A Thesis Entitled Optimization of Operating Parameters of a Material

Home , Reuse, Scrap, Waste collection

A Thesis

entitled

Optimization of Operating Parameters of a Material Recovery Facility using Lean Six

Sigma Techniques

Pukhraj Barnala

Submitted to the Graduate Faculty as partial fulfillment of the requirements for the

Master of Science Degree in Mechanical Engineering

Dr. Matthew J. Franchetti, Committee Chair

Dr. Yong X. Gan, Committee Member

Dr. Ashok Kumar, Committee Member

Dr. Patricia Komuniecki, Dean College of Graduate Studies

The University of Toledo December 2011

This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author.

An Abstract of

Optimization of Operating Parameters of a Material Recovery Facility using Lean Six Sigma Techniques

Pukhraj Barnala

Submitted to the Graduate Faculty in partial fulfillment of the requirements for the Master of Science Degree in Mechanical Engineering

The University of Toledo

December 2011

Lean six sigma is a business improvement methodology which combines tools from both lean manufacturing and six sigma. Lean manufacturing focuses on elimination of waste and thus increases overall speed of the process/operations. Six sigma focuses on quality. By combining the two, the result is better quality, cost efficient, faster and optimized process. Lean six sigma has not been used in recycling to the extent it has been in manufacturing. The objective of this research is to investigate the application and benefits of lean six sigma in the recycling industry. Specifically, the objective of the project is to improve the process for material recovery facility at Toledo, Ohio. This includes aligning and optimizing processes and the removal of process generated defects and errors.

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This thesis revealed the current sigma level, defects per million opportunity and performance at Material Recovery Facility (MRF), Toledo. The case study of material recovery facility at Toledo is discussed. Defects per million opportunities are calculated from the on-site data collection. Using six sigma and statistical quality tools total tons processed per year, standard processing time, current sigma level, and total tons defective and financial aspect are discussed in the thesis.

Acknowledgements

I would like to give special thanks to my advisor, Dr. Matthew J Franchetti, for giving me the opportunity to be involved in his research team. He has offered continuous guidance, support, encouragement, and his patience throughout the preparation of this thesis and my stay at University of Toledo. It has been a pleasure to work under him and his research team. I would also like to thank Dr. Ashok Kumar and Dr.Yong X. Gan for serving on my thesis committee.

In addition, I wish to acknowledge Mr. Christopher Pizza, Manager of the Lucas

County SWMD and Mr. Kevin Burke for their invaluable contributions to this research. I would like to acknowledge Nicholas Roth, Prabhu Kiran, Kristyn Shuster and Alex

Spivak. Their expertise on the subjects of waste management and data collection was critical to the completion of this thesis.

Finally, I would like to acknowledge and thank all of my fellow students at the

University of Toledo’s ECML for their input and support. I would like to specially thank

Sakina Junagadhwalla, Shantanu Rao, Srinivas Kottala and Dr. Amarjit Luniwal for their help and support at graduate school. And a big thanks to my parents without whom it was impossible to study at such an esteemed University.

Contents

Abstract ...... iii

Acknowledgements ...... v

Contents ...... vi

List of Tables ...... ix

List of Figures ...... xi

Abbreviations ...... xiii

1 Introduction ...... 1 1.1 Research Objective ...... 1

1.2 Background of Environmentally Conscious Design and Manufacturing Laboratory (ECML) ...... 2

1.3 Results Expected ...... 6

2 Literature Review ...... 7 2.1 Definition and Composition of Municipal Solid Waste ...... 7

2.2 US EPA MSW Hierarchy Model ...... 10

2.3 Overview of Material Recovery Facility ...... 14

2.4 Lean Six Sigma ...... 16

2.4.1 Overview ...... 16 vi

2.4.2 Definition ...... 17

2.4.3 Measure ...... 18

2.4.4 Analyze...... 18

2.4.5 Improve ...... 19

2.4.6 Control ...... 20

3 Overview of Case Study...... 22 3.1 Definition ...... 23

3.1.1 Project team, selection and goals ...... 23

3.1.2 Gantt Chart ...... 24

3.1.3 Process Overview ...... 26

3.2 Measurement ...... 28

3.2.1 Introduction ...... 28

3.2.2 Pareto Chart ...... 28

3.2.3 Time Studies ...... 30

4 Overall Analysis ...... 33 4.1 Analysis...... 33

4.1.1 Fish bone diagram ...... 33

4.1.2 Method ...... 34

4.1.3 Staff ...... 34

4.1.4 Material ...... 35

4.1.5 Machinery...... 35

4.2 Defects per Million Opportunities - DPMO ...... 37

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4.3 Cost and revenue analysis ...... 39

4.4 Improve ...... 41

4.4.1 Value Added vs. Non Value Added Analysis...... 42

4.5. Capacity ...... 45

4.7. Control ...... 47

4.7.1 Process capability ...... 50

5 Conclusions ...... 52 5.1 Future Research ...... 54

References ...... 55

Appendix ...... 57

A Yellow sheet for May 2010 ...... 57

B Defects per million opportunity chart ...... 59

C Recycling prices for materials...... 61

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List of Tables

2.1: Generation & recovery of MSW for year 2008 and 2009 ...... 8

2.2: Generation, recovery, combustion & discard of MSW, 1960-2009 (in millions of tons)...... 13

3.1: Cause and effect matrix ...... 24

3.2: Recyclable material collected in tons for year 2010 ...... 29

3.3: Time taken for a bale of paper to process through the bailer...... 30

3.4: Time taken for a bale of OCC to process through bailer...... 31

3.5: Time taken for a bale of plastic to bale through process...... 31

3.6: Time taken by forklift to drop baled items to shipping...... 32

4.1: Equipment requirement per material for bailer...... 36

4.2: Annual weight of solid waste or defects from the MRF ...... 37

4.3: Annual volume of solid waste or defects at MRF ...... 38

4.4: Annual utility & maintenance cost for office area...... 40

4.5: Total revenue generated from recycling ...... 40

4.6: Estimated financial benefits ...... 41

4.7: Dollar loss per year due to defects ...... 42

4.8: List of all the non-value added activities ...... 45

4.9: Tons per hour processed for each material ...... 46

4.10: Tons processed per hour after recommendations...... 47

4.11: Final capability chart ...... 51

List of Figures

2-1: Comparison of Recycling rates for year 2008 & 2009 (6) ...... 9

2-2: Municipal Solid Waste composition for 2008 (US) ...... 10

2-3: Recycling Symbol ...... 11

2-4: EPA solid waste management hierarchy ...... 12

2-5: Flowchart of a basic MRF operation ...... 15

2-6: Flowchart of a dirty MRF operation ...... 16

2-7: Cause & effect diagram for determining root cause of flat tire...... 19

2-8: A typical control chart ...... 21

3-1: Material Recovery Facility, Toledo Ohio...... 23

3-2: Gantt chart for the project...... 26

3-3: Process Overview for MRF Toledo operations ...... 27

3-4: Pareto Chart ...... 29

4-1: Fish bone diagram ...... 34

4-2: Annual weight of defect at MRF ...... 39

4-3: Control chart showing time taken to process paper bale ...... 49

4-4: Control chart showing time taken to process the OCC bale...... 49

4-5: Control chart showing time taken to process commingled bale...... 50

A-1: The yellow sheet for May-2010...... 57

A-2: The yellow sheet for May, 2010 showing export prices...... 58

B-1: Sigma values corresponding to defects per million opportunities...... 60

C-1: Recycling market prices for OCC ...... 61

C-2: Recycling market prices for HDPE bottles ...... 61

C-3: Recycling market prices for MOP ...... 62

C-4: Recycling market prices for old newspaper ...... 62

C-5: Defects at MRF Toledo...... 63

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Abbreviations

DPMO------Defects per million opportunity

ECML------Environmentally Conscious Design and Manufacturing Laboratory EPA------Environmental Protection Agency

HDPE------High-density Polyethylene

LCSWMD------Lucas County Solid waste Management district

MOP------Mixed Office Paper MRF------Material Recovery Facility MSW------Municipal Solid Waste

OCC------Old Corrugated Cardboard

PET------Polyethylene Terephthalate

SIPOC------Suppliers Input Process Outputs Customers

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Chapter 1

Introduction

The purpose of this recycling research is to determine recycling opportunities at

The Lucas County, Toledo OH. Despite of a number of benefits environmental, social and economical, recycling continues to face challenges in gaining universal acceptance.

Recycling in simple words can be defined as the process of taking a product at the end of its useful life or period and using all or part of it to make another product which can be utilized again.

1.1 Research Objective

Lean Six Sigma has not been used in recycling to the extent it has been in manufacturing and other sectors. The objective of this research is to investigate the application and benefits of Lean Six Sigma in the recycling industry. Specifically, the objective of the project is to improve the process/operations for Material Recovery Facility at Toledo,

Ohio utilizing Lean Six Sigma techniques. This includes aligning and optimizing processes and removal of process generated defects and errors. These improvements primarily focus on:

 Reduce Municipal Solid Waste (MSW) going to landfill and maximize recycling.

 Optimizing cycle times for the MRF, Toledo.

 Enhancing end user/customer satisfaction.

 Improving efficiency and reducing cost.

 Elimination of errors.

1.2 Background of Environmentally Conscious Design and Manufacturing Laboratory (ECML)

In the environmentally conscious design and manufacturing laboratory, research assistants, students and faculty are functioning and working with local government and businesses to protect the environment by preventing solid waste from entering local landfills. This is accomplished with a grant from the County to aid in making local manufacturers and businesses determine the amounts of their waste streams that are recyclable material at no cost to the business. To promote environmental sustainability within Northwest Ohio, ECML was founded. The business waste reduction assistance program is a unique partnership between the Lucas County Solid Waste Management

District, The University of Toledo’s College of Engineering, and local business and industry. Formed in July 1996, Environmentally Conscious Design and Manufacturing

Laboratory is providing no cost waste and energy assessments to Lucas County commercial and industrial businesses. In addition ECML has begun to expand its research into energy assessments and thus reducing overall carbon emissions of a facility.

During a waste audit or assessment stage, a site is visited, research of whole process is done and their waste stream is determined by a series of measurements and from there, the components are broken down and it is researched as to whether or not the waste materials produced can be utilized for other purposes. The method used to calculate 2

and estimate the annual solid waste streams at any facility involves sample data collection from waste containers. Each of these waste containers are observed and measured to determine the total volume of waste, composition of waste and quantity of each material. From this information, volumes of waste were calculated for each container and each material, by measuring the container dimensions. Using standard packing densities, weights of recyclable materials were estimated. Weights are used to determine the revenue that can be generated by sale of recyclables and volumes are used to calculate the hauling cost savings. Annual waste streams were calculated using the data collected during each of these different days.(15)

Then in the final report composition stage, the annual landfill impact that the company has on the environment is calculated and broken down into what components can be recycled, and therefore kept out of the landfill and the revenue that can be generated from them is calculated. These studies also extend into the realms of energy conservation and proper consumption or utilization. The tasks of a research assistant in this laboratory also may include conducting container audits for the county, optimizing truck routes to determine the most efficient use of time and resources, conducting used oil surveys, energy assessment, landfill audits, as well as the ongoing work to make The

University of Toledo a far greener campus through recycling initiatives. As a program of the county and the university, the major target of the project is to provide a valuable service to the community. The knowledge and expertise of the University’s faculty, staff and students are utilized to identify environmentally friendly solutions and cost savings for local businesses through waste minimization and process efficiency solutions. The following goals are achieved (15):

 Abridge pollution through reduced energy usage and the use of clean and

renewable energy sources.

 Increase overall involvement in recycling.

 Increased manufacturing competitiveness through reduced solid waste disposal

and hauling cost, reduced energy costs and optimized use of raw materials,

resources, packaging and floor space.

 Craving to reduce MSW going to landfill and achieve zero land fill goal.

 Improved corporate image as the companies become greener.

On March 6, 1989 by Lucas County Board of Commissioners under Amended

House Bill 592, The Lucas County Solid Waste Management District was formed. The

Ohio Solid Waste Disposal Act established a structure to plan for the proper disposal of the waste generation in the State of Ohio and reducing the waste going to landfills. The district began to discover new methods to achieve the waste reduction goals. Many innovative solutions were developed and implemented to achieve these goals such as:

 District Drop off Collection Program- The District operates a drop-off collection

system in which Residents throughout Lucas County may recycle any of the

following materials: cardboard, magazines, newspapers, glass, plastic bottles, and

Aluminum Cans.

 City of Toledo Residential Recycling Program- The City of Toledo, LCSWMD,

or their selected representative will provide curbside recycling services to all of its 4

single family households. The service accepts newspaper, milk jugs, aluminum

cans, glass, soda bottles, plastic bottles and detergent bottles.

 Yard Waste Drop off Program- Yard waste drop off program is for citizens with

proof of Lucas County residency and they may recycle grass clippings,

trimmings, leaves, and other yard waste for free of charge. Commercial

Businesses at their own expense are also welcome to use the facilities.

 Commercial/Industrial Outreach and Education Program- the District in

combination with The University of Toledo’s, College of Engineering operates a

Business waste reduction program (ECML). The program offers free waste

assessments, energy assessments and waste minimization assistance to businesses

ranging from manufacturing, healthcare and institutions located within Lucas

County.

 Community outreach Program- The District in combination with some non-profit

organizations like Keep Toledo/Lucas County Beautiful operates an community

outreach program through the Ohio department of Natural Resources Recycle

Ohio Grant Program. Conducting various Outreach programs like Party for Planet

at Toledo Zoo to increase awareness in residents and help recycling.

 Household Hazardous Waste Collection Program-The District continues to offer

its residents a low or no cost options for managing household hazardous wastes

such as paint, used oil, solvents, pesticides, caustics and other toxic items used in

garden and home.

 The Scrap Tire Management Program- Annual collection events for scrap tires,

open to Lucas County residents only are organized by district. The District also

works with local businesses and community in the abatement of scrap tires.

1.3 Results Expected

The results expected from this research are that Lean Six Sigma will reduce the time it takes in the overall process, reduce costs, and increase customer satisfaction with better final products. After use of Lean Six Sigma techniques, the process will have standards, whereby the redundancies and bottlenecks will be identified and eliminated.

As the Material Recovery Facility continues to grow, the whole team will need to become more productive and efficient. This research will help in understanding the current operations and sigma level of the process plus ways to reduce DPMO.

Chapter 2

Literature Review

The literature review includes a number of Six Sigma and Lean manufacturing text books as reference, United States Environmental Protection Agency (EPA) resources, journal articles and internet resources. A wide-ranging literature review is done in order to attain basic background information related to the operation of Material

Recovery Facility plus landfill, energy assessments, waste assessments and recycling.

2.1 Definition and Composition of Municipal Solid Waste

Municipal Solid Waste (MSW) refers to “trash” or “garbage” to most people.

More explicitly MSW can be defined as the everyday items or resources discarded by their users such as old newspaper, mixed office paper, grass clippings, furniture, clothing, bottles, old corrugated cardboard, aluminum cans, product packaging, batteries, appliances etc. (1).The composition of MSW can vary from place to place, like higher percentage of rubber from a tire manufacturer facility and a greater percentage of paper products in metropolitan areas where as a higher percentage of organic matter is found in most of the country sites. Industrial, hazardous and construction waste is not included in

Municipal Solid Waste.

United States Environmental Protection Agency (EPA) is a federal agency with a mission of protecting the environment and human health by writing and implementing regulations based on laws passed by congress. Agency is doing a great job and has collected and reported data on the generation and disposal of waste in US for nearly 40 years. Facts and figures of year 2009 and 2008 are used in this thesis to measure the success of waste reduction and recycling programs throughout the country.

2.1: Generation & recovery of MSW for year 2008 and 2009

Weight Weight generated generated Recovered Recovered Recovery Recovery in 2009 in 2008 in 2009 in 2008 percentage percentage Material (tons) (tons) (tons) (tons) in 2009 in 2008 Paper 68.4 77.42 42.5 42.94 62.1% 55.5% Glass 11.78 12.15 3 2.81 25.5% 23.1% Steel 15.62 15.68 5.23 5.29 33.5% 33.7% Aluminum 3.4 3.41 0.69 0.72 20.3% 21.1% Metals Misc. 1.89 1.76 1.3 1.21 68.8% 68.8% Plastics 29.83 30.05 2.12 2.12 7.1% 7.1% Rubber & Leather 7.49 7.41 1.07 1.06 14.3% 14.3% Textiles 12.73 12.37 1.9 1.89 14.9% 15.3% Wood 15.84 16.39 2.23 1.58 14.1% 9.6% Yard Trimmings 33.2 32.9 19.9 21.3 59.9% 64.7% Food 34.29 31.79 0.85 0.8 2.5% 2.5% Misc. Inorganic wastes 8.46 8.28 1.23 1.15 14.5% 13.9% Total MSW 242.93 249.61 82.02 82.87 33.8% 33.2%

As shown in Table 2.1, (weight is in millions of tons and recovery is in percentage recovered of each material) Americans generated approximately 243 million tons of solid waste for the year 2009 and approximately 250 million tons for the year

2008. As shown in Figure.2-2, MSW contains all kinds of materials as it comes from all different sources and main constituents are paper products like newspaper, magazines, mixed office paper and other major components include (OCC) old corrugated cardboard, aluminum cans, glass, different types of plastics, yard trimmings, rubber and leather etc.

With the data from 2009 and 2008 it is observed a reduction of 2.7% in overall generation of waste, which could be because of the global economy crisis during that period, but an increase of 1.7% in recycling rate is observed. In year 2008 waste recovered/recycled was 82.87 million tons and in year 2009 it was 82.02 million tons.

Recycling Rate

Recycling Rate-2008 Recycling Rate-2009

99 96 74 71 66 65 63 60 48 51 35 35

29 31 28 29 27 28 Recycling rate rate Recycling

Products

2-1: Comparison of Recycling rates for year 2008 & 2009 (6)

Recycling rates for some of the products are shown in Figure 2-1 as per US EPA

Facts and Figures for year 2008 and 2009. From Figure-2-2 it is very much obvious that organic materials continue to be the largest component of MSW. Paper and paperboard

account for 31 percent with yard trimmings and food scraps accounting for 26 percent as per the facts and figures collected by US EPA for year 2008.

Paper Others, Food 3.3% Scraps, Glass 12.7% Yard Paper, 31.0% Metals Trimmings, 13.2% Plastics Wood, Glass, 4.9% 6.6% Rubber, Leather & Rubber, Leather Metals, Textiles & Textiles, 7.9% 8.4% Plastics, 12.0% Wood

2-2: Municipal Solid Waste composition for 2008 (US)

2.2 US EPA MSW Hierarchy Model

US EPA has outlined the most environmentally sound strategies and optimal method for municipal solid waste. The four approaches in order of priority are source reduction , recycling, combustion and finally landfills (8). Source reduction is actually waste prevention or reducing waste at source which includes reuse. Source reduction can be in a form of conserve energy, reduce pollution, reduce toxicity of our waste, save natural resources etc. In day to day life we can relate reduction of source for example reducing number of printing pages by printing both sides, using reusable or washable coffee mugs instead of paper cups. In a manufacturing firm or a company by reducing the packaging material source reduction can be implemented.

Recycling is the second most important approach in MSW management.

Recycling is process of sorting and processing the recyclable products into raw materials.

It includes collection of used, reused and un used material, that would otherwise be considered as a waste.

2-3: Recycling Symbol

Recycling symbol includes three arrows moving in a triangle and each arrow represents a different aspect of the recycling process starting from collection then re- manufacture and then at the end resale. There are a lot of benefits of recycling like it reduces the amount of waste going to Landfill, prevents the emission of many greenhouse gases, supplies valuable raw material to industry as in case of aluminum etc. Recycling also includes composting of organic waste like food waste, yard trimmings etc. Compost is actually a organic waste material which can be used as a soil amendment and it helps in

reducing plant diseases and pests. Recycling also allows for less dependence on virgin raw materials and helps in reducing the need to harvest raw material from the environment. Second most widely used metal in the world today is aluminum and US is the largest consumer of aluminum cans, but it is not found in metallic form in nature. It is found as a mixture of aluminum oxide, iron oxides and clay known which is known as bauxite. Production or manufacturing of aluminum from bauxite is a intricate process and it requires a large amount of energy and resources used in initial manufacture. Making a can from recycled material rather than from bauxite saves 95% of the energy. Despite the popularity of recycling of aluminum cans, the industry is facing a challenge in decreasing recycling rates due to economic conditions all over the globe over the past couple of years. The environmental, economic and social benefits of recycling are well-known and important not only to the United States aluminum industry but to the economy in broad- spectrum and few states in US do have a program like get money back for each can they return back.

2-4: EPA solid waste management hierarchy

The next important step in MSW hierarchy is combustion or incineration. 100 percent of trash cannot be handled by source reduction, recycling or composting. Due to limited space in many communities burning garbage is a viable option of reducing waste by 90% in volume and 75% in weight plus it generates energy too.

The last part of MSW hierarchy is Landfill. EPA has established national standards and federal regulations and they must meet rigid design, operations and closure requirements. Table 2.2 shows the total waste generated in million tons, MSW recovered using recycling initiatives/ techniques and composting, combustion and landfill in US from 1960 to 2009 (11). From the data it is apparent that the amount of MSW going to landfill is reduced from 1990 to till date due to a lot of encouragement received from residents, business and other institutions to keep the globe cleaner and a better place to live.

2.2: Generation, recovery, combustion & discard of MSW, 1960-2009 (in millions of

tons)

ACTIVTY 1960 1970 1980 1990 2000 2005 2007 2008 2009 Generation 88.1 121.1 151.6 208.3 242.5 252.3 255 251 243 Recovery for recycling 5.6 8 14.5 29 53 59.3 63.1 61.8 61.3 Recovery for composting 0 0 0 4.2 16.5 20.6 21.7 22.1 20.8 Total Material Recovered 5.6 8 14.5 33.2 69.5 79.9 84.8 83.9 82.1 Combustion with energy recovery 0 0.4 2.7 29.7 33.7 31.6 32 31.6 29 Discarded to Landfill 82.5 112.7 134.4 145.4 139.3 140.8 138.2 135.5 131.9

2.3 Overview of Material Recovery Facility

A material recovery facility (MRF - pronounced "murf") is a central operation where they receive, sort and prepare recyclable materials for selling to end-users or buyers to meet market specifications for sale. Two types of MRF are clean and dirty MRFs. Figure 2-5 shows a flowchart of a typical clean MRF. As shown in flowchart, a clean MRF uses recyclables already separated at the source by MSW generated by residents and businesses and a clean MRF can be single stream or multi stream. MRF’s are designed such that input material or recyclables from curb side recycling programs can be separated and processed into marketable commodities.

Residents are encouraged to participate in curbside recycling so as higher volumes of materials will be taken from the solid waste management. The MRF discussed here is a basic multi stream MRF with manual sorting of MSW. As shown in Figure 2-5, a multi stream MRF we have source separated commingled containers including ferrous metals and bi products, aluminum cans, PET and HDPE bottles, mixed glass etc. plus there is another paper product stream including OCC, mixed office paper, old newspaper and magazines etc. (3) Non-recyclable and residue from both the streams after manual sorting are collected and sent to landfill. After the manual sortation at Sort station A and B as shown in Figure 2-5, these materials are bailed using a baler and finally ready for shipment to the end users.

2-5: Flowchart of a basic MRF operation

On the other hand, a dirty MRF accepts recyclable and non-recyclable waste from pickup trucks and then whole of the waste collected is subjected to a single sortation process which is a combination of both manual and mechanical sorting. As shown in

Figure 2-6, all the waste from single stream is subjected to sortation process which leads to the higher percentage of material recovery at the source as compared to the basic or clean MRF. So there are number of advantages of dirty MRF over a clean one like it allows 100% of waste to undergo recycling operation, no separate collection of MSW, cheaper collection cost plus the biggest advantage of such a system is that it ultimately reduces the need for sortation by residents.

2-6: Flowchart of a dirty MRF operation

The disadvantage from a dirty MRF includes higher capital investment and higher operating cost. The final product from baler contains higher contaminations as compared to the clean MRF’s final product which is also one of the issues related to dirty MRF’s along with maintaining proper hygiene at the facility.

2.4 Lean Six Sigma

2.4.1 Overview

Lean six sigma is a process improvement methodology that uses both the tools of lean and six sigma. Data and statistical analysis is used to identify and manage process variations. Lean mainly focuses on speed where as six sigma focuses on quality.

Motorola developed six sigma in 1986 and is now used in almost every industry to improve business, reduce errors and overall increasing the customer satisfaction. Lean manufacturing mainly concentrates on reducing non-value added time and thus better customer satisfaction. Six Sigma relies heavily on data, facts, and the use of statistical tools like Pareto analysis, cause and effect diagram, SIPOC analysis, work studies etc. to find out if improvement has been made. Six sigma means 3.4 defects per million opportunities and sigma represents the variation around process mean.(13) In this project for example each aluminum can that is not recycled is considered a defect. The goal is to reduce defect level by understanding the whole process and source of variability. To achieve this goal, six sigma generally comprises of five phases or steps: scope or define, measure, analyze, improve and control. Each of these phases is discussed below with a example to better understand each step.

2.4.2 Definition

Scope or define phase is the initial stage of the project where goal of project, and overall process is discussed and mainly this stage outlines the quality issues and identifies the areas of variability in the existing system. Customer requirements are clearly defined in this stage, so as not to deviate from the goal. This stage is used to learn the process.

Listening to customers, gaining process knowledge and determining the size of problem is actually part of define stage. Gantt chart, affinity diagram, Pareto analysis, work studies etc. are some of the tools used in define stage to gather sufficient information so as to learn about the organizations barriers to solve the problem. For example in a project to improve overall efficiency of the operations of a unit in a hospital and increase

customer satisfaction define stage includes understanding the whole process, identify main barriers for improvement, define a timeline for the project and what all departments to involve in the project are going to be main baseline for the define stage.

2.4.3 Measure

Measure stage means turning the ideas and objectives of the project into a structured process. It includes process and metric definition to define a reliable means of measuring process. Define current state and process plus establish a process baseline.

Finally measurement system is evaluated to validate the reliability of data to reach meaningful conclusions. As discussed the in define stage the same example of a hospital, measure stage would include establishing the baseline, eliminate trivial variables, time studies for different operations and tools used may include basic statistics, measure system analysis, cost to quality SIPOC and critical parameters.

2.4.4 Analyze

After the measure phase data must be collected and analyzed to verify relationships and factors influencing variations in the process. These days various softwares and statistical tools are used during analysis phase. Above all it helps in determining the cause of the problem and how to reduce the gap between desired level of performance and existing or current performance. Regression analysis, cause and effect diagram multi-vary analysis are some of the analysis tools very often used during a

DMAIC approach.

2-7: Cause & effect diagram for determining root cause of flat tire.

Regression analysis is a way to estimate the correlation between variables, variable which are dependent to each other or elimination of trivial variables are some key deliverables of the regression analysis. As shown in figure 2-7, cause and effect diagram helps in determining various factors and variables associated with different problems and process. As an example, the various effects and reasons for tire flat of a vehicle are shown.

2.4.5 Improve

The main objective of the improve phase is to improve the process by eliminating defects. The tools of the improve phase are used to help the team to develop solutions for improving process performance, solutions may vary from a complex experimentation, series of tests and may be simulation etc. Process map is updated and modified to include the missing steps responsible for variability in the process. Design of experiments,

comparative experiments, system thinking and testing of hypothesis are general tools used during improve phase.

2.4.6 Control

The objectives of control phase are to implement the solution, ensure that this solution is sustained and share the lessons learned from the improvement project throughout the organization. Key deliverables of control stage are project documentation, control charts, use of best practices, change management if required, reward and recognition etc. Control phase is used to maintain a solution and key variables within an acceptable range. Control charts are designed so as to identify variations in the process and keep an eye on the process or track of the system. The data is plotted against specified limits to observe the process. As shown in figure 2-8, data measured is plotted against time with a lower and upper control limit. Anything out of this range is considered as a defect or problem to the system.

2-8: A typical control chart

Process improvement, process design or redesign, and process management are three overall targeted solutions of six sigma techniques. Process improvement refers to a strategy of improving a process while leaving the basic structure of the process intact and fixing the problem. Process design and redesign is a strategy used to replace or redesign whole process to fix the problem. Process management refers to the integration of six sigma methods into everyday business. Process improvement and process design and redesign are used to constantly raise the company’s levels of efficiency, competitiveness, and productivity.

Chapter 3

Overview of Case Study

This case study examines a Material Recovery Facility at Toledo, Ohio. MRF is owned by Lucas county solid waste management district. The waste assessment and data collection was conducted during spring 2010 by the research assistants at Environmental

Conscious Design and Manufacturing laboratory, The University of Toledo, Ohio. The

LCSWMD purchased this MRF in 2008 to sort and sell approximately 10,000 tons of recyclable material annually. District funds and maintains around 174 drop off recycling sites throughout Lucas County (12). The drop-off sites are located at apartment complexes, schools, large grocery stores, metro parks, etc. Each drop-off container is owned and maintained by LCSWMD. Daily trucks are dedicated to pick commingled fiber and container stream. As the truck reaches its capacity they unload the material at

MRF, Toledo for sortation and bailing. Before unloading materials trucks are weighed and recorded for payment plus to keep a track of the system. A ticket is generated for each truck and total weight is recorded. The facility has a sortation center, a baler and a warehouse for shipping final products to end users.

3-1: Material Recovery Facility, Toledo Ohio.

3.1 Definition

The objectives of the project were discussed and it is the initial stage of the project where goal of project and overall process is discussed and mainly this stage outlines or drafts the quality issues and determines the areas of variability in the existing system. Initial meetings were mainly to discuss the issues faced at the facility and build a project team including ECML to work towards the goal.

3.1.1 Project team, selection and goals

The members of the Six Sigma team were Dr. Matthew Franchetti PhD., P.E,

Christopher Pizza, Kevin Burke, Julie Riley, Prabhu Kiran and Pukhraj Barnala. Goals for the project were established, basically revolving around the aligning and optimization of the overall process and hence improving efficiency and enhancing better customer satisfaction. Project selection is part of define phase when projects are evaluated and finally a project or group of projects are chosen so as to achieve the objectives of the 23

organization.(14) A cause and effect matrix is used to show all the potential projects and opportunities. As shown in table 3.1, outputs are in the top row and are assigned values according to the importance to the district and business goals. A higher number means more importance and thus quality, waste and runtime are given a rating of 5, 3 & 1 respectively. The first column contains process, second represents the opportunity and then quality, start of waste and runtime. Importance number at the top is cross multiplied with each input from the team and thus summed across each row. As a result, the project for MRF Toledo is chosen which is at the top and higher importance as per the

LCSWMD. The objectives of the project were discussed and finally identifying, quantifying and eliminating the source of variation and defects for the operations at MRF

Toledo are key objectives of the six sigma project.

3.1: Cause and effect matrix

Rating of importance 5 3 1 Start of Process Opportunity Quality Waste Runtime Total MRF Toledo Defects 8 2 1 47 Residents Awareness 4 4 0 32 Landfill audits Recycling 2 1 0 13 Truck routing Optimization 0 2 6 12

3.1.2 Gantt Chart

Gantt chart is a tool to clearly show development and progress of a project graphically. The tasks and subtasks of a project can be quite intricate and dependent on each other. With a project management tool, such as Gantt chart projects are easily managed and all subtasks of a task can be viewed in detail. As shown in Figure 3-2, timeline is shown on x-axis and processes are shown on y-axis. The team established the following timeline for the project to be completed- 24

Define- September to December 2009- Define the project goals and timeline with the focus on improving recycling at Lucas County by establishing the team between MRF,

LCSWMD and ECML.

Measure- November, December (2009), January to May 2010- Includes on-site data collection and identify the current state and current processes. Measure phase also includes the development of a process flow map to baseline the system and to identify defects and bottlenecks. Collect the data including process statistics, surveys, required resources, time studies etc.

Analyze-March, April, May, June 2010- Analyze phase includes the DPMO and sigma level calculation for the process. DPMO includes source of variation and identify Value and Non- Value added activities, which is part of lean manufacturing. Cost and revenue analysis is also done during this phase of the project.

Improve- June, July, August 2010- Team plans to conduct experiments and validate improved processes and develop action plans and standard operating procedures.

Control- October, November 2010- Develop a control plan, implement the solution, ensure that solution is sustained and monitor performance and mistake- proof processes.

DEFINE

MEASURE

ANALYZE

DURATION IMPROVE

CONTROL

3-2: Gantt chart for the project

3.1.3 Process Overview

The process overview includes understanding of the whole process and identifying the reasons for defects and variation in the process. The process begins from curbside pickup of commingled containers including HDPE and PET plastics, glass containers, Aluminum cans etc. plus fiber including Mixed Office paper, OCC,

Newspaper and Magazines. The curbside recycling is totally resident’s dependent so awareness among residents is very important for recycling. Once the recyclable from district owned dumpsters are collected, the district truck brings all of this source separated materials to the MRF which acts as the raw material for the process. At the entry of these trucks, a ticket is generated which shows the weight of the truck. So we know how much amount of recyclable material is in the truck. After that materials are separated from each other by manual sortation of material from 3 to 4 employees as

shown in Process map below. Each material is separated and stored in a box/cage pallet.

Once these pallets are full, pallet are shifted near the bailer area, so as to start the bailing operation after sortation.

The final product are bailed from bailers and stored in a warehouse or storage location for shipping them to the final destinations or customers. The main objectives of the research include aligning and optimizing processes and removal of process generated defects and errors. Improvements primarily focus on reduce MSW going to Landfill and maximize recycling at MRF Toledo, Ohio.

3-3: Process Overview for MRF Toledo operations

3.2 Measurement

3.2.1 Introduction

On-site data collection is part of the measure phase. MRF Toledo, the operation starts at around 7 am, and by around 7.30-7.45 am first truck comes back to the facility.

Truck is weighed and a ticket is issued with weight of the recyclable material on it is marked. Then material is unloaded and sortation process starts. The data from Julie is collected regarding all the expenses of facility. To establish a baseline and eliminate trivial variables are part of this phase. Some of the tools used in the measure phase of the project are-

 Pareto Chart

 Time Studies

 Surveys

 Check sheets

3.2.2 Pareto Chart

A Pareto diagram/chart is a vertical bar graph showing problems or more directly opportunities in a prioritized order, so it can be determined which problems or opportunities should be tackled first. It is a tool used on the 80:20 rule which means usually 80% of the problems come from 20% of the processes. The categories are sorted in decreasing order from left to right in a Pareto Diagram by their count or cost, whichever is displayed.(4)

COLLECTION AMOUNT (IN TONS) 6000

5000

T 4000 O 3000 N S 2000

1000

0 ONP & MOP OCC GLASS PLASTIC STEEL CAN ALUMINUM BOTTLES BOTTLE CAN MATERIAL

3-4: Pareto Chart

3.2: Recyclable material collected in tons for year 2010

MATERIAL TONS PERCENTAGE ONP & MOP 5115 45% OCC 3410 30% GLASS BOTTLES 1748 15% PLASTIC BOTTLE 793 7% STEEL CAN 275 2% ALUMINUM CAN 82 1%

Data from MRF Toledo is collected for the month of March and April 2010 and is assumed for the year. The annual recyclable material to be processed through MRF

Toledo is shown in Figure3-4. From the Pareto Chart, it is visible the most collected material is Old Newspaper, Mixed office Paper and then OCC and Aluminum cans as the last nearly about 82 tons.

3.2.3 Time Studies

Time Study is a continuous surveillance of a job/process, using a timekeeping device (stopwatch used in this project) to record the time taken for accomplishing a job.

After recording the time, the worker’s performance time (level) is recorded and then data is used to make the standard time for the task/process. Standard time (minutes/cycle) for the baler is calculated from the data observed by time studies for each material as shown in Table 3.3 to 3.5.

3.3: Time taken for a bale of paper to process through the bailer.

TOTAL TIME FOR TIME IN TIME OUT PROCESS (MINS) 0.0000 0.0812 4.9 0.0812 0.2787 11.9 0.2787 0.3320 3.2 0.3320 0.3799 2.9 0.5400 0.6600 7.2 0.6070 0.6689 3.7 0.6835 0.7924 6.5 0.3799 0.5400 9.6 AVERAGE STD. TIME 6.2

Table-3.3 shows the average standard time taken for a bale of paper to process through the bailer and get out of the system. And the average time to process and make a bale of paper is around 6.2 minutes.

3.4: Time taken for a bale of OCC to process through bailer.

TOTAL TIME TIME IN TIME OUT FOR PROCESS (MINS) 1.1239 1.2246 6.0 1.2246 1.3215 5.8 1.3215 1.4055 5.0 1.4055 1.4810 4.5 1.4810 1.5635 4.9 1.5636 1.6578 5.7 1.6578 1.7760 7.1 1.7760 1.8590 5.0 1.8590 2.1350 16.6 AVERAGE STD. TIME 6.7

3.5: Time taken for a bale of plastic to bale through process.

TOTAL TIME FOR TIME IN TIME OUT PROCESS (MINS) 2.4101 2.5430 8.0 2.5430 2.6193 4.6 2.6193 2.7000 4.8 2.7000 2.8278 7.7 2.8279 2.9997 10.3 AVERAGE STD. TIME 7.1

Table-3.4 shows the average standard time taken for a bale of OCC to process through the bailer and get out of the system. And the average time to process and make a bale of OCC is around 6.7 minutes. The variation in the system was mainly due to breakdown of cable system that covers the bale, removal of unwanted glass particles coming out from baler etc. Table 3.6 shows the time taken by forklift to drop the baled items from baler area to the shipping/warehouse area from where end products are shipped.

3.6: Time taken by forklift to drop baled items to shipping.

TOTAL TIME FOR TIME IN TIME OUT PROCESS (MINS) 0.1854 0.2139 1.7 0.2140 0.2429 1.7 0.2430 0.2705 1.7 0.8460 0.8800 2.0 0.8900 0.9169 1.6 0.9400 0.9769 2.2 1.8250 1.8500 1.5 1.8501 1.8835 2.0 AVERAGE STD. TIME 1.8

Chapter 4

Overall Analysis

4.1 Analysis

4.1.1 Fish bone diagram

The fish bone diagram is used in this phase to identify the reasons for high variation in the process. After the overall study and data collection from define and measure phase a fish bone diagram is used to jot down all the factors influencing the process. As shown in Figure 4-1, material, machinery, methods and staff are the main factors. Let us discuss each one of these in details.

4-1: Fish bone diagram

4.1.2 Method

From all the surveys and interviewing employees, it is revealed that the manual sortation method used can be improved to help reducing the overall defects. The process takes more time due to complexity of the process, limited quality control, no standard operating procedures etc.

4.1.3 Staff

The only issue with the staff is the staff unavailability, if one operator takes off it is difficult for someone to cover the shift, due to lack of standards and immediate replacement. Staff does need some training regarding the defects in the process. To reduce the DPMO employees should know the whole process and defects like Chinese

OCC, oily OCC, and broken glass pieces which affects mainly the quality of the end product.

4.1.4 Material

As shown in the fish bone diagram raw material used also causes the variability in the system. The main reason is in broken glasses as they contaminate the end products specially paper products and OCC. Mixed Chinese OCC is also one of the major defects in the raw material which is not accepted by the end-customers. Shredded paper is also one of the main defects which is not easy to bale and is just flowing in the system but at the end of the day acts as a residue only.

4.1.5 Machinery

Another variation in process at MRF Toledo is due to break down of bailer, improper resource utilization and single bailer is used for all the materials which are the main reasons for variation in the process. Due to improper maintenance and extensive use of bailer, the cable system that wraps the bale breaks down pretty often. There is a lot of time wasted in fixing the cable wiring system in a shift which is again discussed in the value added and non-value added topic later in this chapter.

Equipment requirements were determined by 3 day on-site data collection. The amount of equipment required for a process is referred to as the equipment fraction. The equipment fraction can be determined for a process by dividing the total time required to perform the process by the time available to complete the process. The total time required to complete the process is product of the standard time for the process and the number of times the operation/process is to be performed. (5)Standard time can be calculated by using time studies and other statistical tools.

Where,

F=number of machines required per shift to perform the task.

S= Standard time (minutes) per unit produced

Q= Number of units to be produced per shift

H= Amount of time available per machine

E= Actual performance, expressed as a percentage of standard time

R= Reliability of machine, expressed as percent “up time”

4.1: Equipment requirement per material for bailer

STANDARD ANNUAL POUNDS BALES AVAILABLE RELAIBILITY EFFICIEN MACHINE TIME TONS TO PER PER TIME PER (R) -CY (E) FRACTION MATERIAL (MIN/CYC) BE BALE YEAR YEAR ( H ) (F) PROCESSED (Q) ONP 6.2 3759 1630 4612 120000 85.00% 90.00% 0.31 OCC 6.7 3410 1700 4012 120000 85.00% 90.00% 0.29 MOP 6.2 1356 1630 1664 120000 85.00% 90.00% 0.11 PLASTIC 7.1 793 1200 1322 120000 85.00% 90.00% 0.10 STEEL CAN 7.1 275 1830 301 120000 85.00% 90.00% 0.02 ALUMINUM CAN 7.1 82 1830 90 120000 85.00% 90.00% 0.01 TOTAL 9675 9820 12000 0.85

Additionally, equipment requirements are a function of the factors like number of

shifts per day, set up time, degree of flexibility, layout type and maintenance etc.

Standard cycle times were measured for current bailer per material so as to determine the

resource utilization. As shown in Table No4.1, baler is used to process 12000 bales per

year. The baler is assumed to be 85% reliable as per the information from Kevin and

workers which means 15% of the time it is machine is not working due to repairs, unscheduled maintenance or to fix the jammed materials. The efficiency in terms of standard cycle time is measured to be around 90% which means machine is processing

10% less than its standard. Based on standard cycle times and 250 working days of 8 hours shift a day, the baler will be utilized 85%.

4.2 Defects per Million Opportunities - DPMO

In process improvement techniques, defects per million opportunities or DPMO are a measure of process performance or non-conformities per million opportunities. A defect is also known as a nonconformance of quality characteristic to its requirement and specification. DPMO is stated in opportunities per million units for convenience.

4.2: Annual weight of solid waste or defects from the MRF

Tons per Percent of Tons Tons Not Component Year Total recycled Recycled Glass 31.85 94.1% 0.00 31.85 Mixed Office Paper 1.37 4.1% 0.00 1.37 Non- recyclable/food 0.22 0.7% 0.00 0.22 Cardboard 0.15 0.4% 0.00 0.15 HDPE (2) 0.13 0.4% 0.00 0.13 Aluminum Cans 0.03 0.1% 0.00 0.03 Newspaper 0.00 0.0% 0.00 0.00 PET (1) 0.10 0.3% 0.00 0.10 Magazines 0.00 0.0% 0.00 0.00 Plastic Wrap 0.00 0.0% 0.00 0.00 Other 0.00 0.0% 0.00 0.00 Other Plastics 0.00 0.0% 0.00 0.00 Total 33.85 100.0% 0.00 33.85

Defects for the processes are shown in Table No-4.2 with annual weight of defects and Table no-4.3 with Annual volume of defects per material. As shown in table

4.2, the main defects of the process are glass particles which come around to be 31.85 37

tons per year. Similarly, with the help of densities for each material we now have figures in cubic yards for the defects as shown in Table 4.3. Now DPMO for the process is calculated and is around 2788 defects per million opportunities which correspond to a sigma level of 4.3 in terms of weight (7).

4.3: Annual volume of solid waste or defects at MRF

Component Cu. Yds./Yr. Percent of Total Cu. Yds. Rec. Cu. Yds. Not Rec. Glass 106.16 63.5% 0.00 106.16 Mixed Office Paper 33.44 20.0% 0.00 33.44 Non- recyclable/food 8.36 5.0% 0.00 8.36 Cardboard 5.85 3.5% 0.00 5.85 Aluminum Cans 1.67 1.0% 0.00 1.67 HDPE (2) 6.69 4.0% 0.00 6.69 PET (1) 5.02 3.0% 0.00 5.02 Newspaper 0.00 0.0% 0.00 0.00 Plastic Wrap 0.00 0.0% 0.00 0.00 Other 0.00 0.0% 0.00 0.00 Magazines 0.00 0.0% 0.00 0.00 Other Plastics 0.00 0.0% 0.00 0.00 Total 167.18 1.0 0.0 167.18

Annual Weight of Solid Waste Streams 35.00 Tons Not 30.00 Recycled

25.00

20.00

15.00

10.00 Weight (Tons) Weight

5.00

0.00

Component

4-2: Annual weight of defect at MRF

4.3 Cost and revenue analysis

The annual expenses and revenue from the facility is estimated by the information gathered from the resources on site and records of the MRF Toledo. Now for revenue analysis an average dollar amount per ton for each recyclable is calculated from the yellow sheets. As per information gathered below is the total annual utility and maintenance cost for the MRF in Table 4.4.

4.4: Annual utility & maintenance cost for office area.

UTILITY COST($)/MONTH ANNUAL COST($) ELECTRIC 2000 24000 GAS 250 3000 WATER 703 8436 SECURITY 278 3333 TELEPHONE 175 2100 MISC. 300 3600 Total 3706 $44,469

Now total annual revenue generated from the sale of recyclables as per average rates per material as per assumptions from The Yellow Sheet for May-2010 is $USD

1012807. The recycling market prices for all the recycling materials from year 1988 to

February 2011 are shown in appendix-C courtesy SMG, Inc.

4.5: Total revenue generated from recycling

DOLLAR PER ANNUAL TONS ANNUAL MATERIAL BALED TON TO BE REVENUE PROCESSED ONP 85 3759 319515 OCC 110 3410 375100 MOP 82 1356 111192 PLASTIC 180 793 142740 STEEL CAN 180 275 49500 ALUMINUM CAN 180 82 14760 TOTAL 9675 1012807

After the recommendations from the six sigma team opportunities in financial terms are calculated from current performance or baseline minus the targeted goals. As shown in table 4.6 the total profit to the LCSWMD after the recommendations from research team will be around $64,048 from selling more recyclable by increasing tons processed per hour plus $971.89 by reducing defects annually. Target dollars earned are 40

based on increasing the efficiency for example from 7.5 to 8.1 in case of tons of paper processed per hour. So a total profit of $65,019.89 annually is estimated after the implementation of recommendations by the ECML team.

4.6: Estimated financial benefits

Annual Annual Dollars Dollars tons tons earned earned Material Baseline Goal (Baseline) (Target) (current) (target) Paper 7.5 8.1 5115 5524 $419,430 $452,968 Commingled 6.5 7.3 1150 1292 $207,000 $232,560 OCC 7.6 7.7 3410 3455 $375,100 $380,050 $1,001,530 $1,065,578 Profit $64,048

4.4 Improve

Once all the data collection is done, whole process is studied and major bottlenecks and defects are identified in a system improve phase is used to now finally decide the proposed solutions and try and reduce defects in the system. Value added and non-value added analysis is a major part of this phase. Table 4.7 shows the dollar loss per year due to residue in the system. This is one of the major reasons for defects in system.

Upon reviewing the data broken glass particles, oily OCC, Chinese OCC, break down of wiring system on bailer, time wasted in transportation of bailed material to warehouse and shredded paper are major reasons for variations in the process.

4.7: Dollar loss per year due to defects

Material Tons per Year Dollar per Baled ton Annual Dollars Lost Glass 31.85 25 $796.25 Mixed Office Paper 1.37 82 $112.34 Cardboard 0.15 110 $16.5 HDPE (2) 0.13 180 $23.4 Aluminum Cans 0.03 180 $5.4 Newspaper 0.00 85 $0 PET (1) 0.1 180 $18 Total 33.63 $ 971.89

4.4.1 Value Added vs. Non Value Added Analysis.

Lean manufacturing is a systematic approach to identifying and eliminating waste which is also known as non-value added activities through proper continuous improvement techniques (10). In simple terms we can say anything which doesn’t add market form or function of a final product is a waste, whereas value-added work is the work that is in fact valuable and results in a final or finished product. A customer is only going to pay for value, if they feel that their money or time is being wasted as a result of unsatisfactory processes, the customer will take his business to somewhere else. Three factors for considering a work to be value added are:

 Capacity-Machinery, resources, tools and employees used in the process must have the necessary capacity to produce a final product adding value to it. In our case we already discuss the utilization for bailer. Capacity is more discussed in the next section as well so as to have a clearer picture.

 Information/Instructions-Employees must know their final product and process to achieve the final product with minimum waste or non-value added activities. Like in our

case study all worker should have a thorough understanding of the overall process plus they should very well understand the difference between recyclables and non-recyclables.

 Materials-The material given to workers should be proper. A worker should know which raw material is good or bad so as to avoid any kind of defects or quality issues later on. On the other hand Non-value added work, also called waste, refers to work that doesn't add value to the overall process or final product. For example a plant manager hires a certified and highly paid welder to improve welding quality and final product. But instead of doing that the welder is just being utilized for regular metal cutting processes which any other less skilled or less paid worker would have done. So, it is a waste of resource and thus adding a more cost to the company for same product. Now customers only want to pay for the work that adds value to the finished product.

The main types of waste or non-value added work are :

1. Overproduction -Overproduction is mainly used to overcome the sudden demand or unbalanced workload. But most of the times is leads to a waste due to unneeded inventory. In our case study overproduction was never a waste; always a truck is scheduled to take the final product out of facility on time so as to avoid any major inventory issues.

2. Defects–Defects are due to various reasons starting from material, inadequate training, labor, sorting or deficient maintenance. In our case study we have seen defects are mainly due to Bailer breakdown at times, defects in raw materials and incomplete engineering specifications. At MRF Toledo, as per the time studies and data collection on site it is observed that 29 percent of the non-value added time is due to time wasted in fixing the cable wire system which wraps the final baled products.

3. Inventory– Excess of inventory yields to more holding cost, more labor hours to again load and reload the material.

4. Transportation–Unwanted movement of materials within the facility makes it a waste or non-value added activity from the customer’s viewpoint.

5. Waiting – The time wasted such as waiting for raw material, waiting time for equipment or tool set ups and instructions, waiting for tooling, waiting for workers etc.

This all adds up to a huge amount of non-value added activities to the final product. As shown in Table-4.8, the total time wasted in waiting for next process or idle time between two processes or time wasted in miscellaneous activities including time for restrooms etc. is 24 minutes which is around 47 percentage of the total non-value added activity or waste.

6. People–Time wasted in not utilizing the abilities of a worker or employee to their fullest potential. Morale and company culture is also one of the main factors for this kind of waste.

7. Motion- Main reasons for waste added due to motion are poor plant layout and workplace design. It includes time wasted in looking for tools, extra product handling, stacking etc. Now time taken by a worker to move baled items from baler to the shipping area can be included in this type of waste.

The main waste or non-value added activities in our case study are time wasted due to fixing the Baler cable system almost 7 to 8 times a day. Below is the list of all the non-value added activities measured at the facility during time studies.

4.8: List of all the non-value added activities

TIME TIME NON VALUE ADDED S. NO IN OUT TIME (MINS) 1 0.0813 0.2222 8.5 2 0.38 0.401 1.3 3 0.38 0.5399 9.6 4 0.4011 0.529 7.7 5 0.54 0.606 4.0 6 0.82 0.88 3.6 7 2.1351 2.41 16.5

4.5. Capacity

Machinery, resources, tools and employees used in the process must have the necessary capacity to produce a final product as per the demand. Due to changing demands of a product an organization need to determine the production capacity.

Capacity can be increased by using modern manufacturing methods, continuous improvement in facility, optimum utilization of machines and workers and there are so many other process improvement techniques depending upon the business to increase efficiency and reduce waste. Now in our case in terms of capacity we did calculate the tons baled per hour for all the different materials. Formula for calculating the tons per hour of a material processed at MRF Toledo is

Where, = tons per hour processed for material x.

= number of bales processed in one hour for material x.

= number of bales processed for material x in total time.

= total time taken to process the material.

= pounds per bale for material x.

4.9: Tons per hour processed for each material

NUMBER MATERIAL TOTAL TIME FOR POUNDS TONS PER OF (x) PROCESSING THE PER BALE HOUR BALES MATERIAL (Px)- (Wx) PROCESSED (Nx) (MINS) (Tx) 9 Paper 58.6 1630 7.5 5 Comingled 35.2 1515 6.5 9 OCC 61 1700 7.6

Tons per hour processed for paper, commingled and OCC are 7.5, 6.5 and 7.6 tons per hour respectively. Now this capacity of processing each material per hour can be increased be reducing the non-value added time and waste from the process. By implementing the recommendations of the research team the tons per hour processed at

MRF Toledo, Ohio can reach to a higher figure than the present. Table no-4.10 represents the tons per hour processed assuming the recommendations and reducing the waste. Now after implementing the recommendations as per the ECML team there will be at least increase in tons processed per hour by 7.3% in paper bales, 12.8 % in commingled bales and 1.6% in OCC bales.

4.10: Tons processed per hour after recommendations.

NUMBER MATERIAL TOTAL TIME FOR POUNDS TONS PER OF (x) PROCESSING THE PER HOUR BALES MATERIAL (Px)- BALE (Wx) PROCESSED (Nx) (MINS) (Tx) 9 Paper 54.6 1630 8.1 5 Comingled 31.2 1515 7.3 9 OCC 60 1700 7.7

4.7. Control

Control phase is basically the implementation of solutions suggested by the process improvement team; discuss the solutions to sustain the improvements. Making control charts, documentation, communicating with all who are involved and will affect the process improvement and discuss recommendations is some of the main aspects of the control phase. (13). Now to be statistically confident that mean cycle time after recommendations will be different from baseline means cycle times, a hypothesis test was carried out. A t – test was carried out with following hypothesis for the mean cycle time of plastic bales Table-3.5.

Null hypothesis, H0 : µ ≤6.5, new mean cycle time is less than or Equal to 6.5

Alternate hypothesis, H1 : µ >6.5, new mean cycle time is greater than 6.5

t = 0

Where, t0– test statistic calculated using the data.

– Sample mean calculated from data in Table 3.5 = 7.1 minutes per bale.

- New assumed mean =6.5 minutes per bale.

s – Standard deviation for the observation=2.4

n –Number of observations, 5 for this test.

tα– Critical test statistic at significance level 5%

We have assumed that the new mean cycle time after recommendations is 6.5 minutes per bale. Test statistic t0comes out to be 0.559. At 5% significance level, critical test statistic tα is 2.776. t – test indicates that do not reject null hypothesis. In other words it means we are 95% confident that the new mean is less than or equal to 6.5 after the recommendations. So it can be concluded that new mean cycle times will be reduced.

Control chart helps in identifying the special cause or bottlenecks in the system.

Control chart for each material is shown in Figure4-3, 4-4 & 4-5. As shown in figure4-3, the average time taken to process the paper bale is 6.2 minutes. The data collected is within the limits, thus we don’t have any special causes for this process. Where as in

Figure 4-4, the control chart is pretty much showing the variations in the process and special causes can easily be seen.

TIME TAKEN TO PROCESS BALE PAPER 21.2 UCL 17.5

16.2

11.2

6.1 CL 6.2

1.1 TIME TIME (MINS)

-3.9 LCL -5.0 -8.9 1 2 3 4 5 6 7 8 SAMPLE

4-3: Control chart showing time taken to process paper bale

TIME TAKEN TO PROCESS OCC BALE 17.6 15.6

13.6 11.6 9.6 UCL 9.1

TIME TIME (MINS) 7.6 CL 6.7 5.6 LCL 4.4 3.6 1 2 3 4 5 6 7 8 9 SAMPLE

4-4: Control chart showing time taken to process the OCC bale.

Now for Figure 4-4, the data points are out of control, and we need to discuss the process and implement some changes in the process so as to make it within the limits and

improve the process. There are like 3 points out of control of 2 standard deviations. This deviation in process is due to the non-value added time from operator. This was the time when baler was idle and there was no operator near the machine to start the next process or cycle. Figure 4-5 represents the control chart showing time taken for the commingled bale process. It’s pretty visible that process looks smooth and under control limits.

TIME TAKEN TO PROCESS COMMINGLED BALE 15.0

UCL 13.1

10.0 CL 7.1

5.0 TIME TIME (MINS) LCL 1.0 0.0 1 2 3 4 5 SAMPLE

4-5: Control chart showing time taken to process commingled bale.

4.7.1 Process capability

The capability of this improvement was traced by measuring tons processed per hour for each material. The final capability Table 4.11 shows the before and improved levels. Cost to poor quality is also a significant figure and total savings from the project are estimated.

4.11: Final capability chart

Before Improved levels Tons processed per hour for paper 7.5 8.1 Tons processed per hour for OCC 7.6 7.7 Tons processed per hour for commingled 6.5 7.3 Total savings (annual) ---- $65,019

Chapter 5

Conclusions

This case study has shown that using Lean Six Sigma could improve the recycling at

Material recovery Facility at Toledo, Ohio. The case study illustrates in detail how

DMAIC approach of Six sigma help in identifying bottlenecks and barriers to the operations of material recovery facility. DPMO and sigma level for the current process are identified and variations to the process were discussed along with the economical and environmental benefits. By reducing non-value added time only tons processed per hour can be increased by 7.3 % for paper bales, 12.8% for commingled bales and 1.6% for

OCC bales. Total profit of $65,019 is estimated form the recommendations of the team by reducing defects and increasing tons processed per hour using lean six sigma techniques. The case study also helped employee participation in process improvement, use of statistical tools to solve problems, increased process knowledge and it motivated people towards six sigma approach.

The methodology used to improve process at MRF, Toledo can be used any manufacturing facility worldwide. Involvement of management and due to cross functional groups involved in six sigma methodology it did help in training and teamwork. Main inputs which the recycling team was getting were through the 52

employees who know the process thoroughly and they did discuss all the issues and defects for process. Main recommendations from the team are:

 A proper maintenance program for the baler wiring system or mechanism used at the facility was one of the main reasons for non-value added activities. 29% of the non- value added time can easily be taken out by implementing a better wiring cable mechanism or a proper maintenance program.

 Glass bottles and the liquid inside them are the main factor which affect the quality of paper bale and OCC bale.

 Use of check lists by employees to keep a track of variation in the process is also recommended. It will help employees to understand reasons for variation and keep them motivated towards the process improvement goal.

 The small glass particles are 94.1% of the overall defects as per the data analysis.

This can only be reduced by increasing more awareness among residents and implementing better ways of sorting glass bottles.

 Shredded paper was also one of the main defects, which came out by interviewing one of the employees who works on baler. There is a significant amount of waste of shredded paper which is out of control and is lost due to the baling process.

 Some standard operating parameters need to be set for a shift or a day. It is observed that after commingled bales there is some moisture in the system and thus paper baled just after that are of lesser quality. So we need to have standard operating parameters which should have a sequence in which materials will be processed at Baler.

5.1 Future Research

The research is able to provide a comprehensive framework for eliminating the waste from the operations at Material Recovery Facility, Toledo, Ohio. A next phase of the project would be ideal to implement and modify the current model. Use of simulation could be a great fit before making changes in the real world for a particular facility. This several areas for suggested research in the future would include:

 Use of energy assessments could be a great fit to reduce cost and help facilities in going greener.

 Predict and research the trends in future that will help in recycling not only from one facilities point of view, but for a bigger picture. Use of manufacturing simulation softwares to create virtual models before implementing changes on live projects.

 Ways of increasing awareness among residents to help recycling and zero land filling is one of the biggest challenges in recycling industry. The main sources of MSW are residents in our case study. If the materials are already source separated from the point of pick up, it will help a lot in increasing overall recycling and thus making this planet greener.

 Carbon emissions impact and revenue generation can be compiled with energy and waste assessments to help business in reducing their waste going to landfill and recycle more.

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Appendix

A Yellow sheet for May 2010

A-1: The yellow sheet for May-2010.

A-2: The yellow sheet for May, 2010 showing export prices.

B Defects per million opportunity chart

Defects per Defects per Defects per Success Sigma 100 10,000 1,000,000 rate Value 93 9,330 933,000 7% 0.0 92 9,190 919,000 8% 0.1 90 9,030 903,000 10% 0.2 88 8,850 885,000 12% 0.3 86 8,640 864,000 14% 0.4 84 8,410 841,000 16% 0.5 82 8,160 816,000 18% 0.6 79 7,880 788,000 21% 0.7 76 7,580 758,000 24% 0.8 73 7,260 726,000 27% 0.9 69 6,910 691,000 31% 1.0 66 6,550 655,000 34% 1.1 62 6,180 618,000 38% 1.2 58 5,790 579,000 42% 1.3 54 5,400 540,000 46% 1.4 50 5,000 500,000 50% 1.5 46 4,600 460,000 54.0% 1.6 42 4,210 421,000 57.9% 1.7 38 3,820 382,000 61.8% 1.8 34 3,450 345,000 65.5% 1.9 31 3,090 309,000 69.1% 2.0 27 2,740 274,000 72.6% 2.1 24 2,420 242,000 75.8% 2.2 21 2,120 212,000 78.8% 2.3 18 1,840 184,000 81.6% 2.4 16 1,590 159,000 84.1% 2.5 14 1,360 136,000 86.4% 2.6 12 1,150 115,000 88.5% 2.7 10 968 96,800 90.32% 2.8 8 808 80,800 91.92% 2.9 7 668 66,800 93.32% 3.0 6 548 54,800 94.52% 3.1 5 446 44,600 95.54% 3.2 4 359 35,900 96.41% 3.3 3 287 28,700 97.13% 3.4 2 228 22,800 97.72% 3.5 59

2 179 17,900 98.21% 3.6 1 139 13,900 98.61% 3.7 1 107 10,700 98.93% 3.8 1 82 8,200 99.18% 3.9 1 62 6,210 99.379% 4.0 47 4,660 99.534% 4.1 35 3,470 99.653% 4.2 26 2,560 99.744% 4.3 19 1,870 99.813% 4.4 14 1,350 99.865% 4.5 10 968 99.903% 4.6 7 687 99.931% 4.7 5 483 99.952% 4.8 3 337 99.966% 4.9 2 233 99.9767% 5.0 2 159 99.9841% 5.1 1 108 99.9892% 5.2 1 72 99.9928% 5.3 48 99.9952% 5.4 32 99.9968% 5.5 21 99.9979% 5.6 13 99.9987% 5.7 9 99.9991% 5.8 5 99.9995% 5.9 3.4 99.99966% 6.0

B-1: Sigma values corresponding to defects per million opportunities.

C Recycling prices for materials

C-1: Recycling market prices for OCC.

C-2: Recycling market prices for HDPE bottles.

C-3: Recycling market prices for MOP.

C-4: Recycling market prices for old newspaper.

C-5: Defects at MRF Toledo.