Simulating a Distribution Chain Civil Engineering

INSTITUTO SUPERIOR

Universidade

Simulating a Distribution Chain

João Granado Marques

Dissertation for the Degree of Master of Civil Engineering

Examination Committee Chairperson: Prof. Joao Torres de Quinhones Levy Supervisor: Prof. Amílcar José Martins Arantes Member of the Committee: Profª Cristina Marta Castilho Pereira Santos Gomes

November 2013

Acknowledgments

I would like to thank my coordinator, Professor Amílcar Arantes for the help and support during the writing of this dissertation.

I would also like to thank my family, Mr. S and Hanz for their support.

iii iv

Abstract

Nowadays, distribution operations play a central role in the performance of any organization, ensuring a significant and decisive impact on the quality of the service provided to the end user.

The aim of this dissertation is to contribute to the enhancement of distribution operations in a retail company through the development of a simulation model, using Simul8, a discrete event- based simulation software.

On the one hand, the results confirm the utility of simulation methodology in solving real problems, random by nature. On the other hand, they make it possible to outline valuable guidelines on the way the study company ought to implement its distribution operation.

Keywords

Logistics, distribution, simulation, supply chain, discrete event based simulation.

As operações de distribuição assumem nos dias de hoje um papel fundamental no desempenhoSumário das empresas, com impacto significativo em termos de nível de serviço ao cliente final.

O objetivo do presente trabalho é o de contribuir para a melhoria das operações de distribuição de uma empresa de venda a retalho através do desenvolvimento de um modelo de simulação, utilizando Simul8, um software de simulação por eventos discretos.

Os resultados, por um lado confirmam a utilidade da metodologia de simulação na resolução de problemas reais, aleatórios por natureza, e, por outro lado, permitem traçar orientações valiosas sobre a forma como a empresa em estudo deve implementar a sua operação de distribuição.

Palavras chave

Logística, distribuição, simulação, cadeia de distribuição, simulação baseada em eventos discretos.

Content

Acknowledgments ...... iii

Abstract ...... v

Keywords ...... v

Sumário ...... vi

Palavras chave ...... vi

Index of Figures ...... ix

Index of Tables ...... xi

1. Introduction...... 1

1.1. Context ...... 1

1.2. Objectives ...... 2

1.3. Methodology ...... 2

1.4. Work development...... 3

2. Literature review ...... 5

2.1. Logistics ...... 5

2.2. Simulation ...... 6

3. The original model ...... 9

3.1. Description ...... 9

3.2. The original model in Simul8 ...... 12

3.2.1. The distribution centre ...... 13

3.2.2. The shops ...... 22

3.2.3. Validation, stabilization and run time ...... 26

4. Improving the model ...... 29

4.1. Alternate routes ...... 29

4.2. Trucks that do not visit every shop ...... 36

4.2.1. Attempt 1 ...... 36

4.2.2. Attempt 2 ...... 39

4.2.3. Attempt 3 ...... 41

4.3. Validation of the final model ...... 44

4.4. Stabilization of the final model ...... 45

vii

4.5. Run time for the final model ...... 46

4.6. Runs per trial ...... 47

5. Results and Discussion ...... 49

5.1. Original model and Final model both with five trucks ...... 49

5.2. Varying the number of trucks available ...... 50

5.3. Final discussion ...... 52

6. Conclusions and recommendations ...... 53

Bibliography ...... 55

Annex 1 – Simul8 – A discrete event-based simulation software ...... 57

Building blocks ...... 57

Start Point...... 58

Queue ...... 60

Activity ...... 62

End ...... 65

Resource ...... 66

Simulation clock ...... 68

Annex 2 – Tables ...... 69

Warm up for the final model ...... 69

Run time for the final model ...... 69

Truck number for the final model ...... 70

viii

Figure 1 Carts filled with supermarket goods ...... 1 FigureIndex 2 Methodology of ...... Figures...... 2 Figure 3 Truck life cycle diagram ...... 10 Figure 4 Truck route ...... 10 Figure 5 Cart life cycle diagram...... 10 Figure 6 Activity cycle diagram ...... 11 Figure 7 Original model ...... 12 Figure 8 Distribution centre ...... 13 Figure 9 start point Orders properties ...... 14 Figure 10 Warehouse properties ...... 15 Figure 11 Schedule limit properties ...... 16 Figure 12 “For_shop_x” properties ...... 17 Figure 13 n_shopX properties ...... 18 Figure 14 Counting properties ...... 18 Figure 15 Grouping properties ...... 19 Figure 16 Loading properties ...... 20 Figure 17 Shop_Flag properties ...... 21 Figure 18 Template of a shop ...... 22 Figure 19 Transport_Sx properties...... 23 Figure 20 Reception_Sx properties ...... 23 Figure 21 Unloading_Sx properties ...... 24 Figure 22 Counting_S x properties...... 24 Figure 23 Route end ...... 25 Figure 24 Transport S5_Warehouse properties ...... 25 Figure 25 Original model stabilization ...... 26 Figure 26 Average time for different trucks numbers ...... 27 Figure 27 Utilization for different truck numbers ...... 27 Figure 28 Truck routes ...... 29 Figure 29 Truck routes example...... 31 Figure 30 Improved model: new route activities ...... 33 Figure 31 new links example ...... 34 Figure 32 Results for the original model ...... 35 Figure 33 Results for the new routes ...... 35 Figure 34 First attempt 1 ...... 36 Figure 35 “Dx” properties ...... 37 Figure 36 “Wx” properties ...... 38 Figure 37 Attempt 1 results ...... 38

Figure 38 Results for the new routes ...... 39 Figure 39 “Wx” properties ...... 39 Figure 40 Storage Area 29 properties ...... 40 Figure 41 Attempt 2 results ...... 40 Figure 42 Attempt 3 ...... 41 Figure 43 Up to 16:50 properties...... 41 Figure 44 “WT” properties ...... 42 Figure 45 Final Model ...... 43 Figure 46 Attempt 3 results ...... 44 Figure 47 Stabilization of the final system ...... 46 Figure 48 Average time vs. Run time ...... 46 Figure 49 Truck utilization vs. Run time ...... 47 Figure 50 Original model results ...... 49 Figure 51 Improved model results ...... 49 Figure 52 Average time and truck utilization vs. Nº of trucks ...... 50 Figure 53 Improved model with four trucks ...... 51 Figure 54 Original model with four trucks ...... 51 Figure 55 Example of a Simul8 model ...... 58 Figure 56 Start Point Properties ...... 58 Figure 57 Start Point Batching ...... 59 Figure 58 Start Point Results ...... 59 Figure 59 Queue Properties ...... 60 Figure 60 Queue Results ...... 61 Figure 61 Queue Content ...... 61 Figure 62 Activity Properties ...... 62 Figure 63 Activity Efficiency ...... 62 Figure 64 Activity Resource ...... 63 Figure 65 Activity Routing In 1 ...... 64 Figure 66 Activity Routing In 2 ...... 64 Figure 67 Activity Results ...... 65 Figure 68 End Properties ...... 65 Figure 69 End Results ...... 66 Figure 70 Resource Properties ...... 66 Figure 71 Resource Travel times ...... 67 Figure 72 Resource Results ...... 67 Figure 73 Clock Properties ...... 68

Index of Tables

Table 1 Route links ...... 30 Table 2 Route links example ...... 32 Table 3 Route links example 2 ...... 32 Table 4 Collected Results for the incremental changes ...... 47 Table 5 Summary of the main options ...... 52

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1. n

The motivation behind the dissertation is mostly personal. However, this document may come in handy forIntroductio others who are searching for information on distribution centres and simulation, as it is likely it will add something relevant to the existing pool of knowledge.

It will also allow me to explore the potential and features of the Simul8 software and expand my knowledge of logistic platforms, more specifically distribution centres and operations.

The present dissertation for the Degree of Master was motivating, rewarding and provided an enriching experience.

1.1. Context

The main focus of the dissertation is an expanded and improved simulation of a distribution centre presented in Ana Moracho Velasco’s Dissertation for the Degree of Master of Industrial Engineering – Design of a Distribution Operation Using Discrete Event-Based Simulation.

The original simulation model of the distribution chain has five shops supplied by truck and a distribution centre that packs the goods in carts (Figure 1).

Figure 1 Carts filled with supermarket goods

The base situation studied in the original model was defined by taking into account information provided by Jerónimo Martins. The model was created using simul8, a discrete event-based software that will be described later. The work items inside the simulation start out as orders, then become carts, then become groups of twenty carts and the only resource in use is the trucks.

Since the shops depend on the distribution chain to keep their stock at the desired levels, the time the carts take to reach the shops once the orders are sent in is a crucial variable in service level.

1.2. Objectives

The main goals of this project are to improve on the current model of the distribution chain while exploring the features that Simul8 has to offer, and to set up recommendations to optimize distribution chains.

1.3. Methodology

The methodology used in this work follows three simple steps (Figure 2):

• Identify something that could be improved; • Incorporate it in the simul8 model; • Compare the results with previous iterations.

Identify possible improvement

Incorporate in Compare results simul8

Figure 2 Methodology

The first improvement in mind will be to increase the model capabilities by expanding on the number of routes (pathways) available for the trucks. The main control variables are the number of trucks needed, the time they are in transit and the amount of time the orders/carts stay in the system from the moment the orders arrive until the carts are delivered at the shops. In order to improve on the results of the current model some upgrades were added and their benefits were gauged.

After that a system is required to ensure trucks don’t have carts for all the shops in order to make the most out of the new quicker paths available. Finally the number of trucks in use must be adjusted to the new reality.

1.4. Work development

After the introduction there is a review of the state of the art concerning logistics and discrete event-based simulation.

Following that there is a chapter describing Simul8, the discrete event-based simulation software used in this dissertation. This chapter aims to explain to the reader how to use the basic features of Simul8.

The next chapter covers simulating a supply chain operation, more specifically a distribution chain. It has two sub-sections: The original model and improving the model. The original model describes the model used by Ana Moracho Velasco in her dissertation, the baseline for this dissertation, describing both the conceptual model and its implementation in Simul8. The section on improving the model contains the enhancements added to the simulation. The first one being the alternative routes made available to the trucks; the second one being the appropriate grouping of carts in order to maximize the possible gain from the alternative routes; and finally the readjustment of trucks available for the preferred models.

Finally, there is a chapter discussing the results followed by the conclusions and recommendations.

2. ew

The most basic definition possible for logistics and simulation can be taken from the Oxford AdvancedLiterature Learners 7th edition Dictionary revi (2005):

− “Logistics: the practical organization that is needed to make a complicated plan successful when a lot of people and equipment is involved. − “Simulation: a situation in which a particular set of conditions is created artificially in order to study or experience something that could exist in reality.”

2.1. Logistics

The definition of logistics given above is rather broad. Many complex operations fall within the definition presented.

The business definition of logistics presented by the Council of Logistics Management (1997) gives a better view of the scope of logistics: “the process of planning, implementing, and controlling the efficient, effective flow and storage of goods, services, and related information from point of origin to point of consumption for the purpose of conforming to customer requirements.” Its wide range of application covers the management of material, service, information or capital.

The Key activities of logistics can be classified into core and supporting. The core activities, essential to the effective coordination and completion of the logistics task, include: Customer service; Transportation; Inventory management; Information flows and order processing. (Lambert et al 1998)

Among the many logistics activities some are more relevant to this dissertation: Warehousing (management of space and stocks); Materials handling (replacement policies, order-picking procedures); Cooperation with production/operations (specify aggregate quantities, sequence and time production output); Information maintenance (info collection, storage and manipulation, data analysis, control procedures).

Logistics is considered to be part of marketing, being central in the elements of marketing concept: customer satisfaction, integrated effort and corporate profit. (Christopher 1992)

In the logistics process design four areas must be considered: Customer service goals, Location strategy, Inventory strategy and Transport strategy. The correct coordination and balance of these areas will ensure the most successful outcome. (Rohde and Wagner 2000)

Therefore, the selection of the best logistics strategy gives a company a competitive advantage.

Logistics in a business aims to provide the best quality of service (service improvement) at the lowest cost (cost reduction), ensuring high product quality and flexibility (service improvement) in a demanding ever-changing market. By providing better services, the logistic services increase the revenues. Thus, the logistics design strategy must be in line with the corporate strategy (Christopher 2005). The impact of Logistics in the success of an organization has been clearly established.

The term ”Supply Chain Management” appeared in 1982 in the literature and pointed to the reduction of inventory within and across firms (Oliver and Webber 1982).

In 1994, The International Center for Competitive Competence (ICCM) defined it as follows: “Supply Chain Management is the integration of business processes from end user through original suppliers that provides products, services and information that add value for customers.”1

Supply chain management, which encompasses all steps of the flow of goods from the raw materials to the end user, becomes central in the success of a business, providing a sustainable competitive advantage.

Supply chains contain the most common examples of complex operations: warehousing or transportation of various goods, trans modal nodes like ports or train stations and distribution centres. On the other hand, distribution chain is the part of the supply chain that starts at the production units or at advanced warehouses until the final clients (shops); it is the logistics of finished goods or services.

Supply chains are also the oldest example of applied logistics, having existed for as long as large scale war and trade have been around. While supply chains were the first instance of logistic, recently its application has spread far outside the supply chain environment, extending to any operation that involves a lot of people and/or equipment. For example, running a hospital with all the shifts and stocks involved (Robinson et al. 2012).

2.2. Simulation

A simulation is run by using a model, the set of conditions previously mentioned. These models can be broadly separated into physical or abstract models.

1 ICCM, Douglas M. Lambert, 1994, quoted by Cooper, Martha (1997), Supply Chain management, The International Journal of Logistics Management

Some examples of physical models are the dam models present at LNEC or the usage of a scale model of an aircraft or bridge in wind tunnels to analyse aerodynamic variables.

An abstract model used to be simply mathematical formulas that allowed you to calculate certain values of interest given key variables. But with the processing power of modern computers and the various programs that have popped up over the years these models have grown considerably.

The simulation program used in this dissertation (Simul8) creates object oriented models. This type of program tends to be visually easy to interact with, which increases the amount of people that are able to run simulations.

Intuitive programs that are designed for non-programmers have allowed many people to run simulations who would otherwise have found it quite difficult.

There are many examples of complex simulations in the current world (Jahangirian et al. 2010). From recreational ones such as the physics engines present in most modern game to massive models that simulate the traffic through entire cities or even the growth of the city.

When running a simulation it is important to make sure you understand the underlying model. This model must be a credible mirror of the simulated system in order to have meaningful results.

While some simulations have no goal other than to simulate the phenomenon – such as the recreational ones – many are used to improve or optimize the system in question.

Since the model is simply an abstract construct, it is easy to try out new configurations and test for specific situations without incurring in any costs.

Another great strength of a sound simulation is the ability to analyse the system’s sensitivity to various factors. This can be vital when planning how to help the system cope with some tough situations.

3.1.The Description original model As stated in the introduction, the main focus of the dissertation is to expand and improve an existent simulation model of a distribution operation.

All the data used in this dissertation came from the original work of Ana Moracho Velasco (Velasco, 2012) and, with that in mind, a brief description of the original model is required to situate the reader.

Annex one can be helpful if the reader is new to Simul8

The distribution chain as presented in the original model includes a warehouse, a distribution centre and five shops.

The orders from the shops arrive at the warehouse at a fixed rate, twenty four hours per day.

During the working hours, eight in the morning to five in the afternoon, carts are loaded according to the orders received and are then grouped into bundles of twenty.

The groups of twenty carts are loaded into trucks and shipped to the stores.

At each store, if the truck carries carts to the store in question, the truck must be received and then unloaded.

The average reception time is fixed regardless of the content of the truck, while the average unloading time is proportional to the amount of carts that truck brings to the shop in question.

If the truck does not carry carts to the store in question, it simply passes by the store without stopping.

Once it visits all the shops it has to, the truck returns to the truck park and awaits the next load.

This means the model includes two entities: one permanent, the Trucks; and another temporary, the Carts.

The life cycle of the trucks is summarized in Figure 3.

Truck Park Truck load Transport Waiting Transporting

Return Park

Transport Waiting Unloading Unload Waiting Reception

Figure 3 Truck life cycle diagram

Additionally, the trucks in the original model follow a single route, regardless of whether or not they need to visit a shop. This path is represented in Figure 4.

Shop 2 Shop 3 Shop 4

Shop 1 Distribution Centre Shop 5

Figure 4 Truck route

Each truck departs from the distribution centre with the carts; Passes by each shop from 1 to 5 in ascending order, stopping only if it has any carts for that shop. Finally they return to the warehouse and wait in the truck park until they are needed again.

The life cycle of the carts is represented in Figure 5.

Outside System Cart load Consolidation

Unloading Grouping Carts

Unload Waiting Load Waiting

Reception Truck load

Reception Waiting Transporting Transport Waiting

Figure 5 Cart life cycle diagram

The activity cycle diagram is represented in Figure 6.

It should be noted that the block circled in purple represents what happens for one shop. In the original model it happens five times, in the final model it will happen n times depending on the shops the truck visits.

Truck Park Truck load Load Waiting

Transport Waiting

Grouping Carts Transporting

Cart Return Reception Waiting Consolidation

Truck

Reception

Cart load

Unload Waiting

Transport Waiting Unloading Outside System

n Shops

Figure 6 Activity cycle diagram

3.2. The original model in Simul8

The overall model (Figure 7) can be divided into the distribution centre and the shops.

Figure 7 Original model

3.2.1. The distribution centre

The distribution centre is the most complex part of the original model (Figure 8).

Figure 8 Distribution centre

A very brief description of the steps this part entails follows:

• The start point Orders generates the orders from the shops which are sent into the Queue for warehouse. • The Warehouse is the point where the orders are converted into carts, for simul8 there is no distinction. • The work hours for the activity Warehouse are limited by the resource Schedule limit. • After passing through the Waiting queue the carts are divided according to the destination through the activities For_shop_1 to 5. • They are then routed to the Queue for Grouping and at the same time to another queue named n_shopx depending on the activity in question. • The Grouping activity groups twenty carts into one bundle and marks the number of carts for each shop in the n_loja_x labels by using the contents of the n_shopx queues. • After a bit the content of the n_shopx queues expires to allow for the next round and the contents are sent into the activity Counting that accepts expired only. • These items are then removed from the system by the Warehouse schedule end. • The bundled carts are sent into the Queue for Loading. They are then loaded into the trucks during the Loading activity. • This is the only activity in which the Truck resource is required. It is not released until the End route. • The final activity, Shop_Flag, flags the shops that are in the bundle using the Lx labels.

All three groups of labels are created in this part. The first group is the label destino, the second group are the n_loja_x labels and the third group is the Lx labels, with x ranging from 1 to 5.

A more detailed explanation of each item follows:

The simulation begins with the start point (Figure 9).

The start point Orders has a fixed distribution; it generates a new order every three minutes.

In order to simulate orders from the five different shops it sets the label “destino” to 1-5 using the probability profile distribution presented below, a uniform distribution

Figure 9 start point Orders properties

After going through the Queue for warehouse, the orders arrive at the Warehouse activity.

This activity has a fixed duration of two minutes and requires the Schedule limit resource to function (Figure 10).

It releases the resource as soon as the task is complete.

Figure 10 Warehouse properties

Since we want to collect bundles of carts, this activity collects twenty of them at a time using the Routing In options.

The resource Schedule limit is shift dependent and uses the Warehouse shift, thus limiting the working hours of the Warehouse activity to 8:00-17:00 (Figure 11).

Figure 11 Schedule limit properties

The carts are routed to five activities, one for each shop (Figure 12).

The activity “For_shop_x” has a special routing in choice. The carts are batched by type using the label “destino” and the fixed value x.

The images are from For_shop_1 so the fixed value is 1.

This separates the carts intended for each shop to allow for separate counting.

This activity routs out to a n_shopX and to the Queue for Grouping. All five “For_shop_x” rout to the Queue for Grouping.

This activity has no real world equivalent, it’s merely a cog in the mechanism used to count the carts per shop in each truck.

As such the duration of the task is a fixed value of zero.

Figure 12 “For_shop_x” properties

The n_shopX queues are different from the rest of the queues in this simulation (Figure 13).

They use shelf life with a value of one. This means any items in this queue will expire after one minute.

Coupled with the activity Counting that accepts only expired items, this queue is assured to keep any items that enters it for exactly one minute providing a temporary measure of the number of carts for each shop.

Figure 13 n_shopX properties

The activity Counting has no duration since it is just there to remove expired items (Figure 14).

Figure 14 Counting properties

The Queue for Grouping leads to the Grouping activity.

This activity collects twenty carts and assembles them into a single item in Simul8 with the Assemble option (Figure 15).

The duration of this task is also set at zero.

The entire “Consolidation of the load and count carts per shop” block can be seen as a single activity with a duration of two minutes set by the Warehouse activity.

Figure 15 Grouping properties

This is also the activity in which the n_loja_x labels are defined.

These are the labels mentioned earlier that identify how many carts each bundle has for each shop.

This is achieved by setting the value of n_loja_x to n_shopX.Count Contents.

This counts the amount of items in the n_shopX queue previously mentioned.

With the twenty carts assembled and the number of carts for each shop recorded in the n_loja_x labels it is time to load the carts into the truck.

The Loading activity uses a normal distribution with an average of thirty minutes and a standard deviation of five (Figure 16).

Since we are loading the carts into the truck, the resource Truck is required.

Note that the resource is not released after the loading activity is over. The truck will remain in use until it reaches the last activity, Transport S5_Warehouse.

Figure 16 Loading properties

The final activity inside the distribution centre part is the Shop_Flag (Figure 17).

In this activity the labels Lx are set to ROUND[[10*n_loja_x]/[1+[10*n_loja_x]]].

This means either 0 if n_loja_x is 0 or 1 if n_loja_x is anything else.

These labels provide a simple way to know whether or not the truck needs to stop at a shop.

Figure 17 Shop_Flag properties

3.2.2. The shops

In the original version all five shops are copies of the same template (Figure 18):

Figure 18 Template of a shop

The template consists of four activities and two queues.

The activity Transport represents the time it takes to reach shop Sx (x: 1 a 5).

The activity Reception represents the reception of the truck at the corresponding shop. As mentioned previously, this task has a duration that is not affected by the number of carts intended for that shop.

The activity Unloading represents the unloading of the carts for that shop. The duration is proportional to the amount of carts in question.

The queues Queue for Counting and Warehouse and the activity Counting are merely there so that we can see how many carts are delivered to Warehouse over the course of the simulation.

The remaining queues have no special function. They are merely there to serve as buffers between activities.

The queues have no special settings that warrant detailing.

A further description of the activities in this template follows:

The journey to the shop does not have a fixed duration.

Therefore the activity Transport has a normal distribution to represent the randomness involved (Figure 19).

An average of fifteen minutes between shops was assumed with a standard deviation of five minutes.

Figure 19 Transport_Sx properties

While the average time is fixed, the reception of the trucks at the store also uses a normal distribution.

This activity has an average of ten minutes and a standard deviation of two and a half minutes (Figure 20).

Both values are multiplied by Lx, a label that identifies whether or not the truck has parcels for the shop x. This Lx can be 0 or 1 and needs to be defined in the distribution centre segment.

Figure 20 Reception_Sx properties

The average length of the unloading process is, as mentioned previously, proportional to the amount of carts being unloaded.

This is achieved by multiplying the average by n_loja_x, a label that identifies the number of carts going to shop x (Figure 21).

This label also needs to be defined in the distribution centre segment.

The distribution used for this activity was an exponential.

This activity routs out to two queues.

Figure 21 Unloading_Sx properties

The activity Counting_S x serves no purpose. It is merely there to count the amount of carts that are deposited in shop x (Figure 22).

Figure 22 Counting_S x properties

After the five shops there is one activity that represents the journey back to the warehouse and an end to the route (Figure 23).

Figure 23 Route end

Like the other transportation activities this final one has a normal distribution (Figure 24).

Figure 24 Transport S5_Warehouse properties

It should be noted that this final activity releases the truck resource. 3.2.3. Validation, stabilization and run time

The model was validated in the original dissertation by testing multiple values, among them expected durations with and without randomness involved.

The model was stabilized by studying average times in system, until the packages were processed by the distribution centre and until the packages have arrived and been processed at the various stores (Figure 25 Original model stabilization).

400,00

350,00 300,00 DC schedule 250,00 Exit S1 200,00 Exit S2 150,00 100,00 Exit S3

Average time time (min) Average 50,00 Exit S4 0,00 End Route 1 2 3 4 5 6 7 8 9 1011121314151617181920 Number of days

Figure 25 Original model stabilization

This leads to a warm up period of four days, after which the model has stabilized.

This warm up period is assumed to be valid for all the improvements that follow until the last one. The last one will be tested to ensure the four day period remains valid.

The truck number selected for the original model was based on the analysis of the previously mentioned average times and truck utilization variation by truck number (Figure 26&Figure 27).

2000,00 1800,00

) 1600,00 1400,00 min DC schedule ( 1200,00 Exit S1 1000,00 Exit S2 800,00 600,00 Exit S3

Average time Average 400,00 Exit S4 200,00 End Route 0,00 1 2 3 4 5 6 7 8 9 10 Truck number

Figure 26 Average time for different trucks numbers

100,00

90,00

80,00

70,00

60,00

50,00 Utilization (%) Utilization

40,00

30,00 1 2 3 4 5 6 7 8 9 10 Truck number

Figure 27 Utilization for different truck numbers

The amount of five trucks was chosen because six trucks showed no significant benefit.

The original run time was one day so this was the run time used during the improvement implementation.

4.1.Improving Alternate routes the model The original model considers that trucks must always follow the same route, regardless of what shops they stop at. The first change aims to fix this by considering that the trucks can take any of the links/routs presented in the figure below. This leads to multiple routes for the trucks depending on the carts they carry (Figure 28).

2 4

1 5

Figure 28 Truck routes

The next table sums up the links available to the trucks and their average duration. It also shows the order in which they will appear in the model.

Since Simul8 requires a single path, logical conditions need to be set to make the links void by setting their average duration and standard deviation to 0 when they are not part of the route currently in use.

The L1 to L5 labels, already established in the original model, are 1 or 0 depending on whether the truck has carts for that shop or not.

The links are not used by the truck in question if the relevant labels have the values presented in the table. The values in the table follow a simple logic. If either of the shops at the ends of a link do not exist (Lx = 0) then the link vanishes. If any of the shops in between the two ends exist (Lx = 1) then the link vanishes as well.

The conditions are of the “or” type, so for link 0-2 if L1 = 1 or L2 = 0 then the link will be annulled. The “ - “ identifies labels that are not relevant for the link in question.

So for example for link 1-4, if L1 or L4 are 0 then the link cannot be in use since either the beginning or the end of the link are not part of the current route. The other condition requires that if either L2 or L3 are 1 then the link cannot exist since the truck always follows an ascending order. If it needs to pass by either shop 2 or 3 then he can’t go straight from 1 to 4.

Duration Link name L1 label L2 label L3 label L4 label L5 label (min)

15 0-1 0 - - - - Shop 1 0 - - - - 15 1-2 0 0 - - - 20 0-2 1 0 - - - Shop 2 - 0 - - - 15 2-3 - 0 0 - - 20 1-3 0 1 0 - - 30 0-3 1 1 0 - - Shop 3 - - 0 - - 15 3-4 - - 0 0 - 20 2-4 - 0 1 0 - 30 1-4 0 1 1 0 - 17 0-4 1 1 1 0 - Shop 4 - - - 0 - 15 4-5 - - - 0 0 20 3-5 - - 0 1 0 30 2-5 - 0 1 1 0 20 1-5 0 1 1 1 0 5 0-5 1 1 1 1 0 Shop 5 - - - - 0 5 5-0 - - - - 0 17 4-0 - - - 0 1 30 3-0 - - 0 1 1 20 2-0 - 0 1 1 1 12 1-0 0 1 1 1 1 Dist.Center (0) - - - - - Table 1 Route links

So, for Link 0-1 for example all values are multiplied by L1 while for Link 1-3 they are multiplied by L1 x (1 - L2) x L3 and so on.

As an example, let us consider one truck has goods for shops 1 and 4. L1 and L4 would be 1 while L2, L3 and L5 would be zero.

The truck would take Link 0-1, followed by Link 1-4 and finally Link 4-0. The connections mentioned are highlighted in Figure 29.

2 4

1 5

Figure 29 Truck routes example

The Links in use shouldn’t meet the annulment conditions while all the other links should meet at least one of those conditions.

In Table 2 in columns L1 to L5 the numbers are red if they match the annulment criteria. If any of the annulment conditions is met the duration is set to 0 and in red in the Duration column.

Duration Link name L1 = 1 L2 = 0 L3 = 0 L4 = 1 L5 = 0 (min)

15 0-1 0 - - - - Shop 1 0 - - - - 0 1-2 0 0 - - - 0 0-2 1 0 - - - Shop 2 - 0 - - - 0 2-3 - 0 0 - - 0 1-3 0 1 0 - - 0 0-3 1 1 0 - - Shop 3 - - 0 - - 0 3-4 - - 0 0 - 0 2-4 - 0 1 0 - 30 1-4 0 1 1 0 - 0 0-4 1 1 1 0 - Shop 4 - - - 0 - 0 4-5 - - - 0 0 0 3-5 - - 0 1 0 0 2-5 - 0 1 1 0 0 1-5 0 1 1 1 0 0 0-5 1 1 1 1 0 Shop 5 - - - - 0 0 5-0 - - - - 0 17 4-0 - - - 0 1 0 3-0 - - 0 1 1 0 2-0 - 0 1 1 1 0 1-0 0 1 1 1 1 Dist.Center (0) - - - - - Table 2 Route links example

After checking every condition and removing those that are voided, the Links that remain in use in this example are the expected ones.

Duration Link name L1 = 1 L2 = 0 L3 = 0 L4 = 1 L5 = 0

15 0-1 0 - - - - Shop 1 0 - - - - 30 1-4 0 1 1 0 - Shop 4 - - - 0 - 17 4-0 - - - 0 1 Dist.Center (0) - - - - - Table 3 Route links example 2

Implementing this change in Simul8 consists in creating activities to represent each link listed in the table, arranged in the order they appear in the table.

These activities replace the previous ones portraying travelling time (Figure 30).

Figure 30 Improved model: new route activities

Each activity has a normal distribution with an average equal to the duration listed in the table and a standard deviation of 5.

Both values are multiplied by the logical conditions in place for each link.

For the two examples mentioned earlier, link 0_1 and link 1_3 (Figure 31):

Figure 31 new links example

With the new routes implemented the model was tested. No deviation from the previous results was expected since a random group of twenty carts will, on average, always contain at least one for each shop.

This means that even though the model allows other routes only the original route will be used at the moment.

As shown in the Figures 51 and 52, the outcome was, as expected, more or less the same, both in time spent by the parcels in the systems on average and in truck utilization. The difference is minimal and is mainly due to the randomness of the trials.

Figure 32 Results for the original model

Results for the model with new routes available (Figure 33):

Figure 33 Results for the new routes

4.2. Trucks that do not visit every shop

In order to take advantage of the improvements in the simulation model, with new routes available, it is necessary to implement in the model a new way of loading the trucks.

The next step is to group the carts into a truck that does not visit every shop in each trip. This will increase the time the carts spend in the system since they will wait longer to leave the warehouse but it should allow for a reduction of the number of trucks in use.

All modifications implemented are situated outside the “Consolidation of the load and count carts per shop” module to avoid disrupting that part of the simulation that already works as desired.

Due to that, the orders need to be separated according to the label “destino” before they enter the warehouse.

A group of twenty must be gathered that fits the conditions desired and sent into the Warehouse activity.

4.2.1. Attempt 1

As a first attempt, the following was added to the beginning of the model (Figure 34):

Figure 34 First attempt 1

The “Dx” activities separate the orders according to the label “destino”, value x goes into “Dx”.

The “Wx” accept a group of 10 at a time from the queue before.

The “Dx” activities are the same as the “For_shop_x” activities (Figure 35).

Routing in is set to batch by type, using “destino” as the label and the fixed value of x.

This gives us five queues with orders separated according to the label “destino”.

Figure 35 “Dx” properties

The “Wx” activities are similar to the Warehouse activity (Figure 36).

The difference being that they group ten instead of twenty orders.

The goal of this alteration would be to have two sets of ten being taken in as a set of twenty by the activity Warehouse.

Figure 36 “Wx” properties

This new simulation gives us the following results (Figure 37):

Figure 37 Attempt 1 results

Comparing them with the previous results (Figure 38):

Figure 38 Results for the new routes

Everything is as expected, the time in the system went up but the average use of the trucks was reduced.

4.2.2. Attempt 2

In order to improve this previous model a small change was implemented.

The activities “Wx” now have zero duration and the queue before the Warehouse activity has its capacity limited to twenty orders (Figure 39 & Figure 40).

This change was prompted by the fact that sometimes the queue was getting thirty orders stored at once, leading to the possibility of having more than two destinies inside a twenty order pack.

Figure 39 “Wx” properties

Figure 40 Storage Area 29 properties

This solution leads to the following results (Figure 41):

Figure 41 Attempt 2 results

The utilization of trucks stays the same but the average time in the system slightly decreases.

4.2.3. Attempt 3

For attempt number three two new resources and a new activity were added (Figure 42).

The new resources are similar to the Schedule limit resource.

They are both shift dependent, with “Up to 16:50” having a 08:00-16:50 shift and “Last 10 min” having a 16:50-17:00 shift (Figure 43).

Figure 42 Attempt 3

Figure 43 Up to 16:50 properties

The “Wx” activities now require the resource Up to 16:50 to function while the new “WT” requires the resource Last 10 min (Figure 44).

Figure 44 “WT” properties

The aim of this change is to remove the excess carts that had to wait overnight due to the restrictive system by allowing the final ten minutes to pack any carts it has into any trucks it needs.

It should lower the time in system of the carts.

This third attempt led to the final model (Figure 45).

Figure 45 Final Model

The results obtained were the following (Figure 46):

Figure 46 Attempt 3 results

These results keep the huge gain of the new solutions in terms of truck utilization, and they come close to the original model in terms of average time within system.

The standard deviation is higher than the original model, but that is to be expected. This method requires some carts to wait longer for a proper group and ensures that those that leave soon after arrival make a quick trip, leading to lower minimums and higher maximums.

4.3. Validation of the final model

The various steps of improving the model serve as the validation of this model.

The incremental implemented changes produced the expected results.

The new routes did not change the results since twenty carts per truck meant on average there would always be one for each shop.

Attempt 1 packed each truck with two sets of ten carts, each for a different shop. As expected the use of trucks dropped drastically thanks to the new routes but the average time of the work items in the system increased.

Note that the total time in system is roughly the time between the placement of an order and its delivery. The time is system only stops counting when the truck returns to the warehouse so it represents a number higher than the delivery time. But it serves its purpose as far as comparisons with the original model and with each incremental change are concerned.

Attempt 2 was a correction of attempt 1.

A simul8 issue, the queue leading to the warehouse had no capacity limit leading it to sometimes accumulate more than two sets of ten carts. This meant that some trucks could take carts for three shops.

As expected, correcting this slightly decreased the time in system.

It was noted that the average number of orders that remained unattended overnight increased.

One extra truckload of orders remained in the warehouse each night, this could account for the increase in time in the system.

This extra truckload was predictable since it is unlikely the last twenty orders will be two sets of ten for two shops.

Attempt 3 dealt with this overnight orders excess.

This excess was fixed by adding a last truck that takes those last twenty carts no matter where they need to go.

This leads to an improvement in the average time in system that comes close to the original model.

Throughout all these changes the model behaved as expected.

4.4. Stabilization of the final model

Even though the four day warm up period is most likely still valid it is better to test the final model just to be sure. With that in mind the average time in system depending on the number of days the system has run was analysed (Figure 47).

The system seems to take longer to stabilize; only around day eight does the system reach a point where the variation of the average time in the system stays in a range of 5% error.

So, in order to account for safety margin, ten days was the chosen warm up period.

430

410 390 370 350 330 310 290

Average time in system (min) system in time Average 270 250 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Days run

Figure 47 Stabilization of the final system

4.5. Run time for the final model

In order to choose the run time for the model the average time in system and the % of truck use were tested depending on the run time. The warm up of ten days was used.

As can be seen in Figure 48 and Figure 49 the results are fairly consistent from day forty onward. The chosen run time for the model was forty days.

Within this simulation a day starts at eight in the morning and lasts fourteen hours.

450

400 350 300 250 200 150 100

Average time in system (min) system in time Average 50 0 0 20 40 60 80 100 120 140 Run time in days

Figure 48 Average time vs. Run time

60,00%

50,00%

40,00%

30,00%

20,00% Truck utilization

10,00%

0,00% 0 20 40 60 80 100 120 140 Run time in days

Figure 49 Truck utilization vs. Run time

4.6. Runs per trial

The runs per trial were calculated automatically by Simul8.

The values presented in the results so far (Table 4) were calculated with a 95% confidence limit. With this demand Simul8 calculated the number of runs needed to ensure the precision required. The number of runs varies with the experiences but the method remains the same throughout the entire dissertation.

Time in System (min) Truck Runs Use Av Max Min Av Max SD # (%) # #

Original Model 214.38 334.77 478.02 83.78 62.38 3.12 5 33 New Routes 214.08 333.97 477.93 84.01 62.22 3.11 5 33 Attempt 1 274.97 425.05 603.40 103.52 47.77 2.39 5 31 Attempt 2 270.34 414.41 593.63 101.75 47.64 2.38 5 30 Attempt 3 244.71 357.76 496.18 75.28 47.12 2.36 5 26 Table 4 Collected Results for the incremental changes

Since theResults goal is to improve andupon it, the Discussion original model provides the baseline for this analysis. The one day run time seems rather out of place considering the forty days arrived at for the final model. Therefore, the original model and the final model need to be run again with the new duration.

The first test to run is to check how the truck utilization and the average time in system vary for this model when you change the number of trucks. The goal of this improvement was to reduce the number or trucks needed. Whatever truck number is chosen, that number should be tested in the original model and compared.

A final comparison of all the results is needed.

5.1. Original model and Final model both with five trucks

Both models were run with the new duration leading to the following results (Figure 50&Figure 51):

Figure 50 Original model results

Figure 51 Improved model results

Although the values changed the differences between the two remain similar to the ones in attempt 3.

The final model has an inferior performance than the original one but the low truck utilization should mean that the impact of removing a truck will be less than in the original model.

5.2. Varying the number of trucks available

The final model has a truck utilization of only 50.88% with five trucks available.

Such a low level of utilization is not desirable.

The model was tested using different numbers of trucks (Figure 52).

450 90,00%

440 80,00%

430 70,00% 60,00% 420 50,00% 410 40,00% 400 30,00%

390 (%) utilization Truck 20,00%

Average time in system (min) system in time Average 380 10,00% 370 0,00% 3 4 5 6 3 4 5 6 Nº of trucks Nº of trucks

Figure 52 Average time and truck utilization vs. Nº of trucks

The minimum of three was considered since the average truck use is higher than two. Going below the average truck use would lead to an eternal accumulation of orders.

There was no point in going higher than six trucks since even five already leads to a truck utilization that is not desirable.

Between these solutions the best is quite likely the one with four trucks. It is impossible to be sure without knowin how much money you actually save by removing one truck and whether there is any loss incurred from the extra time in system.

All parcels are still delivered during the same day, it just takes a bit longer to clear the back orders from the previous day. As long as this extra time does not lead to a rupture in stock getting rid of one truck should end up saving money.

A more complex model using costs and shop stocks would be required to answer these questions.

Following this line of thought one might assume the three truck option should also be considered. After all the parcels are still being delivered in the same day and you have fewer trucks. The average time in system becomes rather high, but without measures to compare the costs, it is hard to compare the two situations in theory.

In practice the three trucks solution is not viable since it has no leeway. With only three trucks available any unexpected situation, a sudden increase in orders or a truck having mechanical problems, could mean a rupture in the distribution chain if it goes on for too long.

So if the final model is run using four trucks (Figure 53), it has similar truck utilization to the original model with five trucks, one less truck, and a higher average time in system.

Figure 53 Improved model with four trucks

So, could the original model also lose one truck?

After running the original model with four trucks (Figure 54) it becomes clear that with four trucks the final model is more desirable.

Figure 54 Original model with four trucks

5.3. Final discussion

A summary of the results of all the options has been collected into Table 5:

The values in the table are the average values, the number of trials run to obtain them is presented in the last column.

Note that the maximum number of trucks used is the number of trucks available.

Time in System (min) Truck Runs Model in use Use Av Max Min Av Max SD # (%) # #

Original Model 155.54 360.94 613.91 89.05 63.57 3.18 5 12

Original Model 207.38 407.60 629.35 72.42 78.90 3.16 4 17

Final Model 197.94 384.43 700.07 88.05 50.88 2.54 5 14

Final Model 202.16 396.73 704.10 88.33 63.06 2.52 4 15 Table 5 Summary of the main options

The improved model is more efficient in terms of truck use, this leads to a better performance with fewer trucks as seen in Table 5. With five trucks the original model has a better minimum and average time in system, with four trucks the final model becomes the better option.

Due to waiting for the two sets of ten the final model ends up with some orders that take quite long to process, this leads to higher maximum times in system.

Truck use is obviously lower in the final model due to the more efficient truck use. The original model with five trucks and the final model with four trucks have similar utilization rates.

Between these two solutions it is assumed that thirty minutes will not cause stock rupture making the final model with four trucks the more economical one.

As mentioned previously it is not known how much money you actually save by removing one truck and whether there is any loss incurred from the extra time in system.

Since all orders are being delivered in the same day, as long as this extra time in system does not lead to a rupture in stock there is no downside to it.

A more complex model using costs and shop stocks would be required to analyze these questions.

Note that we are not taking into account the money saved in fuel by the new routes.

Conclusions and This dissertation focused on the improvement of an already established model of a distribution chainrecommendations originally presented in Ana Moracho Velasco’s master dissertation.

Getting a grasp of some of the aspects involved in the process, especially in supply chain management, which would be of interest for a goods manufacturer, retailer or different organizations, gave me a broader perspective of the aspects involved.

By implementing and testing different solutions for this model, I was able to explore the potential and features of the Simul8 software. Simul8 is quite flexible and user friendly and allows a wide range of simulations to be run using the basic block.

One thing that was found lacking was the routing in option of the activities. I wanted to rout in groups of 20 items from 2 different sources but the options merely allow for single source groups.

While many companies prefer fixed routes for simplicity’s sake, this simulation shows that adaptive routes provide a more efficient, in terms of truck use, way of distributing the goods to the shops in this situation. This means, the adaptive routes work better with lower number of trucks than the fixed routes.

As mentioned previously the fuel consumption was not taken into account; this variable would tip the scale further to the side of routes that avoid certain shops.

For a more complete solution this and other peculiarities would have to be taken into account. For instance, the stock of the shops being supplied might limit the time available for the goods to arrive in order to avoid a rupture of stocks.

The overall company strategy plays an important role in logistics planning and this simulation is but a simplified example of a supply chain.

In line with the goals of logistics the aim was to provide a cost effective and versatile system. With the limited variables considered it was assumed that having fewer trucks in use would be more cost effective since no stock rupture is expected in the shops due to the extra delay. With that assumption the final solution was reached.

This dissertation has helped expand my knowledge in the area of logistics and gave me a better understanding of the Simul8 software.

As further expansions to this model a mode where each truck carries carts for three shops would be a good place to start. This could result in a lower time in system.

Following that the model could be expanded to simulate the stock of the shops. This would require further information from real world examples.

Simulating the stock of all the products available in each shop would be far too complicated so future work should focus on the most critical products. These products would be identified by the relationship between the amount in stock and the typical rates of consumption.

A simplistic approach to the shops could keep the current mechanism for generating orders and just include the shops as queues and the public consumption as an activity. This would not be very accurate since shop 1 could have a higher consumption for a while leading to increasingly shorter supplies while the orders were created with a lack of occurrences for that particular shop. This means this simplistic approach would be far more likely to lead to rupture than a realistic one. Keep in mind this means that if this approach does not lead to rupture then further complexity is unnecessary.

A more complex approach would be to simulate the shops completely. Shops would still be a queue but they would start with a full inventory. The activity that represents public consumption would lead to another queue that would represent the “empty inventory”, items in that queue would no longer be products but orders to be. Once that queue had enough work items they would be sent back to the beginning of the simulation as orders, replacing the entry point. This means that the new simulation would be a cycle.

If the previously mentioned expansion revealed small rupture of stock problems, costs would have to be factored in to make a final decision.

Only costs that vary between the models have to be accounted for.

Bibliography Carvalho, José Crespo (2010), Logística e Gestão da Cadeia de Abastecimento, Edições Sílabo, Lisboa.

Christopher M, Peck H., (2004), Building the Resilient Supply Chain, Cranfield School of Management, International Journal of Logistics Management, Vol. 15, Iss. 2, pp. 1-14.

Christopher, M. (1992). Logistics and Supply Chain Management: Strategies for Reducing Cost and Improving Service. London: Pitman Publishing.

Christopher, M., (2005). Logistics and Supply Chain Management: Creating Value-Adding Networks. Harlow: Financial Times Prentice Hall.

Concannon K., Elder M., Hindle K., Tremble J., Tse S., (2009), Simulation modeling with SIMUL8, Simulation Solutions.

Cooper, Martha (1997), Supply Chain management, The International Journal of Logistics Management, Vol. 8, Iss. 1, pp. 1-14.

Council of Logistics Management, (2013) – Definitions, http://scmanagement.blogspot.pt /2004/10/clm-definition-of-supply-chain.html, accessed on 8 October 2013.

Jahangirian, Mohsen, Tillal Eldabi, Aisha Naseer, Lampros K. Stergioulas, and Terry Young. (2010). "Simulation in manufacturing and business: A review." European Journal of Operational Research no. 203 (1):1-13.

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Annex 1 – – A event-based Simul8 discrete This anex aims to provide the readersimulation with a basic idea of what Simul8 software can do.

The entities that Simul8 works with are called work items - . They can represent many things depending on the simulation in question.

In this project they are orders, carts and bundles of twenty carts.

Building blocks

Simul8 has five building blocks available for the user:

Start Point

An entry point that produces work items;

Queue

Stores work items between activities;

Activity

Any work happens within this building block

End

Exit point from the system;

Resource

Any particular resource required by an activity within the system.

In order to better explain the building blocks a small example model was created (Figure 55).

Figure 55 Example of a Simul8 model

All the building blocks have three shared sections:

Memo – In this section the user can write notes describing the building block in question;

Results – In this section the user can review the results related to that building block after running the model;

Graphics – In this section the user can adapt the look of the building block selected.

Start Point

A Start Point creates work items (Figure 56).

The inter-arrival time is the time between arrivals of each consecutive work item.

This time can follow several standard distributions or even a customized one.

The first work item arrives according to the distribution picked. If the user desires a work item to arrive at the start of the simulation, he needs to tick the “First at start time” option.

Usually a Start Point feeds a Queue, if the unlimited arrivals option is chosen. However, the Start Point should rout out to an activity.

With the unlimited arrivals option the Start Point gives work items as the activity it routs out to requires.

Figure 56 Start Point Properties

You can turn off automated arrivals with the none option and set arrivals according to a file. Or you can have arrivals according to a Schedule Sheet.

The Start Point does not need to create a single work item at a time. With the batching option you can create a fixed amount of work items each time or have the number vary according to a distribution. By default it has a fixed distribution with a value of 1 (Figure 57).

Figure 57 Start Point Batching

You can also use the Actions section to set any label you desire on the newly created work items.

The results presented for a Start Point detail the number of work items that entered the system during the simulation and the number that was lost (Figure 58).

Figure 58 Start Point Results

Routing out details where the work items go to from the Start Point.

Queue

Queues are the storage bins of the program (Figure 59).

Their function is usually to store work items until the following activity can process them.

The capacity of a queue is infinite by default but it is possible to set a maximum value of work items it can hold at any given time.

Note that if a start point routs out to a queue that has limited capacity, it is possible that the queue will not have the space needed to store an incoming work item, and this work item will be lost.

Figure 59 Queue Properties

Shelf life starts out as none. If defined as a certain value, any work item that spends more time in the queue than that value will expire.

It is possible to set a queue that stores work items until they expire.

The minimum wait time forces the work items to wait at least that amount of time before they leave the queue. It can be set as a fixed value, as a formula or even as a value dependant on other building blocks.

By default the queues have a FIFO (first in first out) policy.

If a different order is desired the LIFO (last in first out) option will send out first the last work item to arrive at the queue.

The prioritize option organizes the items in the queue according to a label, giving priority to those with higher values.

The Start-Up section allows the queue to start with a number of work items already in store.

Figure 60 Queue Results

Queue results cover the number of work items in the storage and the queuing times throughout the simulation (Figure 60).

It is also possible to segregate results according to one label.

The content section allows you to see the current contents of the queue. The work items, their respective labels and the label content.

It can be quite useful to examine what is going on during the simulation run (Figure 61).

Figure 61 Queue Content

Activity

An activity is where work happens (Figure 62).

The timing section has several different distributions available to represent the task you have in mind.

Like the start point, the activity has an actions section where you can change the labels or set new ones.

The replicate button allows you to “create” similar activities.

If the replicate value is higher than 1, then the simulation will consider the activity represents that number of similar activities running side by side.

Figure 62 Activity Properties

Priority defines the priority this activity has within the simulation. By default all have the same priority but that can be changed if the user desires.

It is even possible to have the priority be determined by the work item currently being worked on.

Efficiency allows you to simulate breakdowns (Figure 63).

By default the efficiency is set at 100%, meaning no breakdowns, but it is possible to have randomly occurring breakdowns or fixed timing breakdowns.

Figure 63 Activity Efficiency

The resources section is where you define whether or not a resource is required for the activity to function (Figure 64).

It is possible to require the resource at one activity and release it in another, representing a courier for example that carries the work items.

You can also require one resource and release it as a different resource.

Figure 64 Activity Resource

The routing in and out sections regulate where work items come from and where they go to.

Aside from the multiple choices of routing out modes, you can multiply the number of work items leaving the activity by using the batching option in the routing out section.

The routing in section also provides many modes (Figure 65).

The default is priority.

Expired only is available to remove expired items from queues as previously described.

Collect allows you to collect multiple work items and even assemble them into a single new work item. This comes at a loss in label information since only one of the values can be saved in the new work item.

Plenty of other disciplines are available for other needs.

Among other options you can also batch by type, receiving only work items that have the desired label value (Figure 66).

Figure 65 Activity Routing In 1

Figure 66 Activity Routing In 2

The shifts section allows you to set a shift for the activity.

The results of an activity give you the number of work items in the activity, the number of completed jobs and a distribution of the time according to use (Figure 67).

In this example the activity spent 8.49% of its time awaiting work.

Figure 67 Activity Results

End

The end is an exit point and the simplest building block available (Figure 68).

Like the queue it can segregate results based on a label.

If you wish you can end the simulation early by selecting Halt simulation at limit. This will shut down the simulation once the set number of work items has been processed.

Figure 68 End Properties

The end results give you the amount of work completed and the statistics concerning the time in system (Figure 69).

Figure 69 End Results

Resource

Resources are anything that is required to complete the tasks the activities represent (Figure 70).

They can be shift dependent, with the number available depending on the shift they are currently on.

They can be pooled together. If a resource is pooled then all members of the pool will be acceptable for an activity that requires this one.

Figure 70 Resource Properties

If desired, the resource can have travel time between its storage area and its place of use (Figure 71).

Availability is to resources what efficiency is to activities. The resource is by default 100% available but you can set absences.

Resource results show the percentage of utilization the resources had and the units of resource in use during the simulation (Figure 72).

Figure 71 Resource Travel times

Figure 72 Resource Results

Simulation clock

In the clock properties you can select the time unit in use and the overall look of the time display in your simulation (Figure 73).

More importantly you can select the running time.

The results collection period is the duration of the simulation during which results are collected. Before that you have the warm up period.

This separation is available because many systems take a while to stabilize and the relevant data can only be collected after the system is stable.

So the warm up period must be adjusted to allow the system to stabilize.

Figure 73 Clock Properties

Annex 2 – Tables

Warm up for the final model

Days Minutes Av Time Runs 1 840 318.41 7 2 1680 389.07 10 3 2520 402.32 6 4 3360 406.47 4 5 4200 405.82 4 6 5040 399.7 4 7 5880 393.03 4 8 6720 389.33 4 9 7560 385.67 4 10 8400 382.55 4 11 9240 383.12 4 12 10080 382.34 4 13 10920 379.76 4 14 11760 377.35 4 15 12600 376.46 4 16 13440 375.6 4 17 14280 374.53 4 18 15120 374.26 4 19 15960 378.33 4 20 16800 379.56 4

Run time for the final model

Days Minutes Av Time Truck Runs Ut 1 840 375.73 50.04% 10 22 2 1680 372.89 51.12% 7 12 3 2520 366.26 49.09% 5 8 4 3360 365.93 48.91% 4 5 5 4200 365.36 48.45% 4 5 6 5040 364.14 48.24% 4 6 7 5880 363.58 48.10% 5 6 8 6720 363.85 47.68% 4 5 9 7560 373.56 49.13% 4 5 10 8400 376.72 49.69% 4 5 11 9240 380.34 50.19% 4 5 12 10080 383.14 50.55% 4 5 13 10920 385.42 50.91% 4 5 14 11760 387.11 51.17% 4 5

15 12600 388.67 51.44% 4 5 16 13440 390.19 51.60% 4 5 17 14280 391.86 51.82% 4 5 18 15120 394.15 52.10% 4 5 19 15960 395.9 52.32% 4 5 20 16800 396.96 52.39% 4 5 21 17640 397.45 52.43% 4 5 22 18480 397.18 52.33% 4 5 23 19320 397.32 52.57% 4 4 24 20160 395.73 52.27% 4 4 25 21000 394.07 52.05% 4 4 26 21840 393 51.97% 4 4 27 22680 391.79 51.78% 4 4 28 23520 390.91 51.64% 4 4 29 24360 389.77 51.44% 4 4 30 25200 388.66 51.26% 4 4 35 29400 385.1 50.81% 4 4 40 33600 382.31 50.40% 4 4 45 37800 380.98 50.34% 4 4 50 42000 379.48 50.09% 4 4 55 46200 378.4 49.92% 4 4 60 50400 377.07 49.77% 4 4 70 58800 375.37 49.62% 4 4 80 67200 374.09 49.49% 4 4 90 75600 372.74 49.29% 4 4 100 84000 371.71 49.14% 4 4 120 100800 371.31 49.17% 4 4 140 117600 378.53 50.10% 4 4 160 134400 383.3 50.71% 4 4 180 151200 387.18 51.22% 4 4 200 168000 390.23 51.59% 4 4

Truck number for the final model

Trucks Av Time Truck Runs Ut 3 438.09 81.56% 4 4 395.49 62.86% 4 5 383.02 50.59% 4 6 380.14 42.33% 4