systems modelling and simulation

0409576 System Modelling and Simulation

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MN5543

Module Title: System Modeling and Simulation Assignment 1: Individual Assignment Student Number: 0409576 Student Name: Hassan Saif Course Title: MSc Engineering Management

Table of Contents

1 Flowchart process ------------------------------------------------ 2-8

2 Results ------------------------------------------------- 9-11

3 Bottleneck process ------------------------------------------------- 11-12

4 Proposal for improvement ------------------------------------------------- 12-14

5 Discussion ------------------------------------------------- 14-16

6 Conclusion ------------------------------------------------- 17

7 References ------------------------------------------------- 18


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1. Flowchart Process

The problem definition requires modelling of a small manufacturing company with a

animation display. The goal is to model the production system of the company, as well

as modelling major products without great detail. The flowchart module was

approached by breaking down the problem definition. There are three major parts to

this overall model the first one being the arrival of components, the second part of the

model is processing of components. While the third part of the model is the main

production process of the products P1, P2 and P3, this stage requires full details with

the resources and scheduling.

The diagram below illustrates the full simulation model based on the given problem

definition. As seen there are three parts to the model with full animation display of

manufacturing process. The reason for choosing key modules will be discussed later in

the report.

Full animation display of manufacturing process is shown in this screenshot


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Flowchart process of manufacturing system divided into three parts


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The screenshot below shows the Create module as the starting point for the

production; here the orders are the entities. From the given description, in the time

between arrivals the type has been set to random (expo) with a mean value of 50 and

units are selected in minutes. We assume that the products arrive in singles so we set

the Entities per arrival to 1. Similarly create modules for Components 2, 3,4 and 5 have

been defined.


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This next screenshot shows the Assign module; here different attributes have been

assigned for the entity to follow. As it is known that later components (entity) have to

follow a predefined sequence therefore attribute value for a sequence has been defined.

The Decide module in the below screenshot is used to decide which machine will

prepare materials for the 5 components. Since there are three possible outcomes, the N-

way by chance option from type box has been selected. Here the condition is that for

components C1, C2, C3, C5 let machine prepare the material otherwise for C4 pass the

order to assembly point.


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Once the station is determined for a particular order the Process module is used to

indicate the preparation of the product material. The resource here is the cell 1 machine

1 and the time needed for preparation is given in triangular distribution, for instance

process 1 needs TRIA(11,15,18) hours to prepare the materials. Exactly the same

methodology has been used for other process only the preparation time is different.

Next the station module is used when the material arrive at stations. Soon after that

Route module is used to transfer the materials to the production process. Additionally,

the transfer time is given at 2 minutes the destination here is an attribute value “By

sequence”. This attribute allows the entity to move along in a predefined sequence.


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The diagram below highlights the process for one of the five machines; the same

methodology applies other machines. The Process module indicates the main

processing method, for manufacturing process it is logical to choose (Seize Delay and

Release) action for the resource. Notice, the delay time is expression and the expression

to be evaluated is an attribute called “Process Time” which varies depending on the type

of operation. This expression is defined in Sequence data module and will be shown

later. Station and Route modules are used as to transfer the entities at a given specific

time.

This screenshot shows Entity data module as the entities have to be redefined in a

Model. The first entity is the orders generated then its materials for the components P1

to P5.


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This screenshot illustrates Resource data module, it shows six resources. All the

resources for Company production process are based on a fixed schedule, which is

defined as two 8 hours shift. Moreover, machine 5 also has a failure rate that is defined

in a failure module.

The sequence module in the below screenshot shows the sequence for five

components. Each raw material has a set sequence that it must follow in order to

produce a component. As it is illustrated for C1 it goes through machine 1,2,3,4 and each

machine has a unique time allocated to it. In this case when C1 goes through machine 1

it follows a time of triangular distribution of TRIA(5,6,8) minutes. Similarly when it goes

through machine 2 it will follow a time of TRIA(3,4,5) minutes

As Machine 5 has a given failure rate of mean uptime of 120 minutes and it has a repair

time of 4 minutes these values are reflected by this failure module.


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2. Results

For the SIMAN summary report please refer to Appendix 1, however some key results

for the manufacturing process are given below.

Key Results


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3. Bottleneck Process

The simulation model for this manufacturing process indicates bottleneck at machine 2.

This can be justified from the results obtained and by examining the key performance

indicators. The most noticeable results are shown by the queue times in the category

overview report Appendix 2 generated by simulating the model. It can be observed that

queue time for product 1 is highest at the average of around 481 minutes. The machine

activity time for product 2 is at around 69 minutes whereas the queue times for other

product is reasonable relative to products 1 & 2 thus indicating satisfactory throughput

times. On the other hand the resource utilization generally is quiet high. Only machine 4

has a resource utilization level of less than others. The graph below highlights the

resource utilization. Another statistics which highlights the bottleneck at machine 2 is

shown in the queue section of the report known as “Number Waiting”. This indicates the

numbers not the time and it shows that the numbers waiting at machine 1&2 is much

higher than the rest further reflecting the possible occurrence of bottleneck at machine

2. Furthermore, while animating the simulation model it is quite evident from the length

of the queues that materials tend to wait longer and at larger numbers at machines 1&2,

although slightly more so at machine 2.


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4. Proposal for system performance improvement

Before recommending improvements several approaches were taken into account. One

such adapted method included changing the capacity of the resources and observing its

effect on the animations and in final results. This method was found to be quiet long

therefore another approach using an in-built function called process analyser was used.

Process Analyser was method that was used to work towards system improvement.

This tool was advantageous compared to the manual method of changing capacity of the

available resources. Here original model was easily compared to the altered scenarios of

different capacities and key responses (KPI) were added to measure the performance.

Several scenarios were evaluated but only with reasonable results are shown here. In

the screenshot below there are in total five scenarios. The parameters include five

controls which were the resources from machine 1 to 5. In addition, four responses

were added to the process analyser as follows WIP, queue time for the machines,

resource utilisation and system number out. In most cases changing the capacity of one

machine reduced the queue time for that machine but the queue time for another

machine increased. The aim is to reduce waiting time, and WIP times while keeping the

resource utilisation high.


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The listed scenarios reveal interesting results around the chosen parameters. Few

chosen scenarios are discussed below

Scenario 1

In scenario 2 the capacity for machine 2 was increased while the others remained the

same, this considerably reduced the waiting time at machine 2. However, the queue

time increased for machine 1 & 5. Moreover, the WIP has reduced for all the

components but has not maintained a reasonable resource utilisation level. This

solution is less costly but doesn’t satisfy the requirements.

Scenario 2

This scenario gives more of reasonable results at a sound financial price. The queue

times have spread more evenly consequently reducing bottleneck. The WIP process has

considerably decreased for some components while it has increased for the others. The

resource utilisation is quiet notable with four machines having a good utilisation. The

system number out has increased by therefore indication of reduced throughput time

with same schedule.

Scenario 5

This scenario gives the best results among all the others which were considered. The

WIP time has reduced considerably for all the components the queue time is more

evenly spread therefore bottleneck has been reduced. This scenario in an ideal world


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would be recommended without hesitation. However due to cost implications this

scenario can be expensive in real world.

5. Discussion

For model proposal different approaches were taken and various scenarios were

considered. Some of the reasonable rationale behind the scenarios was to decrease the

bottleneck at the particular machine and to reduce the WIP for the components. The

reason for choosing the queue time parameter is that it gives a clear indication of the

bottleneck at any one machine, whereas the WIP time is only for a given process of a

component.

In an ideal world from the results of process analyser scenario 5 was clearly the best

option as it satisfied more or less all the desired parameters. In practical world however

there are various constraints attached to any system in this case the financial

implications of buying new machines might not be a practicable solution. There are

many more internal management issues which could effect on the final decision. If the

company is expanding then in return it expects higher output and production, therefore

scenario 5 could apply to company strategic plan.

As it is described that based on the current output the company would like to reduce

throughput and WIP times with high utilisation. Therefore scenario 2 was chosen, it

doesn’t address all the issues but it does provide more balanced solution. The capacity


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for two machines was doubled it is recognised that this solution might not be financially

attractive given the machine depreciation and return of investment.

In addition to capacity increase the process time for machine 1. The rationale behind

reducing the process time is that if a certain operation in that machine is updated than it

would significantly reduce the bottleneck.

The graph for resource utilisation shown below further emphasises the rationale behind

choosing scenario 2. From the graph apart from scenario 5 which is expensive to

implement scenario 2 gives more even resource utilisation at given machines thus

reducing bottleneck.


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6. Conclusion

The aim of this report was to simulate a manufacturing process of a particular product.

Software called Arena was used to model and simulate the manufacturing process. The

problem was analysed by breaking down the process into simple steps and thus

implementing it in a logical manner. Many system level programming concepts were

understood and used in the simulation model. It was established earlier in the course

the vast potential and application of this software is immense as it could simulate both

discrete and continuous systems and can be used to suggest improvements.

For any real world system the main challenge is to gather data, verify and validate the

model. In this model data was given therefore the requirement was to model the system

with appropriate animations. After modelling the system bottleneck in the system was

recognized by observing KPI in the system.

During simulation of the model different issues were looked at such as bottleneck, Work

In Progress and throughput times in the systems. It was established that there were

only three possible factors which could improve the system performance first was

increasing the capacity, second being the schedule increase and the last one reducing

the process time. All three of these factors had to be weighed up carefully as financial

and resource constraints were attached to it.. At the end immense improvement was

made to the original model as the bottleneck was dealt with and WIP and throughput

time was reduced successfully.

According to the work presented in this report it is possible to improve the overall

performance of the system. In Arena the simulation was performed and it showed the

behaviour the manufacturing system. Modelling of this manufacturing system has given

a great insight in the world of system modelling, and has also given more confidence in

terms of tackling similar problem in the future. Although initially it was a bit of struggle

to understand the main concepts once learnt the experience of modelling was found to

be valuable.


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7. References

[1]. Dr Ali Mousavi Notes and Model Examples, (1/12/08)

http://people.brunel.ac.uk/~emstaam/ .

[2]. David W. Kelton; Randall Sadowski ; David T Sturrock, Simulation with Arena (4th

edition).

[3]. Arena 10.0 Software, Arena Online Help.

http://people.brunel.ac.uk/~emstaam/


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Appendix A: SIMAN summary report

ARENA Simulation Results

Authorised User - License: 1953000875

Summary for Replication 1 of 10

Project: Assignment 1 Run execution date :12/19/2008

Analyst: Hassan Saif Model revision date:12/19/2008

Replication ended at time : 57600.0 Minutes

Base Time Units: Minutes

TALLY VARIABLES

Identifier Average Half Width Minimum Maximum Observations

___________________________________________________________________________________________________

Part 1.VATime -- -- -- -- 0

Part 1.NVATime -- -- -- -- 0

Part 1.WaitTime -- -- -- -- 0

Part 1.TranTime -- -- -- -- 0

Part 1.OtherTime -- -- -- -- 0

Part 1.TotalTime -- -- -- -- 0

Part 2.VATime -- -- -- -- 0

Part 2.NVATime -- -- -- -- 0





Part 3.VATime -- -- -- -- 0

Part 3.NVATime -- -- -- -- 0



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Part 4.VATime -- -- -- -- 0

Part 4.NVATime -- -- -- -- 0





Part 5.VATime -- -- -- -- 0

Part 5.NVATime -- -- -- -- 0





product 1.VATime 79.374 (Corr) 72.139 86.096 523

product 1.NVATime .00000 .00000 .00000 .00000 523

product 1.WaitTime 8032.5 (Corr) 111.10 13315. 523

product 1.TranTime 28.000 7.2475E-15 28.000 28.000 523

product 1.OtherTime .00000 .00000 .00000 .00000 523

product 1.TotalTime 4492.5 (Corr) 104.60 8088.8 523

product 2.VATime 79.688 .27786 71.016 88.211 532

product 2.NVATime .00000 .00000 .00000 .00000 532

product 2.WaitTime 6232.2 (Corr) 73.948 11791. 532

product 2.TranTime 28.000 1.4056E-14 28.000 28.000 532


product 2.TotalTime 3655.8 (Corr) 83.871 6654.5 532

product 3.VATime 91.201 .20514 83.280 99.881 577

product 3.NVATime .00000 .00000 .00000 .00000 577

product 3.WaitTime 10315. (Corr) 705.23 15103. 577

product 3.TranTime 30.000 .00000 30.000 30.000 577


product 3.TotalTime 6926.2 (Corr) 401.50 10141. 577


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Cell 3.Queue.WaitingTime -- -- -- -- 0

Batch 2.Queue.WaitingTime .00000 .00000 .00000 .00000 1596


Select 2.Queue1.WaitingTime 3424.2 (Corr) .00000 6589.7 532



Process 5.Queue.WaitingTime 4.3887 .79750 .00000 58.253 1632

Route from Cell 1.Queue.WaitingTime -- -- -- -- 0

Start Sequence.Queue.WaitingTime -- -- -- -- 0





Cell 1.Queue.WaitingTime -- -- -- -- 0


Select 3.Queue2.WaitingTime 6845.6 (Corr) 224.97 10068. 577









DISCRETE-CHANGE VARIABLES

Identifier Average Half Width Minimum Maximum Final Value

___________________________________________________________________________________________________

Part 1.WIP 35.657 (Corr) .00000 71.000 60.000

Part 2.WIP 80.707 (Corr) .00000 142.00 142.00

Part 3.WIP 37.913 (Corr) .00000 68.000 59.000


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Part 4.WIP 78.719 (Corr) .00000 120.00 110.00

Part 5.WIP 28.528 (Corr) .00000 50.000 20.000

product 1.WIP .17285 .01796 .00000 3.0000 .00000

product 2.WIP .17229 .02000 .00000 3.0000 .00000

product 3.WIP .19530 .02042 .00000 3.0000 .00000

Cell 5 New.NumberBusy .41609 .02233 .00000 1.0000 .00000

Cell 5 New.NumberScheduled 1.0000 (Insuf) 1.0000 1.0000 1.0000

Cell 5 New.Utilization .41609 .02233 .00000 1.0000 .00000

Cell 3 Machine.NumberBusy .56072 .01921 .00000 1.0000 .00000

Cell 3 Machine.NumberScheduled 1.0000 (Insuf) 1.0000 1.0000 1.0000

Cell 3 Machine.Utilization .56072 .01921 .00000 1.0000 .00000

Cell 2 Machine.NumberBusy .46345 .01593 .00000 1.0000 1.0000


Cell 2 Machine.Utilization .46345 .01593 .00000 1.0000 1.0000

Cell 1 Machine.NumberBusy .51037 .01727 .00000 1.0000 .00000


Cell 1 Machine.Utilization .51037 .01727 .00000 1.0000 .00000

Cell 5 Old.NumberBusy .00000 (Insuf) .00000 .00000 .00000

Cell 5 Old.NumberScheduled .00000 (Insuf) .00000 .00000 .00000

Cell 5 Old.Utilization .00000 (Insuf) .00000 .00000 .00000

Cell 4 Machine.NumberBusy .57755 .02050 .00000 1.0000 1.0000


Cell 4 Machine.Utilization .57755 .02050 .00000 1.0000 1.0000

Cell 3.Queue.NumberInQueue .00000 (Insuf) .00000 .00000 .00000

Batch 2.Queue.NumberInQueue .00000 .00000 .00000 3.0000 .00000


Select 2.Queue1.NumberInQueue 34.409 (Corr) .00000 68.000 60.000

Select 2.Queue2.NumberInQueue .63365 (Corr) .00000 18.000 .00000


Process 5.Queue.NumberInQueue .12435 .02713 .00000 4.0000 .00000

Route from Cell 1.Queue.NumberInQueue .00000 (Insuf) .00000 .00000

.00000


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Start Sequence.Queue.NumberInQueue .00000 (Insuf) .00000 .00000

.00000


.00000



.00000


Cell 1.Queue.NumberInQueue .00000 (Insuf) .00000 .00000 .00000



Select 3.Queue3.NumberInQueue 1.8082 (Corr) .00000 18.000 .00000

Select 1.Queue1.NumberInQueue .07675 (Corr) .00000 6.0000 .00000





.00000



OUTPUTS

Identifier Value

_____________________________________________________________

Part 1.NumberIn 1115.0

Part 1.NumberOut 1055.0







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product 1.NumberIn 523.00

product 1.NumberOut 523.00





Cell 5 New.NumberSeized 1632.0

Cell 5 New.ScheduledUtilization .41609

Cell 3 Machine.NumberSeized 5285.0

Cell 3 Machine.ScheduledUtilization .56072





Cell 5 Old.NumberSeized .00000

Cell 5 Old.ScheduledUtilization .00000



System.NumberOut 1632.0


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Appendix 2: Category overview

systems modelling and simulation

Engineering

system modelling

simulation model

process module

production system

simulation assignment

system modeling

flowchart module

bottleneck process