This document summarizes the results of simulating a manufacturing system model with three parts: arrival of components, processing of components, and the main production process. The simulation found a bottleneck at machine 2, with the longest queue times. Several proposals to improve system performance were explored using a process analyzer tool, with scenario 2 found to spread queue times more evenly and reduce bottlenecks at a reasonable cost, and scenario 5 found to give the best results by further reducing wait times and balancing utilization across resources.

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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.

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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 2.WaitTime -- -- -- -- 0
Part 2.TranTime -- -- -- -- 0
Part 2.OtherTime -- -- -- -- 0
Part 2.TotalTime -- -- -- -- 0
Part 3.VATime -- -- -- -- 0
Part 3.NVATime -- -- -- -- 0
Part 3.WaitTime -- -- -- -- 0

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Part 3.TranTime -- -- -- -- 0
Part 3.OtherTime -- -- -- -- 0
Part 3.TotalTime -- -- -- -- 0
Part 4.VATime -- -- -- -- 0
Part 4.NVATime -- -- -- -- 0
Part 4.WaitTime -- -- -- -- 0
Part 4.TranTime -- -- -- -- 0
Part 4.OtherTime -- -- -- -- 0
Part 4.TotalTime -- -- -- -- 0
Part 5.VATime -- -- -- -- 0
Part 5.NVATime -- -- -- -- 0
Part 5.WaitTime -- -- -- -- 0
Part 5.TranTime -- -- -- -- 0
Part 5.OtherTime -- -- -- -- 0
Part 5.TotalTime -- -- -- -- 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.OtherTime .00000 .00000 .00000 .00000 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.OtherTime .00000 .00000 .00000 .00000 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
Batch 3.Queue.WaitingTime .00000 .00000 .00000 .00000 1731
Select 2.Queue1.WaitingTime 3424.2 (Corr) .00000 6589.7 532
Select 2.Queue2.WaitingTime 68.605 (Corr) .00000 1421.3 532
Select 2.Queue3.WaitingTime 2705.2 (Corr) .00000 5917.8 532
Process 5.Queue.WaitingTime 4.3887 .79750 .00000 58.253 1632
Route from Cell 1.Queue.WaitingTime -- -- -- -- 0
Start Sequence.Queue.WaitingTime -- -- -- -- 0
Route from Cell 2.Queue.WaitingTime -- -- -- -- 0
Process 1.Queue.WaitingTime 3.6478 .34960 .00000 49.068 4173
Route from Cell 3.Queue.WaitingTime -- -- -- -- 0
Process 2.Queue.WaitingTime 1.6536 .23025 .00000 23.479 5287
Cell 1.Queue.WaitingTime -- -- -- -- 0
Select 3.Queue1.WaitingTime 3250.3 (Corr) .00000 5860.1 577
Select 3.Queue2.WaitingTime 6845.6 (Corr) 224.97 10068. 577
Select 3.Queue3.WaitingTime 180.51 (Corr) .00000 1977.7 577
Select 1.Queue1.WaitingTime 8.4529 (Corr) .00000 443.46 523
Select 1.Queue2.WaitingTime 4353.7 (Corr) .00000 8027.3 523
Select 1.Queue3.WaitingTime 3636.7 (Corr) .00000 5687.6 523
Process 3.Queue.WaitingTime 2.7930 .34160 .00000 32.680 5285
Route from Cell 4.Queue.WaitingTime -- -- -- -- 0
Batch 1.Queue.WaitingTime .00000 .00000 .00000 .00000 1569
Process 4.Queue.WaitingTime 3.2945 .43654 .00000 48.337 5286
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.NumberScheduled 1.0000 (Insuf) 1.0000 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.NumberScheduled 1.0000 (Insuf) 1.0000 1.0000 1.0000
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.NumberScheduled 1.0000 (Insuf) 1.0000 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
Batch 3.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
Select 2.Queue3.NumberInQueue 25.398 (Corr) .00000 47.000 20.000
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
Route from Cell 2.Queue.NumberInQueue .00000 (Insuf) .00000 .00000
.00000
Process 1.Queue.NumberInQueue .26428 .02934 .00000 8.0000 .00000
Route from Cell 3.Queue.NumberInQueue .00000 (Insuf) .00000 .00000
.00000
Process 2.Queue.NumberInQueue .15178 .02330 .00000 5.0000 .00000
Cell 1.Queue.NumberInQueue .00000 (Insuf) .00000 .00000 .00000
Select 3.Queue1.NumberInQueue 34.536 (Corr) .00000 64.000 53.000
Select 3.Queue2.NumberInQueue 77.997 (Corr) .00000 119.00 109.00
Select 3.Queue3.NumberInQueue 1.8082 (Corr) .00000 18.000 .00000
Select 1.Queue1.NumberInQueue .07675 (Corr) .00000 6.0000 .00000
Select 1.Queue2.NumberInQueue 44.779 (Corr) .00000 89.000 89.000
Select 1.Queue3.NumberInQueue 36.300 (Corr) .00000 67.000 58.000
Process 3.Queue.NumberInQueue .25627 .03421 .00000 6.0000 .00000
Route from Cell 4.Queue.NumberInQueue .00000 (Insuf) .00000 .00000
.00000
Batch 1.Queue.NumberInQueue .00000 .00000 .00000 3.0000 .00000
Process 4.Queue.NumberInQueue .30235 .04941 .00000 8.0000 .00000
OUTPUTS
Identifier Value
_____________________________________________________________
Part 1.NumberIn 1115.0
Part 1.NumberOut 1055.0
Part 2.NumberIn 1242.0
Part 2.NumberOut 1100.0
Part 3.NumberIn 1114.0
Part 3.NumberOut 1055.0
Part 4.NumberIn 687.00

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Part 4.NumberOut 577.00
Part 5.NumberIn 1129.0
Part 5.NumberOut 1109.0
product 1.NumberIn 523.00
product 1.NumberOut 523.00
product 2.NumberIn 532.00
product 2.NumberOut 532.00
product 3.NumberIn 577.00
product 3.NumberOut 577.00
Cell 5 New.NumberSeized 1632.0
Cell 5 New.ScheduledUtilization .41609
Cell 3 Machine.NumberSeized 5285.0
Cell 3 Machine.ScheduledUtilization .56072
Cell 2 Machine.NumberSeized 5287.0
Cell 2 Machine.ScheduledUtilization .46345
Cell 1 Machine.NumberSeized 4173.0
Cell 1 Machine.ScheduledUtilization .51037
Cell 5 Old.NumberSeized .00000
Cell 5 Old.ScheduledUtilization .00000
Cell 4 Machine.NumberSeized 5286.0
Cell 4 Machine.ScheduledUtilization .57755
System.NumberOut 1632.0

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Appendix 2: Category overview