IN DEGREE PROJECT MECHANICAL ENGINEERING,SECOND CYCLE, 30 CREDITS

, STOCKHOLM SWEDEN 2021

Discrete Event Simulation of Cabinet Assembly at ABB Robotics and Discrete Automation

VENKHAT ABHISHEK ALLAMKOTA VIJAYAPRASAD

KTH ROYAL INSTITUTE OF TECHNOLOGYSCHOOL OF INDUSTRIAL ENGINEERING AND MANAGEMENT

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Discrete Event Simulation of Cabinet Assembly at ABB Robotics and Discrete Automation

VENKHAT ABHISHEK ALLAMKOTA

KTH Supervisor: Daniel Tesfamariam Semere

ABB Supervisors: Andreas Larsson and Emma Sundström

KTH Examiner: Daniel Tesfamariam Semere

© VENKHAT ABHISHEK ALLAMKOTA, 2021.

Department of Production Engineering and Management School of Industrial Engineering and Management KTH ROYAL INSTITUTE OF TECHNOLOGY Stockholm, Sweden 2021

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Abstract

Planning of the optimised production system is an intricate task. The reason behind this is changing

demand, variations, and other disturbances. The main purpose of this study the current performance in the

existing production system and provide recommendations for optimising the production in order to

accommodate a new product. Since, the addition of new products may lead to bottlenecks in the flow which

affects the output performance. The bottlenecks would lead to ineffective results, while tying up the capital

in the production downstream as inventory gets stagnated in the assembly lines. The performance

evaluation is a tedious process however, the study probes into the use of flow simulation tool to analyse

the production performance.

In this thesis work, Discrete Event Simulation (DES) is utilised as a tool to examine the performance of

the production systems and to determine the cost consuming areas. To achieve that, the production system

is replicated as a functional model into the DES system with the appropriate logics and parameters, with

the thorough understanding of the existing workflows. To supplement it, the data from the orders,

resources, and parts are charted.

In the later part, production flow is analysed with the additions of determined improvisations to understand

the impact on the output. Later, investigations are performed to identify the challenges and applicable

changes required to meet future output. As an outcome, the production system is restructured and

optimised, thus getting an overview of the future production setup as a digital factory layout.

Keywords:

Bottleneck, Flow Simulation, Discrete Event Simulation, Functional Model, Digital Factory

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Sammanfattning

Planering av det optimerade produktionssystemet är en invecklad uppgift. Orsaken bakom detta är

förändrad efterfrågan, variationer och andra störningar. Huvudsyftet med denna studie är den nuvarande

prestandan i det befintliga produktionssystemet och ger rekommendationer för att optimera produktionen

för att rymma en ny produkt. Eftersom tillägg av nya produkter kan leda till flaskhalsar i flödet som påverkar

uteffekten. Flaskhalsarna skulle leda till ineffektiva resultat, samtidigt som kapitalet i produktionen binds

nedströms när lagret stagnerar i löpande band. Prestationsutvärderingen är en tråkig process, men studien

undersöker användningen av flödessimuleringsverktyg för att analysera produktionsprestanda.

I detta avhandlingsarbete används Discrete Event Simulation (DES) som ett verktyg för att undersöka

produktionssystemens prestanda och bestämma de kostnadskrävande områdena. För att uppnå det

replikeras produktionssystemet som en funktionell modell i DES -systemet med lämpliga logiker och

parametrar, med en grundlig förståelse av de befintliga arbetsflödena. För att komplettera det kartläggs data

från order, resurser och delar.

I den senare delen analyseras produktionsflödet med tillägg av bestämda improvisationer för att förstå

effekten på produktionen. Senare utförs undersökningar för att identifiera de utmaningar och tillämpliga

förändringar som krävs för att möta framtida produktion. Som ett resultat omstruktureras och optimeras

produktionssystemet och får därmed en överblick över den framtida produktionsuppsättningen som en

digital fabrikslayout.

Nyckelord:

Flaskhals, Flödesimulering, Diskret Händelsessimulering, Funktionell Modell, Digital Fabrik

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Acknowledgement

Firstly, I would like to thank my supervisors at ABB Robotics and Discrete Automation (Västerås), Andreas

Larsson and Emma Sundström for providing this opportunity. Their continuous support and feedback

were pivotal to drive this project work forward. And I would like to also mention Jonathan Nilsson and

other employees at ABB for their assistance and contribution in explaining the processes remotely which

were very crucial especially during the COVID-19 Pandemic.

I would also like to thank my KTH supervisor, Daniel Tesfamariam Semere. His mentorship and feedback

have guided me in developing the simulation model. Finally, I would like to thank my friends and family

for their continuous support.

- Venkhat

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Contents

Chapter 1 – Introduction ............................................................................................................................................ 1

1.1 Theoretical Background .................................................................................................................. 1

1.2 Company Introduction ................................................................................................................... 1

1.3 Company Background .................................................................................................................... 2

1.4 Problem Statement ......................................................................................................................... 3

1.5 Aim ................................................................................................................................................ 3

1.6 Delimitations .................................................................................................................................. 4

Chapter 2 – Theoretical Framework ......................................................................................................................... 5

2.1 Discrete Event Simulation .............................................................................................................. 5

2.2 Discrete Event Simulation for Performance Management............................................................... 5

2.3 Process Mapping ............................................................................................................................ 6

2.4 Conceptual Model .......................................................................................................................... 6

2.5 Evaluating Conceptual Models ....................................................................................................... 7

2.6 Data Collection and Organisation ................................................................................................... 7

2.7 Goals for Modelling........................................................................................................................ 9

2.8 ExtendSim ...................................................................................................................................... 9

2.9 Factory Layout ............................................................................................................................. 10

2.10 VSM for Products ....................................................................................................................... 11

Chapter 3 – Methodology ......................................................................................................................................... 12

3.1 Research Approach ...................................................................................................................... 12

3.2 Background Study ........................................................................................................................ 13

3.3 Conceptual Modelling ................................................................................................................... 13

3.4 Data Management ........................................................................................................................ 14

3.5 Building Simulation Model ........................................................................................................... 17

3.6 Verification and Validation .......................................................................................................... 23

Chapter 4 – Testing and Results .............................................................................................................................. 24

4.1 Warm-up Period ........................................................................................................................... 24

4.2 Simulation .................................................................................................................................... 26

4.3 Observations ................................................................................................................................ 26

Chapter 5 – Analysis .................................................................................................................................................. 30

5.1 Status Analysis .............................................................................................................................. 30

5.2 Validation ..................................................................................................................................... 32

5.3 Recommendations ........................................................................................................................ 35

Chapter 6 – Conclusion ............................................................................................................................................ 36

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6.1 Research Findings ......................................................................................................................... 36

6.2 Future Work ................................................................................................................................. 37

References ................................................................................................................................................................. 38

Nomenclature .......................................................................................................................................................... 39

Appendices ................................................................................................................................................................ 40

Appendix A: Process Maps ................................................................................................................ 40

Appendix B: VSM .............................................................................................................................. 42

Appendix C: DES Models ................................................................................................................. 44

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List of Figures

Figure 1 Production Units .......................................................................................................................................... 2

Figure 2: Conceptual Modelling Framework ........................................................................................................... 7

Figure 3: Changes in the accuracy of model over time .......................................................................................... 7

Figure 4: Data Management Model .......................................................................................................................... 8

Figure 5: Factory Layout ........................................................................................................................................... 10

Figure 6: Triangular Distribution ............................................................................................................................ 14

Figure 7: Log-logistic Distribution .......................................................................................................................... 15

Figure 8: Shutdown Operator .................................................................................................................................. 17

Figure 9: TBF and TTR Distribution ..................................................................................................................... 18

Figure 10: Model Overview (Hierarchical blocks represented as the factory layout) ..................................... 18

Figure 11: Takt Gates ................................................................................................................................................ 19

Figure 12: Takt Logic ................................................................................................................................................ 19

Figure 13: Cycle Time Variation Operator ............................................................................................................ 20

Figure 14: Resource Call Logic ................................................................................................................................ 21

Figure 15: Example of a Resource Pool ................................................................................................................. 22

Figure 16: Warm-up Graph ...................................................................................................................................... 24

Figure 17: Average Cycle Times - Final Assembly ............................................................................................... 26

Figure 18: Average Wait - Final Assembly (Current) ........................................................................................... 27

Figure 19: Current Utilisation - Pie Chart .............................................................................................................. 29

Figure 20: Current Output........................................................................................................................................ 29

Figure 21: Mean Cycle Times - Improved (Assumption) .................................................................................... 31

Figure 22: Average Wait - Improved Model .......................................................................................................... 32

Figure 23: Production Output (Improved Model) ............................................................................................... 33

Figure 24: Overall Utilisation - Improved Model ................................................................................................. 34

Figure 25: IRC5 Flow Chart ..................................................................................................................................... 40

Figure 26: IRC5C Flow Chart .................................................................................................................................. 41

Figure 27: Cabinet Assembly VSM ......................................................................................................................... 42

Figure 28: Parallel Flow VSM .................................................................................................................................. 43

Figure 29: Final Assembly - ExtendSim Model .................................................................................................... 44

Figure 30: Gable Assembly - ExtendSim Model................................................................................................... 44

Figure 31: Door Assembly - ExtendSim Model ................................................................................................... 45

Figure 32: Computer Assembly - ExtendSim Model ........................................................................................... 45

Figure 33: Test Rigs - ExtendSim Model ............................................................................................................... 45

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List of Tables

Table 1: Process Maps ................................................................................................................................................. 6

Table 2: ExtendSim Libraries ..................................................................................................................................... 9

Table 3: Cycle Time Distribution ............................................................................................................................ 14

Table 4: Disturbance Distribution .......................................................................................................................... 15

Table 5: Resource Allocation ................................................................................................................................... 21

Table 6: Resource Utilisation – Current ................................................................................................................. 28

Table 7: Resource Utilisation – Improved ............................................................................................................. 33

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Chapter 1 – Introduction

In this chapter, a detailed background study is illustrated from both theoretical and company standpoint. Subsequently, the

conundrum is elaborated as the problem statement including the specifications. Later, the objective of this study is translated as

research questions. The final part discusses the challenges and limitations.

1.1 Theoretical Background The production system design plays a crucial role for the profitable and efficient running of the company

(Kadane & Bhatwadekar, 2011). The elements of the production system such as activity stations, buffers,

resource allocation, etc must be designed to deliver high performance and less processing time to obtain

high productivity. It is essential to consider and account all the events and activities taking place in the

production process. The production system must be designed to be flexible and must have the capability

to adapt to the future demand. Designing the production system with the modularity and futuristic aspects

is beneficial while averting regular changes and costs involved to meet the future requirements.

Kadane and Bhatwadekar, states that optimisation of the production system is the process of enhancing

the performance output and attain the higher level of efficiency. Optimisation drives the process of

analysing and avoiding the non-value-added activities in the production flow. Optimisation of the

production flow manually is a tedious process (Choudhury et al., 2020). Because it is required to completely

understand all the logics and processes taking place in the actual production environment. However, the

process can be aggregated to a certain levels based on the viewpoints required for the optimisation.

Extensive research has been done over the years to determine a standard framework for analysing and

optimisation of the production processes. Though there are very limited studies which showcases the effect

of addition of new products to the existing production setup. But the technological advancements have led

to the development of various digitals tools to model and simulate the actual processes. In the past, the

naivety of the simulation tools has restricted its application in the industrial scenarios.

The modern developments of the simulation tools and their agile characteristics had fostered the utilization

of Discrete Event Simulation (DES), Continuous Event Simulation (CES), Agent Based Simulation (ABS)

and System Dynamics (SD) in the business (Banks, 1998). Furthermore, it is enables to envisage the

tentative events which are not possible in analytical models; these simulation models have the ability to

serve as decision support systems. In the production environment, simulation could be utilised to analyse

the existing production facility and determining the areas for improvement by inputting the appropriate

data into the model. Simulation also helps in comparison and study of various setup change, inclusion, and

reconfiguration ideas (Kadane & Bhatwadekar, 2011).

DES is used widely in the industry due to the agility it possesses and the ability to manipulate various

production flow issues (Sweetser A, 1999). Also, the study performed by Banks, 1998 explains briefly about

the flexibility of DES modelling and the integration of data from the external platforms; this property of

DES offers a constructive simulation architecture thereby producing reliable simulations results. Therefore,

DES is selected for performing this thesis work.

1.2 Company Introduction ABB Robotics and Discrete Automation, which is referred as ABB in this report is the member of the ABB

Group(ABB AB). ABB Robotics and Discrete Automation predominantly works on the fields of machine

and factory automation. ABB is working with the technologies and providing solutions towards the Industry

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4.0, Smart and Flexible Factories by producing smart equipment. The advanced automation and robotics

technologies help the users to enhance the efficiency, capability, and productivity. ABB has been a global

leader in this sector, due to the ability to and knowledge in the domain to translate the customer needs,

challenges, and other opportunities into innovative products.

ABB provides products and solutions for various sectors of the industries ranging from automotive,

electronics, logistics and more. Being a global leader in the domain, the company has a presence in over 53

countries and has a workforce of over 11000 personnel. ABB has delivered over 600,000 robots to the

customers worldwide.

1.3 Company Background The ABB Robotics and Discrete Automations division at Västerås, produces industrial robots, end

effectors, and robot controllers. The production facility consists of various assembly lines namely small

and medium manipulator, cabinet line, and large manipulator with respective testing and packaging stations.

The production units and the order flow are shown in Figure 1.

Figure 1 Production Units

The focus production unit here is Cabinet Assembly, where the industrial robot controllers are produced

which are associated with the robots produced at assembly lines. There are two types of products(Industrial

Robot Controllers) at present which are, IRC5 (regular model) and IRC5C (compact model). IRC5 and

IRC5C has multiple variations and configurations since the products are customised based on the customer

needs. Many variations of the models and the configurations of the product units has been a challenge for

the production department. ABB is committed to achieve lean production by implementing lean concepts

and by making continuous improvements. As the demand rises with the time, the target is clear which is to

increase the productivity, utilisation, and efficiency.

Cabinet assembly is a hybrid assembly line which comprises of both robot automation and manual labour

to perform production. The model variety in the Industrial Robot Controllers (IRC) have a large variety of

variants and hence there is a big difference in workload and number of the assembly processes. The

workload can also vary for different products which are determined from the need of the customers which

impacts the throughput rate, workforce requirement, and lead time for part sourcing & production. All of

the mentioned factors make it more complex to standardise and optimise the production. In cabinet

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assembly, the products are made to order i.e., products are pulled through the final assembly line however,

the parts are pushed and stored as inventory in a few sub-assembly stations to avoid the part shortages

since the workload is high. The logistics and sales department supports the production with the forecast

data.

There are two types of flow in the Cabinet assembly line which are product flow and the parallel flow. The

main product is assembly in the product flow line and its parts are produced in the parallel flow lines. The

demand for the product in the past ranges between 12500 and 14000 units annually. The production

development group has decided to improve the production lines, throughput, and layout to meet the

changing demand in future and new products.

1.4 Problem Statement The addition of new products into the current production line would make the product variety wider, thus

making the assembly more complex. The increased variety of products will impact the throughput,

utilisation and can generate bottlenecks in the flow which in turn affect the total output. Therefore, it is

vital to study the production flow and determine the impacted/sensitive areas which affects the output rate.

Additionally, production department at ABB has plans for modifying and improving the current production

line in the future for which through investigation of the production setup is essential. However, studying

the production flow and gathering the data from the production setup is a very complex task in reality.

Fortunately, the industry 4.0 digital tools could be utilised to develop an aggregated model of the actual

production setup to understand the dynamics and other challenges in the production line. This

understanding is important to plan the changes which are required to be done in order to meet the future

demand. By understanding the basic element of the problem and previous experiences has led ABB to take

advantage of flow simulation, particularly DES modelling to perform a detailed analysis of the production

which is very difficult with the analytical models.

Since there are different variants of Industrial Robot Controllers (IRCs), the need for resources and process

times differs along with the sequence of assembling the parts. In order to fit the assembly processes inside

the takt-time and balance the workload, a complete bottleneck analysis is essential. It is also crucial to

assimilate Production Effectiveness to consider the fact that the operators are able to manage the workload

without hurrying. In order to ensure the quality of the product and avoid any kind of errors.

ABB is a global leader in the robotics & automation sector, and the products from ABB have a high

reputation and demand in the global market due to its quality, reliability, and robustness. The increasing

demand and product mix challenges must be tackled with the aid of Lean principles. Since ABB is focussing

on the shift towards Industry 4.0, the flow model could serve as the digital twin of assembly line providing

the information and data.

1.5 Aim The first objective of this thesis is derived from the problem statement, that is to analyse the production

flow with the aid of Discrete Event Simulation for ABB’s Industrial Robot Controller assembly line. This

lays the foundation for the formulation of the first research question:

RQ1: How DES could be utilised to support the performance improvement process of an assembly line?

For addressing RQ1, a simulation model of the assembly line has to be built accurately with all the important

parameters. The model is then validated and verified by assessing it with the actual assembly line to ensure

the reliability of the results. From this, the assembly line could be analysed which drives the second research

question:

RQ2: What are the bottlenecks in the assembly line and the optimal buffer size?

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After analysing the results from the developed model, the other key aspect from the problem statement has

to be addressed which is to know the effectiveness of DES for studying the effects of product variation.

This could be framed as another research question as follows:

RQ3: How efficient is DES to optimise the material flow in production?

Finally, solutions must be provided for reducing the impacts and to improve the productivity of the

assembly line. Additionally, investigations are to be performed for obtaining an optimised production setup.

Thus, providing a base for final research question:

RQ4: What changes in the assembly line is required for meeting future demand?

1.6 Delimitations There were various limitations and constraints in this thesis which are stated below. These constraints were

set in order to fit the project timeline within the allocated timeframe.

Firstly, the flow logics is aggregated from the actual production logistics however to an accepted level

without creating an impact on the results. This is done to reduce the complexity in the modelling.

Secondly, the raw material and part replenishment activities from external suppliers are considered as

instant/constant. Meaning that there will we no shortages of supply from the vendors. So, the lead-time

for the arrival of parts from the vendors are not taken into account. Thus, does not consider the impact of

material shortages, which is accepted with the guidance from ABB.

Thirdly, the Discrete Event Simulation is performed only in the ExtendSim (Version 10.0.7) software which

has its own merits and demerits. So, the modelling is performed within the scope of the software.

Lastly, although the resource and operator allocation are modelled as per the actual logics provided by ABB.

There are certain cases/exceptions in which the logics differ from the standard practice which are

complicated to model in DES. Therefore, some of the logics are not included in the model.

Moreover, the COVID 19 pandemic has brought new restrictions for performing the thesis work. The 90%

of the thesis work is performed from distance which made communication, data gathering and

comprehending the information challenging.

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Chapter 2 – Theoretical Framework

This chapter provides the background study of Discrete Event Simulation is used as a tool for managing the performance. The

later part describes the selected approaches for carrying out the thesis work and describes about the factory layout the general

flow of the products.

2.1 Discrete Event Simulation Discrete Event Simulation has an agility which aids the planners to take effective and data driven decisions

in the production areas which makes it a robust tool (Huynh, Akhtar and Li, 2020). In reality, there are

various factors which affects the production performance, such as variations in demand, disturbances, and

additions/changes in the products. Therefore, it is beneficial to have a flexible, efficient, and optimised

production setup. DES is a valuable tool to analyse and optimise the production performance (Bokrantz J

et al., 2018). DES tools helps in building a digital model of the actual production setup by assigning the

right logics and values observed from the production system. This provides a clear view to the planners to

check the feasibility for developing/changing the current setup or to build a new setup for production

(Sweetser A, 1999). DES also gives an opportunity to forecast the potential performance of the future

production setup (Negahban and Smith, 2014).

The results from a valid DES model can be a good base for making decision at all the stages, and the

production system could be analysed at various levels and perceptions (Lidberg et al., 2020). But the

reliability of the results is reliant on the input process conditions and variables, which is essential to produce

accurate simulations. Therefore, careful consideration must be given while gathering and assigning the

process parameters to the model. The quality of logics and input parameters defines the quality of outcome

from the simulation run.

2.2 Discrete Event Simulation for Performance Management Discrete Event Simulation can be used as a tool to aid the reconfiguration of the production system to meet

the demand or to accommodate the product changes (Huynh, Akhtar and Li, 2020). From various research

and testing done in the past, it is evident that the modelled developed using DES tools can be a good base

to evaluate the conceptual facility arrangement and configuration, also to analyse and optimise the item

flows (Huynh, Akhtar and Li, 2020). There are various sources showcasing the benefits of applying DES

to measure the production performance, yet there are very limited studies about how it has impacted in the

real-time performance. However, the reliability of the results is questioned due to the aggregation of the

model, and other factors such as response time, data acquisition, and processing challenges the

implementation of DES in the actual production settings (Negahban and Smith, 2014).

Recently, Huynh et al. have performed a case study to fill the void between theoretical research and real-

time applications about the management and utilisation of the DES data. Based on this specific study, it is

demonstrated that application of DES is effective for analysing the production performance and supports

the capacity planning activities. In this master thesis work, DES software Extendsim (Version 10.0.7) is

used for performing flow simulation of the production activities of industrial robot controllers at the

production facility of ABB Robotics located at Västerås, Sweden. By modelling and simulating the current

production system it is possible to test and verify the various configuration just be varying the variables.

The following parts of the report illustrates how DES has been taken to advantages to achieve desired

results.

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2.3 Process Mapping A process map is an elementary tool for gaining insights about the activities through illustrating all the

processes in a production system. There are different types of process mapping used during this thesis work

depending on the nature of the entities and the information. The process map ensures that the rightful

information in the system thus reducing the risks of incorrect flow of information during the production

run. The below table lists the various mapping tools used for performing the thesis along with their scope

and usage description.

Table 1: Process Maps

Scope Type Description

Process Value Stream Mapping • Indicates the timing attributes (cycle time and takt time values).

• Operator allocation.

• Order triggers.

• Inventory description.

Product Deployment Map • Illustrates the process sequence for various product variants.

• Workflow and material requirement.

Organisation SIPOC Map • Supplier relationship.

• Customer relationship.

• Inter-department interactions.

It is crucial to develop the map from the very initial stage of the project in order to obtain a better clarity

of the system in place. The process map enables to comprehend the flow of information and the links

better which is key to produce a model that is as close to reality. The maps are really useful in translating

the gathered information through unstructured interviews into visual representations. It is also very crucial

in the verification and validation phase of modelling to avoid any risks that caused complications in the

later stages of the work.

2.4 Conceptual Model A conceptual model is an aggregated representation of an actual system. Conceptual model is vital in any

work that requires simulation in order to gain better knowledge about the work structure in a system. The

conceptual modelling is essential in order to replicate the real-life parameters from the problem statement

which could also be stated at the conditions for a model. The conceptual modelling is performed between

the data gathering and simulation phases of the project. The aim of conceptual modelling is to achieve a

model which is a close to the actual system by considering various factors such as working parameters,

input & output values, and also by making assumptions of missing information/simplifying the complex

logics. The conceptual model should be developed from the perspective to reach the end goal without any

deviations. Also, the activities which does not impact the goal of the project may not be included.

A well-structured conceptual model reduces the risks of obtaining error prone results which are caused by

making high level of assumptions, incorrect logics, and improper assignment of the conditions/parameters.

Thus, helps in developing a reliable model for simulation. The below figure illustrates the framework

developed by Robinson et al., 2010 for designing a conceptual model.

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Figure 2: Conceptual Modelling Framework

2.5 Evaluating Conceptual Models The conceptual model was assessed by discussing with the production planners and controllers responsible

for the specific assembly line. The logics, parameters and constraints are evaluated in order to obtain a

credible model. Based on the discussions and received input changes are made to develop a best match for

the assembly processes in reality. The significance of evaluating the conceptual model is to ensure that the

modelling is done to the accepted level of abstraction. The following graph represents how the accuracy of

the model changes along with time.

Figure 3: Changes in the accuracy of model over time

2.6 Data Collection and Organisation A simulation project requires huge amount of data for model input and management of the gathered

information is essential to construct a reliable model which is as close to the operational reality. Hence, the

first phase of the thesis deals with complete study about the operation background and collection of data

from various sources. Gathering and compiling of these data is a tedious and continuous process. It is often

time consuming and affects the other parts of the project. The lack of quality data input may have a direct

impact on the results and reliability of the model. This thesis works follows the methodology framed by

Skoogh & Johansson, 2008 for data gathering and management. This methodology is illustrated in the figure

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shown below. The objective of this study is to manage the data management process more streamlined and

efficient by using a structured approach.

Figure 4: Data Management Model

The above flow chart explains the various levels and processes involved in the data management which

consist of three main levels. It is important to collect all the available relevant data to the highest extent and

ensure that the quality of the gathered data by checking its criticality in operational reality. In case of

unavailability of the data, decisions are made to engage alternate methods for example,

probability/randomness. The most important data in this DES project is the cycle times of the processes

which require intricate time study. The other important data in this DES project work include the

downtimes for the production which can occur due to various reasons. MTBF and MTTR are calculated

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form the 2020 breakdown history data which includes over 14000 samples from the shopfloor. All these

statistics are exported into the DES database as readable values. Some of the data requires further

processing of the information and conversion into mathematical form in order to impost into the model.

In order to trigger the variations/deviation which are natural at the operational reality statistical tool and

random generators are utilised to make the work simpler. Data gathering and verification is a cyclic process

which is performed until the model is developed completely.

2.7 Goals for Modelling There are two most important aspects which are needed to be understood before starting to develop a

conceptual model which are: proper comprehension of the problem statement and identifying the

objectives for modelling. The objective for modelling here is to simulate the discrete events which may take

place during the operations in order to carry out the improvement/optimisations of the processes.

2.8 ExtendSim The software used in this project for performing simulations is Extend, which can process various

simulations methods and is therefore a commonly used tool in the industry. However, the focus in this

project will be just on the discrete event simulation. The version of the software used for this project is

ExtendSim 10.0.7. The ExtendSim contains various libraries with large number of tools which is needed

for modelling.

Table 2: ExtendSim Libraries

The ExtendSim has the feature which allows the user to define some custom function are not available in

the pre-defined library entities. It can be done my redefining the pre-defined code in the block entities as

the ExtendSim allows to change the program structure from the foundation. This makes the work easier

for the user for defining the complex processes. The programming language used in the ExtenSim is called

ModL which is an adapted variation of the C++ programming language. ModL is modified from C++ with

added features and extensions which are appropriate for the simulation environment (Sackett et al., 2013).

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The ExtendSim has an inbuilt statistics tool called Stat::fit, which contains various mathematical models

and distributions inside it (Sackett et al., 2013). The Stat::fit has the ability to the select and assign the best

distribution based on the nature of the processes. It is designed in order to processes up to 50000 values &

data points to determine the best suited distribution (Mountain, 2020). ExtendSim also allows the users to

import the data points to the database from excel. Stat::fit is an external tool which is integrated into the

ExtendSim as an add-in feature.

2.9 Factory Layout The production facility i.e., cabinet assembly is made up of various assembly lines, sub-assembly lines, and

testing rigs. In the main flow the products are pulled (made-to-order) and in some of the sub-assemblies

the items are pushed and stored in the buffer. The figure 5 is the factory layout of the cabinet assembly in

current condition.

Main Assembly:

• Gable Assembly

• Final Assembly

• ES & Test Rigs

Sub-assembly:

• Computer Assembly (CA)

• Door Assembly (DA)

• Gable Assembly (GA)

• Rear Hood

• Pre-assembly Electronics

• Drive unit

Figure 5: Factory Layout

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Analysing the production facility is a very important step in order to proceed further with the project. It is

impossible to make decisions or develop the model without the proper understanding of the layout since

the assembly processes depend on the design of the layout. Here, the focus was on the routing of the

products. There are two type of products namely IRC5 and IRC5C which follows different routing. The

order trigger point of the IRC5 is at the Gable Assembly whereas for IRC5C the process begins directly at

the final assembly. The processes map for the products are provided in the Appendix A for better

understanding. From the study, it is observed that the layout of factory can be improved as the place of the

zones are not ergonomic meaning that the two zones associated with each other placed at different place

and accessibility is less. The makes the material movement/communication difficult which lead to increased

time consumption. So, there is an opportunity for making improvement in the layout.

2.10 VSM for Products The Value Stram Map for the products are attached in the Appendix B.

12

Chapter 3 – Methodology

This chapter describes the research approach and the justification for the selection of the approach. The first part of the chapter

explains the research methodology and the following parts discusses the various stages of the work performed during the course

of this project elaborately.

3.1 Research Approach The approach in this thesis work is mainly divided into three top level phases which are data gathering &

verification, modelling & simulation of the production line using a computer software, and result analysis

along with future plans/improvements. The research approach chosen here is described as quantitative

technique which is decided based on the various factors. Since the cabibet’s production process is complex

and the workload variation is high, a streamlined research approach is selected. The research process begun

with having several semi-structured interviews and investigations to gain a complete understanding of the

production operations at various stages and assembly lines. Then the gathered information is organised and

the represented in the form of process maps. The modelling is performed simultaneously, as it is crucial to

resemble the assembly line as accurate as possible. Until the model was verified, the modelling and the

investigations were conducted in a cyclic manner. After the model has been verified and validated, the last

segment of the research which is the analysis of the model has been performed.

In order to respond to RQ1: “How DES could be utilised to support the performance improvement process of an assembly

line?”, the entire production process in cabinet assemblu is mapped as a material flow model. This model

acts as a digital replication of the production line and enables to study the material flow activities. The

model includes the Production Planning from which the orders are sent to the Gable Assembly Line or

Final Assembly Line depending on the type of the product. The logics and statistics defined to the model

is of very high intensity and quality in order to match the model to reality.

The objective of the modelling if replicate all the logics and details, but it is very challenging to include

some complex activities or events which occurs at operation reality. Therefore, these complexities are

aggregated or simplified to certain levels which have been described in detail in the following segments of

the report. From this, it drives to provide an answer for the RQ1 which is: the DES model resembles the

operations of the cabinet assembly line, which serves as a base to study the performance.

After the model has been developed, verified, and validated with the help of all the available resources. The

next segment in the project comes as RQ2: “What are the bottlenecks in the assembly line and the optimal buffer

size?”. Firstly, to get the reliable results, the historical data inputted to the relevant areas of the model before

performing the simulation. In order to determine the answer for RQ2, results from the model (Activity

Statistics) are analysed and compared with the desired values and conditions.

After the results are extracted from the model and analysis is performed, the following part of the thesis is

to determine how effective and reliable is DES in the production performance improvement process. This

is framed as RQ3: “How efficient is DES to optimise the material flow in production?”. In order to answer this

question, the findings from the models are compared with the actual operational conditions by conducting

interviews and obtaining feedback from the shopfloor.

Lastly, after studying the results from the simulation the critical parts of the production are identified and

the different scenarios and changes were tested with the model until the desired output is obtained. The

best operations structure and conditions are determined by this method. These findings are recommended

as suggestions to meet future demand at the end of this thesis. Therefore, it answers the final research

question RQ4: “What changes in the assembly line is required for meeting future demand?”.

13

3.2 Background Study In order to gain the complete understanding of the production activities, the inputs are collected from the

various sources working on cabinet assembly at ABB. Structured and semi-structured interviews with the

production workers, planners, and engineers were the foundations for the background research process.

The information from these sources is elemental for gaining a complete understanding of the production

activities, layout, and functions in the assembly line. In order to get a better understanding of the material

flow and replenishment strategies the inputs from the logistics department are taken. The flow charts

representing the process flow for different product and variants are collected from the process planning

department. These flow charts are very crucial to understand the various flows and process required for

production of different variants of the products. Also, with the help of discussion with the production

operators and process planners complete understanding is gained. Using all these information, various

process maps are developed. The process maps are developed for two main products IRC5 and IRC5C,

with sub-flows representing the different models of the main products. Flowing that VSM is studied to

understand the process cycle-times, order trigger points, type of the assembly, buffer size and locations.

The VSM study is essential to get the model right. The data and statistics related to the breakdowns and

failure is collected from the ERP and Shopfloor data from the year 2020. With the help of all the gathered

information and having discussions with various experts, the complete picture of the production activities

is understood which laid the foundation for this project work.

3.3 Conceptual Modelling There are three important stages in the conceptual modelling. The initial stage is the development of the

framework for the conceptual modelling, the next phase is to the construct the conceptual model and the

final stage is the assessment of the built models.

Framework

The initial step in this thesis work was to get a clear understanding of the current problems and the

objectives from the thesis work, from this the goals and the scope of the modelling is framed. Factory visit

is an integral part of this work in order to get a complete understanding of the actual processes and the

operational problems. The reports and documents of the previous project work are studied completely and

discussion with the relevant persons at ABB has helped in formulation of the project goals and objectives.

Several discussions and studies were done in order to ensure that no crucial information is missed in the

process of building the model. Once all the basic specifications were formulated, the experimental variables,

requirements, and outcome for the model was found out. After getting the complete understanding about

the processes, the next phase to build the model is started.

Conceptual Model Construction

Conceptual models were built from the scratch, which started by developing the process maps and IDEFO

activity models containing all the input, output, control, and medium for the assembly operations. Next, all

the parameters, interactions, history statistics, and disturbances were studied and formulated before the

allocation of these variables in the model. Later, these variables are transformed into the DES software

accepted form to an extent that the variables do not affect the reliability of the model. Necessary precautions

and measures were taken to ensure that maximum level of details is included in the model. Then these

models were developed with digital tools for visual depiction.

14

Assessing Conceptual Model

Assessment of the conceptual model is an important step in the model development process to ensure the

credibility. Various review meetings are conducted to evaluate the model and for clarification of the unclear

and complex parts of the assembly processes. Feedback and suggestions are received and taken into

consideration to rebuild the model. Several methods and ways of models were presented and the most

appropriate method for the company was carefully selected.

3.4 Data Management In order to replicate the assembly line to a maximum possible accuracy it is vital to gain the complete

understanding of the data flow in the assembly processes. Although, the access was given to the historical

data and analytics it was important to get a clear picture of the uncertain areas. Virtual discussions and

interviews were held with the process planners who created the documents and process plans for the cabinet

assembly. Once, the complete understanding of the logics was attained the insights were reflected on the

model.

Mathematical Characteristics

The two main data required for this simulation work are cycle times and the stop times which are crucial

for production. The information transfer of these kinds of data are done as excel documents(sheets). From

the provided information a spreadsheet was created for all the workstations and the times for varying

assembly processes from which the mean, maximum and minimum process times are determined.

With the gathered process time data, it determined that the triangular distribution is feasible for such

scenarios. Triangular distribution is best suited for the cases when there is only a limited information is

available, and specifically when the relationship of the variables is known but other information is lacking.

This kind of situations are common since the data gathering can be complex, time consuming and cost

ineffective. Triangular distributions are widely utilised for business simulations in scenarios where only the

upper and lower limits are known.

Table 3: Cycle Time Distribution

Triangular Distribution Only three values: upper limit, lower limit, and most common values are required for the triangular distribution. The triangular distribution is not required to be symmetric with the mean point. The triangular distribution is best suited when the complete data is not available, and it is identified that the runtime variables are not uniform.

Figure 6: Triangular Distribution

15

The shutdown values which are the disturbances in the material flow cannot be easily determined unlike

the process times and set-up times. Although the disturbance data is available in abundance from the shop

floor, they can become obsolete after a cycle/period of time due to various factors. It is also very common

that there can be errors in the disturbance data due to the under reporting. It is important to carry out

investigations using DES to the study the impact of the disturbances on the productivity.

Careful considerations are taken in choosing the well-suited distribution for the DES. There are various

distributions namely Weibull, Exponential, Gamma, and Triangular which are commonly used for

allocation of the disturbance values. The DES model was run with various distributions and calculation

were made to rank the well-suited distribution. Although from the through research it is known that the

standardly deployed distribution for failures is negative exponential distribution (Lie et al., 1977). However,

based on the investigations Log-Logistic distribution is most appropriate for this project scenario.

Table 4: Disturbance Distribution

Log-Logistic Distribution The log-logistic distribution is similar to the log-normal probability distribution, but the random variables have a logistic characteristics. The distribution rate increases gradually and decreases later. This can be used as a foundation for the accelerated failure time model.

Figure 7: Log-logistic Distribution

Before specifying a probability distribution in the DES model, it is essential to check if the selected

probability distribution generates value in co-relation with the provided disturbance data. This is an

important to step in order to ensure that the representation is accurate to the real system. Kolmogorov-

Smirnov (KS) test and Anderson-Darling (AD) have given a framework for the fitness testing. This

framework for testing works by finding the intervals(distance) between two points from which the

suitability is checked. KS and AD are best to validate the distributions which are based on cumulative

probability.

𝐾𝑆𝑛 = √𝑛𝑠𝑢𝑝𝑥|𝐹𝑛 (𝑥) − 𝐹(𝑥)| (1)

Where,

𝐹 (𝑥) = Theoretical value for the distribution at point x.

𝐹𝑛 (𝑥) = Empirical value of the distribution for size n.

The null distribution is calculated when in the null hypothesis i.e, if the 𝐾𝑆𝑛 is larger the critical value.

16

Anderson Darling (AD) test is used for validating a sample data which is obtain from a specific distribution.

This method is derived from the Kolmogorov Smirnov (KS) test. Unlike the KS test the critical values are

not based on the which distribution is under the test. The AD method possesses an advantage of providing

accurate results of since the test is more sensitive. The formula for the AD method is given below:

S = ∑(2𝑖−1)

𝑁

𝑁

𝑖=1[𝐹(𝑥𝑖) + ln(1 − 𝐹(𝑥𝑁+1−𝑖))] (2)

Where,

𝐹 represents the cumulative probability function of testing distribution and 𝑥𝑖 is the ordered data

that starts from the initial point to the last point ‘n’ [x1 < …< xn] which is the lot size.

Data Structures

In order to manage the complex transfer of information within the model between various elements the

dynamic modelling method is followed. Which means that the data is driven in the database with the help

of relational linking. Relational linking helps to connect and transfer the information of the table in a parent

and child manner. Relationship database and dynamic database enables to link two relevant properties from

different data sources.

The relationship is very useful in the modelling to link the Product ID, Model Number (represents the

variation type of the model), Cycle Time, and Production Planning. The Production Planning is linked with

the Product ID to indicate the product which must be processes. The Product ID is linked with the Model

Number which in turn is linked with Cycle Time, which enables to assign the correct process and cycle

times to the station based on the model selected.

Each element in the model is allocated with a separate database based on the nature. The shutdown

times/failures, results, produced unit, and time stamps are linked to the database which can be extracted as

a spreadsheet whenever required. The database acts as a control system since the variables such as KPIs

and other parameters which defines the mathematical characteristics have a dedicated data pool in the

system database.

17

3.5 Building Simulation Model Since the cabinet production consists of various assembly line and also the assembly processes are complex

to model, it is decided to build the model in a modular manner. Therefore, the different assembly lines are

modelled separately one by one as an individual hierarchical blocks. The assembly lines in facility are:

• Computer Assembly (CA)

• Door Assembly (DA)

• Gable Assembly (GA)

• Final Assembly (FA)

• Panel Assembly (PA)

• Test Rigs

3.5.1 Shutdown

The above-mentioned assembly lines and buffers are built as individual hierarchical blocks, later the

appropriate connections were made with the help of ‘throw and catch items’ tools in the ExtendSim, thus

representing the overall assembly processes. For all of these assembly lines and workstations, a dedicated

RBD models were built to stimulate and regulate the shutdowns based on the historical data and

probabilities. Various types of showdowns are triggered by these RBD model. The TBF and TTR are

generated by Random generator, which are triggered by log-logistic distribution.

Figure 8: Shutdown Operator

18

Figure 9: TBF and TTR Distribution

3.5.2 Hierarchical Blocks

The hierarchical blocks are collection of workstations which from an assembly line. The hierarchical blocks

help in creating a better user interface and provides a clear picture for navigation. The hierarchical blocks

are customised with the layout pictures to match view of the actual factory layout.

Figure 10: Model Overview (Hierarchical blocks represented as the factory layout)

19

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3.5.3 Takt-Time and Cycle Times

The takt-time is only accounted in the final assembly line. The takt-time in the final assembly line is 7.5

minutes and the cycle times for the workstations in the final assembly line vary from 240 up to 500 seconds.

However, the cycle times of some of the products could be different depending on the customer

requirement and the complexity of the assembly process.

In order to control the takt-time during the simulation takt-gates were modelled for the very workstation

in the final assembly line. The takt gates uses the values from the time of entry from the workstation till the

time of completion of the assembly activity. Once the station has finished the job and is ready for the next

product the end takt gate throws a value ‘1’ to the master takt controller. The product waits in the station

until other station ready for processing this is checked by the value from the master control, the value is

represented as ‘Takt_In’ in the figure 8. Once the stations are ready for processing ‘Takt_In’ receives value

1 which connected to the sensor gate which operates based on the demand.

Figure 11: Takt Gates

The takt-time logic is controlled using equation tool which check for the required value to send signal to

the workstations. The logic equation is shown in the figure 9. Also, the pulse tool sends a value for every

7.5 minutes and gets reset when all the workstations are ready. It is also made sure that if the takt value is

exceed due to a discrepancies in any of the workstation, the value tank stores the excess values therefore

the count does not affect the logics in the other workstations.

Figure 12: Takt Logic

20

The cycle time varies from 240 to 500 seconds and in some case the workload in the workstations changes

based on the complexity of assembly processes. Since, there is high frequency of variation in the cycle times

it is quite complex to allocate model specific cycle times. Therefore, in order simplify the complexity of

allocating cycle times a standard baseline formed by obtaining the mean value by using the upper and lower

limits. Later, in order to match the processes to the reality the cycle times are distributed using a cycle time

variation operator. The cycle time operators multiply the average cycle time with the triangulation factors

(The maximum and minimum multiplying factor) and generates a random values in the triangular

distribution.

Figure 13: Cycle Time Variation Operator

In order to effectively manage and change the cycle times all the important parameters are dynamically

linked to the ExtendSim database. The values are imported from the master excel sheet containing all the

important parameters. Therefore, it makes the external/new users to easily change the parameters to change

the characteristics for simulation by just changing a single value. To create a better simulation user interface

to manage the setting a data panel has been setup in the model view containing all the vital links.

21

3.5.4 Resource Pool

There are ten worker pools consisting of varying number of workers allocated perform the processes in a

dedicated assembly line. However, there are some exceptions where the workers move from the dedicated

line to another area which is considered as their secondary tasks which are required to manage the high

demand or if the production is overwhelmed due to various reasons.

Table 5: Resource Allocation

Assembly Line Total Workers

Door Assembly 5

ES Test 2

Gable Assembly 9

Material Team 1

Panel Assembly 2

Spot Welding 1

Final Assembly (WS 1-7) 7

Final Assembly (WS 8-14) 7

Test 5

Variation Workstations 3

As stated above the workers have a secondary tasks to perform at certain conditions which makes the

modelling complex and challenging. Through experimenting various method for modelling the allocation

of workers, it is decided to build separate resource block consisting of different worker pools inside it.

Throw and Catch tools are used to assign the workers when there is demand and catch block are used to

collect the dispatched item from the workstation.

Figure 14: Resource Call Logic

22

As it can be seen in figure 13, a sensor gate is set up at the starting of the workstation which sends a signal

to the corresponding resource pool which acts as a call for a worker. Next, the item waits in the batching

block until the worker has gathered to processes the activity. Once, the batch leaves the block a reset value

is sent to the resource pool in order to release the demand hold. At the end of the workstation the un-

batching is done from where the resource item is sent back to the pool.

The figure 14 represents the resource pool design used for this project. The resource items are managed in

the resource pool block, there is a demand gate after resource pool which checks for the demand signals

from all the gates. The demand is stored in the value tank and ‘or’ operator checks for the demand and

passes the signal to the demand. In order for the resource item enter the correct gate ‘select item out’ block

is specified with connector priority. Also, as said earlier in some cases the workers have a secondary tasks

in external assembly lines, in order to replicate this in the model a value is assigned to the resource item

which makes is easier for handling and identifying the pool to which it belongs.

Figure 15: Example of a Resource Pool

23

3.6 Verification and Validation Several measures and checks were performed in a structured manner to evaluate the model and validate it

before moving to the analysis phase.

Objective

After the model has been completely developed the model must be verified and validated in the presence

of the company supervisor and experts. The purpose of this process is to ensure that the developed model

is functioning similar to the reality, compliant to answer the stated problems, and reliable.

Functioning Logics and Statistics

In order to the check the characteristics of the model the simulation results by running it for a factory year

i.e., 254 days were run for 4 times. The statistics from the simulation run where compared with the actual

values from the past production. For example, in order to ensure that the model works in the similar manner

to the reality the production rate and output were compared with each other. Statistical graphs and plots

generated by the model were compared to the data in company database (ERP) and statistical reports. The

shutdown and disturbance statistics were given by the company supervisors, these statistics were gathered

from the shopfloor database which are connected to PLC systems. The history statistics from the cabinet

assembly consists of time period starting from 11/01/2020 to 15/12/2020 which were used to determine

the minimum, maximum, and mean values.

To evaluate the functional logics for the assembly processes and the other events associated with it,

meetings were held with the related company personnel. The model was presented and explained

elaborately in the presence of the experts and shop floor worker to verify the logics and rectify if any

inaccuracies are identified.

Sensitivity Analysis

The below listed factors are scrutinized in order to eliminate any negative impacts and to measure the

performance, this is done by sensitivity analysis (Law, 2019).

• Parameter Value

• Selection of probability distribution

• Movements of items in the system

• Level of detail of sub-systems

24

Chapter 4 – Testing and Results

This chapter contains the general information about the testing of the model and results from the simulation will be discussed

in detail. Firstly, the warmup period required for the model is calculated. Here, the results from the current model would be

studied and areas for optimisations will be identified.

4.1 Warm-up Period Warm-up period is the time which is required to overcome the starting phase of the production. Simulations

of the model must be run with an optimum warm up period as the simulation usually starts with zero

items/empty in the stations. So, in order to the reach the current rate of the production it is required to

calibrate the model to the current level by running the simulation for a warm-up period. Careful

considerations are given in order to not account the results from the warm-up periods to the end results.

Therefore, the results and statistics are cleared from the database after the warm-up period.

A certain procedure is followed to determine the warm-up time. The steps involved in determining the

warm-up period are stated below:

1. Performance Indicators

The first step is to identify the key performance indicators which are very relevant to the processes in order

to measure the performance. Some of the performance indicators are buffer quantity, machine utilisation,

production rate, etc which are considered during the warm-up.

2. Short Run

The model is tested by running for different period of time in order to check to the number of units

produced and production rate at a specific point of time. This step is necessary to determine the time period

in which the expected quantity (which the rate of production in reality) of the products emerge out of the

line.

3. Test Run

Now, knowing the reach point the model is again simulated for a full time and calculation is done to

determine the production rate per day which is done by finding the number of units produced between day

10 and day 20. From the gather data a chart is plotted to the illustrate the data.

Figure 16: Warm-up Graph

25

From the graph, with the numbers collected from the simulation it is very easy to determine the threshold

point. From the figure 13, it is evident that when the simulation time reaches 20 days the model has

produced slightly over 830 products, meaning that the production rate is on average around 43 units/day

which is very close the actual production average rate of 46 units/day.

Although in this case the model has worked consistently and there is a steady increase in the production

rate, it is not always possible to obtain such results. The model is built a property of randomness which can

create variation in the production output. So, it is natural to expect some deviation which is common in

reality.

It is not very easy to determine the warm-up period by looking just at the graph since the model possess

randomness in its characteristics. Also, the performance could become unstable at times due to the

randomness which makes the measurement challenging. There can even be possibilities of the model being

inconsistent even after the warm-up period. Hence, the basic principle is to make certain that the simulation

is not affected by the peculiar conditions during the initial stages for example, lack of materials/items in

the workstations.

4. Safety Margin

Since the model processes the characteristics of being random which causes slight deviations (usually) in

the warm-up time. By analysing the warm-up period by running the model for various number of times it

is determined that it is essential to add 20% as a safety margin to the determined warm-up time.

Warm-up Period = Determined Time + Safety Margin

= 20 days + (20 x 0.2) days = 24 days

Addition of Safety margin to the warm-up time is significant because it is technically difficult to check the

warm-up time for each time you run simulation. Therefore, it reduces a time taken to recheck the warm-

up time considerably. But the disadvantage is it leads to increase time of simulation run.

26

4.2 Simulation To study the current production conditions in order to answer the questions framed in the problem

statement the model was simulated with actual work constraints. In order to test the weak points in the

production some tests were done with the model to understand the what-if scenarios. The conditions for

simulation are specified below:

• Run Time: 254 days/year (5 days/week and 13.22 hours/day)

• Runs: 4

• The production order is initialised through the production plan which the imported into the

database.

• Some of the statistics i.e., the percentage of sub-products and processes are assigned as

probabilities in the model. For example, only a small portion of total produced needs to be spot

welded in the end of production. These kind of data are assigned based on the historical

information and forecasting.

4.3 Observations The results from the simulation are presented and the current conditions are described. The results are

taken by running the simulation without changing any parameters. The simulation time was set for one year

in order to get the complete understanding of the behaviour of the system. It is checked that the products

emerging out of the model at the end of simulation is near to the actual annual production amount.

4.3.1 Cycle Times

The cycle times are studied here to figure the areas where the bottlenecks are created. The focus here is

mainly on the final assembly line since it is determined from the basic analysis of simulation that the major

reason for the blockages in the production is dues to the disruptions in the final assembly. Therefore, it

makes sense to observe the average cycle times carefully in order to understand the reasons for the

blockages in the flow.

Figure 17: Average Cycle Times - Final Assembly

0

1

2

3

4

5

6

7

Min

ute

s

Cycle Times

27

As shown in the figure 17, the bottleneck stations are indicated with the orange colour while other are

indicated with the blue colour. Although, the average cycle-times are well under the takt-time based on the

detailed observation of the flow simulation it is found that the bottleneck stations block the flow of the

products of a varying workload. Consequently, this is affecting the flow of the products and increasing the

time between items. The blockages can be also party due to the various other reasons such as lack of

workers, materials, etc which are analysed in detail in the later part.

It is also evident that some stations like variation stations have a very less cycle times compared to the other

stations. However, the variation stations are not just workstation, but they are also considered as a buffer

in the flow. The variation stations are dedicated mainly to performed additional tasks to the product based

on the customer requirement. The cycle time balancing is done through the Avix software.

4.3.2 Average Wait

The average wait indicated the waiting time of the product at the end of the workstation until the next

workstation is ready for processing. As described before in the cycle times section the focus here is also in

the final assembly for the same reasons. Here, the average wait times are calculated with the help of the takt

gates which collects these information.

Figure 18: Average Wait - Final Assembly (Current)

In this case the bottlenecks are the stations right next to the stations with the highest waiting times. The

average wait time of the products in the flow is around 18.32 minutes which is considered very high. This

has a direct effect on the productivity and the cost of the product as well. Therefore, the processes must

be optimised to increase the rate of flow in the production. As it can be seen in the chart (Figure 18), the

average maximum wait time is over 28.36 minutes.

Through the detailed analysis of the flow, one of the major reasons for the delays are due to the

unavailability of the variation workers since they also have a secondary duties another area. Secondary

reason is due to the processes delays in the next station which occurs due to the lack of the materials or

increased workload.

0

0.005

0.01

0.015

0.02

0.025

FA,S1

FA,S2

FA,S3

FA,S4

FA,S5

FA,V1

FA,S6

FA,S7

FA,S8

FA,S9

FA,S10

FA,S11

FA,V2

FA,S12

FA,S13

FA,V3

FA,V4

FA,S14

Day

s

28

Therefore, it is crucial to analyse the activities completely starting from the background activities which is

a key factor for the optimisation.

4.3.3 Resource Utilisation:

The resource utilisation corresponds to the utilisations of the workers at the end of simulation of the model

in current condition.

Table 6: Resource Utilisation – Current

Resource Pool Utilization (%)

Door Team 87.46

ES Team 27.747

Gable Workers 98.391

Material Team 25.836

Panel Team 54.173

SW Team 15.712

Team 1-7 99.28

Team 8-14 99.6

Test Team 39.363

Variation Team 59.416

From table 6, it can be seen that the highest utilisation is in the gable assembly, final assembly, and Door

assembly whereas the lowest utilisation is found in the ES, material pool, solid welding, and test rigs. It is

very obvious from the results that some areas are highly utilised due to the increased workload in contrast

there are majority of the station which are poorly utilised.

The high utilisation of the workers may not be also positive since in this case the high utilisation means that

the workers are stagnated in the workstations due to various delays and disturbances. On the other hand,

it can be a reason that very less utilisations of the sub-flow causes the delays in the main flow. Hence, it is

important to determine the root causes for the problem in order the optimise the entire flow. Therefore, it

is important to proceed with checking every bottleneck station and their background processes carefully in

order to determine the problem generating processes which in turn gives an idea about how to optimise

them. There are also possibilities to allocate more tasks or to transfer the workers to another segment in

order to make the production more effective.

29

4.3.4 Overall Performance

The below given pie chart represents the total utilisation of the production line from the high-level view.

The result from the simulation shows that the current utilisation is around 71%. However, the goal for this

thesis work is to increase the production utilisation to 85%.

Figure 19: Current Utilisation - Pie Chart

The other performance results are indicated below:

• Yield Rate = ~ 45.3 units/day

• TBI = 17.54 minutes

• Average Annual Output = ~11500 - 12000 units

Figure 20: Current Output

Idle~29%

Busy ~71%

0

50

100

150

200

250

300

14

00

79

91

19

81

59

71

99

62

39

52

79

43

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33

59

23

99

14

39

04

78

95

18

85

58

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98

66

38

56

78

47

18

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27

98

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08

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99

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89

57

79

97

61

03

75

10

77

41

11

73

Day

s

Produced Units

30

Chapter 5 – Analysis

In this chapter, the obtained results are analysed as well as the improvement methods for the productions are described. The

improved model is validated, and the performance compared with the current state. Finally, based on the analysis the

recommendations are presented.

5.1 Status Analysis

5.1.1 Gable Assembly

It is observed from the simulation that average waiting time of the product in the buffer at the end of the

gable assembly if found to be around 72.6 minutes. It is mainly due to the disruptions/deviations in the

final assembly line. Therefore, it is not really concerned in the gable assembly rather the optimisations must

be performed in the final assembly line.

The other bottlenecks identified are Folding Robot-cell and Beams/Lift-Beams stations which have cycle

times higher than the other stations in the gable assembly. The trigger points of the sub-flows in the gable

assembly namely, Right Side Plates, Roof, Floor, Contact Plate and Extension Plate can be moved to one

step prior which reduces the production time considerably. The optimisations can give a possibility if

bringing the gable assembly under the Takt-Time values which increases the productivity.

5.1.2 Computer Assembly

The orders are pushed in the computer assembly and the current buffer size is minimum 10 units and

maximum are 20 units. The simulation results show that the buffer utilisation in the computer assembly is

around 96% percentage. Which indicates that the computer assembly is being over utilised, meaning that

computers are produced and stored in more numbers that what is actually required in the final assembly.

The computer assembly also has higher processing times which requires operator presence at all times. The

computer assembly is also performed by the variation workers from the final assembly. Therefore, the over

utilisation leads to the unavailability of the operators for the variation stations.

In order to control the over production, the buffer sizes of the computer assembly must be reduced. Based

on the calculations performed using the utilisation rate and also checking the results by simulating through

the complete time period it is determined that reducing the buffer size to minimum 5 units and maximum

10 unit is optimal. Careful considerations are taken when calculated to ensure that there is no outage of

computers.

5.1.3 Final Assembly

The final assembly is the most complex section in the production which consists of lots of interactions and

micro processes which defines the overall production efficacy and performance. Therefore, it required to

investigate the processes very closely in every stations which was time consuming and complex. However,

it was very beneficial at the end as it has yielded some important information in order to optimise the

processes to meet the future demand. The goal for optimising the final assembly is to reach the OEE to

85% from the current performance in order to attain the high-performance status.

To begin with, the cycle time study is performed based on the results observed. There is high level of

variation in the workload depending on the type of the product and customer requirements for the product.

However, there is no control over the product and orders in terms of the process times/assembly. Also,

the order cannot always arrive optimised for the assembly. Therefore, it is important to distribute the

workload evenly in the workstations in order to have a uniform flow in the assembly.

31

The bottleneck processes identified from the cycle time study are found to workstation 10 and variation

station 3 where the workload variation is very high in comparison with the other stations in the final

assembly. Station 10 and Variation 3 are the main contributors to the delays in the production which often

crosses the Takt time. Therefore, the processes must be reworked in the Avix and some of the process

must be distributed with the consecutive stations in order to attain the uniformity in the flow. When the

cycle times are balanced there is opportunity for the final assembly to reduce the Takt-Time to 6.5 minutes

from the current Takt-Time of 7.5 minutes. Later the model is simulated with the assumed cycle times as

stated in the figure 21, in order to test and validate the improvement suggestions.

Figure 21: Mean Cycle Times - Improved (Assumption)

After the cycle time balancing is done, the next step is to analyse the secondary processes in order to make

the production Leaner. The close observation of the processes helped in identifying the material outage

events during the simulation. It is observed that the YuMi-robot cell supplying fan holder parts to the final

assembly based on the product to either Station 8 or Station 9 is made to order. Moreover, YuMi-robot cell

has a mean cycle time of 8.5 minutes which 1 minute over the Takt- Time. Which is constantly delaying the

activities in the flow.

Finally, the resource(workers) utilisation and availability are checked where it is found that there is

unavailability of the workers in the variation stations due the over utilisation in the computer assembly as

describes in the section 5.1.2. Which is leading to the delays in the production flow.

0

1

2

3

4

5

6

7

Min

ute

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5.2 Validation Before making the recommendation, it is essential to validate the findings in order to ensure the reliability

and prevent cost wastage. The simulation model is modified with the identified parameters and changes

required. Since, the model was constructed from the base to adapt to the change it made process lot easier

since the parameters are only changed in the database. After necessary changes are made the model is

simulated with newly defined parameters in order to validate the results. The results from the improved

model are described and compared with the current results below:

Figure 22: Average Wait - Improved Model

The above given chart shows the figures of average wait times of the items in the stations gathered from

the improved simulation model. From the chart above, it can be seen that the mean wait time in the final

assembly is around 11.5 minutes and the maximum wait is observed in the variation 4 station with 15.8

minutes. The wait times have been reduced considerably when compared to the current activity statistics

which has the mean wait time of 21 minutes and the maximum waiting time is around 28.4 minutes.

The reduction of the wait times is achieved due to the optimisations done from the sub-level processes

which were creating problems. The wastes in the processes are eliminated using the lean principles and the

modifications are done to the workstation in order to achieve these results, the major takeaways of the

simulations are provided as recommendation in section 5.3.

0

0.002

0.004

0.006

0.008

0.01

0.012

FA,S1

FA,S2

FA,S3

FA,S4

FA,S5

FA,V1

FA,S6

FA,S7

FA,S8

FA,S9

FA,S10

FA,S11

FA,V2

FA,S12

FA,S13

FA,V3

FA,V4

FA,S14

Day

s

33

Table 7: Resource Utilisation – Improved

Block Utilization

Door Team 89.852

ES Team 33.058

Gable Workers 93.91

Material Team 30.7

Panel Team 72.348

SW Team 23.636

Team 1-7 99.779

Team 8-14 99.991

Test Team 53.151

Variation Team 60.323

The above given table shows the utilisations of the workers in the operator pool of the improved model.

The first impression from the table can provide a false idea that there is not much difference in numbers

when compared to the current statistics. However, it is crucial to have a complete comprehension of the

assembly processes in order to compare the result to understand the actual improvements in the utilisation

and workers performance.

The figure in the table shows that there is a considerable increase in the ES workers and Test Team (which

is the considered as the exit of the flow), meaning that the frequency of the item arrival is increased in the

test rigs which is considered as an improvement. Although there is only a marginal increase in the figures

of the other pool namely, Variation, SW, Panel and Material team it suggests that the consumption of the

parts and materials required for the production is more which could be translated as more products are

being produced with the updated model. The below give chart (figure 23) shows the output from the

production.

Figure 23: Production Output (Improved Model)

0

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250

300

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34

The output graph shows that the output is approximately 15000 units annually. However, the average

output from the improved model is around 14300 – 15000 units annually. Which is approximately 19 –

26% increase in the yield in comparison to the current production average output of 11500 – 12500 units.

Consequently, the throughput rate has also increased from 45.3 units/day in current production to 59.1

units/day. The frequency of items i.e., Time between Items (TBI) is decreased from 17.54 minutes to 13.5

minutes. As ABB is going to introduce a new product into the same line the improved line can now

accommodate new or additional processes without affecting the current products.

11%Idle

89%Busy

Yield Rate = ~ 59.1 units/day

TBI = 13.5 minutes

Avg Annual Output = ~14300 – 15400 units

% Increase in Yield = ~19 – 26% ≈ 23%

Figure 24: Overall Utilisation - Improved Model

35

5.3 Recommendations

In this section, the major takeaways are provided in order to achieve high performance in cabinet assembly

facility:

• Addition of 1 worker to the variation pool (Operator from SW could be utilized).

• Bottleneck Stations – 2,3,4,6 & 14 in FA must be balanced to attain potential Takt-Time of 6.5 minutes.

• Processes from Variation 3 could be shared with Variation 4 to even out the workload.

• Lowering of Buffer Sizes of Computer Assembly to half.

• No layout changes are necessary since it does not affect the productivity much.

• Reduce waiting time for products from gable to enter Final Assembly.

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Chapter 6 – Conclusion

This chapter provides conclusion to this master thesis project by answering the questions from the problem formulation. Finally,

the suggestions and ideas for the future works is presented.

6.1 Research Findings

Here, the research questions form the problem formulation are answered.

RQ1: How DES could be utilised to support the performance improvement process of an assembly line?

For this whole idea behind this master thesis project, the foundation was this question. DES is commonly

for analysing the current system in the system, at least not the very less in the production domain. The

objective here was to replicate the ABB Robotics’ Cabinet Production as a digital model. In DES all the

processes and activities in the real system are represented with all the parameters to the model with this a

functioning model could be developed. The logics and mathematical characteristics should be assigned to

the model to get the reliable results. As described in the above chapters, developing DES can be an intricate

task therefore it is important to understand the objective and scope of the project. Which can drive the

practitioner to develop the DES model from the required perspective. In this case not the entire activities

from the production system are represented and the processes are aggregated to accepted levels. Later the

model is validated, and mathematical characteristics are evaluated. By this way, it is possible to create the

production system as a digital twin in the DES software to carry out the performance study which can aid

the decision makers.

RQ2: What are the bottlenecks in the assembly line and the optimal buffer size?

From the start of this project work, the main objective was to determine when, why and where the problems

are occurred in the production system. The major challenged during in the production system was dues the

variations of the products which has different flows. Therefore, there were a lot of problems in the

production which were identified. It is also crucial to the root causes of this problem in order to tackle the

issues.

Firstly, the bottlenecks were identified. The bottlenecks were observed in various forms and areas in the

production system. Major bottlenecks were due to imbalanced workload in the assembly lines. And the

other bottlenecks were due to the lack of workers and materials/parts which were needed in timely fashion

for the uniform flow.

There were some areas in the production where the buffer sizes were changed, and these changes are clearly

defined in the section 5.3 of this report. There were areas where the buffer sizes were reduced and, in some

cases, new buffer storage were suggested in order optimise the production flow.

37

RQ3: How efficient is DES to optimise the material flow in production?

DES is very effective tool in production development as it can be witnessed from the ‘Chapter 5 – Analysis’.

The DES is very effective in replicating the functioning of the material flow and also allows to test the

what-if scenarios which is very challenging to test in reality. DES is both cost and time effective as it helps

in comparing the current and improved states through just digital representations. Therefore, Discrete

Event Simulation is a very effective tool to test the conditions before it can be implemented into the real

system. There are various other benefits in using DES such as the planning and the resources needed to

optimise the process is very less. Also, the DES is very tool to carry out the future work since the

maintenance requirement for the DES is very minimal. The simulation is time effective and fast, but the

simulation speed depends on the computer configuration and performance. As shown in section 5.2, end

results from the DES have yielded increased productivity.

RQ4: What changes in the assembly line is required for meeting future demand?

The critical points in the assembly flow such as bottlenecks, etc have been analysed thoroughly in the

meantime the changes in the parameters have been determined in the process. Later, the changes are

implemented, and some parameters were modified in the model to study the results. These changes and the

parameters which contribute to the optimised production layout are presented in section 5.3. Changes

required for the assembly line include building/rescaling the buffer storages, relocating the order trigger

points, reassignment of workers, etc. To avoid scenarios that cause the system to operate in an unstable

condition, it is also vital to evaluate these parameters within which the system can function in stability.

6.2 Future Work

• In the future the model can be directly integrated with the ERP and PLM system to monitor the

production plan, inventory levels, and shop-floor data (Statistics). This acts as a method to

automate the simulation processes.

• As the next step in the project work the buffer levels and filling rates can be analysed thoroughly

for different systems by dedicated monitoring blocks.

• The scope of the project narrowed as there were delays in the introduction of new products.

Therefore, new products can be added to the database and could be simulated in order to get a

better perspective for meeting the future demand.

• Simulation model could be fine tuned based on the level of details required in the future which

may be missing in the current model.

• The model could be extended to the material handling and internal logistics in order to measure

their effect on the production performance.

38

References

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Bokrantz J, Skoogh A, Lamkull D, Hanna A, Perera T. ‘Data quality problems in discrete event simulation

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39

Nomenclature

DES: Discrete Event Simulation

ERP: Enterprise Resource Planning

OEE: Overall Equipment Effectiveness

VSM: Value Stream Map

IRC: Industrial Robot Controller

MTBF: Mean Time Between Failure

MTTR: Mean Time To Repair

TBI: Time Between Items

PLC: Programmable Logic Controller

SW: Spot Welding

FA: Final Assembly

GA: Gable Assembly

DA: Door Assembly

WS: Workstation

PLM: Product Lifecycle Management

40

Appendices

Appendix A: Process Maps

Figure 25: IRC5 Flow Chart

41

Figure 26: IRC5C Flow Chart

42

Appendix B: VSM

Figure 27: Cabinet Assembly VSM

43

Figure 28: Parallel Flow VSM

44

Appendix C: DES Models

Models for visualizing the workstations utilization and throughput.

Final Assembly:

Figure 29: Final Assembly - ExtendSim Model

Gable Assembly:

Figure 30: Gable Assembly - ExtendSim Model

45

Door Assembly:

Figure 31: Door Assembly - ExtendSim Model

Computer Assembly:

Figure 32: Computer Assembly - ExtendSim Model

Test Rigs:

Figure 33: Test Rigs - ExtendSim Model

TRITA -ITM-EX 2021:559

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