indicated by the KPI Value added work (VAW), it has increased by this modifications but still not reached 100% (level of ideal lean system) due to non-value adding processes like rework lugs cutting, stringer sealant and splice correction process, bagging and debagging. In future these processes also need to be modified by using solution like different transport frame with direct panel holding mechanism, out of autoclave curing, different adhesive and prepreg placing arrangements, etc.

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8.3 Reflection on the research project execution

This section discusses the various choices I made for the project and what things I learned from this project.

The choice regarding the methodology for this research project:

The main aim of this research was to design the lean production system, but the information and data available for it was based on the current A380 system. The dimension and panel structure of A380 panels is different than A320 panels, so same processing times cannot be used for future system design. So, the current system was analyzed to get more insight into the process and know more about inefficient (waste) processes. The collected data was then extrapolated for A320 panel’s basecase calculation. By the bottleneck analysis, constrained resources were identified. Then process modifications were researched, but before arriving to final conclusion, the future state lean system has to be validated. As the production system is dynamic; any change in one process affect the production line and to measure that discrete event simulation model needs to be used. Therefor the research methodology used was combined methodology of lean and discrete event simulation methodology. The advantage of using combined approach was that the process interactions were easy to identify for future system, so the effect of line balancing by alternatives like flexible workers was easy to evaluate. Moreover, it was easier to quantify the lean KPIs of the future production system. Furthermore, it gives more dynamic picture of the future system, instead of static value stream chart.

Decision regarding simulation software:

The Simio simulation software was used in this research project. Based on the last 6 months of work experience with this software, I can say that it is very user friendly software. The intelligent object based modelling saves lot of time, while modelling complex systems like that of Fokker production system. The real time visualization of the process at the time of modelling helps in debugging and verification. The only disadvantage is that the bugs of some objects like vehicle are hard to solve. However, by using different modelling techniques it could be avoided.

The research part which I could have done differently?

The equipment’s of the production system have accidental breakdowns and occurrence of breakdown also depends on the stage of the life cycle of the equipment. In this research due to insufficient data availability, the accidental breakdowns effects are tested with data obtained from interviewing Fokker experts and machine operators. This data is the estimated data from their experience of A380 system equipment’s. If the sufficient historical data would have been available then I could have done more detailed analysis on the breakdown of the equipment. The Weibull analysis for breakdowns which takes into account the life cycle of equipment (bath tub curve) would have been better choice.

Things I learned from this research project:

 Different approach for solving problems, using scientific theories and methodology.  New software’s like Simio, Geogebra, Q CAD, etc. and theories like AHP for decision making.  Managing the project activities within set time frame.  “Language is not a barrier” – The data collected and various interactions with people during this research were in Dutch language, but use of translation tools made it easy to translate to English.

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Appendix 1: Current production processes of GLARE

Fig. 136 Detailed Process Chart

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Appendix 2: Value Stream Mapping Activities Details

Activities Details Value Added Activities Customer Value adding Non Value Added Activities Business Value added (Cannot be avoided) & Waste Activities (Can be avoided) 1) Lay Up Value added activities Non Value added activities 1) Alum. lay-up X-Y laser projection co-ordinate 1) Get production information 2 )Prepreg 0° Lay-up X-Y laser projection co- 2) Preparation of aluminum sheets ordinate 3) Prepreg 90° Lay-up X-Y laser projection co- 3) Preparation prepreg & adhesive ordinate 4) Adhesive lay-up X-Y laser projection co-ordinate 4) Preparation mold degrease and pressure tooling 5) Preparation index laser projection and pilot pins 6) Record production information 2) Bagging Value added activities Non Value added activities 1) Separation foil mold 2) putting (dummy panel) 3) Pressure splice tooling 4) Putting Airweave 5) Sealing GS213 6) Autoclave foil, fold 7) Vacuum connection test 8) Mold material handling 9) Record production information 3) Autoclave Value added activities Non Value added activities 1) Curing Process 1) Loading molds on autoclave carrier 2) Move carrier into autoclave 3) Connect vacuum hoses 4) Connect thermocouples 5) Disconnect thermocouples 6) Disconnect vacuum hoses 7) Unload carrier out of autoclave 8) Unload molds from carrier 4) Debagging Value added activities Non Value added activities 1) Get production information 2) Remove pressure tooling 3) Product debagging 4) Remove adhesive from product 5) Remove foil 6) Remove breather cord + airweave 7) Cleaning adhesive remainder 8) Drilling transportation holes 9) Drilling pilot holes 10) Frame panel 11) Hoist panel 12) De Electrify panel 13) Clean mold & cover molds 14) Visual inspection of the panel 15) Rotate frame to vertical position

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5) C Scan Value added activities Non Value added activities 1) Scanning 1) Start-up C-scan 2) Review and evaluation of results 2) Crane drives product from buffer to scanning 3) Adjust product at place 4) Making Calibration File 5) Running Program 6) Align jets 7) Test Scan 8) Disconnect the product from scanning 9) Crane drives product from scanning to Buffer 6) Machining Value added activities Non Value added activities 1) Milling 1) Transport product from buffer to machine 2) Drilling 2) Deframing the panel 3) Deburring skin edge with sandpaper 3) Placing panel on the machine bed 4) Deburring windows with rotating scraper 4) Adjust actuators as per panel type 5) Deburring holes with rotating scraper 5) Cleaning table 6) Deburring holes with drill (manual) 6) Remove product 7) Deburring holes with drill (driven by air) 7) Framing the panel 8) Nails placed for stringer or doubler positioning 9) Elastics placed keeping nails in place 10) Clean skin 5B) Bond Testing (Stringer Panel) Value added activities Non Value added activities 1) Scanning 2) Material, equipment and documentation handling 2) Review and evaluation of results 7) Painting & Finishing Value added activities Non Value added activities 1) Splice sanding with eccentric sander (air-driven) 1) Read the operation sheet 2) Spray primer on splices 2) Transport the panel inside the paint box 3) Fill splices with Glue 3) Clean the panel 4) Sanding of the panel 4) Clean splice 5) Water brake test 5) Create primer 6) Apply Primer 6) Create cold Glue 7) Apply topcoat 7) Quality Control 8) Layer thickness measurement 8) Material handling and apply tape either template 9) Wait for 30 minutes flash-off drying 10) Wait for 30 minutes to dry at 70oC 11) Wait for 30 minutes flash-off drying (Top Coat) 12) Wait for 60 minutes to dry (Top Coat) 13) Create topcoat 14) Remove and dispose frame protection clothes 15) Record Information 16) Transport panel out of paint box 8) Remove Lugs Value added activities Non Value added activities 1) Removing Plate lugs 1) Transport product from buffer to machining point 2) Sanding 2) Preparation of equipment 3) Applying Alodine 4) Applying side protectors

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Appendix 3: Doublers Combination

A320 Individual Doublers

Hidden, due to confidential data

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A320 Doubler Combination

Hidden, due to confidential data

Here colour indicates,

Spliced Doublers (next to each other) SC doublers stacked DC doublers

SC doublers stacked in 1 panel are:

Hidden, due to confidential data

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A321 Individual Doublers

Hidden, due to confidential data

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A320 Doubler Combination

Hidden, due to confidential data

Here, colour indicates,

Spliced Doublers (next to each other) SC doublers stacked

DC doublers

SC doublers stacked in 1 panel are:

Hidden, due to confidential data

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Appendix 4: AHP Model

Experts Criteria Ratings

Criteria Ratings Criteria Expert 1 Expert 2 Expert 3 Expert 4 Expert 5 1 Reliability 16.81% 14.89% 21.70% 27.21% 28.04% 2 Investment Cost 12.04% 9.30% 5.70% 14.85% 12.85% 3 Operation Cost 5.56% 6.68% 11.37% 13.89% 14.31% 4 Factory Space 3.50% 2.44% 1.88% 5.12% 6.33% 5 Automation 5.59% 11.53% 2.80% 4.81% 4.06% 6 Maintenance 2.78% 2.06% 6.34% 4.79% 5.66% 7 Flexibility 1.68% 5.16% 11.33% 5.31% 6.13% 8 Technical Adaptability 29.48% 24.41% 19.79% 12.97% 8.85% 9 Logistics Parameters 20.47% 21.44% 17.92% 9.90% 12.57% 10 Continuous Delivery 2.08% 2.09% 1.17% 1.15% 1.20%

Equipment ratings

Conveyor AGVs Manual Overhead Cranes Carts 1 Reliability 0.14 0.25 0.41 0.21 2 Investment Cost 0.25 0.07 0.51 0.17 3 Operation Cost 0.36 0.36 0.07 0.22 4 Factory Space 0.07 0.21 0.26 0.46 5 Automation 0.26 0.41 0.05 0.28 6 Maintenance 0.09 0.22 0.55 0.14 7 Flexibility 0.05 0.37 0.49 0.09 8 Technical Adaptability 0.28 0.22 0.30 0.20 9 Logistics Parameters 0.14 0.36 0.30 0.20 10 Continuous Delivery 0.73 0.08 0.08 0.11

Final Results

1 Reliability 0.030 0.054 0.088 0.045 2 Investment Cost 0.027 0.008 0.056 0.018 3 Operation Cost 0.037 0.037 0.007 0.022 4 Factory Space 0.003 0.008 0.010 0.018 5 Automation 0.015 0.023 0.003 0.016 6 Maintenance 0.004 0.010 0.024 0.006 7 Flexibility 0.003 0.022 0.029 0.006 8 Technical Adaptability 0.054 0.042 0.058 0.037 9 Logistics Parameters 0.024 0.060 0.049 0.032 10 Continuous Delivery 0.011 0.001 0.001 0.002 Total 0.207 0.265 0.325 0.202

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AHP Model

Based on the AHP theory the model was made in the Microsft excel, its snapshot is attached below.

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Appendix 5: Simulation model

A 5.1 Interface

Input:

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A 5.2 Description of the simulation model This section will give a description of the simulation model, where the various components used in the Simio software are elaborated.

1) Modules

The basic building blocks for Simio model are modules (called common steps in interface), are in form of flowcharts and objects, they define the simulation process. Entities come in, flow and go out of the modules. An example of popular modules is assign, decide, batch, unbatch, delay, etc.

2) Objects

By combining these modules objects like workstations, transport vehicle, etc. are programmed.

3) Entities

An entities are object that flow through modules, where their values get updated. In the simulation model panels and doublers were modelled as entities.

4) Resources

In the model, resources provide service to the entities. An entity seizes the resource when available and releases it when finished. Resources are like workers, tools, etc.

5) Queues

When the resources are seized or objects are constrained, entity (panels) cannot move on, it needs place to wait, they are put in queue. The queues can also have maximum capacity constraints, but are not used in this model. So, the properties of the queues like maximum number waiting, average waiting time, etc. can be logged.

Example of Working of model

By combining the modules as shown figure above (common steps), process logic is build up for the objects. Above example is the decision loop (process trigger) of the autoclaves, where panels are sorted out according to the sizes and directed to the assigned autoclaves.

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Appendix 6: Cost Model

Investment Costs for the future production system

Hidden,HHiddenHiHidHiddidden,dden,den,en,, duedue toto confidentialconfidenticonfidcoconfidentiaconfidenconfnfidentialidentialentialtialall datadata

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Investment Costs for the A320 system based on A380

Equipment Unit Costs (in Units Total costs (in euros) needed euros) Decoil station 270000 1 270000 2D framing station 350000 2 700000 Chemical treatment line 9950000 2 19900000 Primer, Flash off & Curing 2250000 1 2250000 Deframing position 350000 1 350000 Buffer for storage Hidden, due50000 to confidential data1 50000 2D milling (Portatec) 1700000 2 3400000 Scrap separation unit 100000 2 200000 Cleaning Station 850000 2 1700000 Reclaim robot 445000 1 445000 Kit Trolley 157500 1 157500 Total Costs WP 2 29422500 Autoclave 5666250 4 22665000 C scan 1000000 7 7000000 Layup 200000 30 6000000 Debagging & Framing 700000 1 700000 Milling 6000000 2 12000000 Paint box Costs 200000 12 2400000 Rework Lugs Cutting 10000 3 30000 Transport Equipment’s 12052200 1 12052200 Tool Costs 150000 60 9000000 Buffer doublers & Kitting 200000 1 200000 Prepreg cutter 250000 4 1000000 Total Costs WP 3 & 4 73047200 Factory Construction Costs Cost/m2 Floor Space 750 72760 54570000 Clean room space & Chemical line area 750 14000 10500000 space extra charges Total factory costs 65070000 Total Costs 167,539,700

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Appendix 7: Simulation results

1) Alternative 1 (Basecase system): Production with 7days

When the panels of A320 and A321 are made with A380 GLARE production technology and factory operates for 7 days (21 shifts a week), then number of process station needed are shown in Fig 137. It shows the two results, one for Ideal production system with no defects and second is for production system with defects, the probability of defect used in simulation was based on defects data (shown in section 4) obtained from A380 production system. The number of process stations needed for layup and painting & finishing decreases as compared to 5 days production as Takt time increases due to more available working hours. For resources which do not decrease in number, their utilization rate decreases. Though the investment costs will decreases for 7 days shift but process costs cost increases due to addition of 6 extra shifts every week.

Number of Process stations Ideal Case Case with Defects 25

20 15 10

No ofStations No 5 0

Fig. 137 No of process station needed for production with 7 days working per week

Ideal Case Utilization rate Case with defects

100.0 80.0 60.0 40.0 20.0

Utilization Utilization (%)Rate 0.0

Fig. 138 Utilization rate of process stations

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Fig 139 shows the 3 group of panels with their processing time, waiting time and lead time. Here also the maximum waiting time is due to batching of Autoclave, as panels have to wait before and after the autoclave. Then panels also have to wait in buffers before processes like C scan, machining, painting and lugs cutting. Fig 141 shows the amount of waiting panels (queuing pattern before servers). The total waiting times are less by (one and half hour) than compared to 5 days production, as Takt is higher and utilization of equipment is less. The in process waiting time is result due to sealant drying & topcoat drying, it will stay the same. The higher lead times results to high work in process Inventory, the inventory is also 20% percent less than 5 days production but it is still very high (shown in Fig 140). It will still need 49 layup tools and 69 vertical frames for production of panels.

Small Panels Waiting time & Lead time Medium Panels Large Panels 140 120

100 80 60

Time (Hours) 40 20 0 Processing times Idle Waiting times Inprocess waiting Lead times times

Fig. 139 Waiting time and Lead Time

Inventory 100 90 80

70 60 50

40 No ofPanels No 30 20 10 0 Work in process Inventory Waiting Inventory Total Inventory

Fig. 140 Inventory of Panels

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Queuing Pattern

Fig. 141 Panels waiting in between processes

Fig. 142 Panels Waiting in Drying Buffers

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Buffer Storages

Based on queuing patterns the buffer storages needed before process station is shown in Fig 143. The buffer storages needed are less than scenario of production with 5 days due to less queuing and less waiting times. The maximum waiting buffer is for drying of panels and for panels waiting before and after the autoclave.

Buffer Storage 35

25 20 15

Panel Storage Panel 10 5 0

Fig. 143 Buffer Storages

Production Costs:

The recurring cost of GLARE panel production is determined according to the method as described in section 5.2. The material costs would also be same as scenario with 5 days. The process costs and depreciation rates changes as number of equipment’s and full time employees (FTE) are different than scenario with 5 days due to extra shifts. The detailed list of the Investment costs is provided in Appendix 6. The recurrent costs are calculated per panel for A320 and A321.

Table 42 Process Costs

The depreciation on investment per shipset decreases as compared to scenario with 5 days of production due to decrease in investment as resources needed are less. However, the operation costs increases as 6 extra shifts would need extra full time employees (FTE).

Based on the material costs, process costs and depreciation costs, the recurrent costs for A320 and A321 panel are calculated (shown in Table 44). The material costs and process costs are the major contributor for the recurrent cost.

Table 43 Recurrent costs with 7 days per week production

Cost Item A320 A321 Material Costs per Shipset € 77238 € 109378 Process Costs per Shipset € 162556 € 183308 Depreciation Costs per € 25241 € 28463 Shipset Total Cost per Shipset € 265035 € 321150

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Alternative 2 (Future system): Production with 7 days

If the panels of A320 and A321 are made with future production system with new automated production technology and factory operates for 7 days (21 shifts a week), then number of process station needed are shown in Fig 144. It shows the two results, one for Ideal production system with no defects and second is for production system with defects. The defects caused at the layup station by workers due to wrong process application are eliminated. The defects will be reduced from 3.2% to 1% by automating the layup. The number of process stations needed for layup and painting & finishing decreases as compared to 5 days production as Takt time increases due to more available working hours. For resources which do not decrease in number, their utilization rate decreases.

Ideal Case Number of process stations Case with Defects 5

No ofStations No 1

Fig. 144 No of process stations

Ideal Case Utilization of stations Case with defects 80 70 60 50 40 30

Utilization Utilization % 20 10 0

Fig. 145 Utilization rate

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In simulation model all the panels were modeled, but for representation panels are shown in group based on the sizes. Fig 146 shows the 3 group of panels with their processing time, waiting time and lead time. Two different waiting times are used: one is ideal waiting time when panels are not in process and are waiting as process stations are not free. Second is in process waiting time when panels are waiting as part of process like for drying. The waiting time is reduced as compared to base case scenario due to autoclave batching optimization and Flexible worker modifications. Fig 148 shows the amount of waiting panels (queuing pattern before servers). The in process waiting time is result due to sealant drying & paint drying. The drying times are same for all panels irrespective of their sizes. The in process waiting times are reduced due to use of the infrared dryers. The overall lead times are reduced resulting to less work in process Inventory. This also means less number of molds and transport frames would be needed.

Small Panels Waiting time & Lead time Medium Panels 45 40

30 25 20

15 Time(Hours) 10 5 0 Processing times Idle Waiting times Inprocess waiting times Lead times

Fig. 146 Waiting time & Lead time

Inventory of panels 40 35

25 20

15 No of panels of No 10 5 0 In process Inventory Waiting Inventory Total Inventory

Fig. 147 Inventory of panels

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Queuing pattern

Fig. 148 Queing at the process stations

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Based on queuing patterns the buffer storages needed before process station is shown in Fig 149. The buffer storages needed are less than scenario of production with 5 days due to less queuing and less waiting times. The maximum waiting buffer is for waiting before autoclave.

Storage Buffer 6

5 4 3 2 Noof Positions 1 0

Fig. 149 Storage Buffer for process stations Production Costs:

Table 44 Process costs

Processes FTE Labour Hours Total Costs (€) Decoiling 11 16320 1250112 Chemical Treatment line 16 24480 1875168 Primer & Curing 11 16320 1250112 Deframing 11 16320 1250112 Milling 6 8160 625056 Kit Delivery 6 8160 625056 Layup 11 16320 1250112 Doublers Layup & Pre compacting 26 40800 3125280 & Bagging

Stringer Layup 6 8160 625056 Autoclave 11 16320 1250112 Debagging & Doublers Framing 11 16320 1250112 C Scan 6 8160 625056 Milling 6 8160 625056

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Deburring, Stringer Sealant, 26 40800 3125280 Cleaning Position, Quality Control, Rework Lugs cutting Sanding Robots &Painting Robots 6 8160 625056 Heating Systems 6 8160 625056 Doublers Alodine and Kitting 6 8160 625056 Total 182 20626848

Table 45 Depreciation per shipset

Depreciation on Investment Investment in Fixed Assets € 151,582,200 Asset lifetime for use 5000 Residual Value (%) 10 Residual Value € 15,158,220 Depreciation/shipset € 27285

Based on the material costs, process costs and depreciation costs, the recurrent costs for A320 and A321 panel are calculated (shown in Table 47). The material costs and process costs are the major contributor for the recurrent cost.

Table 46 Recurrent costs with 7 days per week production

Cost Item A320 A321 Material Costs per Shipset € 77238 € 109378 Process Costs per Shipset € 64631 € 72882 Depreciation Costs per Shipset € 25648 € 28922 Total Cost per Shipset € 167516 € 211181 Total Cost per panel € 11168 € 14079

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Decoupling point Inventory

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