Improving Operational Service Delivery at Stellenbosch Traffic Department

Improving Operational Service Delivery

at Stellenbosch Traffic Department

Luguen E. Gass

Department of Industrial Engineering

University of Stellenbosch

Study Leader: James Bekker

Final year project presented in partial fulfilment of the requirements for the

degree of Industrial Engineering at Stellenbosch University

B. Eng Industrial

December 2012

http://www.ie.sun.ac.za

http://www.sun.ac.za

ToStellenbosch Traffic Department

and the National Department of Transport...May this be another step forward in

government service delivery.

i

Declaration

I, Luguen E. Gass, hereby declare that the work contained in this final

year project is my own original work and that I have not previously, in its

entirety or in part, submitted it at any university for a degree.

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

LE. Gass Date

ii

ECSA Exit Level Outcomes Reference

Outcome Reference

Sections Pages

1. Problem solving: Demonstrate competence toidentify, assess, formulate and solve convergentand divergent engineering problems creatively andinnovatively.

All All

5. Engineering methods, skills and tools, includ-ing information technology: Demonstrate com-petence to use appropriate engineering methods,skills and tools, including those based on informa-tion technology.

2, 3, 4, 5 & 6 9 – 63

6. Professional and technical communication:Demonstrate competence to communicate effec-tively, both orally and in writing, with engineeringaudiences and the community at large.

All All

9. Independent learning ability: Demonstratecompetence to engage in independent learningthrough well developed learning skills.

2, 3 & 4 9 – 46

10. Engineering professionalism: Demonstratecritical awareness of the need to act professionallyand ethically and to exercise judgment and takeresponsibility within own limits of competence.

7 64 – 66

iii

Acknowledgements

My greatest thanks goes to God; without Him I could not.

I would also like to acknowledge Mr. (soon to be Dr.) James Bekker for

sharing his wealth of knowledge, answering all my questions timeously, and

for wearing his heart on his sleeve. It was comforting to have had his support

all the way.

Mr. Royi of the Stellenbosch Traffic department; thank you for providing a

playground in which to perform this final year project. A special thanks to

all the staff at the department who had to endure my nosiness for extended

periods of time.

My dearest friends who provided some trivial knowledge which usually re-

sulted in some good ideas; the days together make life worth so much more.

A special mention to Ulla who provided me with enough distraction to take

this project lightly, to Nina who makes “cray-cray” normal, and Caelli, who

so dutifully stands by me no matter what (and who converted my messy

cartoon ideas into a reality). I love you.

A last thanks to my parents responsible for producing this brain and brawn;

capable of producing and affecting this world - hopefully in a positive way.

To those not mentioned - know that you have impacted my life even if in

even the smallest way, and for that I am grateful.

iv

Abstract

The Stellenbosch Traffic Department offers a municipal service to approxi-

mately 40 000 vehicle owners in the area. Numerous complaints about ser-

vice delivery, specifically referring to the long times waited by customers at

the department, have been reported. The focus of this report is on the queu-

ing dilemma at the department and aims to investigate alternative models

to reduce extensive waiting times. A decision support system (DSS) in the

form of a stochastic, discrete-event simulation model is developed. Using

the DSS, four alternative models are experimented with. Results analysed

by TOPSIS show that the current queue model implemented at the de-

partment is sub-optimal and that a multiple-server-single-queue model is

likely to be a better solution; reducing the time in system for customers by

almost four times. Structural changes to the Stellenbosch facility are also

recommended to accommodate the multiple-server-single-queue model. Fi-

nally, managerial recommendations are provided such that employee morale

and leadership may be increased to further improve customer service at the

department.

v

Opsomming

Die Stellenbosch Verkeersdepartement lewer ’n munisipale diens aan ongeveer

40 000 voertuig eienaars in die area. Talle klagtes oor dienslewering, spesi-

fiek met betrekking tot die lang tye gewag deur kliente by die departement,

is aangemeld. Die fokus van hierdie verslag is op die toustaan-dilemma by

die departement en het ten doel om ondersoek in te stel na alternatiewe

modelle om uitgebreide wagtye te verminder. ’n Besluitnemingsonderste-

uningstelsel in die vorm van ’n stogastiese, diskrete simulasiemodel is on-

twikkel. Die Besluitnemingsondersteuningstelsel is gebruik om met vier

alternatiewe modelle te eksperimenteer. Resultate ontleed deur TOPSIS

toon dat die huidige toustaan model wat by die departement geimple-

menteer is, sub-optimaal is, en dat ’n meervoudige-bediener-enkeltou model

waarskynlik ’n beter oplossing is; wagtye is ongeveer vier keer vermindered.

Dit beveel ook strukturele veranderings aan die Stellenbosch-fasiliteit aan

om die meervoudige-bediener-enkeltou model te akkommodeer. Ten slotte,

bestuursaanbevelings is gegee sodat werknemermoraal en leierskap kan toe-

neem om verdere kliente diens by die departement te verbeter.

vi

Contents

1 Introduction 1

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

1.2 Business Process Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 3

1.3 Solving Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3.1 Queuing Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3.2 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.4 Project Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.5 Report Road Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2 Literature Review 9

2.1 Queuing Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.1.1 Fundamental Concepts . . . . . . . . . . . . . . . . . . . . . . . . 9

2.1.2 Customer Behaviour . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2.2.1 Characterising the Problem . . . . . . . . . . . . . . . . . . . . . 13

2.2.2 Input Analysis and Parameters . . . . . . . . . . . . . . . . . . . 15

2.2.3 Validation and Verification . . . . . . . . . . . . . . . . . . . . . 18

2.3 Queuing Theory vs. Simulation . . . . . . . . . . . . . . . . . . . . . . . 19

2.4 TOPSIS Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

2.5 Box Plot Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.6 Managerial Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.6.1 Customer Waiting Time . . . . . . . . . . . . . . . . . . . . . . . 23

2.6.2 Organisational Behaviour . . . . . . . . . . . . . . . . . . . . . . 25

2.6.2.1 Work Motivation . . . . . . . . . . . . . . . . . . . . . . 25

2.6.2.2 Stress Management . . . . . . . . . . . . . . . . . . . . 27

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CONTENTS

2.6.2.3 Leadership . . . . . . . . . . . . . . . . . . . . . . . . . 27

2.7 Summary of Literature Review . . . . . . . . . . . . . . . . . . . . . . . 28

3 Benchmarking 29

3.1 Stellenbosch Traffic Department . . . . . . . . . . . . . . . . . . . . . . . 29

3.2 Bellville Traffic Department . . . . . . . . . . . . . . . . . . . . . . . . . 32

3.3 Durbanville Traffic Department . . . . . . . . . . . . . . . . . . . . . . . 34

3.4 Malmesbury Traffic Department . . . . . . . . . . . . . . . . . . . . . . 36

3.5 Summary of Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . . . 39

4 Proposed Queue Models 41

4.1 Queue Design Considerations . . . . . . . . . . . . . . . . . . . . . . . . 41

4.2 Alternative Queue Designs . . . . . . . . . . . . . . . . . . . . . . . . . . 42

4.3 Summary of Proposed Changes . . . . . . . . . . . . . . . . . . . . . . . 44

5 Simulation Study 46

5.1 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

5.2 Analysis of Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 48

5.3 Validation and Verification . . . . . . . . . . . . . . . . . . . . . . . . . 51

5.4 Summary of Simulation Study . . . . . . . . . . . . . . . . . . . . . . . . 53

6 Conclusions and Recommendations 54

6.1 Queueing Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

6.2 Facility Layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

6.3 Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

6.3.1 Work Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

6.3.2 Stress Management . . . . . . . . . . . . . . . . . . . . . . . . . . 59

6.3.3 Leadership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

6.4 Further Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . 61

7 Closing Summary 63

7.1 Project summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

7.3 Contribution to Society . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

7.4 Lessons Learnt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

viii

CONTENTS

7.5 Denouement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

References 69

A Supporting Information 70

A.1 Newspaper Articles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

A.2 Project Plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

A.3 Time Study Template . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

B Queuing Models 76

B.1 Alternative Queuing Models . . . . . . . . . . . . . . . . . . . . . . . . . 76

B.1.1 Multiple Servers, Multiple Queues . . . . . . . . . . . . . . . . . 76

B.1.2 Multiple Servers, Single Queue . . . . . . . . . . . . . . . . . . . 78

C Simulation Model Notes 82

C.1 Functional Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

C.1.1 Operational Sections . . . . . . . . . . . . . . . . . . . . . . . . . 82

C.1.2 Servers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82

C.1.3 Customers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

C.1.4 Transactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

C.1.5 Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

C.1.6 Schedules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

C.2 Input and Output Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

C.2.1 Input Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

C.2.2 Output Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

C.3 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

C.4 Model Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

C.4.1 Design 1 — Single Stage, Multiple Queue, Single and Multiple

Server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

C.4.2 Design 2 — Single Stage, Multiple Queue, Single Server . . . . . 87

C.4.3 Design 3 — Multiple Stage, Single Queue, Single Server . . . . . 87

C.4.4 Design 4 — Single Stage, Single Queue, Multiple Server . . . . . 88

C.5 Data Distributions Summary . . . . . . . . . . . . . . . . . . . . . . . . 88

C.6 TOPSIS Calculations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

ix

CONTENTS

D Administration of the

Final Year Project 92

D.1 Meetings with the Study Leader . . . . . . . . . . . . . . . . . . . . . . 92

D.2 Summary Time Sheet . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

x

List of Figures

1.1 Graphic Summary of the Problem Statement, Methodology and Result. 8

2.1 Triangular Distribution as Estimated by Servers . . . . . . . . . . . . . 17

2.2 Herzberg’s Two Factor Theory of Motivation . . . . . . . . . . . . . . . 26

3.1 Current Business Process Flow and Facility Layout . . . . . . . . . . . . 31

3.2 Bellville Traffic Department: Licence & Registration, Reed Str . . . . . 33

3.3 Bellville Traffic Department: Drivers Licences, Bailey Rd . . . . . . . . 34

3.4 Durbanville Traffic Department: Licence & Registration, Oxford Str . . 35

3.5 Durbanville Traffic Department: Drivers Licences, Church Str . . . . . . 36

3.6 Malmesbury Traffic Department: All Transactions . . . . . . . . . . . . 37

4.1 Design 1 — Single Stage, Multiple Queue, Single and Multiple Server . 43

4.2 Design 2 — Single Stage, Multiple Queue, Single Server . . . . . . . . . 44

4.3 Design 3 — Multiple Stage, Single Queue, Single Server . . . . . . . . . 45

4.4 Design 4 — Single Stage, Single Queue, Multiple Server . . . . . . . . . 45

5.1 Box Plots Comparing TIS of Designs 1 and 4 (left), Designs 2 and 4

(right). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

6.1 Recommended Business Process Flow and Facility Layout . . . . . . . . 57

A.1 “Rotten service detrimental to the economy.” . . . . . . . . . . . . . . . 70

A.2 “Service doesn’t exist.” . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

A.3 “Officers react to complaints.” . . . . . . . . . . . . . . . . . . . . . . . 72

A.4 Planned Tasks and Deadlines for the Project. . . . . . . . . . . . . . . . 74

A.5 Time Study Template for Servers . . . . . . . . . . . . . . . . . . . . . . 75

xi

LIST OF FIGURES

D.1 Extract of Meeting Minutes: Meeting 6. . . . . . . . . . . . . . . . . . . 93

D.2 Summary Time Sheet as on 21 October 2012. . . . . . . . . . . . . . . . 94

xii

List of Tables

4.1 Summary of Alternative Queuing Models . . . . . . . . . . . . . . . . . 42

5.1 Summary of Simulation Results . . . . . . . . . . . . . . . . . . . . . . . 47

5.2 Ranked TOPSIS Analysis of 75th Percentile Results . . . . . . . . . . . 49

5.3 Actual vs. Simulated TIS: Design 1 . . . . . . . . . . . . . . . . . . . . . 52

5.4 Actual vs. Simulated Entities Created: Design 1 . . . . . . . . . . . . . 52

6.1 Comparison of Simulation Results of Current- and Proposed Queue Designs 55

B.1 P (j ≥ S) for the M/M/s Queueing System . . . . . . . . . . . . . . . . 79

C.1 Summary of (Fitted) Time Study Data Distributions . . . . . . . . . . . 89

C.2 TOPSIS Analysis of 75th Percentile Results . . . . . . . . . . . . . . . . 91

C.3 TOPSIS Analysis of 75th Percentile Results (continued) . . . . . . . . . 91

xiii

Nomenclature

Acronyms

CIS Customers in System

eNatis Electronic National Traffic Information System

HRK Hassler Register Kassen: Retail Information System

OPUS Information system used for capturing fines

TCS Total Control System

TIS Time in System

Greek Symbols

λ Average arrival rate

µ Average service rate

πi Steady-state probability where there are i entities in the

system

ρ Workload rate or traffic intensity

Roman Symbols

Ab Best alternative

Aw Worst Alternative

dib Distance between target alternative i and the best con-

dition Ab

xiv

Nomenclature

diw Distance between target alternative i and the worst con-

dition Aw

L Average number of entities in system (in queue and in

service)

Lq Average number of entities in queue

P (j ≥ S) Probability there are more entities in the system, j, than

servers, S, given a certain ρ

p-value Probability of obtaining a test statistic at least as ex-

treme as the observed statistic

rij Normalised matrix

Sib Similarity to best condition

S Number of servers in system

Tij Weighted normalised matrix

vj Performance measure ,j

W Average time in system (in queue and in service)

Terminology

Drivers Licence Section Operational department processing all transactions re-

lated to drivers’ licenses, including learners’ licences

Entity Material existence referring to customers in the simula-

tion

Fines Section Operational department processing all transactions re-

lated to fines

Floor Customer queuing and waiting area

Toxin Handler Person willing to listen to an individual’s issues

Idle Not in service

xv

Nomenclature

Indifference Zone Area of difference between the quartiles of box plots.

In queue Waiting in line but not yet in service

In Service Currently being served by a server

Licence & Registration Section Operational department processing all transactions re-

lated to licenses, registrations and roadworthies

Mode Number that appears most often in a set

Multiple Server More than one server

Queueing Discipline Nature in which entities move from the queue to service

Section Independent operational department

Server A resource for which entities compete. A teller.

Social Justice Justice exercised within a community based on princi-

ples of equality

Teller A resource for which entities compete, usually in queu-

ing. A server.

Utilisation (of server) is the time-average number of individual servers,

divided by the total number of servers

Variates A random variable with a numerical value that is defined

on a given sample space

xvi

Chapter 1

Introduction

This final year project aims to apply techniques, skills and vantages of Industrial Engi-

neering to recommend improvements to Stellenbosch Traffic Department in creating a

business process which flows naturally and serves clients efficiently. The proposed im-

provements should increase customer satisfaction by reducing time waited in queues and

improving customer service. Existing structures and information systems will be con-

sidered in proposing a re-engineered queuing system, facility layout, and management

style. It is expected that this project will make use of operations research methods,

simulation and facility design.

This chapter describes the background of the problem at the Stellenbosch Traffic

Department and the need to improve its processes, the possible use of queuing theory

and simulation as problem solving methodologies, as well as this project’s objectives and

road map. The Stellenbosch Traffic Department may be referred to as “the department”

throughout this report.

1.1 Background

The Stellenbosch Traffic Department serves a local population in excess of 200 000 cit-

izens (Stats SA, 2007). It provides a municipal service to the Stellenbosch community

in the form of testing and issuing roadworthy validations, renewal of licences, registra-

tions, issuing and renewal of new drivers’ licences, facilitation of learners’ licence tests

and processing payment of fines. The department experiences large volumes of human

traffic on a daily basis, in part due to the fact that it is situated in a university town

1

1.1 Background

catering for over 20 000 university and college students; they are at the most popular

age to acquire drivers’ licences (University of Stellenbosch, 2012). In doing so, it is

required to cater for indigenous residents of Stellenbosch as well as those originating

from neighbouring municipalities, but residing temporarily in Stellenbosch.

The author realised an opportunity to improve service delivery and reduce customer

waiting time at the department after experiencing the frustration of the department’s

current system. Recent articles published by the Eikestad News newspaper on 26

April, seen in Figures A.1 and A.2 in Appendix A, illustrate the reality of the issue at

hand and emphasize the frustration of the general public who perceive that the waiting

lines at the department are unnecessarily long. The reaction from the department is

also included in Figure A.3. The articles are in Afrikaans and are not translated on

the assumption that the reader is literate in Afrikaans, but also to ensure that the

expressions of the articles are left untainted.

An interview with the Administrative Traffic Chief at the department, Mr. A. Royi,

highlights a few issues regarding the processes at the department which contribute to

ill service delivery (Royi, 2012):

Enquiries The department has a general enquiries desk to which persons entering the

building report. Customers are advised as to what documentation is necessary

and are directed to join a queue at the required section: Fines, Licence & Reg-

istration or Drivers Licences. Enquiries has only one server who is able to assist

one client at a time. This means that a single server at Enquiries is required to

serve a queue containing as many clients as there are in three other queues which

are served by multiple tellers. This causes the queue at Enquiries to “explode”,

increasing the time customers spend at the department.

Fine Processing System The system which processes and receives payment of fines

operates independently from the national system used for all other transactions.

Fines are processed by a system called OPUS or TCS, whereas all other trans-

actions are processed using eNatis. The problem is that fines can only be paid

using the OPUS/TCS system.

It is here that an opportunity to integrate the OPUS/TCS and eNatis systems is realised

by the author.

2

1.2 Business Process Analysis

Drivers Licence Section There is a lack of capacity at the Drivers Licence section.

Currently, learners’ licence tests are only performed on Wednesdays, while drivers’

licences are issued and renewed on the remaining days due to a lack of physical

space.

The author realises the opportunity to re-engineer the layout of the facility.

Employee Absenteeism The department is experiencing a high occurrence of in-

termittent and “unnecessary” absenteeism. It is suspected that employees are

exhausted and that the department is under staffed.

The author is interested to determine whether the department is in fact understaffed

or whether its current staff are simply under utilised.

Payment Facilities Payment facilities are limited to cash, cheque or debit card only.

There is no provision made for credit cards as it is associated with high cash

handling fees. “The installed debit card facilities are also unreliable and therefore

signs are placed at the tellers to notify customers that no card facilities [are]

available.” Mr. Royi informed that all prices are set and governed by the National

Department of Transport and that they cannot implement any other fees locally

without it being approved nationally.

The limitation of payment methods serves as a frustration to clients, and could place

strain on the queuing system as numerous clients are required to draw money at an

ATM and re-enter the queue.

It was also highlighted that the department understands that service delivery is a

key priority, but that it has limited funds to make necessary improvements, and strict

laws and controls which prohibit implementation of any new systems that have not

been approved by the National Department of Transport.

What follows is an “as-is” analysis of the business events carried out at the Stellen-

bosch Traffic Department.

1.2 Business Process Analysis

The way in which the department currently executes transactions and the facility layout

which supports this, will be analyzed in this section.

3

1.3 Solving Methodologies

The department executes its services with a modular approach; each section (Fines,

Licence & Registrattion, Drivers Licences) is independent from the other, with separate

tellers and queues for each. For example, a customer who wants to pay a fine, but also

renew a license, is required to queue for each transaction separately by first queuing at

the Fines section and paying the fine, and then having to queue again at the Drivers

Licence section in order to complete the renewal process. The Fines-, Drivers Licence-,

and Licence & Registration sections are mutually exclusive and therefore cannot process

any other transaction types other than their own.

The structural facility layout supports the current modular style of executing busi-

ness events; each section has its own queue and waiting area, albeit insufficient and

confusing for customers. It is speculated by the author that customers are confused

because there is a lack of logical systems and flow within the building.

The next section will take a look at the assumed methodologies required to solve

the issues faced by the department as discussed in sections 1.1 and 1.2.

1.3 Solving Methodologies

This section will consider possible techniques applicable to the problem at the depart-

ment, as well as the reasoning behind their use. It is speculated that the root cause of

the lengthy waiting lines described in section 1.1 is the modular layout of the various

sections at the department, more so than the issues highlighted by Mr. Royi. This

section will explore two main Industrial Engineering techniques – queuing theory and

simulation – as problem solving methodologies.

1.3.1 Queuing Theory

Queuing theory is practical in measuring a system’s performance by calculating the

time a client can expect to wait in the line and the total time spent in the system;

that is time spent queuing and being served (Gross et al., 2008). These values aid in

designing a near optimal system. It is in this project’s interest to balance idle time

of servers against minimized customer waiting time, and the resources associated with

these. The design of the structural facility which will house the servers, the waiting

line and waiting room are also to be considered.

4

1.4 Project Objectives

The analysis of the waiting line at the department is one of many complexities; one

being the distribution of arrivals which varies non-uniformly with time. In attempting

to design an improved queuing facility and layout it will be necessary to iterate queuing

analysis to compare results for each proposed solution in order to identify near optimal

solutions (Kelton et al., 2010). Analysis by queuing theory forces the analyst to make

simplifying assumptions. Kelton et al. (2010) suggests that in making such assumptions,

the complexities of the system are avoided, thus questioning the validity of results. It

is here that the use of simulation is suggested in order to build a more valid model of

reality.

The next section considers the use of simulation for this project.

1.3.2 Simulation

Computer simulation is the imitation of the operation of a system and its internal

processes over time to draw conclusions about the system’s behaviour. Simulation

models are often used to predict the effect of changes to existing systems, as well as

predict the performance of new systems. Simulation gives an adequate analysis of

complex systems, more so than queuing theory. (Kelton et al., 2010) It is therefore

considered to be applied to this project in analysing the current system, and the effects

of proposed changes.

The next section describes the aim and scope of this project, followed by a project

road map.

1.4 Project Objectives

The main aim of this project is to re-design the transactional flow of the Stellenbosch

Traffic Department such that customers are served promptly and resources are used

near-optimally. This will require analysis of current processes and systems, and finding

better ways of executing them. It will consider the queuing system, layout of the

department and general management.

The author envisions a complete turn-around in service delivery and customer sat-

isfaction. It is also envisioned to propose that the outcomes of this project be imple-

mented at traffic departments nationally; an appeal made to the National Department

of Transport.

5

1.5 Report Road Map

The project plans to deal with the following aspects with the described level of

detail:

Waiting Line Analysis of the waiting line using queuing theory and simulation must

be done in detail. The only way to expect a valid and realistic result is by using

accurate data. Data distributions will be approximated, but within statistically

accepted error bands.

Facility Layout The project will include a redesign of the facility’s layout, especially

at the Drivers Licence section which is experiencing capacity constraints, as ex-

plained in section 1.1.

Resource Management Organisational behaviour and general management advice

will be provided to aid management at the department to implement proposed

solutions, and also to improve employee morale. The author suspects that the

high occurrence of sick-leave is as a direct result of poor company culture and

uncomfortable work environment. The most valuable resource of a business is its

human capital; an idea often ignored by management. This report will provide

an understanding of organisational behaviour to the reader and suggest straight

forward socially, psychologically and physically implementable improvements.

Results It is desired to provide results in quantitive tables, graphs and summaries.

The simulation model will include animation. These will be used to convince

external parties who are less technically inclined on the subject of its results and

consequences.

The project plan which outlines the specific activities, durations and due dates for this

project is supplied in Appendix A.2.

1.5 Report Road Map

This chapter stated the problems to be addressed by this final year project. It is realised

that simulation and queuing theory can be used to re-design the Stellenbosch Traffic

Department’s business processes in order to increase customer satisfaction.

Chapter 2 explores literature on queuing theory and simulation as problem solving

tools and briefly introduces managerial aspects. Chapter 3 details operations at the

6

1.5 Report Road Map

department as well as three local traffic departments in the Western Cape which are

used for benchmarking, while Chapter 4 develops proposed changes to be made at the

Stellenbosch Traffic Department, including strategic and operational changes. Chapter

5 presents an analysis of the simulation of the proposed changes suggested in Chapter 4.

Chapter 6 draws conclusions and makes recommendations to the department. Chapter

7 provides an overview of the final year project from the author’s perspective.

Figure 1.1 illustrates a summary of the problems experienced at the department,

and the road map followed in this report.

7

1.5 Report Road Map

Figure 1.1: Graphic Summary of the Problem Statement, Methodology and Result.

8

Chapter 2

Literature Review

The previous chapter introduced the problem to be solved at the Stellenbosch Traffic

Department and the aim of this report. It mentioned specific problem areas at the

department as identified by the Administrative Traffic Chief, Mr. Royi, and mentioned

that queuing theory and simulation can be used as tools in finding solutions for the

department. The project plan was also introduced.

This chapter includes a literature study of queuing theory and simulation. More

specifically, it elaborates on discrete-event modeling as solving means. Thereafter,

literature on managing queues and human capital is explored. Lastly, analysis by

TOPSIS and box plots is discussed.

2.1 Queuing Theory

This section covers the basics of queuing theory rather than queuing simulation. Fa-

miliarity with queuing and its terminology is imperative to building queuing models.

Most models of real operations are of queuing systems, whether it be queues of physical

objects or information. In this report, queueing will be considered for the flow of people

(the customers). Queuing theory is also used in the verification of simulation models,

to be discussed later in this report. (Kelton et al., 2010)

2.1.1 Fundamental Concepts

It is assumed that the reader is familiar with basic probability theory and the following

concepts: experiments, sample space, events, random variables, probability distribu-

9

2.1 Queuing Theory

tions, expected values, and steady-state.

A queue system is one in which entities arrive, wait in one or more queues, are

served, and then leave. If there are more than one servers serving a queue, the system

is called a multiple server queueing system. An entity is in service when it has left the

front of the queue and is being served by a server, and in queue if it is waiting in the

line but not yet in service. The system refers to the sum of the queue and in service

aspects. The queuing discipline stipulates the nature in which entities move from the

queue into service. These include last-in-first-out (LIFO), general discipline, and first-

in-first-out (FIFO), the latter being applicable to the Traffic Department problem since

customers who enter the waiting line are served in order of first arrival. (Kelton et al.,

2010)

This project aims to minimize customers’ time in system (TIS). This is the time

an entity (the customer) spends in the waiting line and in service. The time in system

of customers will be used as performance measure of the success of various queueing

designs suggested in this report because this is the main contributor to the lack of

customer satisfaction, in the author’s opinion. It is the only objective which aims to

be minimized in this project, describing this project as a single objective optimization

problem. However, it must be noted that having this as the only objective could result

in requiring infinitely many servers. To prevent this, the proposed re-designed facilities

will be analyzed while considering utilization of the servers. It would be economically

infeasible to employ an infinite number of servers, but have the servers under utilized

and waiting idle for most part of the day. (Kelton et al., 2010)

In general, queueing theory analysis is done for steady-state conditions, which in-

clude a few basic symbols. These symbols are defined in the Nomenclature of this

report for ease of reference. However, it is necessary to be acquainted with some queu-

ing terminology before it can be discussed:

Wq Average time in queue (excluding service time)

W Average time in system (in queue and in service)

Lq Average number of entities in queue

L Average number of entities in system (in queue and in service)

ρ Workload rate or traffic intensity

10

2.1 Queuing Theory

λ Average arrival rate

µ Average service rate

Relationships between these steady-state measures exist which make computing and

estimating other queueing characteristics fairly simple. The first and most important

of these relationships is Little’s Law which has been proven in detail by Ross (1970).

Little’s Law is

L = λW

Kelton et al. (2010) highlights the interesting fact that Little’s Law relates a time

average (W ) to an entity-based average (L). More specifically, Little’s Law can be

extended as

Lq = λWq

and intuitively,

W = Wq + E(S)

where E(S) is the expected service time. This simply says that the expected time of

an entity in the system is the sum of the expected time in the queue and the expected

time in service. These formulae enable one to algebraically calculate any of Wq, W , Lq

or L, if only λ and a single value of these is known.

2.1.2 Customer Behaviour

Customer behaviour plays a very important role in queuing systems. The above sections

assume that every customer entering a queue will remain in the system until served, and

that the customer remains in only one waiting line. However, a more realistic approach

considers bulk arrivals, balking, reneging and jockeying, as explained by Gross et al.

(2008):

Bulk Arrivals More than one customer enters the queue at an instant.

11

2.2 Simulation

Balking If customers wanting to enter the queue see k customers ahead of them,

they do not join the queue. Customers have different discouragement limits, k.

However, the customer’s discouragement is not only due to the number of people,

k, but also the speed with which the queue advances. A long, but fast-moving

queue might be deemed worth joining while a short, slow-moving queue might

not.

Reneging A customer joins a queue and then estimates whether the waiting time will

be intolerable. If it is, the person leaves the queue. Reneging can be described

by a pure mathematical function.

Jockeying A customer moves back- and forth between several queues to attain the

shortest waiting time. As realistic as this behaviour is, it is very difficult to pursue

analytically since the probability distributions describing the jockeying process

become complicated.

It is necessary to establish whether the behaviours described here are in fact realised

at the department. If so, it must then be decided which of these occurrences to include

during the simulation and analysis. It may, or may not be acceptable to ignore the

effect it will have on the accuracy of the methodology used.

2.2 Simulation

This section considers the power of simulation in queueing problems, methods of gath-

ering input data, and validation and verification.

Many computerised tools are available which are useful in making decisions to

solve real problems. One of these decision techniques is simulation. Central to most

simulation models is the queue. (Kelton et al., 2010)

Queues occur because facilities lack in capacity to handle the demand placed on

them. It is also difficult to accurately predict the demand placed on these facilities and

how much time is required to render service. Queueing analysis is usually characterised

by uncertainty of (Kelton et al., 2010):

• Level of demand

• Service time

12

2.2 Simulation

• Behaviour of entities (reneging customers, bulk arrivals, etc.)

The purpose of applying queueing analysis is to identify what is needed to create

an adequate service facility. If a service facility is too generous it will result in idle

employees. If the service facility is inadequate, excessive waiting time could result in

a loss of goodwill of the customers and could discourage customers from entering the

queue at all. (Kelton et al., 2010)

There are numerous algorithms which are useful in solving queueing problems. How-

ever, Proctor (1994) and Kelton et al. (2010) agree that simulation is a better suited

analytical means for complex systems because it provides a near optimal solution which

is more realistic than those acquired by pure mathematical models (queuing theory).

A system is deemed “complex” when it either cannot be expressed mathematically

without making unreasonable assumptions, or the formulation is too involved for eco-

nomical and practical purposes. Simulation does not make use of any algorithm, but

rather illustrates the performance of a system given a set of input parameters. (Kelton

et al., 2010) These parameters are discussed in greater detail later in this section.

Simulation is the preferred choice of business analysts because of its degree of realism

and the ease with which it can be understood by non-technical decision makers. It is the

ultimate solution for decision makers to experiment with various factors and scenarios

of a problem to determine a near optimal solution without physically interfering with

the system. (Hoover & Perry, 1989)

Simulation requires the taking of random samples from a probability distribution

which represents the real-world system being analysed. Before a simulation can be

performed, the distribution of events needs to be determined for the real-world problem.

(Proctor, 1994)

Various approaches to simulation and methods of acquiring the distribution of these

events are discussed in the following section.

2.2.1 Characterising the Problem

This section characterizes the simulation for this project in detail.

A model of a scenario in simulation is characterized into three classes (Kelton et al.,

2010):

• Static vs. Dynamic

13

2.2 Simulation

• Continuous vs. Discrete

• Deterministic vs. Stochastic

A static simulation is one in which time has no effect on the model’s structure and

operation. This implies that such a model can be simulated without considering the

effect of time. A dynamic model is where the issue of time is central to the changes

and flows in a system. (Kelton et al., 2010)

Dynamic models usually have state variables that describe the state of a simulated

system. For a queueing system, these variables would indicate the length of the queue,

the times of arrivals of customers, or whether a server is idle or in service. A system

is considered to be continuous if these variables change continuously over time. A

common example would be the flow of water in and out of a tank. If states of these

variables change only at specific instances of time, rather than continuously, then the

model is discrete. This is most applicable to queueing problems since state variables

only change value at the time of occurrence of discrete events such as a customer

entering the queue, or a server going from in service to idle. (Kelton et al., 2010)

A deterministic model is one in which all input values of the model are constant,

and non-random. A deterministic model will always return the same results, regardless

of the number of times the model is re-run. Such models are very rare and somewhat

unrealistic. Simulation models where input values vary randomly, or from some prob-

ability distribution, are stochastic. This is typical of most queueing problems in that

the service time at the counter varies for each client. However, these service times can

be characterised by a probability distribution. This means that a stochastic model is,

in essence, run by a random draw of a distribution of data. (Kelton et al., 2010)

That said, running such a model only once would show only what could happen.

As Kelton et al. (2010) explains, this would be like tossing a die only once, observing a

number 4, and concluding that the die is biased to always result in a 4. This shows that

the simulation model of a stochastic problem must be run multiple times to conclude

the system’s behaviour. However, simply re-running the simulation of the stochastic

model will actually give the exact same results. This is due to the fact that random

number generators created by software are in fact not random at all, and will produce

the exact same random numbers each time. The solution to this is to replicate the model

multiple times within the same execution. This will produce a different random output

14

2.2 Simulation

from each replication while the simulation software keeps track of the uncertainty in

the results. (Kelton et al., 2007)

It can be concluded that the queueing problem at the Stellenbosch Traffic Depart-

ment is one which is dynamic, discrete, and stochastic:

Dynamic Time plays a role in the model of the queuing system converting client

arrivals to clients having been served by the system.

Discrete Clients arrive and leave at specific time intervals. Decision variables are

allocated only integer values.

Stochastic The system is modeled as one with some random inputs (random arrivals

of clients requiring random service times).

This type of problem is most typically solved by simulation. In fact, simulation

software is specifically designed for such problems (Kelton et al., 2010).

2.2.2 Input Analysis and Parameters

This section aims to describe the data required for the simulation of the queuing prob-

lem at the department, method for determining data distributions, and random number

generation to run the simulation. It will also take a brief look at verification of simulated

results.

Kelton et al. (2010) says that the distribution of service times must be specified. It

is ideal to have such data available, or made available by a time study.

Since a time study requires that real world data be collected, it would seem most

logical to simply use this collected data as input for the simulation model, rather

than a more indirect approach of fitting a probability distribution to the data, and

then generating random variates from the fitted distribution. Kelton et al. (2010) and

(Bekker, 2012a) give a few reasons for using a fitted distribution:

• Simulations are required to run for very long times and many replications are

run to ensure statistically valid results. The simulation would simply run out

of real-world data unless a probability distribution is fitted from which infinitely

many random variates can be generated.

15

2.2 Simulation

• The collected data represents only the time period for which the data is physically

collected. That data does not necessarily show what could have been observed

at other times. Using only the collected data would limit the simulation, and

question its validity.

• Collected data is usually only observed for certain times and periods; the sample

size is small. This could result in “gaps” in the data where events could possibly

have occurred, but which simply were not observed during the physical data

collection.

It is realized that it is more convenient to fit a probability distribution to some collected

data, and then generate random variates from this. It also ensures validity of the

simulation results.

Two methods of fitting a distribution to collected data are considered for this

project:

Option 1: Estimated Service Time, Physical Time-Study of Arrivals

This method is as suggested by the study leader. It is suggested that the servers be

asked to estimate their shortest-, typical-, and longest service times. This data would

then be used to create a triangular distribution of service time, as in Figure 2.1, from

which the simulation can generate variates. This is an internationally accepted practice

(Bekker, 2012b). Physical collection of data for arrivals of customers could be done by

means of a time study.

Option 2: Physical Time-Study of Service Time and Arrivals

This method follows the typical procedure for determining data distributions as ex-

plained in Kelton et al. (2010). A physical time study of the service time of each server

is done, and the same for inter-arrivals of customers. This method will require more

time and effort than detailed in Option 1, and is less convenient.

Option 2 is chosen as it provides the most accurate input data. The following

paragraph outlines a few important guidelines for doing time studies correctly.

Freivalds & Niebel (2009) say that the analyst conducting the time study must be

able to inspire confidence, exercise judgement and develop a personal approach with

everyone with whom s/he comes into contact with in order to ensure its success. The an-

alyst should also be familiar with and understand the operations being studied. Among

16

2.2 Simulation

Probability, f(t)

Service Time Shortest Typical Longest

Figure 2.1: Triangular Distribution as Estimated by Servers

many useful guidelines, Freivalds & Niebel (2009) make a few suggestions applicable to

time studies at the department:

The Operator The person being studied should be made familiar with time study

procedures and practices, and should be convinced of the advantages of cooper-

ating – having confidence in time study methods, as well as the analyst.

At the Stellenbosch Traffic Department this was done by fostering a comfortable re-

lationship with the servers, while also informing them of the uses and benefits of the

proposed outcomes of this project. Employees were approached in a friendly manner,

giving opportunity to ask questions which were answered frankly and patiently. The

author realised that explaining time study techniques and its aims to the employees at

the department allowed for the most valid data to be captured.

Recording The analyst should record all information and allow for remarks and

sketches on the time study form. The analyst should be sensitive to writing

notes when the time-study subject is present; it creates a sense of suspicion.

The author considered this; only making notes once out of line of sight of the interviewee

or time study subject.

17

2.2 Simulation

Position of the Analyst The analyst should preferably stand out of line of sight

of the server. Also, the analyst should refrain from any conversation with the

employee during operation as this can cause distraction and affect the validity of

the data.

A time study form in which servers actually conduct their own time study was con-

sidered by the author. The form required servers to tick a box each time they serve a

customer. The template is shown in Appendix A.3. This would give an indication of

customers served per hour.

2.2.3 Validation and Verification

It is important to bear in mind that the simulation needs to be valid. If the simulation

is invalid, it is not a true representation of reality, and forfeits its use as a solution to

any problem being modeled (Kelton et al., 2010). In creating the simulation model,

the validity of the model must be considered at all times.

Kelton et al. (2010) emphasizes the importance of verifying that the simulated

model is a valid representation of reality. It is proven by this source that simulation

does provide near-optimal, true results. However, every model must be checked to

ensure that the specific simulation is correctly formulated and that the results are

true. Bekker (2012a) states that “verification allows us to confirm that we have built

the model right, whereas validation allows us to confirm that we have built the right

model.”

It is suggested that a set of expectations of the model’s results be set up before the

actual simulation. These expectations are commonly determined by common sense and

by use of analytical means such as queueing theory. Once the simulation is performed,

the expected results should be compared to the simulation’s results. If the results do

not correlate, a few reasons for the differences should be considered, and adjustments

made to the model.

A few reasons could include (Kelton et al., 2007):

• The model is incorrectly created in the simulation software (i.e. there is an error

in the model itself).

18

2.3 Queuing Theory vs. Simulation

• The assumption that the simulation should match the expected results is incor-

rect. Kelton et al. (2010) explains that a “warm-up period” can be used to remedy

this.

• A sampling error could exist. The simulation’s results correlate with the expec-

tation probabilistically, but the model has not been run for long enough, or the

results are being interpreted incorrectly.

If results correlate realistically, the simulation is deemed valid by verification (Kelton

et al., 2010).

2.3 Queuing Theory vs. Simulation

In comparing queueing theory to simulation it is noticed that queuing theory falls short

for complex systems. Calculation by queuing theory alone is exact and not subject to

statistical uncertainty. Simulation, on the other hand, is not exact and is associated

with statistical uncertainty. (Kelton et al., 2010) This will be discussed in greater detail

later in this report.

Queuing theory requires that assumptions be made, and in many real cases (es-

pecially complex systems) such assumptions can be incorrect and invalidate results.

Simulation is made to deal with short-term analysis of queueing, and allows for a more

realistic data distribution input. It requires fewer generalizations and assumptions such

that the most appropriate inter-arrival and service time distributions can be used to

almost identically mimic the real system. There is, however, a negative aspect of sim-

ulation; all results are statistical estimates and therefore must be analyzed by proper

statistical means in order to draw justified conclusions. (Kelton et al., 2010)

A brief overview of queuing theory and how it compares to simulation was given

in this section. The following section introduces TOPSIS as a means of analysing the

best alternative queueing model, once simulation results are acquired.

2.4 TOPSIS Analysis

The “Technique for Order of Preference by Similarity to Ideal Solution” (TOPSIS) is a

multi-criteria decision analysis method, related to Analytical Hierarchy Process (AHP)

decision making, which was developed by Hwang and Yoon in 1981 (Jahanshahloo

19

2.4 TOPSIS Analysis

et al., 2006). It is mentioned by Kim & Nelson (2001) that statistical procedures based

on ranking and selection theory, such as TOPSIS, are popular when the number of

alternative designs is small as they are easy to apply and interpret.

It is useful in choosing a best- or worst case of numerous alternatives. It is based

on the idea of choosing an alternative which has the shortest geometric distance from

the ideal solution, and the longest geometric distance from the least preferred solution.

It compares alternatives based on weightings of relative importance of a set of crite-

ria, normalising the scores for each criterion, and calculating the geometric distance.

(Jahanshahloo et al., 2006)

The following describes how TOPSIS analysis is performed (Jahanshahloo et al.,

2006):

Firstly, a matrix of m alternatives by n criteria is developed, where each combina-

tion is given as Xij , resulting in a matrix (Xij)mxn.

The matrix is then normalised to the form:

R = (rij)mxn,

where rij = Xij/Pmax(vj),

Pmax(vj) = max{vj},

for i = 1, 2, ...,m,

j = 1, 2, ...,n.

where vj refers to the performance measure, j.

Next, a weighting is assigned to each criterion, n, and the weighted normalised matrix

is calculated:

T = (tij)mxn,

= (wjrij)mxn,

for i = 1, 2, ...,m,

where wj = Wj/

n∑j=1

Wj,

n∑j=1

wj = 1.

Wj is the weight given to vj.

20

2.4 TOPSIS Analysis

Next, the best alternative (Aw) and worst alternative (Ab) is calculated using:

Aw = {〈max(tij |i = 1, 2, ...,m)|j ∈ J−〉, 〈min(tij |i = 1, 2, ...,m)|j ∈ J+〉}

≡ {twj |j = 1, 2, ..., n}

Ab = {〈min(tij |i = 1, 2, ...,m)|j ∈ J−〉, 〈max(tij |i = 1, 2, ...,m)|j ∈ J+〉}

≡ {tbj |j = 1, 2, ..., n}

where J+ = {j = 1, 2, ..., n|j associated with benefitting criteria,

J− = {j = 1, 2, ..., n|j associated with negative (cost) criteria.

The distance between the target alternative, i, and the worst condition, Aw, is

calculated using:

diw =

√√√√ n∑j=1

(tij − twj)2

and the same for the best condition, Ab:

dib =

√√√√ n∑j=1

(tij − tbj)2

for all i = 1, 2, ...,m.

The similarity to the best condition is calculated:

sib = diw/(dib + diw), 0 ≤ sib ≤ 1, i = 1, 2, ...,m.

Lastly, the alternatives are ranked according to sib (i = 1, 2, ...,m) where the alter-

native with the highest sib is the overall winner.

An important assumption of the TOPSIS method is that the criteria are mono-

tonically assigned; the weightings sum to 1, or 100%. The TOPSIS analysis consid-

ers trade-offs between criteria values of the outcomes which will be outputted by the

simulation. It provides a realistic method to analyse alternatives, compared to other

decision process models which might not consider the relative importance of criteria.

(Jahanshahloo et al., 2006)

TOPSIS will be used to determine the best alternative between the queue designs

simulated. The criteria will relate to the performance measures chosen to measure the

improvements of the model and will typically include:

21

2.5 Box Plot Analysis

• Time in system (TIS)

• Number of customers in system (CIS)

• Utilisation of servers

• Percentage of customers not served

The next section considers the use of box plots to analyse alternatives.

2.5 Box Plot Analysis

A box plot is a convenient way of graphically illustrating numerical data as a five

number summary; the sample minimum, lower quartile, median, upper quartile and

the maximum. It can be used to compare alternatives without making assumptions of

the statistical distribution of the data (Frigge et al., 1989).

In comparing box plots, when there is no overlap in the spread of data it can be

said that there is a definite difference between the alternatives compared. With boxes

overlapping, but not the medians, it is likely that there is a difference, but this is

not definite. Should boxes overlap with both medians, no difference can be claimed.

(Nayland College Mathematics, 2012) Somewhat contradictory, Kim & Nelson (2001)

say that choosing an alternative is not as definite as previously described. Instead, it

is suggested that there be an indifference zone set by the experimenter such that a

difference in boxes should be greater than the said indifference zone, else the difference

between the data should be considered practically insignificant (Kim & Nelson, 2001).

It is recommended by Bekker (2012b) that this zone be set to approximately 5%.

The next chapter introduces literature on customer waiting time and organisational

behaviour.

2.6 Managerial Aspects

The previous sections discussed literature on queueing theory, how it compares to sim-

ulation, simulation’s function as decision tool, data required for the simulation study,

TOPSIS analysis, and the use of box plots. This section provides evidence that cus-

tomer waiting time is a valid performance measure to be considered in order to improve

22


the quality of service at the department. It also explores literature of organisational

behaviour.

2.6.1 Customer Waiting Time

This section explores the time a customer waits in the system as performance measure

and considers other factors contributing to perceived waiting time, in addition to actual

waiting time.

Customer waiting time is regarded as one of the most critical aspects of quality

in service. In the modern day, society is more time-constrained than ever before.

A competitive world in which the expectation to do more in less time is unlikely to

diminish. (Sheu et al., 2003) Extended waiting has been cited as an important source

of customer dissatisfaction in many service industries (Murdick et al., 1990). Customer

evaluation of service quality is partly determined by the time waited for a service,

therefore many companies have included waiting time as a measure of service quality

(Sheu & Babbar, 1996). This motivates why it is appropriate to use customer waiting

time as a performance measure for this project.

Service providers, such as the Stellenbosch Traffic Department, realise that cus-

tomers value time. A customer having to wait an “unreasonable” amount of time is

considered to be “wasting” time and this could prevent customers from entering the

queue at all. This is essentially saying that a customer waiting in a line is a lost cus-

tomer (Sheu et al., 2003). Any private organisation would lose this customer to its

competition.

However, this is slightly different for traffic departments in South Africa; vehicle

owners and drivers are obligated to perform certain transactions such as renewing their

licences or paying fines, by law, with no option to make use of a competing service

provider – all drivers residing in Stellenbosch are obligated to use the Stellenbosch

Traffic Department. This means that customers are forced to enter the waiting line at

some or other stage, regardless of the expected waiting time. The only motivation for

the department to reduce waiting time is to encourage all citizens to comply to South

Africa’s traffic laws. Improving the current system will encourage road users to pay

fines, roadworthy their vehicles, renew licences and acquire legal licenses – all of which

increase revenue for the municipality and make South African roads safer to use. It

will also reduce employee fatigue and improve morale. The advantages are “infinite”.

23


Changes to a process can result in improvements with regards to actual waiting time

for a customer, but customer satisfaction might not be realised unless this improvement

is perceived by the customer. An article by Luo et al. (2003) suggests that perceived

waiting time is a more accurate predictor of customer satisfaction and is quite often

different from the actual waiting time, depending on how and what customers are

waiting for. An instance at Disney World is described (Luo et al., 2003):

“In Disney World, for instance, a number of popular rides make visitors wait for at

least 45 minutes to take a 3 minute ride, but most visitors are quite satisfied with their

experience. This is because the distractions employed by Disney make visitors feel that

they did not wait that long.”

This raises the question of what can be done, other than improving the actual

waiting time, to influence customers’ perceived waiting time at the traffic department.

It is suggested to change the service environment (Katz et al., 1991), engage with

customers during the wait (Dube & Schmidt, 1996), and to provide feedback of expected

waiting time (Hui & Zhou, 1996). Another example shows where feedback reduced the

dissatisfaction of waiting (Luo et al., 2003):

“Hui & Zhou (1996) conducted an experiment in which university students were

instructed to use an online course registration system with system delay. Under one

condition, students were informed about how long the delay was going to be, and under

another, there was no delay information. The results showed that delay information did

not change students’ perceived waiting time, but students felt they had more control

over the wait. Providing delay information also reduced students’ dissatisfaction with

the delay.”

Maister (1985) has found that both customer perception and expectation about

a service operation play a role in determining customer satisfaction. If the customer

perceives that the service has exceeded his/her expectations, the customer is satisfied.

This says that a customer’s level of satisfaction can be influenced by adjusting his/her

expectation or perception of the service. On the same topic, Baker & Baker (1996)

suggest that a customer’s perception of waiting time can be influenced by changing a

customer’s perception of time, or of the queue. It is proposed that spatial layout, queue-

ing progress, and social justice are variables which can alter a customer’s perception of

a queue.

24


On the other hand, a customer’s perception of time can be influenced by using

music, lighting, colour, employee visibility, and social interaction. The use of music is

found to have positive effects on a customer’s emotions toward waiting in a queue, but

has no effect on the perceived waiting time. (Hui & Dube, 1997)

In summary, in redesigning a service process, not only the actual waiting time, but

the perceived waiting time should also be considered. In the author’s opinion, perceived

waiting time cannot be quantified in a simulation and can only be measured once the

redesigned process has been implemented.

This section has shown that actual waiting time should not be the only focus of

process improvement at the department, but that perceived waiting time and customer

satisfaction should also be considered. It is also important to bear in mind that process

improvement might bring about unintended results. A small scale “pilot project” of

the proposed process change could initially be implemented in order to establish any

unintended effects.

2.6.2 Organisational Behaviour

This section discusses behavioural aspects of people in a work environment. The work

environment is required to facilitate organisational diversity and motivation, stress

management, leadership and communication within each employee.

2.6.2.1 Work Motivation

The author perceives that the employees of the department do not show interest in their

work and are generally unmotivated. The following literature discusses work motivation

and ways to improve it.

Motivation refers to the forces from within an individual that causes the person to

wilfully and persistently direct efforts toward achieving a goal, where the goal is not

achievable merely by the person’s physical abilities (Hitt et al., 2011).

Two theories for motivation exist: content- and process theories. Content theories

of motivation focus on identifying specific factors that motivate people. It is a straight

forward and traditional approach which includes McClelland’s Needs Theory, Alderfer’s

ERG Theory, and Herzberg’s Two-Factor Theory. (Hitt et al., 2011)

McClelland states that each person has a need for achievement, affiliation and power

in order to be motivated. These three needs are independent, meaning that a person

25


Motivators

Hygienes

Satisfaction

No dissatisfaction

No Satisfaction

Dissatisfaction

Figure 2.2: Herzberg’s Two Factor Theory of Motivation

(Management Study Guide, 2012)

can be in varying stages of each need. Alderfer’s ERG Theory is similar to Maslow’s

well-known “Hierarchy of Needs” in that it puts forward basic needs which build on

one another. The levels are, starting from the most basic need, existence, relatedness,

and growth. Only once the need of existence is satisfied, can a person progress to

satisfy his/her need of relatedness, and then growth. Herzberg’s Two-Factor Theory

emphasizes the rewards and outcomes of a situation as motivator for performance.

Rewards are related to job satisfaction or job dissatisfaction, independently. (Hitt

et al., 2011) In other words, the antonym of job satisfaction is not dissatisfaction, but

rather low satisfaction, as illustrated in Figure 2.2.

In contrast to content theories, process theories consider the process by which fac-

tors result in motivation, rather than the factors themselves. This includes Vroom’s

Expectancy Theory, Equity Theory and Goal Setting.

Expectancy Theory states that there are multiple, complex sources of motivation.

It suggests that people consider three factors in establishing their level of effort: the

probability that effort will lead to performance (expectancy), the ratio of rewards likely

to be received for a particular performance level (instrumentality), and the relative

importance or value of the outcome (valence). (Hiriyappa, 2011)

Equity Theory simply states that a person’s motivation depends on his/her opinion

of how fair a situation is, and how s/he is being treated. Each person calculates the

ratio of equity in the expected outcomes versus their inputs, compared to other people

in the organisation. It also says that individuals adjust their effort according to their

opinion of equity. (Hitt et al., 2011)

Goal Setting Theory suggests that goals enhance human performance because they

26


channel focus, attention and effort (Miner, 2005). Hitt et al. (2011) states that the

positive effects of goal setting on work motivation is “one of the strongest findings

in research on organisational behaviour.” It recommends that goals setting include

consideration of the goal difficulty, specificity, commitment, participation of associates,

and feedback of performance.

2.6.2.2 Stress Management

Mr. Royi mentions in section 1.1 that the department’s high occurrence of absen-

teeism could be attributed to the fact that employees are exhausted and stressed. This

paragraph discusses solutions in managing stress.

Hitt et al. (2011) suggests reducing organisational stress by increasing individu-

als’ autonomy and control, ensuring individuals are fairly rewarded for their effort,

maintaining job demands and requirements at healthy levels, ensuring that employees

have adequate skills for the job, increasing employee involvement in decision making,

improving physical work conditions, providing job security, career development and

healthy work schedules, improving communication throughout all job levels, encour-

aging managers to be “toxin handlers” who can listen and lend advice to individuals,

and implementing wellness programmes. It is important to realise that these actions

require involvement from management.

Individual stress management is also an important consideration. It is again sug-

gested by Hitt et al. (2011) that individuals participate in regular exercise, practise a

lifestyle which consists of a proper and balanced diet, involve themselves in social net-

works for support, and make use of relaxation techniques. This should be encouraged

by management at the department.

2.6.2.3 Leadership

It is suspected by the author that the department is experiencing a lack of organisa-

tional leadership. An article in Hitt et al. (2011) by Maria Yee, CEO of a furniture

manufacturing company in the USA, expresses her belief that leadership development

throughout the organisation is one of the top five factors contributing to gaining a com-

petitive advantage in the market. Warren Bennis, a leadership expert, says that leaders

should be “doing the right things” and not so much “doing things right” (Bennis, 2003).

27

2.7 Summary of Literature Review

Hitt et al. (2011) put forward various types of leadership theories which demonstrate

different behaviours and styles. In summary of this information, it is concluded that

leaders have traits and actions in line with:

• creating and communicating a vision of what the organisation should be,

• communicating with and gaining the support of each part of the company,

• persisting with a decided direction, regardless of the conditions,

• creating a company culture which supports the business and obtains results,

• having a drive and motivation to deliver,

• personal characteristics of integrity, confidence, knowledge of the business, and

cognitive ability, and

• being open to new experiences and solutions.

2.7 Summary of Literature Review

This chapter reviewed literature on simulation and queueing theory extensively. It

convinced the author that simulation is necessary to solve the queuing problem at the

department. It also discussed TOPSIS and box plots for analysing simulation results.

It then briefly explored literature on managing queues and people. The next chapter

describes the findings of benchmarking performed by the author at various local traffic

departments.

28

Chapter 3

Benchmarking

The previous chapter justified the use of simulation and customer waiting time as a

performance measure. It also explained behavioural aspects which could contribute to

improving service delivery at the department.

This chapter describes the process flows, operational management, and facility lay-

out of the Stellenbosch department and three local traffic departments, namely: Bel-

lville, Durbanville and Malmesbury. Bellville and Durbanville form part of the City

of Cape Town Municipality, while the latter is governed by Swartland Municipality.

The concept of benchmarking is used to investigate whether other traffic departments

are experiencing similar problems to the Stellenbosch department. It is also used in

generating alternative queue models to be simulated.

3.1 Stellenbosch Traffic Department

The Stellenbosch Traffic Department provides services to 41 883 customers; the total

number of registered vehicles in the district as on 30 September 2012 (Royi, 2012). The

department’s current facility layout, shown in Figure 3.1, does not allow all transactions

to be performed every day. The current layout at the Drivers Licence section limits

learners’ tests to be facilitated only on Wednesdays, while drivers’ licences are processed

on the remaining days.

The process flow of the current layout indicates numerous crossing paths which

prevent easy flow of customers in the system. The waiting area at the Drivers Licence

section serves no definite purpose; it is simply a “general” waiting area. Drivers Licence

29


enquiries and payments are done at the rear of the building which is confusing for

customers; most customers walk into the building having to search for the teller or

enquiries desk.

In Figure 3.1, notice the separation of the Fines-, Licence & Registration, and

Drivers Licence sections. The arrows indicate the sequential flow of customers. Ap-

proximately 10% of Licence & Registration customers require authorisation, and must

then re-enter at the front of the queue.

A few key operations are detailed:

Operating Hours From 08:00 – 15:00 for vehicle registrations at the Licence & Reg-

istration section, 08:00 – 15:30 for all other transactions, while employee working

hours are 07:30 – 16:00. Vehicle registrations are assumed to be the longest trans-

action type; this is the reason given for closing such applications 30 minutes prior

to other transactions (Royi, 2012).

Payment Method Cash or Cheque is preferred. Debit card facilities are available,

but are not used because they are “unreliable”. Credit card payments are not

allowed.

Authorisation Some transactions require authorisation from a supervisor before it

may be performed by a server. Customers are directed to the authorisation office

by the teller once the customer has already waited in the queue. Authorisation is

usually a lengthy process requiring an average of 10 minutes to process. A queue

usually forms outside the authorisation office, as shown in Figure 3.1.

Meetings These are held during operational hours and exclude participation of servers

who have to remain in operation to serve customers.

Lunch Breaks Each teller is allowed a maximum of one 30 minute lunch break, and

a 15 minute tea break, at self-decided times.

30


Enq

uir

ies

Licence & Registration Servers

Au

tho

risa

tio

nO

ffic

eSt

ore

1

Off

ice

Off

ice

Off

ice

Off

ice

Fin

es

Serv

er

Off

ice

Off

ice

Vau

lt

Dri

vers

’ Li

cen

ces

Serv

er

Lear

ner

s’ T

est

Ro

om

(Wed

nes

day

s)

Eye

Test

Wai

tin

g A

rea

(Mo

n,T

ues

, Th

urs

,Fri

)

Eye

Test

R

oo

m 1

Eye

Test

R

oo

m 2

Dri

vers

Lic

ence

sG

ener

alW

aiti

ng

Are

a

Sto

re 2

Off

ice

Off

ice

Off

ice

Sto

re Dri

vers

Lic

ence

s W

aiti

ng

Are

a

ENTR

AN

CE

(Mai

n B

uild

ing)

ENTR

AN

CE

Application Forms Station


Fin

es

Serv

er

Telle

r 2

Telle

r3

Enq

uir

es

Dri

vers

Lice

nce

sSe

rver

Enq

uir

ies

Telle

r1

Telle

r3

Au

tho

risa

tio

n

Serv

er

Eye

Test

O

ffic

er 1

Eye

Test

O

ffic

er 2


Off

ices

, Par

ade

Ro

om

, Kit

chen

Enter

Exit

Queue

Enter

Eye

Test

ExitDriv

ers Licences

Fines

Licence

& Regist

ratio

n

Authorisation

Exit

Fin

esLi

cen

ce &

Reg

istr

atio

nD

rive

rs L

icen

ces

Figure 3.1: Current Business Process Flow and Facility Layout

31

3.2 Bellville Traffic Department


Bellville Traffic Department is split into three sections, not unlike the current layout

of the Stellenbosch department. Fines- and Licence & Registration transactions are

handled at the main building in Reed Street, with fines and roadworthies processed at

a single teller and licences and registrations performed at four other tellers, while all

Drivers Licence related transactions are performed in a building two streets away, in

Baily Road.

Licence & Registration type transactions are split into two parts: the application

and payment, and then the issuance. The customer goes to a server to apply and pay

for a transaction, and then waits in another queue to be issued the document, as shown

in Figure 3.2. This separation of roles was most likely introduced to prevent corruption

at the department. This gave the author the idea of an alternative queuing model in

which transactions are split into three parts: application, payment and issuance.

The Drivers Licences section at Bailey Road did not reveal any hassles with respect

to service delivery. On the contrary, the tellers at this section admittedly are idle quite

often. This suggests to the author that the excessive waiting time at the Licence &

Registration section may be reduced by making use of the Drivers Licences section’s

idle tellers. This would require integrating the Licence & Registration and Drivers

Licences sections in a single facility. The Drivers Licences section at Bellville has a

simple layout, as in Figure 3.3. The figure excludes the area for eye tests because

optimization of eye test administration is beyond the scope of this report. It was,

however, mentioned by Bellville department’s staff that the newly implemented eye

test machinery has lengthened the drivers licence process, and that the public should

be urged to make use of a service offered by optometrists in South Africa; an “Eye

Test Screening Certificate” can be obtained free of charge and used instead of having

to wait in a long queue at the Traffic Department for an eye test.

Employees and supervisors at the Licence & Registration section at Reed Street

refused to converse with the author which made obtaining any information difficult.

However, Mrs. Bronwyn Pieterson at the Drivers Licences section in Baily Road was

extremely insightful and willingly provided the author with information.

A few other key operations are highlighted below, to compare to that of the Stel-

lenbosch department (Pieterson, 2012):

32


Teller 5Collection

Teller 4Application & Payment

Teller 6Collection

Teller 2Application Payment

Fines

Fines & Roadworthy

Start(Licence & Registration)

Start(Fines, Roadworthy)

Figure 3.2: Bellville Traffic Department: Licence & Registration, Reed Str

Operating Hours From 08:00 – 15:30 for all transactions, but employee working

hours are 07:30 – 16:00.

Payment Method Cash or Cheque only. Payment cannot be made using debit- nor

credit cards.

Authorisation There is insufficient information regarding authorisation at Reed Street.

Only on the rare occasion is authorisation required at the Drivers Licence section

on Baily Road.

Meetings No information about meetings at the Reed Street section was given. The

Drivers Licence section at Baily Road hosts short meetings approximately every

second week, before the start of the business day.

Lunch Breaks Each server is entitled to a maximum of 30 minutes for a lunch break,

and another 15 minutes for tea. The servers decide among themselves when they

will take these breaks.

Tellers are given only 30 minutes to complete their financial cash-ups at the end

of the business day. The tellers at the Drivers Licence section said that it usually

does not take longer than 15 minutes, but should the cash-up not balance, it can take

33

3.3 Durbanville Traffic Department

Teller 1

Teller 2

Drivers Licences

Figure 3.3: Bellville Traffic Department: Drivers Licences, Bailey Rd

up to a maximum of 30 minutes. It was also advised that all cash-ups are captured

on the HRK management system at the end of the day by one of the tellers or the

supervisor. (Pieterson, 2012) HRK is a cash management system which was noticed to

be implemented only at City of Cape Town Municipality Traffic Departments.

Mrs. Tanya Reid of Bellville Traffic Department mentioned that she has been

receiving numerous complaints from customers who have had to wait in excess of 30

minutes in the queue at the Licence & Registration section in Reed Street (Reid, 2012).

The process layout of this section is shown in Figure 3.2. The long waiting time is

synonymous with complaints at the Stellenbosch department. This instantiates the

author’s suspicion that the current separation of operational sections (Fines-, Licence

& Registration, Drivers Licences) is detrimental to the service level of the Stellenbosch

Traffic Department, as it is at Bellville.


Durbanville Traffic department is also split into three sections, similarly to the Stel-

lenbosch department. Licensing and registrations are done at a satellite office within

the Durbanville Municipality Administrative Offices building in Oxford Street, while

drivers licences, roadworthies and fines are processed at the main building in Church

34


Teller 3 Teller 4Teller 2 Teller 5

Teller 1

Start

Figure 3.4: Durbanville Traffic Department: Licence & Registration, Oxford Str

Street. The satellite office has approximately three open tellers who process Licence &

Registration type transactions, as in Figure 3.4.

The Drivers Licence and Roadworthy section in Church Street is perceived to be a

smooth running operation. Two enquiry tellers also accept payment of fines, while two

other tellers process drivers licences and roadworthy transactions. Incorporating fines

into enquiries may be a feasible option for Stellenbosch. The layout of Durbanville’s

Drivers Licence and Roadworthy section is shown in Figure 3.5.

This department is also open on two Saturdays of every month. A few operational

notes were made during the visit at the traffic department, as below:

Operating Hours From 08:00 – 15:30 for all transactions, but employee working

hours are 07:50 – 16:00.


credit cards.

Authorisation Insufficient information regarding authorisation at Reed and Church

Street.

Meetings Approximately every two weeks, if required. All personnel are included.


a 15 minute tea break, self-decided.

35

3.4 Malmesbury Traffic Department

Teller 4Drivers Licences &

Roadworthy

Enquiries& Fines

Enquiries& Fines


Roadworthy


Roadworthy


Roadworthy

Enquiries & Fines

Drivers Licence & Roadworthy

ExitExit

Figure 3.5: Durbanville Traffic Department: Drivers Licences, Church Str


This traffic department is governed by the Swartland Municipality, unlike the aformen-

tioned municipalities which are governed under the City of Cape Town Municipality. As

shown in Figure 3.6, Malmesbury Traffic Department integrates all transaction types;

while fines are payable at one specific teller, all other transactions – licence & regis-

tration, roadworthies, and drivers’ licences – are done at any of four tellers at the end

of a single queue. Customers are required to enter one queue only, while tellers are

able to perform any transaction. Mr. Nico Edas and Mrs. Anita Nieuwoudt of the

Malmesbury department assisted in providing insightful information about the facility

and its operations (Edas, 2012) (Nieuwoudt, 2012):

Operating Hours From 08:00 – 15:00, closing 30 minutes before the previously dis-

cussed departments, but employee working hours are 07:50 – 16:00. On Fridays,

the department closes at 14:00, to compensate for shortened lunch breaks (see

“Lunch Breaks” below).


credit cards.

36


Teller 2 Teller 3Teller 1 Teller 4

Fines

Start

Enqueries

Proposed NewEnqueries

Desk

Fines (Outsourced)

All Transactions:Licence & Registration, Drivers Licences, and Roadworthy Tellers Enqueries

Figure 3.6: Malmesbury Traffic Department: All Transactions

Authorisation Performed by the municipal representative or supervisor. Customers

are not required to go to a separate authorisation office, instead it is done while

the customer waits at the teller.

Meetings Every second Friday, at the end of the business day, all personnel included.


instead of a 15 minute tea break, the servers are allowed to finish work one hour

earlier on Fridays. Usually two of the four tellers take lunch at a time, in these

time slots: 12:15 – 13:00 or 13:00 – 13:45.

This traffic department has a dedicated Enquiries server, as Stellenbosch does.

Learners’ licence tests are written Mondays through Thursdays, but appointments

can be made any day of the week. Drivers licences can also be renewed every day.

This is unlike Stellenbosch which only allows learners’ licence tests to be performed on

Wednesdays, and drivers’ licences to be renewed on the remaining days.

37


A few innovative and interesting ideas are being applied at Malmesbury Traffic

Department:

• The supervisor uses a Bluetoothr earpiece to answer all incoming calls and trans-

fers this to the Enquiries desk when unavailable to take calls. This allows the

supervisor to be flexible; remaining visible and assisting customers in the queue,

while being able to answer incoming calls.

• All application forms are issued to customers at the Enquiries desk. This guides

the customer to have all documentation ready for hassle-free service at the teller

and prevents frustration for the customer; usually customers enter the queue and

wait in the line only to be informed that they have insufficient documentation.

• This department has applied for a dynamic queue regulator which sends visual-

and voice commands to the customers in the queue to state which server is avail-

able.

• The supervisor tries to be active on the “floor” where most customer interaction

occurs. The supervisor is able to answer technical questions which Enquiries

might not have the answers to, is able to identify clients who require authorisation

even before they reach the teller, or allow customers who merely want to renew

their licences to bypass the queue because such transaction requires less than

60 seconds to process. This also puts customers at ease; literature in section

2.6.1 explains that having employees active and visible results in customers more

willing to bear the wait in the queue.

• In the case of authorisation, when the supervisor is not “on the floor”, the teller

simply phones the supervisor through the switchboard, and relays information

for the supervisor to process the authorisation without requiring the customer to

physically go to an authorisation office.

• This department also makes use of highly effective signage which guides customers

through the building. This ensures that customers are not confused about where

to go or what to do.

• All processed application forms are scanned and stored as soft copies on a database

for future reference.

38

3.5 Summary of Benchmarks

• There is a good work ethic and culture at Malmesbury Traffic Department; staff

are friendly and appear to enjoy their work environment.


The previous sections detailed the key operations of various traffic departments sur-

rounding and including the Stellenbosch department. It provides a good perspective

of the challenges faced and operations at departments under the municipalities of the

City of Cape Town and Swartland.

Operating hours are relatively uniform. The Stellenbosch department is unique

in that it closes applications for vehicle registrations 30 minutes prior to the actual

closing time. The author perceives this as being unnecessary considering that three

other traffic departments do not take this approach, yet still manage to cash-up on

time.

Payment methods exclude the option to pay by debit- or credit card across the

benchmarks. This is due to the fact that the National Department of Transport excludes

cash handling fees in calculating transaction fees for Western Cape traffic departments.

The departments operate on strict budgets and therefore prefer curbing losses incurred

by card facility cash handling fees. Stellenbosch Traffic Department does have card

facilities, but does not accept credit cards. The department is also reluctant to use the

card facilities because they are unreliable (Royi, 2012).

Malmesbury Traffic Department displays truly innovative ways of delivering excep-

tional service and optimising resource utilisation. Their method of processing transac-

tions which require authorisation is unique in that it is customer-oriented, minimizing

the need for the customer to enter and re-enter the queue or proceed to a separate

office.

Meetings that include servers can only be held outside operational work hours. The

scenarios described in this chapter reveal that very few departments hold meetings

that include servers, if any. The author identified consensus amoung staff from all the

departments discussed in this chapter, that they would prefer to be included in weekly

or daily meetings, even if outside operational hours.

Lunch breaks vary slightly between the departments discussed in this chapter. It

can be said that servers are entitled to 45 minutes which includes a separate 15 minute

39


tea break. Each department handles this differently. Malmesbury’s policy to close one

hour earlier on Fridays in exchange for servers sacrificing their tea breaks, provides one

hour additional serving time of the queue which contributes to the exceptional service

and short waiting times at the Malmesbury department.

Benchmarking has proven to be valuable to the author in generating ideas of alter-

native queueing designs which are revealed in the next chapter. It has also shown the

author what is possible and provides hope for what the Stellenbosch department may

achieve in the near future.

40

Chapter 4

Proposed Queue Models

This chapter considers various queuing models to be considered for Stellenbosch Traffic

Department. The first section introduces process design elements which should be

considered, and the second proposes queueing layouts as experiments to be analyzed

by simulation.

4.1 Queue Design Considerations

Service process design refers to the way in which facilities are laid out and the process

through which a service is delivered (Ramaswamy, 1996). Fitzsimmons & Fitzsim-

mons (2000) have suggested that when demand is highly fluctuating and peak demand

regularly exceeds capacity, cross-training of personnel should be considered.

The concept of cross-training requires every server to perform all transaction types,

as at Malmesbury. When this was mentioned, Mr. Royi indicated that there is concern

of such cross-training in terms of opportunity for corruption because employees will

have authority in a broad range of transactions. In the author’s, opinion a well designed

information system can control and monitor employees in such a way that corruption

and fraud are almost entirely curbed.

A further suggestion by Fitzsimmons & Fitzsimmons (2000) is to incorporate flexi-

bility into the design of the service process so as to respond to demand variations. This

will assist with optimising personnel utilization while reducing customer waiting time.

The following section introduces queue designs to be simulated.

41

4.2 Alternative Queue Designs

Table 4.1: Summary of Alternative Queuing Models

Idle Server (%) Time in System (minutes)

Example 1 25 12

Example 2 56.72 3.8


Various queue designs are introduced in this section to be investigated through simu-

lation in order to determine the most effective design in terms of minimised customer

waiting time.

The author developed these designs using ideas generated as a direct result of bench-

marking, as well as a simplified mathematical comparison of multiple-server-multiple-

queue and multiple-server-single-queue model examples using queueing theory. The

queuing theory analysis is presented in detail in Appendix B, and is summarised in

Table 4.1.

Comparing the idle time (and therefore the utilization) and waiting time in each

example, the advantage of multiple servers serving a single queue rather than multiple

queues, can be seen. In Example 1, where multiple servers serve multiple queues,

servers are found to be idle 25% of the time, and customers spend an average of over

twelve minutes in the system. In Example 2, where multiple serves serve a single queue,

servers are idle over 50% of the time while customers are in the system for under four

minutes. Not only are the servers better utilised, but the customers also spend almost

four times less waiting in the system in the second example. Sheu & Babbar (1996)

suggests that a single-queue-multiple-server design (Example 2) always outperforms a

multiple-queue-multiple-server system (Example 1) in terms of customer waiting time.

Mr. Royi mentioned that the department is understaffed. By changing the layout

of the department to be one in which servers serve a single queue, the demand will be

reduced, thus reducing the need to employ more staff. Not only this – customers could

also expect to spend less time in a queue.

For this reason, this project considers a multiple-server-single-queue model as a

possible solution to the long waiting times at the department.

The author developed four designs to be simulated:

42


Fines

Licensing & Registration

Licensing & Registration

Drivers Licences

Figure 4.1: Design 1 — Single Stage, Multiple Queue, Single and Multiple Server

Design 1 – Single Stage, Multiple Queue, Single and Multiple Servers This is

the current layout of the Stellenbosch Traffic Department. Example 1, analysed

in section B.1.1, represents the essence of Design 1; separate waiting lines are

formed at each section. Each server is limited to only one transaction type, as

seen in Figure 4.1.

The illustrations represent customers as circles and servers as squares.

Design 2 – Single Stage, Multiple Queue, Single Server Customers all enter into

any of four separate queues and can perform any type of transaction at a single

server. See Figure 4.2.

Design 3 – Multiple Stage, Single Queue, Single Server Stages of the transac-

tion are separated into the processing of the application, receiving of monies, and

issuing of documents, with a dedicated server at each stage. See Figure 4.3. This

is similar to the approach of a drive through restaurant; one person takes the

order, another receives payment, and the last person delivers the food.

43

4.3 Summary of Proposed Changes

All Transactions(Fines, Licensing &

Registration, Roadworthy,

Licences)



Licences)



Licences)



Licences)

Figure 4.2: Design 2 — Single Stage, Multiple Queue, Single Server

Design 4 – Single Stage, Single Queue, Multiple Server Customers all enter a

single queue and can be served by one of multiple servers – which ever server is

available next. See Figure 4.4. The essence of this design is captured in Example

2 of section B.1.2.

Design 3 could be a solution to Mr. Royi’s concern for corruption within the depart-

ment. Separating roles into three sections (application processing, payment, issuing)

would mean that any fraudulent transaction would have to be approved by three em-

ployees. The likeliness that three employees concede to fraudulent activity is probably

somewhat less than that of one person having sufficient authority to commit a fraudu-

lent transaction.


This chapter introduced the reader to queue models which form part of the solution

set in reducing customer waiting time. These models are developed and simulated in

the following chapter.

44


Issuance of Documents

Payment

Application

Figure 4.3: Design 3 — Multiple Stage, Single Queue, Single Server



Licences)



Licences)



Licences)



Licences)

Figure 4.4: Design 4 — Single Stage, Single Queue, Multiple Server

45

Chapter 5

Simulation Study

The previous chapters provided knowledge acquired leading up to the decision to sim-

ulate the current queue design at the department, as well as three alternatives. The

functional specification, detailed explanation of each model simulated and a summary

of input data used are provided in Appendix C. In this chapter, the simulation results

and the validation and verification of the models are presented and discussed.

5.1 Simulation Results

The previous section briefly introduced the models to be simulated. This section pro-

vides the reader with quantitive results as outputted by the simulations of each model.

The study leader recommended that the author use 75th percentile results from the

simulation for analysis. This was recommended to ensure statistically sound argument

(Bekker, 2012b). The simulation results are summarised in Table 5.1.

46

5.1 Simulation Results

Tab

le5.

1:S

um

mar

yof

Sim

ula

tion

Res

ult

s

Scenario

Perform

anceM

easu

re

Average

75th

Percentile

Min

imum

Maxim

um

Half

Wid

th

Design

1A

llS

erver

sU

tilisa

tion

,A

vg

(%)

66.0

375

68.5

458

56.5

275

77.2

406

0.2

321

Cu

stom

ers

inS

yst

em(C

IS),

Avg

18.3

760

21.5

247

6.2

401

36.2

884

0.3

023

Cu

stom

ers

Not

Ser

ved

(%)

13.3

574

15.5

488

3.3

537

22.8

916

0.2

002

Tim

ein

Syst

em(T

IS),

Avg

(min

ute

s)21.1

348

24.9

133

8.7

314

41.8

979

0.3

706

Oth

erPerform

anceM

easu

res

Cu

stom

ers

inS

yst

em(C

IS),

Max

52.0

000

59.0

000

20.0

000

89.0

000

0.6

668

Dri

ver

sL

icen

ceS

erver

Uti

lisa

tion

,A

vg

(%)

97.0

499

99.3

101

83.0

653

100.0

000

0.1

873

Fin

esS

erver

Uti

lisa

tion

,A

vg

(%)

15.3

582

17.5

589

6.6

633

25.9

288

0.2

086

Lic

ence

&R

egis

trati

on

Ser

ver

Uti

lisa

tion

,A

vg

(%)

75.8

710

80.7

000

54.7

406

99.9

982

0.4

439

Dri

ver

sL

icen

seC

IS,

Avg

8.4

205

11.2

778

1.5

894

24.5

390

0.2

666

Fin

esC

IS,

Avg

0.1

931

0.2

228

0.0

733

0.4

559

0.0

033

Lic

ense

&R

egis

trati

on

CIS

,A

vg

9.7

624

11.3

437

3.0

622

22.6

736

0.1

519

Lic

ence

&R

egis

trati

on

TIS

,A

vg

(min

ute

s)22.0

499

25.2

106

8.9

592

48.8

562

0.3

151

Design

2A

llS

erver

sU

tilisa

tion

,A

vg

(%)

60.7

245

62.9

562

50.3

348

72.4

108

0.1

909

Cu

stom

ers

inS

yst

em,

Avg

3.1

763

3.3

588

2.2

779

4.4

745

0.0

186

Cu

stom

ers

Not

Ser

ved

(%)

0.9

679

1.2

739

0.0

000

3.4

056

0.0

347

Tim

ein

Syst

em(T

IS),

Avg

(min

ute

s)4.4

010

4.5

860

3.5

378

5.8

870

0.0

193

Design

3A

llS

erver

sU

tilisa

tion

,A

vg

(%)

59.3

575

59.9

899

56.5

854

62.0

126

0.0

555

Cu

stom

ers

inS

yst

em,

Avg

49.0

895

55.0

176

19.6

259

82.3

618

0.5

763

Cu

stom

ers

Not

Ser

ved

(%)

28.7

502

31.4

465

16.3

763

39.4

595

0.2

336

Tim

ein

Syst

em(T

IS),

Avg

(min

ute

s)68.3

966

75.9

809

28.5

249

105.1

046

0.6

835

Design

4A

llS

erver

sU

tilisa

tion

,A

vg

(%)

60.7

649

63.0

218

52.0

465

70.2

152

0.1

971

Cu

stom

ers

inS

yst

em,

Avg

3.0

846

3.2

747

2.3

823

4.4

503

0.0

190

Cu

stom

ers

Not

Ser

ved

(%)

0.9

686

1.2

903

0.0

000

2.9

499

0.0

342

Tim

ein

Syst

em(T

IS),

Avg

(min

ute

s)4.2

706

4.4

396

3.5

655

6.2

614

0.0

203

47

5.2 Analysis of Simulation Results


The simulated results of Design 1 show that the Fines server is under utilised at only

17.56%, while the Drivers Licence server is operating at near maximum capacity with

a utilisation of 99.31%. The servers at the License & Registration section are only

utilised at 80.70%. From this, it is speculated that the waiting time can be reduced

and customer satisfaction improved by mobilising the Fines server to other sections. It

is with this intention that Design 2 and 4 make use of all servers for all transactions.

It is also seen here that the department operates with an average of 21.52 customers

in the system, and a maximum of 89, while 15.55% of all customers who enter the system

are not served. The fact that 15.55% of customers are not served is unacceptable. This

simulation once again proves the dire need for a re-design of the business processing

system at Stellenbosch Traffic Department and echoes the intention of this project.

Table 5.1 shows the significant improvement of Design 2 in contrast to Design 1;

customers are expected to spend an average of 4.4 minutes in the system, compared to

21.13 minutes of the current model implemented. There is also an expected reduction

in the average percentage of unserved customers in the system from 18.38% to 3.18%.

The simulation results for Design 3 are worse than that of the status quo in every

respect. See Table 5.1 for specific results.

At first glance, the results of Design 4 are marginally better than those of Design

2. This implies that the queuing models which allow servers to process all types of

transactions are superior to the other models simulated.

In order to identify and justify the best design, it is necessary to analyse the results

for each model simulated. As explained in section 2.4, TOPSIS is a multi-criteria

decision analysis method used in choosing a best- or worst case of numerous alternatives.

It compares alternatives based on weightings of relative importance of a set of criteria,

normalising the scores for each criterion, and calculating the geometric distance of

each. The TOPSIS calculations are shown in Appendix C.6, and the results ranked

and summarised in Table 5.2.

As mentioned previously, TOPSIS makes use of a weighting criterion. The author

has chosen the average time in system (TIS) to carry a weighting of 75%, with average

number of customers in system, server utilisation and percentage of customers not

48


Table 5.2: Ranked TOPSIS Analysis of 75th Percentile Results

TOPSIS Outcome

Model dib diw Sib Rank Sib Model

Design 1 0.20771 0.50838 0.7099 1 0.9949 Design 4

Design 2 0.00389 0.71359 0.9946 2 0.9946 Design 2

Design 3 0.71499 0.01040 0.0143 3 0.7099 Design 1

Design 4 0.00369 0.71502 0.9949 4 0.0143 Design 3

served carrying the remaining weight in equal proportions. Time in system (TIS) is

chosen as the majority criteria as this is the main concern at the department.

The TOPSIS analysis summary of Table 5.2 shows Design 4 as the superior queue

model because it has the greatest similarity to the best condition, Sib. Design 2 and

Design 4 differ marginally; a difference in scores of only 0.0003. From this it suspected

that Designs 2 and 4 are equally successful as a difference of 0.0003 is negligible in

statistical terms.

As a secondary method of evaluating the alternatives, box plots of the time in

system (TIS) are used to graphically identify whether there is a significant difference

between Designs 1 and 4. It is also used to further analyse whether there is a significant

difference between Designs 2 and 4, which differ insignificantly by TOPSIS analysis.

Refer to Figure 5.1.

The box plots show no overlap of data in comparing Design 1 and 4; this reaffirms

that Design 4 is superior by obtaining the lowest time in system (TIS). It is also clear

that the difference between the boxes are well over the 5% indifference zone, as discussed

in section 2.5. The comparison of Designs 2 and 4 show overlapping of medians; this

means that no difference between the time in system of these designs can be claimed.

However, it must be considered that the simulation of Design 2 assumes that cus-

tomers entering the system make perfect logical calculations in entering the shortest

of four queues. It is unlikely that every customer entering the system will make an

accurate calculation, the result being that the time in system (TIS) of Design 2 is likely

to be greater than outputted by the simulation. It is with this reason that the author

chooses Design 4 as the best queue design.

This section has shown from two perspectives that Designs 2 and 4 outperform

Design 1 in terms of time in system (TIS).

49


0

5

10

15

20

25

30

35

40

45

Design 1 Design 40

1

2

3

4

5

6

7

Design 2 Design 4

Figure 5.1: Box Plots Comparing TIS of Designs 1 and 4 (left), Designs 2 and 4 (right).

50

5.3 Validation and Verification


Kelton et al. (2010) recommends that simulation models should be verified and vali-

dated. This is done by creating a set of expected values before the actual simulation,

and comparing these to the simulated output. Model verification consists, in large part,

of debugging and is therefore done throughout model development.

In order to build a credible model, the problem must be formulated precisely, as

presented in Appendix C.4 (Law, 2005). When the author initiated the simulation, the

exact problem was not completely understood in its finest detail, which is usually the

case, as agreed by Law (2005). As the problem progressed, greater detail was added

for accuracy.

A subject matter expert (SME) in simulation, the study leader, assisted the author

in gaining a complete understanding of the system to be modeled. It is assumed that

information from SME’s is usually correct.

The author also ensured that the simulation modeled the systems correctly by doing

a structured walk-through, as well as interacting with the staff and other decision

makers at the department. All concepts, changes, and assumptions were documented

throughout the project to ensure that true information was recorded and used for the

simulation.

The author chose to verify and validate the simulation of the current system imple-

mented at the department (Design 1) by comparing its values to true observed values.

It is assumed that if this model is verified and validated, that the data distributions

used in this simulation remain verified for the remaining Designs 2, 3, and 4.

As previously mentioned, an old-fashioned clock card machine was used to calculate

the time in system (TIS) of every customer entering each queue. This actual time in

system was compared to the time in system outputted by the simulation for Design 1,

as shown in Table 5.3.

The actual TIS for the Fines section is not included in the time study, but is realis-

tically estimated at 2.5 minutes, and the simulation agrees. The Licence & Registration

outputs also correlate reasonably. The time in system (TIS) at the Drivers Licences

section requires some explanation. The average observed waiting time is 14 minutes,

whereas the simulated waiting time is 25 minutes. The difference is explained as fol-

lows; the time study to obtain the inter-arrival information and the time in system

51


Table 5.3: Actual vs. Simulated TIS: Design 1

Section Actual Simulated

(minutes)

Fines 2.5 2.42

Licence & Registration 22.8 22.05

Drivers Licence 14.2 25.26

Table 5.4: Actual vs. Simulated Entities Created: Design 1

Section Actual Simulated

(units)

Fines 31 36

Licence & Registration 142 141

Drivers Licence 141 148

(TIS) of each customer was done one week prior to the time study of the service times

at the Drivers Licence server. The service time is independent of the arrival rate,

therefore it is reasonable to use the data from these separate time studies in a single

simulation. However, the observed waiting time is 11 minutes less than the simulation

output because an additional server was used during the time that the time study of

inter-arrivals and waiting time was done. The time study of TIS was done at a partic-

ularly busy period at the Drivers Licence section, leading to the need for an additional

teller. However, this is not the usual case, thus the simulation includes only one server.

It is therefore reasonable that the simulation outputs the time in system (TIS) as 11

minutes longer than the observed waiting time.

The number of entities created by the simulation is also compared to the number

of entities observed, shown in Table 5.4. The actual and simulated results correlate

satisfactorily.

The simulation of Design 1 is therefore verified and validated. Designs 2, 3, and 4

were verified using logical argument, and the assumption that inputs of Design 1 are

valid for the remaining models.

52

5.4 Summary of Simulation Study

5.4 Summary of Simulation Study

This chapter provided the results of the simulations of Designs 1 to 4. It also showed

that Design 4 is the best alternative queue design in terms of average time in system

(TIS), by TOPSIS analysis. Lastly, it described validation and verification of the models

simulated.

The next chapter draws conclusions from the simulation study and makes recom-

mendations to the department with respect to queue design, facility layout and man-

agement.

53

Chapter 6

Conclusions and

Recommendations

This chapter summarises the outcome of the simulation study, recommends a re-

engineered facility layout and then provides managerial advice for implementation.

The Stellenbosch Traffic Department requires much more than only an overhaul of the

queue design, but also an improved management who consider the importance of the

work environment.

6.1 Queueing Model

This project included a simulation of various queue models. Ideas of queue design were

generated from benchmarking and then simulated using real world data obtained by

physical observations. An analysis by TOPSIS shows Design 4 as most optimal between

the current queue model used at department, and the three alternative models.

Design 4 is a single stage, single queue, multiple server model. In this model, all

customers enter a single queue, regardless of the type of transaction to be performed.

The customers are then served on a first-in-first-out (FIFO) basis from this single

queue to the next available of multiple servers who are able to perform all types of

transactions; fines, licences, registrations and drivers’ licences.

The simulation of Design 4 boasts an average time in system of only 4.44 minutes

per customer; a quarter of the current 22 minutes experienced at the department. This

is the most important criteria for improvement at the department as it is the greatest

54

6.2 Facility Layout

Table 6.1: Comparison of Simulation Results of Current- and Proposed Queue Designs

Current Proposed

Performance Measure Design 1 Design 4

TIS (Avg, Minutes) 24.9133 4.4400

CIS (Avg, Number) 21.5247 3.2747

Utilisation (%) 68.5459 63.0218

Customers Not Served (%) 15.5488 1.2903

source of complaints from customers. A summary of the verified simulation results

comparing the currently used queue design (Design 1) and the proposed design (Design

4), are shown in Table 6.1. This outcome shows that the department is in fact not

under staffed, but that staff are under utilised.

This proves the advantage of implementing Design 4. However, in order to im-

plement this queue design, a few structural changes to the building are required, as

detailed in the following section.

6.2 Facility Layout

The proposed layout accommodates Design 4 while making only a few structural

changes, as shown in Figure 6.1. It includes the removal of three existing walls of

“Store 2”, the Drivers Licences cubicle, as well as the Fines cubicle. Refer to Figure 3.1

to compare the proposed- to the existing layout. All transactions are to be processed by

at least six servers in the main building, including two servers stationed at the existing

Enquiries desk.

There is sufficient space to accommodate all entering customers; the simulation of

Design 4 shows a maximum of 27 customers in the system. In contrast, the simulation

of Design 1 shows a maximum of 89 customers in the system. The maximum number

of customers in the system is not given in the tables of Chapter 5, but is deducted

from the simulation output by the author. The Fines cubicle of the current design is

to be converted to a storage room to account for the loss of storage space due to the

demolishment of “Store 2” of the current design. Additional Application Form Stations

are also supplied in the void of the Fines cubicle. A waiting area for eye tests is created,

as well as an area for participants and partners of persons undertaking learners’ licence

55

6.2 Facility Layout

tests, at the previously Drivers Licence section. This allows learners’ licence tests and

eye tests to be conducted every day of the week.

Complete installation of the proposed Design 4 will require that all servers have ac-

cess to the eNatis as well as the OPUS and TCS information systems in order to perform

all transactions. Should the department prefer to have fines processed independently

from the remaining transactions, it is recommended that all fines be processed by En-

quiries, as done at Durbanville Traffic Department.

There are two options for queueing at the waiting area for the eye tests; either the

seating is arranged such that persons sit in the next available seat in chronological order

of arrival (this is the same as a normal queue, except that each person is seated), or

a ticket issuing system can be implemented in which each person entering is issued a

ticket with a customer number, and an automated prompt calls out the next customer

to be directed to the eye test facility. Effective signage in all operational areas are

highly recommended to guide customers through the building.

A company, Qmatic, was approached by the author to discuss the feasibility of using

such a ticketing system. The company director, Mr. Eugene Swanepoel, provided en-

couraging insight. Qmatic has already installed their solutions at Johannesburg Metro

Police Department and Western Cape Department of Transport for public licences in

Bellville. Installations were in progress at Mpumalanga Department of Transport at

the time of correspondence. The Qmatic solution is also part of the “blue print” to

be implemented at traffic departments nationally. This was approved by the National

Department of Transport, but has not been implemented. (Swanepoel, 2012)

Thus far, it has been realised that a more optimal queuing design is advantageous

in reducing the time in system of each customer. It better utilises the servers, and the

flow is more logical and thus easy for customers to understand.

The queue design and facility layout consider only the physical and logical compo-

nents of the system at the department.

The following section focuses on less tactile strategies to improve customer service

at the department; the management and other human capital.

56

6.2 Facility Layout

Enq

uir

ies

All Transactions Servers

Au

tho

risa

tio

nO

ffic

eSt

ore

1

Off

ice

Off

ice

Off

ice

Off

ice

Sto

re 2

Off

ice

Off

ice

Vau

lt

Lear

ner

s Li

cen

ce

Wai

tin

g A

rea

Lear

ner

s’ T

est

Ro

om

(Eve

ry D

ay)

Eye

Test

R

oo

m 1

Eye

Test

R

oo

m 2

Eye

Test

Wai

tin

g A

rea

(Eve

ry D

ay)

Off

ice

Off

ice

Off

ice

Sto

re

ENTR

AN

CE

Fin

es, L

icen

ce &

Reg

istr

atio

n

ENTR

AN

CE

Dri

vers

’ Lic

ence

s


Telle

r 6

Telle

r 2

Telle

r3

Enq

uir

esTe

ller

5En

qu

irie

s

Telle

r1

Telle

r3

Au

tho

risa

tio

n

Serv

er

Eye

Test

O

ffic

er 1

Eye

Test

O

ffic

er 2

Off

ices

, Par

ade

Ro

om

, Kit

chen

Enter

Queue

All Tra

nsacti

ons

Ap

plic

atio

n F

orm

s St

atio

n


Customer # Call-Out Prompt

“Cu

sto

mer

Nu

mb

er 1

23

, ple

ase

pro

ceed

to

Eye

Tes

t R

oo

m 2

"

Exit

Eye Test

# Is

sue

Authorisation

Eye

Test

Learners Tests

Figure 6.1: Recommended Business Process Flow and Facility Layout

57

6.3 Management

6.3 Management

The introduction to this report mentioned that the department was experiencing a high

occurrence of absenteeism. It was also mentioned in section 2.6.2 that employees at

the department seemed to show little motivation and complained of being over-worked

and stressed. It is suspected that there is a lack of leadership at the department; the

author noticed that the tellers, supervisors and the Administrative Traffic Chief do

not support one another. There is also a functional divide, creating “silos” of sections

(Fines, Licence & Registration, Drivers Licences) with employees refusing to assist one

another. Much commentary along the lines of “It is not my section, so it is not my

problem” was noticed.

In any business, human capital is its most important asset, therefore this is some-

thing which should be given much attention. The time in system is likely to be reduced

by changing the queue design, but in order to deliver excellent customer service, the

employees at the department must be satisfied and comfortable in their work environ-

ment.

This section will focus on providing a recommendation to the department in order

to improve the quality of work delivered by the employees. It includes advice relating

to work motivation, stress management and leadership.

6.3.1 Work Motivation

Various models of motivation reveal that a person requires certain needs to be satisfied

in order to become motivated; specific factors such as physical work condition, payment,

safety, social and belongingness, but also the need to achieve, be recognised, and exercise

authority. There is also consensus that, despite having all their needs satisfied, people

also want to be treated fairly, with an expectation of the value or rewards for every

job done. A major success of theories relating to motivation is the use of goal setting.

(Hitt et al., 2011)

These factors and theories are used to develop a recommendation to improve work

motivation at the department:

Physical Needs The physical work environment at the department is satisfactory.

However, it is recommended to introduce standards of tidiness, cleanliness and

58

6.3 Management

comfort to foster pride within employees. The employees are provided job security

by the mere fact that they are government employees.

Recognition The department makes no room for recognising and rewarding a job well

done. It is recommended that an “Employee of the Month” scheme be used to

give recognition to the top three performers each month. This will also create a

sense of healthy competition amongst the employees. This effectively “infects”

employees with an internal drive to achieve.

Authority The department is hierarchically orientated – this is typical of government

institutions. Without tampering with the major hierarchical structure, it is rec-

ommended to allow all employees to have authority in some area of their work.

This will also require the employees to take responsibility for their work and

reduces the opportunity to shift blame amongst themselves.

Goal Setting Employees of the department do not practise any form of goal setting.

Targets should be set weekly to unite them as a team to work together at achieving

specific numbers of customers served, transactions performed, minimised duration

of cash-ups, performance ratings from customers, etc. All goals should enforce

participation from all employees. It is said by Gryna et al. (2007) that actions

to “motivate” employees are of little value if they are not put in a position of

self-control; provided with knowledge of what they are supposed to do, feedback,

and a means with which to regulate their performance. These must be supplied

by management.

6.3.2 Stress Management

Stress should be managed at an organisational- and individual level. Section 2.6.2.2

provides a few solutions to reduce stress at the work environment, as suggested by Hitt

et al. (2011). The following is also recommended:

Autonomy, Control & Decision Making Hitt et al. (2011) recommends that in-

creasing employees’ autonomy and control while including them in managerial

decision making is also a means of reducing stress. It is recommended that daily

meetings for the servers of all sections be held at the department at 07:30, before

the start of the business day. Inclusion of employees’ opinions and allowing them

59

6.3 Management

to make decisions is a great shortfall experienced at the department. The Chief

Traffic Administrator could be the host of these daily meetings, and must take

cognisance of the servers’ ideas, and allow them to make decisions. This will allow

employees to take ownership of their work and foster inter-relational bonds at the

workplace.

Toxin Handlers It is also recommended that individuals be nominated as “toxin han-

dlers” who can listen and lend advice to employees, even on a personal level. Mr.

Royi should also encourage supervisors to nominate themselves as “toxin han-

dlers” to their immediate staff.

Career Development Currently, the department does not provide opportunity for

advancement. It is recommended that opportunities for employees are created.

Another form of advancement can be introduced by providing related skills train-

ing. Obligatory training on customer service is highly recommended. The staff

currently do not have a customer focus; a crucial aspect to service delivery in a

high customer interaction business such as the department.

Workload Reduction Employees feel that they are overworked. Should the depart-

ment implement the queue system proposed in this report (Design 4), the demand

on servers will be much reduced.

6.3.3 Leadership

It has been noticed that there is a major weakness in the leadership at the department,

which is deemed the responsibility of Mr. Royi. It is with this in mind that the author

recommends the following:

Communication The leader is urged to communicate daily with the servers of all

sections, including his vision for the department; one in which there are no cus-

tomer complaints, employees are focused on the customers’ needs, servers work

as a team, and transactions are processed hassle-free with every employee satis-

fied in their work environment. The leader must also engage with all sections of

the department to gather support and align everyone to a common goal; deliver-

ing excellent service by serving every customer efficiently, effectively and with a

personal touch.

60

6.4 Further Recommendations

Company Culture The work culture at the department is one without pride, esteem,

motivation or focus. It is the responsibility of the leader to foster a culture in

which each individual experiences belongingness and contributes to the company

with integrity, pride, quality and persistence. Above all, there must be a sense of

urgency and initiative on quality throughout all levels of the organisation (Gryna

et al., 2007). The author feels that the department will be able to make the

greatest improvements with respect to service delivery by improving company

culture. It should contribute to reducing absenteeism and increase motivation.

Motivation to Deliver It is noticed by the author that Mr. Royi has personal char-

acteristics in line with being a good leader: integrity, confidence, knowledge of

the industry and cognitive ability, but lacks in enforcing, persisting and demand-

ing performance. There is little “drive” to improve service delivery. The leader

needs to be inspired, and inspire his employees. Here it is recommended that

Mr. Royi do an exchange with the Chief Traffic Administrator at Malmesbury

Traffic Department. This will provide an opportunity for Mr. Royi to experience

a department which works well and should inspire him to achieve the same at the

Stellenbosch department.

Openness to new Solutions The leader is urged to be open to new ideas. These

ideas could stem from employee opinions, customer suggestions, advice from man-

agement, or even recommendations of this report. The only way anything can

change, is if it is allowed to.


The previous sections make the most important recommendations, while the following

section discusses additional recommendations to even further improve service delivery.

Literature in section 2.6.1 discussed the significance of perceived waiting time as

opposed to actual waiting time. In order to create a perception by customers that the

wait in the queue is shorter than it actually is, it is recommended to:

• play calming music throughout the building

• use lighting such that customers are comfortable in their environment

61


• have employees visible to customers in the queue with the supervisor active on

the “floor” and engaging with customers

• allow for social interaction in queues

• provide feedback to customers as to the expected duration until s/he will be

served

A few other recommendations include:

• Credit card facilities should be made available at all traffic departments. Reluc-

tance to implement these facilities due to cash handling fees are an ill-defined

excuse; the cash handling fees imposed by financial institutions can simply be

“built in” to the costing structure of transaction fees by the National Depart-

ment of Transport.

• Operating hours should be modified to allow all transactions to be done from

08:00 – 15:30. Employee working hours are then from 07:30 – 16:00. Servers

should be given permission to leave work once the cash-up is done, and not be

forced to remain at work until 16:00. Benchmarking showed that cashing-up

rarely required more than 30 minutes.

• Lunch breaks should remain as they are; 30 minute lunch break with a 15 minute

tea break. However, it is preferable that servers have their tea while performing

transactions.

• The department should consider using Bluetoothr earpieces for servers at En-

quiries, and the supervisor.

• The use of Eye Test Screening Certificates should be advertised to the public

so as to reduce the workload at the eye test facility and reduce time waited by

customers.

It is important to note that all changes should first be approved by the National

Department of Transport.

62

Chapter 7

Closing Summary

Previous chapters contain, amongst others, literature studies, suggested queuing mod-

els, details on simulation of the models, analysis of the results and a recommendation

to the department. This chapter summarises the final year project. It also shows how

this project contributes to society and that it was a major learning tool for the author.

7.1 Project summary

The primary objective of this project was to reduce time spent waiting in queues at

the Stellenbosch Traffic Department. A secondary objective was to improve overall

customer service by creating a business process which flows naturally, processes trans-

actions efficiently and serves clients without hassle. It was also a priority to make use

of Industrial Engineering tools, such as queuing theory, simulation and other principles.

The author studied literature on queuing theory, and self-studied simulation and the

Simio software package with the aid of the study leader. Chapter 3 explored the oper-

ations of three local traffic departments in comparison to the Stellenbosch department.

This was used to suggest alternative queue models to be simulated – the results of

which are analysed in Chapter 5. Finally, a three dimensional recommendation is given

which includes a near-optimal queue model, improved facility layout, and managerial

advice for improved customer service.

In the interest of the examiner, Appendix D provides an extract of meetings with

the study leader, as well as a summary time sheet.

63

7.2 Future Work

7.2 Future Work

The scheduling of lunch times is self-decided at all traffic departments. It would be

advantageous to develop a method in which to determine optimal times at which servers

should go on lunch. The author would have liked to have been able to simulate such

a problem and find a near-optimal solution using “OptQuest”; an optimizer in Simio.

Unfortunately, this could not be done as input data was calendar independent. An

Excel analysis of arrival data collected over a one week period also showed that there

is little predictability in arrivals; the sum of data from one week resulted in a uniform

distribution. However, the author is of the opinion that there is opportunity for such

a solution to be researched.

Further research could also include identifying unexpected effects of the implemen-

tation of the recommendations made in this report.

7.3 Contribution to Society

Many traffic departments across South Africa deliver unsatisfactory service. The Stel-

lenbosch Traffic Department is one not to be excluded in terms of customer service and

time waited in queues. It has caused much frustration to the residents of Stellenbosch

and criticism directed at the department’s management.

The simulations developed and the recommendations made by this report should

enable the Stellenbosch Traffic Department to deliver all services effectively. This could

encourage more road users to renew licences, roadworthy vehicles, and pay fines – all

of which contribute to creating a conforming society, increasing revenue for the state,

and allowing for safer cars on South African roads.

7.4 Lessons Learnt

Performing the final year project was one which has taught and enabled the author on

various levels. It is difficult to describe every enrichment in only one paragraph. A

major realisation was that of the author’s independent learning ability. A reflection of

a few key lessons learnt are listed:

• Everything always takes longer than expected.

64

7.5 Denouement

• Assumptions can be the master of all faults, but valuable in finding approximate

outcomes.

• There is never a perfect solution – it can always be done better.

• Conversation with persons unrelated to the problem being researched often results

in innovative ideas being realised.

• American vs. South African language conventions are a source of irritation.

• Simio, used for simulation in this project, was self-studied. The author had had

no prior experience with any sort of simulation.

• LATEX, in combination with WinEdt6, was used for typesetting as prescribed by

the study leader. Yet another invaluable tool was added to the author’s skill set.

The author realised that there is still much to learn and feels that this project has

provided equipment for a successful future in Industrial Engineering.

7.5 Denouement

This chapter provided a summary of the project and described items for future research.

This project’s contribution to society is briefly described, as well a few key lessons learnt

by the author.

The author hopes that you have had an enjoyable read.

65

References

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consumer perception of waiting time: An integrative review and research preposi-

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22

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69

Appendix A

Supporting Information

This appendix contains newspaper articles of complaints and reactions directed at the

Stellenbosch Traffic Department. It also contains activity diagrams drawn by the author

in understanding the process flow of the department, and the project plan as updated

continuously by the author during execution of this project. The template used in

server self-time study is also included in this appendix.

A.1 Newspaper Articles

Figure A.1: “Rotten service detrimental to the economy.”(Eikestad News, 2012)

70


Figure A.2: “Service doesn’t exist.”(Eikestad News, 2012)

71


Figure A.3: “Officers react to complaints.”(Eikestad News, 2012)

72

A.2 Project Plan

A.2 Project Plan

Various due dates were identified and tasks scheduled to meet these, as in Figure A.4

(overleaf). The project was executed in order of “nearest due date”. The Gantt chart

was used to give the author an indication of progress of the final year project, and

to prioritize activities. It was updated continuously as changes to the project were

realised.

73

A.2 Project Plan

IDT

ask N

am

eD

ura

tio

nS

tart

Fin

ish

1Topic Reg

istration

1 day

Feb

ruary 24

Feb

ruary 24

2Project Proposa

l3

Literature Research: Simulation &

Arena

4 da

ys?

Feb

ruary 24

Feb

ruary 29

4Library Resea

rch: Related

topics

8 da

ys?

March 07

March 16

5Plann

ing & Study Lea

der Mee

tings

3 da

ys?

March 15

March 19

6Project Plan

1 da

y?March 19

March 19

7Problem

Statemen

t from Mr. Royi

1 da

y?March 14

March 14

8Report W

riting

2 da

ysMarch 20

March 21

9Progress

Rep

ort

10

Literature Research: Que

uing

&

Simulation

96 days

April 24

Aug

ust 3

0

11

Literature Research : P

erf M

easures,

Fac Layou

t13

2 da

ysApril 24

Octob

er 18

12

Literature: LaT

eX13

2 da

ys?

April 25

Octob

er 19

13

Activity Cha

rting

3 da

ys?

April 26

April 30

14

Que

uing

The

ory Ana

lysis

5 da

ysAug

ust 3

1September 06

15

Report W

riting

2 da

ys?

October 22

Octob

er 23

16

Exa

ms

15 day

s?May

21

June 08

17

Holid

ay W

ork

18

Sho

rt Cou

rse on Simio/Simulation

1 da

yJune

11

June

11

19

Time Study at D

epartmen

t21

days

June

11

July 06

20

Ben

chmarking

5 da

ysJuly 02

July 06

21

Simulation

6 da

ys?

July 23

July 30

22

Update Report

5 da

ys?

October 19

Octob

er 25

23

70% Draft

24

Further simulation

2 da

ys?

Aug

ust 1

8Aug

ust 2

025

Add

ition

al data requ

ired

5 da

ys?

Aug

ust 2

0Aug

ust 2

426

Advice from

Lan

guage Centre

1 da

y?Aug

ust 2

4Aug

ust 2

427

Improve 70

% Rep

ort

3 da

ysAug

ust 2

3Aug

ust 2

728

Pee

r Rev

iews

5 day

s?August 27

August 31

29

Prelim

inary Exa

m Copy

30

Fee

dback ad

justmen

ts15

days?Sep

tembe

r 11

September 30

31

Ben

chmarking

1 da

y?Sep

tembe

r 17

September 17

32

Add

. Simulation

3 da

ys?Sep

tembe

r 19

September 22

33

Facility Design

2 da

ys?Sep

tembe

r 23

September 24

34

TOPSIS

2 da

ys?Sep

tembe

r 27

September 30

35

Edit R

eport

1 da

y?October 30

Octob

er 30

36

Final Rep

ort

37

Simulation Stats Ana

lysis

8 da

ys?

October 01

Octob

er 10

38

TOPSIS 75th %ile

6 da

ys?

October 08

Octob

er 15

39

Edit R

eport

16 days?

October 16Novem

ber 06

40

Exa

ms

15 day

s?October 29Nove

mber 16

41

Summary Slid

e2 day

s?Nove

mber 16Nove

mber 19

No

vJan

Mar

May

Jul

Sep

Nov

1st

Qua

rte

r3rd

Qua

rter

1

Task

Split

Mile

sto

ne

Sum

mary

Pro

ject

Su

mm

ary

Exte

rna

l T

asks

Exte

rnal M

ilesto

ne

Inactive T

ask

Inactive M

ilesto

ne

Ina

ctive

Su

mm

ary

Ma

nua

l T

ask

Dura

tion

-only

Man

ual S

um

mary

Ro

llup

Man

ual S

um

mary

Sta

rt-o

nly

Fin

ish-o

nly

Pro

gre

ss

De

adlin

e

Page

1

Pro

ject:

Skri

psie

Pla

nnin

gD

ate

: O

cto

ber

20

Figure A.4: Planned Tasks and Deadlines for the Project.

74

A.3 Time Study Template

A.3 Time Study Template

Sect

ion

1A

uth

ori

sati

on

2Fi

nes

3Li

cen

ces

4R

egis

trat

ion

& L

icen

sin

g(R

oad

wo

rth

y/Fi

nes

/Lic

ence

s/R

egis

trat

ion

&Li

cen

sin

g)

Dat

ed

dm

my

yy

yD

ay

Tota

l

8am

-9

am1

23

45

67

89

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

9am

-1

0am

12

34

56

78

91

01

11

21

31

41

51

61

71

81

92

02

12

22

32

42

52

62

72

82

93

0

10

am-

11

am1

23

45

67

89

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

11

am-

12

am1

23

45

67

89

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

12

am-

1p

m1

23

45

67

89

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

1p

m-

2p

m1

23

45

67

89

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

2p

m-

3p

m1

23

45

67

89

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

3p

m-

3.3

0p

m1

23

45

67

89

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

26

27

28

29

30

Co

mm

ents

: Fee

l fre

e to

co

nve

y an

y su

gges

tio

ns

you

hav

e to

imp

rove

th

e se

rvic

e an

d f

low

of

peo

ple

at

the

Stel

len

bo

sch

Tra

ffic

Dep

artm

ent

her

e.

You

r co

mm

ents

will

rem

ain

an

on

ymo

us!

Inst

ruct

ion

s

Ple

ase

com

ple

te t

he

form

sta

tin

g th

e ap

pro

pri

ate

sect

ion

(R

oad

wo

rth

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nes

/Lic

ensi

ng/

Lice

nce

&R

egis

trat

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) w

ith

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ate

and

day

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ase

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e ap

pro

pri

ate

bo

x ea

ch t

ime

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sto

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is s

erve

d b

y yo

u a

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r. Y

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r n

ame

do

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ear

on

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e fo

rm, a

nd

will

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info

rmat

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ly b

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sed

to

det

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ine

the

cap

acit

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f th

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affi

c d

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t, N

OT

to m

on

ito

r yo

ur

per

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- w

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ow

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at y

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xtre

mel

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for

Lugu

en G

ass

Tim

e S

tud

y: H

ou

rly

Cu

sto

me

r V

olu

me

at

Telle

rs (

Serv

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Tim

e)

Nu

mb

er o

f C

ust

om

ers

Tota

l

Sect

ion

nu

mb

er 1

or

2 o

r 3

or

4 -

->

Mo

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ues

/Wed

/Th

urs

/Fri

/Sat

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mb

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OA

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N

Figure A.5: Time Study Template for Servers

75

Appendix B

Queuing Models

B.1 Alternative Queuing Models

This section attempts to convince the reader that alternative queuing models are worth

considering to improve operational flow at the department. Two basic queuing models

will be explored in this section. The first model is that which is currently implemented

at the department; where clients are required to enter various (multiple) queues, de-

pendant on the type of transaction to be performed: fines, licence and registration, or

drivers licence transactions. The second model goes to show a different example where

customers need only enter a single queue, regardless of the type of transaction.

The following examples calculate the effect of queue design on customer waiting

time and server utilisation. It aims to convince the reader that considering alternative

queue designs is worthwhile.

B.1.1 Multiple Servers, Multiple Queues

This section refers to the current layout of the Stellenbosch Traffic Department in which

a client stands in one queue to pay a fine, another queue to renew a license, or another

queue to register a vehicle, as in Figure 4.1.

This model is an M/M/1 described by the Kendall Lee notation where the inter-

arrival and service times are assumed to be exponential. Winston (2004) explains that

an exponential distribution of arrival and service times is reasonably assumed when

no specific data of the nature of the inter-arrival and service times is available. The

capacity of the queue and its discipline are not of importance for the purpose of this

example.

76


Queuing theory relating to this type of model and the model in the next section can

be found in most related textbooks, such as Winston (2004) and Gross et al. (2008).

The following symbols are fundamental to queuing theory:

λ Average arrival rate of clients (clients per time unit)

µ Average service rate of clients (clients per time unit)

ρ Probability (or proportion of time) server is in service, or workload rate of server,

or traffic intensity

The probability, ρ, is a ratio described as:

ρ = λ/µ

The specific formulae for the M/M/1 queuing model are given below. The proba-

bility that the server is idle (not in service) is

1− ρ.

The mean number of customers in the system is given by L, where

L = ρ/(1− ρ).

By Little’s Law of section 2.1.1, the average time a customer spends in the system,

that is waiting in the queue and being served, is given by W :

W = L/λ

= (ρ/1− ρ)/λ

= ρλ/(1− ρ)

A multiple queue example is presented to give the reader an idea of the effects of

such a queuing system on the time a customer spends in the system, and the utilization

of the servers. For this reason the arrival and service times are assumed.

Example 1 — Multiple Queues, Multiple Servers Assume that 45 customers en-

ter the traffic department each hour and that a server takes 3 minutes to serve a

client, on average. The inter-arrival and service times are reasonably assumed to

be exponentially distributed (Winston, 2004). There are three separate queues

handling the transactions.

77


From this, it can be noted that

λ = 45/3 = 15 clients/hr per queue and

µ = 60/3 = 20 clients/hr per queue

Therefore,

ρ = λ/µ

= 15/20

= 0.75

The proportion of idle time of each server is then described by

1− ρ = 1− 0.75

= 0.25

This means that the server is idle approximately 25% of the time.

The average time the customer spends in the system:

W = L/λ

= (ρ/1− ρ)/λ

= ρ/[(λ)(1− ρ)]

= 0.75/[(15)(1− 0.75)]

= 0.2 hours

= 12 minutes

B.1.2 Multiple Servers, Single Queue

This section introduces the reader to a queueing model in which clients stand in one

single queue to be served, regardless of the transaction type. This model illustrates a

possible alternative solution to the queuing issue at the Stellenbosch department. In

this queueing model, the client stands in one queue in which all transactions can be

done; a fine can be paid, or a license renewed, etc. See Figure 4.4.

This model is an M/M/S according to the Kendall Lee notation where the inter-

arrival and service times are assumed to be exponential and there are S servers serving

the queue. Again, this is reasonably assumed (Winston, 2004).

78


Table B.1: P (j ≥ S) for the M/M/s Queueing System

ρ S = 2 S = 3 S = 4 S = 5 S = 6 S = 7

.10 .02 .00 .00 .00 .00 .00

.20 .07 .02 .00 .00 .00 .00

.30 .14 .17 .04 .02 .01 .00

.40 .23 .14 .09 .06 .04 .03

.50 .33 .24 .17 .13 .10 .08

.55 .39 .29 .23 .18 .14 .11

.60 .45 .35 .29 .24 .20 .17

.65 .51 .42 .35 .30 .26 .21

.70 .57 .51 .43 .38 .34 .30.75 .64 .57 .51 .46 .42 .39.80 .71 .65 .60 .55 .52 .49.85 .78 .73 .69 .65 .61 .60.90 .85 .83 .79 .76 .74 .72.95 .92 .91 .89 .88 .87 .85

(Winston, 2004)

From this type of queueing model, the probability that the server is idle (not in

service) is calculated using a steady-state probability. A steady-state probability is one

in which the probability is calculated as though the queueing model runs to infinity and

reaches a steady-state where values remain approximately constant. The steady-state

probability is represented by πj where there are j entities in the system.

The following formulae are applicable:

π0 = S!P (j ≥ S)(1− ρ)/(Sρ)2 (B.1)

πi = (Sρ)iπ0/i! (B.2)

i = 1, 2, ..., j.

S refers to the number of servers in the system, and the values for P(j ≥ S) can be

found in Table B.1.

The probability that a server is idle is the same as the probability that there is no

entity at the server. For this queuing system in which there are three servers (S=3),

the probability that a server is idle is equal to the probability that there is no entity in

the queue plus the probability that if there is one entity in the queue, that one of the

other two available servers will serve that entity, plus the probability that if there are

79


only two entities in the queue, that they will be served by the other two servers. This

is illustrated mathematically as follows:

P (idle) = π0 +2

3π1 +

1

3π2

Example 2 — Single Queue, Multiple Servers Assume that 45 customers enter

the traffic department each hour and that a server takes 3 minutes to serve a client,

on average. The inter-arrival and service times are assumed to be exponentially

distributed. This is the same scenario as Example 1, except that there is only

one queue, but which handles all types of transactions.

From this, it can be said that λ = 45 clients/hr and µ = 60 clients/hr.

Therefore,

ρ = λ/µ

=45

60

Now, calculating π0, π1, and π2 using (B.1) and (B.2):

π0 = S!P (j ≥ S)(1− ρ)/(Sρ)2

= 3!(0.57)(1− 45/60)/(3× 45

60)2

=38

225

π1 = (Sρ)1π0/1!

= 3(45

60)(

38

225)

=19

50

π2 = (Sρ)2π0/2!

= [(3× 45

60)2(

38

225)]/2

=171

400

80


Therefore,

P (idle) = π0 +2

3π1 +

1

3π2

=38

225+ (

2

3)(

19

50) + (

1

3)(

171

400)

= 0.5672

This means that each server is idle approximately 56.72% of the time.

The average length of the queue for this example is given by:

Lq = P (0 ≥ S)ρ/1− ρ

= (0.57)(45

60)/1− (

45

60)

= 1.71 customers

In order to calculate the average time the customer spends in the system, the average

number of customers in the system is required. The average number of customers in

the system is equal to the number in the queue, (Lq), and the number of customers in

service, (λ/µ).

L = Lq +λ

µ

= 1.71 +45

60= 2.46 customers

From Little’s Law, one can calculate the average time the customer spends in the

system:

W = L/λ

= 2.46/45

= 0.0546 hours

= 3.28 minutes

A summary of this analysis is provided in Section 4.2.

81

Appendix C

Simulation Model Notes

This appendix contains information relating to the simulation of the queue designs. It

includes a functional specification, assumptions of-, and describes the models developed

for simulation. Lastly, it contains a summary of the distributions fitted to data obtained

by physical time studies.

C.1 Functional Specification

It is recommended by Kelton et al. (2010) to create a functional specification early in

the simulation modeling process. It is said to assist the modeler in conceptualising and

translating information pertaining to the simulation, details the system by considering

the process flow, resources, and operations involved, and identifies which data inputs

and outputs are required (Kelton et al., 2010). The following sub-sections detail the

functional specification.

C.1.1 Operational Sections

The Stellenbosch Traffic Department is modularised into specific areas of transactions.

Fines, Licence & Registration, and Drivers Licence sections operate independantly, and

perform only specific transactions.

C.1.2 Servers

Clients who enter the department compete for service from servers or tellers of the

department at a specific section. The servers complete transactions at a certain service

rate; the time it takes to serve a customer.

82

C.2 Input and Output Data

C.1.3 Customers

Customers arrive at the department requiring to perform a certain type of transaction,

such as paying a fine, renewing a license, or applying for a roadworthy certificate.

Customers arrive independently, and sometimes in bulk.

C.1.4 Transactions

Customers have specific transactions which they would like performed. Each transac-

tion type is unique, and associated with a service time.

C.1.5 Flow

The physical layout and operational flow of queues consider the way in which customers

are expected to queue and how servers are distributed between sections, as well as the

transaction types performed by each server.

C.1.6 Schedules

The Stellenbosch Traffic Department operates from 08:00 – 15:30. Servers are entitled

to a 30 minute lunch break after at most 5 hours of continuous work, and a 15 minute

tea break.


Considering each design to be simulated, as introduced in section 4.2, each data input

is identified. The two main data inputs to the simulation are the customer arrivals,

and the service rates of the servers.

C.2.1 Input Data

The input data is acquired by physical time-motion studies of customer arrivals, server

service rates, and other observations over a three week period from 18 June to 6 July

2012. The time studies are conducted in collaboration with Ms. Renette de Villiers, a

third-year Mechanical Engineering student at Stellenbosch University.

A description of which data, and how it was collected, is described:

83


Customer Arrivals As a customer entered a queue at the department, the time at

which the customer arrived was recorded using Microsoft Excel’s “time stamp”

function. Bulk arrivals were also noted. The time study was done at each opera-

tional section (Fines, Licence & Registration, Drivers Licences) for a week, each;

totalling a three week physical time study period.

Server Service Rate Also using Microsoft Excel, the time at which a server began

a transaction and the termination of a transaction was recorded. The difference

between the times gives the service time for each transaction. This was done

for all servers for a full 712 hour shift. At first a time study template was given

to each server on which the server was to tally the number of customers served

per hour, as discussed in 2.2.2. This concept originated after the author noticed

such a template being used by the South African Post Office. The template is

included in Appendix A.3. Unfortunately, this self-time study method failed as

the tellers were reluctant to cooperate, and often forgot to tally the customers.

A time study performed by the author soon followed.

Transaction Segment Times In the same way server service times were studied, the

times for each segment of a transaction were recorded. This provided the times

for Design 2 in which each server has only one function: application, payment, or

issuance. Each transaction was divided into these three segments at each section.

Time in System The time at which a customer entered the queue was stamped onto

a clock card using an old-fashioned clock-card machine. As the customer exited

the system, after having been served, the card was once again stamped to de-

termine the actual time the customer spent in the system. This is also used for

validation and verification of the simulation of Design 1: the current layout of

the Stellenbosch Traffic Department.

ARENA, a simulation package, offers an “Input Analyzer” which was used to fit distri-

butions to the physical time study data. To ensure that distributions are statistically

sound, only distributions with p-values > 0.05 were used. In the event that no such

distribution could be found, an empirical distribution was used. A summary of data

distributions used in the simulations is supplied in Appendix C.5.

84

C.3 Assumptions

C.2.2 Output Data

In order to validate the simulation, it is required to have the following output statistics:

• Number of customers entering the system (entities created)


• Server processing time

In order to make an informed decision on which design (1, 2, 3, or 4) is best, the

following is required:


• Number of customers in system (CIS)

• Utilisation of resources (the servers)

• Percentage of customers not served

Output data is assumed to be statistically viable since 1000 replications of each

experiment are performed. This ensures that h-values are minimised, resulting in nar-

rowed confidence intervals.

C.3 Assumptions

In developing the simulations it is necessary to make some simplifying assumptions,

but which do not compromise the integrity of the simulation. Assumptions are only

made where they intuitively have little or no effect on the accuracy of the simulation.

These include:

Bulk Arrivals Each bulk arrival is noted as being an arrival of exactly two customers,

no more. Effects of balking, reneging, and jockeying are ignored on advice from

the study leader (Bekker, 2012b).

Data Distributions Distribution curves fitted to the measured data are assumed to

be reasonable since p-values of each are well above 0.5, and have expected values

within 5% of the actual observed values. All distributions are assumed to be

independent.

85

C.4 Model Experiments

Service Time The service rate of the tellers is assumed to be representative of any

day, not only the day on which the observations were made. This also implies

that the study ignores the Hawthorne effect; a change in natural occurrences due

to the mere physical presence of the author doing a time study.


The previous sections discussed functional specifications of the simulation models,

which data is required, and described the assumptions to put the simulation in context

for the reader.

The simulation models built for this project consist of the designs discussed in sec-

tion 4.2 and are “built” in separate models using a simulation package, Simio. Each

model is described by one common process; a customer arrives at the Traffic Depart-

ment, enters at the back of a queue, is served in a first-in-first-out (FIFO) fashion by

a server, and exits the system. The difference between the models is that of the layout

of the queues, the nature of entering the queue, and the nature of service.

Customers performing Licence & Registration type transactions are also subject

to authorisation at another step in the serving process. The authorisation service

time is included in all simulations. Due to the fact that only approximately 10% of

all customers requiring to perform a Licence & Registration type transactions require

authorisation, a large variation in Authorisation service rates is realised between the

models (or designs). To ensure that comparison between designs is fair, it was decided

to use the average service time of 9.18 minutes as a mean service rate. All customers

who require authorisation after being served by a teller then re-enter the queue. They

re-enter at the front of the queue, rather than at the back. A customer only enters at

the back upon first entry of the queue.

Further details of each model experiment and respective outcomes are presented in

the following subsections.

C.4.1 Design 1 — Single Stage, Multiple Queue, Single and MultipleServer

This model represents the current queue design at the department, as in Figure 4.1.

A customer entering the department is required to enter a specific queue, depending

on the type of transaction s/he would like to perform. To pay a fine the customer is

86


obligated to enter the queue at the Fines section, and to renew a drivers’ licence the

customer must enter a separate queue at the Drivers Licence section. This model is

necessary to be simulated as it forms part of the reference point for verification and for

comparison to measure the success of the proposed designs 2, 3 and 4.

Customers who intend on performing a Licence & Registration type transaction

assemble in one queue and are then distributed to the next available of two servers.

On the other hand, customers at the Fines- or Drivers Licences section form a queue

directly at a single server.

C.4.2 Design 2 — Single Stage, Multiple Queue, Single Server

This is the first of three proposed alternative queueing designs. All customers can

choose to enter any queue and can perform any transaction at the server. Refer to

Figure 4.2.

The model is simulated such that a customer will choose the shortest queue; one

with the least number of entities in service and waiting for service. However, it must

be noted that it is unlikely that customers will first make an accurate calculation of the

number of customers in each queue before they decide to enter one. This implies that

the waiting time in reality is likely to be larger than that outputted by the simulation.

Again, it is approximated that 10% of all customers performing Licence & Registration

type transactions require authorisation after being served, and then have to re-enter

the queue.

C.4.3 Design 3 — Multiple Stage, Single Queue, Single Server

Each segment of a transaction is assigned to a separate server. A customer enters

the queue and waits his/her turn to be served by the first server, the Applications

server. Here the customer hands over the documentation to the server and any other

representation required. Once the Application server has completed the application

segment of the transaction, the customer moves to the next server. A customer can

only move to the next server to perform the next segment of the transaction once the

next server is available. This is similar to the queue of a drive-through restaurant.

Now, at the Payment server, the customer is informed of the amount payable, and

pays. Once the payment is confirmed, the receipt is given to the customer, and s/he

again moves to the next (Issuing) server and collects the final document or item. Ten

87

C.5 Data Distributions Summary

percent of all customers performing a Licence & Registration type transaction are first

routed to the Authorisation server, before moving to the Issuing server.

C.4.4 Design 4 — Single Stage, Single Queue, Multiple Server

Customers all enter into a single queue, regardless of the type of transaction they

would like to perform, and can be served by one of multiple servers – which ever

server is available next (see Figure 4.4). The customer enters at the back of the queue.

Customers go to the next available server once they are at the front of the queue. Again,

the need for authorisation of Licence & Registration type transactions is considered.


A summary of all distributions fitted, using ARENA, to data of the physical time

studies, are shown in Table C.1 (overleaf).

88


Tab

leC

.1:

Su

mm

ary

of(F

itte

d)

Tim

eS

tudy

Dat

aD

istr

ibu

tion

s

Day/Segm

ent

Mean

Bulk

Arrivals

Distrib

ution

(Arena)

p-valu

eExpecte

dValu

e

Fin

es

Inte

rarr

ival

Tim

eM

on

18

Ju

ne

15.4

583

6/31

0.5

+G

am

ma(1

2.7

,1.1

8)*

*0.0

515.4

860

Tu

e19

Ju

ne

12.5

806

1/32

0.5

+E

xp

on

enti

al(

12.1

)0.1

25

12.6

000

Wed

20

Ju

ne

7.4

211

12/79

0.5

+G

am

ma(5

.67,1

.22)*

*0.5

36

7.4

174

Thu

rs21

Ju

ne

9.6

889

7/53

0.5

+G

am

ma(8

.09,

1.1

4)*

*0.4

55

9.7

226

Fri

22

Ju

ne

10.0

476

3/46

0.5

+W

eib

ull(9

.39,0

.964)*

*0.1

69

10.0

400

Ser

vic

eT

ime

Fri

29

Ju

ne

1.9

427

Gam

ma(0

.93,2

.09)*

*>

0.1

51.9

437

Seg

men

ted

Ser

vic

eT

ime

Ap

plica

tion

0.9

615

Exp

on

enti

al(

0.9

61)

>0.1

50.9

610

Paym

ent

0.6

156

Wei

bu

ll(0

.601,0

.953)*

*>

0.1

50.6

140

Issu

an

ce0.3

656

0.0

1+

Gam

ma(0

.169,

2.1

)**

>0.1

50.3

649

Licence&

Registration

Inte

rarr

ival

Tim

eM

on

18

Ju

ne

3.9

000

31/142

0.5

+W

eib

ull(3

.61,

1.1

8)*

*>

0.7

53.9

100

Tu

e19

Ju

ne

3.3

111

56/192

0.5

+G

am

ma(1

.75,

1.6

1)*

*0.0

51

3.3

175

Wed

20

Ju

ne

3.1

972

47/190

0.5

+E

xp

on

enti

al(

2.7

)0.0

75

3.2

000

Thu

rs21

Ju

ne

2.9

045

45/203

0.5

+11*B

eta(0

.926,

3.3

1)

0.5

36

2.9

046

Fri

22

Ju

ne

2.9

530

30/180

0.5

+W

eib

ull(2

.62,

1.1

9)*

*0.6

41

2.9

600

Ser

vic

eT

ime

Tu

e26

Ju

ne

3.9

640

Wei

bu

ll(4

.27,

1.1

7)*

*0.4

44.0

400

Seg

men

ted

Ser

vic

eT

ime

Ap

plica

tion

2.4

962

11*B

eta(0

.574,

1.9

6)

0.1

36

2.4

917

Paym

ent

0.7

474

Exp

on

enti

al

(0.7

47)

0.6

55

0.7

470

Issu

an

ce0.7

211

Exp

on

enti

al

(0.7

21)

0.0

712

0.7

210

DriversLicences

Inte

rarr

ival

Tim

eM

on

2Ju

ly3.5

378

22/141

0.5

+11*B

eta(0

.926,

2.4

3)

0.7

45

3.5

352

Tu

e3

Ju

ly3.6

583

10/130

0.5

+W

eib

ull(3

.38,

1.2

2)*

*0.5

42

3.6

600

Wed

4Ju

lyL

earn

ers

Lic

ence

sO

nly

(Wed

nes

days)

Thu

rs5

Ju

ly3.7

288

15/133

0.5

+W

eib

ull(3

.44,

1.1

9)*

*0.2

97

3.7

400

Fri

6Ju

ly3.7

701

6/93

0.5

+E

xp

on

enti

al(

3.2

7)

0.1

13.7

700

Ser

vic

eT

ime

Thu

rs28

Ju

ne

3.2

330

1.1

6+

Gam

ma(0

.409,

5.0

7)*

*0.6

99

3.2

336

89


Day/Segm

ent

Mean

Bulk

Arrivals

Distrib

ution

(Arena)

p-valu

eExpecte

dValu

eS

egm

ente

dS

ervic

eT

ime

Ap

plica

tion

1.8

900

0.3

2+

Gam

ma(0

.295,5

.32)*

*0.3

57

1.8

894

Paym

ent

0.2

660

Em

pir

ical

sin

ce¡0

.005

Not

Su

itab

le(1

/12,3

/77,1

3/30,1

0/11,4

7/60,7

4/77,1

7/15,7

5/77,8

9/60,7

6/77,1

1/6,7

6/77,1

31/60,7

6/77,3

8/15,1

)Is

suan

ce1.0

750

Gam

ma(0

.491,2

.19)*

*0.0

931

1.0

753

LearnersLicences

Inte

rarr

ival

Tim

eW

ed4

Ju

ly8.2

917

35/83

0.5

+W

eib

ull(5

.73,0

.678)*

*0.1

72

7.9

800

Ser

vic

eT

ime

Wed

27

Ju

ne

2.2

460

Em

pir

ical

sin

ce¡0

.005

Not

Su

itab

le(5

3/30,1

/35,7

91/360,1

7/35,4

73/180,3

1/35,3

67/120,3

4/35,1

57/45,3

4/35,1

411/360,3

4/35,8

7/21,1

)

Auth

orisation

Inte

rarr

ival

Tim

eJu

ne

Ass

um

pti

on

—10%

of

Lic

ence

&R

egis

trati

on

arr

ivals

Ser

vic

eT

ime

Fri

29

Ju

ne

9.1

8N

ot

Su

itab

le.

Con

stant

serv

ice

tim

een

sure

sfa

irco

mp

ari

son

of

mod

els

9.1

800

**

Ind

icate

sth

at

the

para

met

erm

ust

be

inver

ted

for

use

inS

imio

90

C.6 TOPSIS Calculations

C.6 TOPSIS Calculations

Table C.2: TOPSIS Analysis of 75th Percentile Results

Performance Measures Matrixvj TIS (Avg, Minutes) CIS (Avg, Number) Utilisation (%) Customers Not Served (%)

Design 1 24.9133 21.5247 68.5459 15.5488Design 2 4.5860 3.3588 62.9562 1.2739Design 3 75.9809 55.0176 59.9900 31.4465Design 4 4.4400 3.2747 63.0218 1.2903

Normalised MatrixPmax(vj) 75.9809 55.0176 68.5459 31.4465

rij = 0.3279 0.3912 1.0000 0.49450.0604 0.0610 0.9185 0.04051.0000 1.0000 0.8752 1.00000.0584 0.0595 0.9194 0.0410

Weighted Normalised MatrixWeighting 0.7500 0.0833 0.0833 0.0833

Tij = 0.2459 0.0326 0.0833 0.04120.0453 0.0051 0.0765 0.00340.7500 0.0833 0.0729 0.08330.0438 0.0050 0.0766 0.0034

TIS (Avg, Minutes) CIS (Avg, Number) Utilisation (%) Customers Not Served (%)(Cost) (Cost) (Benefit) (Cost)

Ab = 0.0438 0.0050 0.0729 0.0034Aw = 0.7500 0.0833 0.0833 0.0833

Table C.3: TOPSIS Analysis of 75th Percentile Results (continued)

Model dib diw Sib

Design 1 0.20771 0.50838 0.7099Design 2 0.00389 0.71359 0.9946Design 3 0.71499 0.01040 0.0143Design 4 0.00369 0.71502 0.9949

91

Appendix D

Administration of theFinal Year Project

This appendix provides the reader with an extract of minutes of meetings held between

the author and the study leader, as well as a summary time sheet detailing the activities

and durations of tasks completed in creating this final year project. The agendas and

time sheets illustrate the author’s independent project management ability.

D.1 Meetings with the Study Leader

It was recommended by the study leader that agendas be drawn up for meetings to

ensure that they are effective, and also to serve as an archive for ideas and advice given.

It was said to be used in the event of any disputes during the execution of the project.

An extract of of meeting minutes is supplied. The reader is, however, invited to request

the complete collection of meeting minutes; the author has this filed.

92

D.1 Meetings with the Study Leader

Figure D.1: Extract of Meeting Minutes: Meeting 6.

93

D.2 Summary Time Sheet

D.2 Summary Time Sheet

The author continuously recorded the activities and durations contributing to this final

year report, a summary of which is shown in Figure D.2. Kindly request the complete

breakdown of activities from the author, should it be required.

Name Luguen Gass Total Hours 319.8333333

Student Number 15771598 Hours Per Month 35.53703704

Hours Per Term 79.95833333

Hours (1st Semester) 62.5

June/July Holiday 108

Hours (2nd Semester) 149.3333333

Dissertation Time Sheet | SUMMARY

Figure D.2: Summary Time Sheet as on 21 October 2012.

94

Improving Operational Service Delivery at Stellenbosch Traffic Department

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