Improving Operational Service Delivery at Stellenbosch Traffic Department by Luguen Gass

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

To Stellenbosch Traffic Department and the National Department of Transport... May this be another step forward in government service delivery.

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

ECSA Exit Level Outcomes Reference Outcome 1. Problem solving: Demonstrate competence to identify, assess, formulate and solve convergent and divergent engineering problems creatively and innovatively. 5. Engineering methods, skills and tools, including information technology: Demonstrate competence to use appropriate engineering methods, skills and tools, including those based on information technology. 6. Professional and technical communication: Demonstrate competence to communicate effectively, both orally and in writing, with engineering audiences and the community at large. 9. Independent learning ability: Demonstrate competence to engage in independent learning through well developed learning skills. 10. Engineering professionalism: Demonstrate critical awareness of the need to act professionally and ethically and to exercise judgment and take responsibility within own limits of competence.

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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 resulted 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 â&#x20AC;&#x153;cray-crayâ&#x20AC;? 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.

Abstract The Stellenbosch Traffic Department offers a municipal service to approximately 40 000 vehicle owners in the area. Numerous complaints about service delivery, specifically referring to the long times waited by customers at the department, have been reported. The focus of this report is on the queuing 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 department 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. Finally, managerial recommendations are provided such that employee morale and leadership may be increased to further improve customer service at the department.

Opsomming Die Stellenbosch Verkeersdepartement lewer ’n munisipale diens aan ongeveer 40 000 voertuig eienaars in die area. Talle klagtes oor dienslewering, spesifiek 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 Besluitnemingsondersteuningstelsel in die vorm van ’n stogastiese, diskrete simulasiemodel is ontwikkel. 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 geimplementeer 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 toeneem om verdere kliente diens by die departement te verbeter.

Contents 1 Introduction

1.1

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1.2

Business Process Analysis . . . . . . . . . . . . . . . . . . . . . . . . . .

1.3

Solving Methodologies . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1.3.1

Queuing Theory . . . . . . . . . . . . . . . . . . . . . . . . . . .

1.3.2

Simulation

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

1.4

Project Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1.5

Report Road Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2 Literature Review 2.1

Queuing Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.1.1

Fundamental Concepts . . . . . . . . . . . . . . . . . . . . . . . .

2.1.2

Customer Behaviour . . . . . . . . . . . . . . . . . . . . . . . . .

Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.2.1

Characterising the Problem . . . . . . . . . . . . . . . . . . . . .

2.2.2

Input Analysis and Parameters . . . . . . . . . . . . . . . . . . .

2.2.3

Validation and Verification . . . . . . . . . . . . . . . . . . . . .

2.3

Queuing Theory vs. Simulation . . . . . . . . . . . . . . . . . . . . . . .

2.4

TOPSIS Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.5

Box Plot Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.6

Managerial Aspects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

2.6.1

Customer Waiting Time . . . . . . . . . . . . . . . . . . . . . . .

2.6.2

Organisational Behaviour . . . . . . . . . . . . . . . . . . . . . .

2.6.2.1

Work Motivation . . . . . . . . . . . . . . . . . . . . . .

2.6.2.2

Stress Management . . . . . . . . . . . . . . . . . . . .

2.2

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CONTENTS

2.6.2.3 2.7

Leadership . . . . . . . . . . . . . . . . . . . . . . . . .

Summary of Literature Review . . . . . . . . . . . . . . . . . . . . . . .

3 Benchmarking

3.1

Stellenbosch Traffic Department . . . . . . . . . . . . . . . . . . . . . . .

3.2

Bellville Traffic Department . . . . . . . . . . . . . . . . . . . . . . . . .

3.3

Durbanville Traffic Department . . . . . . . . . . . . . . . . . . . . . . .

3.4

Malmesbury Traffic Department

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

3.5

Summary of Benchmarks

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

4 Proposed Queue Models

4.1

Queue Design Considerations . . . . . . . . . . . . . . . . . . . . . . . .

4.2

Alternative Queue Designs . . . . . . . . . . . . . . . . . . . . . . . . . .

4.3

Summary of Proposed Changes . . . . . . . . . . . . . . . . . . . . . . .

5 Simulation Study

5.1

Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5.2

Analysis of Simulation Results . . . . . . . . . . . . . . . . . . . . . . .

5.3

Validation and Verification

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

5.4

Summary of Simulation Study . . . . . . . . . . . . . . . . . . . . . . . .

6 Conclusions and Recommendations

6.1

Queueing Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6.2

Facility Layout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6.3

Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

6.3.1

Work Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . .

6.3.2

Stress Management . . . . . . . . . . . . . . . . . . . . . . . . . .

6.3.3

Leadership . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

Further Recommendations . . . . . . . . . . . . . . . . . . . . . . . . . .

6.4

7 Closing Summary

7.1

Project summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7.2

Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7.3

Contribution to Society . . . . . . . . . . . . . . . . . . . . . . . . . . .

7.4

Lessons Learnt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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CONTENTS

7.5

Denouement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

References

A Supporting Information

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

A.2 Project Plan

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

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

B Queuing Models

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

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

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

C Simulation Model Notes

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

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

C.1.2 Servers

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

CONTENTS

D Administration of the Final Year Project

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

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

List of Figures 1.1

Graphic Summary of the Problem Statement, Methodology and Result.

2.1

Triangular Distribution as Estimated by Servers . . . . . . . . . . . . .

2.2

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

3.1

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

3.2

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

3.3

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

3.4

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

3.5

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

3.6

Malmesbury Traffic Department: All Transactions . . . . . . . . . . . .

4.1

Design 1 — Single Stage, Multiple Queue, Single and Multiple Server

4.2

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

4.3

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

4.4

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

5.1

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

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

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

A.2 “Service doesn’t exist.”

6.1

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

A.3 “Officers react to complaints.”

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

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

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

LIST OF FIGURES

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

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

xii

List of Tables 4.1

Summary of Alternative Queuing Models . . . . . . . . . . . . . . . . .

5.1

Summary of Simulation Results . . . . . . . . . . . . . . . . . . . . . . .

5.2

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

5.3

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

5.4

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

6.1

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

B.1 P (j â&#x2030;Ľ S) for the M/M/s Queueing System

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

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

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

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

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

Average service rate

πi

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

Workload rate or traffic intensity

Roman Symbols Ab

Best alternative

Worst Alternative

dib

Distance between target alternative i and the best condition Ab

xiv

Nomenclature

diw

Distance between target alternative i and the worst condition Aw

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

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 extreme as the observed statistic

rij

Normalised matrix

Sib

Similarity to best condition

Number of servers in system

Tij

Weighted normalised matrix

Performance measure ,j

Average time in system (in queue and in service)

Terminology Drivers Licence Section

Operational department processing all transactions related to drivers’ licenses, including learners’ licences

Entity

Material existence referring to customers in the simulation

Fines Section

Operational department processing all transactions related to fines

Floor

Customer queuing and waiting area

Toxin Handler

Person willing to listen to an individual’s issues

Idle

Not in service

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 related 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 principles of equality

Teller

A resource for which entities compete, usually in queuing. 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 Engineering to recommend improvements to Stellenbosch Traffic Department in creating a business process which flows naturally and serves clients efficiently. The proposed improvements should increase customer satisfaction by reducing time waited in queues and improving customer service. Existing structures and information systems will be considered 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 citizens (Stats SA, 2007). It provides a municipal service to the Stellenbosch community in the form of testing and issuing roadworthy validations, renewal of licences, registrations, 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 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 & Registration 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 transactions 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.

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 intermittent 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 Stellenbosch 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.

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 business 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 department, 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.

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 systemâ&#x20AC;&#x2122;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 satisfaction. It is also envisioned to propose that the outcomes of this project be implemented at traffic departments nationally; an appeal made to the National Department of Transport.

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 facilityâ&#x20AC;&#x2122;s layout, especially at the Drivers Licence section which is experiencing capacity constraints, as explained 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 Departmentâ&#x20AC;&#x2122;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

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 authorâ&#x20AC;&#x2122;s perspective. Figure 1.1 illustrates a summary of the problems experienced at the department, and the road map followed in this report.

1.5 Report Road Map

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

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. Familiarity 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-

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 firstin-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 include 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 queuing terminology before it can be discussed: Wq

Average time in queue (excluding service time)

Average time in system (in queue and in service)

Average number of entities in queue

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

Workload rate or traffic intensity

2.1 Queuing Theory

Average arrival rate

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.

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 gathering 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

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. However, 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 economical 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

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 probability 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

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 Department 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 problem 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: â&#x20AC;˘ 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.

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

2.2 Simulation

Probability, f(t)

Shortest

Typical

Longest

Service Time

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 cooperating â&#x20AC;&#x201C; having confidence in time study methods, as well as the analyst. At the Stellenbosch Traffic Department this was done by fostering a comfortable relationship 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.

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 considered 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).

2.3 Queuing Theory vs. Simulation

• The assumption that the simulation should match the expected results is incorrect. 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 expectation 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 (especially 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 simulation; 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

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 criteria, 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 combination 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 , = (wj rij )mxn , for i = 1, 2, ..., m, n X where wj = Wj / Wj , j=1 n X

wj = 1.

j=1

Wj is the weight given to vj .

2.4 TOPSIS Analysis

Next, the best alternative (Aw ) and worst alternative (Ab ) is calculated using: Aw = {hmax(tij |i = 1, 2, ..., m)|j ∈ J− i, hmin(tij |i = 1, 2, ..., m)|j ∈ J+ i} ≡ {twj |j = 1, 2, ..., n} Ab = {hmin(tij |i = 1, 2, ..., m)|j ∈ J− i, hmax(tij |i = 1, 2, ..., m)|j ∈ J+ i} ≡ {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

v uX u n = t (tij − twj )2 j=1

and the same for the best condition, Ab : v uX u n dib = t (tij − tbj )2 j=1

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 alternative with the highest sib is the overall winner. An important assumption of the TOPSIS method is that the criteria are monotonically assigned; the weightings sum to 1, or 100%. The TOPSIS analysis considers 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:

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 simulation, 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 customer waiting time is a valid performance measure to be considered in order to improve

2.6 Managerial Aspects

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 customers 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 customer (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”.

2.6 Managerial Aspects

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

2.6 Managerial Aspects

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

2.6 Managerial Aspects

Motivators Satisfaction

No Satisfaction

Hygienes No dissatisfaction

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 factors 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

2.6 Managerial Aspects

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 absenteeism 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 individuals’ 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, encouraging 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 suggested 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 networks 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 organisational 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 competitive 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).

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.

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 layout of the Stellenbosch department and three local traffic departments, namely: Bellville, 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

3.1 Stellenbosch Traffic Department

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. Approximately 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 & Registration 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 transaction 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.

Office

Fines Server

Fin es Enquires

Enquiries

ENTRANCE (Main Building)

Exit

Office

Teller3

Teller 2

Teller1

Authorisation Server

Vault

Authorisation Office

Licence & Registration

n tio tra gis Re

Figure 3.1: Current Business Process Flow and Facility Layout 31

Fines

Office

Fines Server

Application Forms Station

Exit

Office

Enter

Store 1 Drivers Licences Server

Exit

Eye Test

Eye Test Officer 1

Eye Test Room 1

Eye Test Officer 2

Eye Test Room 2

Eye Test Waiting Area (Mon,Tues, Thurs,Fri)

Learners’ Test Room (Wednesdays)

Drivers Licences

ENTRANCE

Drivers Licences General Waiting Area

Store 2

Enquiries

Drivers’ Licences Server

Drivers Licences Waiting Area

Office

ice nc es Dr ive rs L

Application Forms Station

& ce en Lic

Queue

Application Forms Station Authorisation

Office

Enter

Offices, Parade Room, Kitchen

Licence & Registration Servers

Store

3.1 Stellenbosch Traffic Department

3.2 Bellville Traffic Department

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 Stellenbosch department (Pieterson, 2012):

3.2 Bellville Traffic Department

Teller 6 Collection

Teller 5 Collection

Teller 4 Application & Payment

Teller 2 Application Payment

Fines

Start (Licence & Registration) Fines & Roadworthy

Start (Fines, Roadworthy)

Figure 3.2: Bellville Traffic Department: Licence & Registration, Reed Str Operating Hours From 08:00 â&#x20AC;&#x201C; 15:30 for all transactions, but employee working hours are 07:30 â&#x20AC;&#x201C; 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

3.3 Durbanville Traffic Department

Teller 1

Drivers Licences

Teller 2

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 authorâ&#x20AC;&#x2122;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.

3.3

Durbanville Traffic Department

Durbanville Traffic department is also split into three sections, similarly to the Stellenbosch 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

3.3 Durbanville Traffic Department

Teller 2

Teller 3

Teller 4

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. Payment Method Cash or Cheque only. Payment cannot be made using debit- nor credit cards. Authorisation Insufficient information regarding authorisation at Reed and Church Street. Meetings Approximately every two weeks, if required. All personnel are included. Lunch Breaks Each teller is allowed a maximum of one 45 minute lunch break, and a 15 minute tea break, self-decided.

3.4 Malmesbury Traffic Department

Teller 4 Drivers Licences & Roadworthy

Teller 3 Drivers Licences & Roadworthy

Teller 2 Drivers Licences & Roadworthy

Teller 1 Drivers Licences & Roadworthy

Enquiries & Fines

Exit

Drivers Licence & Roadworthy Enquiries & Fines

Enquiries & Fines

Figure 3.5: Durbanville Traffic Department: Drivers Licences, Church Str

3.4

Malmesbury Traffic Department

This traffic department is governed by the Swartland Municipality, unlike the aformentioned 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 & registration, 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 discussed 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). Payment Method Cash or Cheque only. Payment cannot be made using debit- nor credit cards.

3.4 Malmesbury Traffic Department

All Transactions: Licence & Registration, Drivers Licences, and Roadworthy Tellers

Teller 1

Teller 2

Teller 3

Enqueries

Teller 4

Enqueries

Start

Proposed New Enqueries Desk Fines (Outsourced)

Fines

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. Lunch Breaks Each teller is allowed a maximum of one 45 minute lunch break, and 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.

3.4 Malmesbury Traffic Department

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 transfers 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 visualand voice commands to the customers in the queue to state which server is available. • 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.

3.5 Summary of Benchmarks

â&#x20AC;˘ There is a good work ethic and culture at Malmesbury Traffic Department; staff are friendly and appear to enjoy their work environment.

3.5

Summary of Benchmarks

The previous sections detailed the key operations of various traffic departments surrounding 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 exceptional service and optimising resource utilisation. Their method of processing transactions 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

3.5 Summary of Benchmarks

tea break. Each department handles this differently. Malmesburyâ&#x20AC;&#x2122;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 alternative 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.

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 & Fitzsimmons (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 authorâ&#x20AC;&#x2122;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 flexibility 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.

4.2 Alternative Queue Designs

Table 4.1: Summary of Alternative Queuing Models

Example 1 Example 2

4.2

Idle Server (%)

Time in System (minutes)

25 56.72

12 3.8

Alternative Queue Designs

Various queue designs are introduced in this section to be investigated through simulation 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 benchmarking, as well as a simplified mathematical comparison of multiple-server-multiplequeue 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 â&#x20AC;&#x201C; 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:

4.2 Alternative Queue Designs

Fines

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 transaction 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.

4.3 Summary of Proposed Changes

All Transactions (Fines, Licensing & Registration, Roadworthy, 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 department. Separating roles into three sections (application processing, payment, issuing) would mean that any fraudulent transaction would have to be approved by three employees. The likeliness that three employees concede to fraudulent activity is probably somewhat less than that of one person having sufficient authority to commit a fraudulent transaction.

4.3

Summary of Proposed Changes

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.

4.3 Summary of Proposed Changes

Issuance of Documents

Payment

Application

Figure 4.3: Design 3 â&#x20AC;&#x201D; Multiple Stage, Single Queue, Single Server

All Transactions (Fines, Licensing & Registration, Roadworthy, Licences)

Figure 4.4: Design 4 â&#x20AC;&#x201D; Single Stage, Single Queue, Multiple Server

Chapter 5

Simulation Study The previous chapters provided knowledge acquired leading up to the decision to simulate 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 provides 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.

All Servers Utilisation, Avg (%) Customers in System, Avg Customers Not Served (%) Time in System (TIS), Avg (minutes)

Design 3

Design 4

Other Performance Measures Customers in System (CIS), Max Drivers Licence Server Utilisation, Avg (%) Fines Server Utilisation, Avg (%) Licence & Registration Server Utilisation, Avg (%) Drivers License CIS, Avg Fines CIS, Avg License & Registration CIS, Avg Licence & Registration TIS, Avg (minutes)

Performance Measure All Servers Utilisation, Avg (%) Customers in System (CIS), Avg Customers Not Served (%) Time in System (TIS), Avg (minutes)

Design 2

Scenario Design 1

60.7649 3.0846 0.9686 4.2706

59.3575 49.0895 28.7502 68.3966

60.7245 3.1763 0.9679 4.4010

52.0000 97.0499 15.3582 75.8710 8.4205 0.1931 9.7624 22.0499

Average 66.0375 18.3760 13.3574 21.1348

63.0218 3.2747 1.2903 4.4396

59.9899 55.0176 31.4465 75.9809

62.9562 3.3588 1.2739 4.5860

59.0000 99.3101 17.5589 80.7000 11.2778 0.2228 11.3437 25.2106

75th Percentile 68.5458 21.5247 15.5488 24.9133

Table 5.1: Summary of Simulation Results

52.0465 2.3823 0.0000 3.5655

56.5854 19.6259 16.3763 28.5249

50.3348 2.2779 0.0000 3.5378

20.0000 83.0653 6.6633 54.7406 1.5894 0.0733 3.0622 8.9592

Minimum 56.5275 6.2401 3.3537 8.7314

70.2152 4.4503 2.9499 6.2614

62.0126 82.3618 39.4595 105.1046

72.4108 4.4745 3.4056 5.8870

89.0000 100.0000 25.9288 99.9982 24.5390 0.4559 22.6736 48.8562

Maximum 77.2406 36.2884 22.8916 41.8979

0.1971 0.0190 0.0342 0.0203

0.0555 0.5763 0.2336 0.6835

0.1909 0.0186 0.0347 0.0193

0.6668 0.1873 0.2086 0.4439 0.2666 0.0033 0.1519 0.3151

Half Width 0.2321 0.3023 0.2002 0.3706

5.1 Simulation Results

5.2 Analysis of Simulation Results

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

5.2 Analysis of Simulation Results

Table 5.2: Ranked TOPSIS Analysis of 75th Percentile Results TOPSIS Outcome Model Design Design Design Design

1 2 3 4

dib

diw

Sib

Rank

Sib

Model

0.20771 0.00389 0.71499 0.00369

0.50838 0.71359 0.01040 0.71502

0.7099 0.9946 0.0143 0.9949

1 2 3 4

0.9949 0.9946 0.7099 0.0143

Design 4 Design 2 Design 1 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 customers 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).

5.2 Analysis of Simulation Results

40 6

35 5 30

15 2 10

1 5

0 Design 1

Design 4

Design 2

Design 4

Figure 5.1: Box Plots Comparing TIS of Designs 1 and 4 (left), Designs 2 and 4 (right).

5.3 Validation and Verification

5.3

Validation and Verification

Kelton et al. (2010) recommends that simulation models should be verified and validated. 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 SMEâ&#x20AC;&#x2122;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 implemented 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 realistically 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 follows; the time study to obtain the inter-arrival information and the time in system

5.3 Validation and Verification

Table 5.3: Actual vs. Simulated TIS: Design 1 Section

Actual

Simulated

(minutes) Fines Licence & Registration Drivers Licence

2.5 22.8 14.2

2.42 22.05 25.26

Table 5.4: Actual vs. Simulated Entities Created: Design 1 Section

Actual

Simulated

(units) Fines Licence & Registration Drivers Licence

31 142 141

36 141 148

(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 particularly 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.

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 recommendations to the department with respect to queue design, facility layout and management.

Chapter 6

Conclusions and Recommendations This chapter summarises the outcome of the simulation study, recommends a reengineered 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 driversâ&#x20AC;&#x2122; 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

6.2 Facility Layout

Table 6.1: Comparison of Simulation Results of Current- and Proposed Queue Designs

Performance Measure TIS (Avg, Minutes) CIS (Avg, Number) Utilisation (%) Customers Not Served (%)

Current

Proposed

Design 1

Design 4

24.9133 21.5247 68.5459 15.5488

4.4400 3.2747 63.0218 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 implement 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

6.2 Facility Layout

tests, at the previously Drivers Licence section. This allows learnersâ&#x20AC;&#x2122; 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 access 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 Enquiries, 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 encouraging 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 â&#x20AC;&#x153;blue printâ&#x20AC;? 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 components 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.

Office

Store 2 Enquires Enquiries

Enquiries Exit

Teller 6

on cti

Office

Queue

Teller 5

s Teller3

Teller3

Teller 2

Teller1

Vault

Authorisation Server

Authorisation Office Store 1

Learners Licence Waiting Area

# Issue

Eye Test

Eye Test Officer 1

Eye Test Room 1

Eye Test Officer 2

Eye Test Room 2

“Customer Number 123, please proceed to Eye Test Room 2"

ENTRANCE Drivers’ Licences

Eye Test Waiting Area (Every Day)

Office

Learners’ Test Room (Every Day)

Office

Customer # Call-Out Prompt

ENTRANCE Fines, Licence & Registration

Application Forms Station

Enter

l Al an Tr

Eye Test

Office

Authorisation

Office

All Transactions Servers

Offices, Parade Room, Kitchen

Learners Tests

Store

6.2 Facility Layout

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 something 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 environment. 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

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 recommended 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 increasing 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

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 handlers” who can listen and lend advice to employees, even on a personal level. Mr. Royi should also encourage supervisors to nominate themselves as “toxin handlers” 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 training. 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 department 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 customer complaints, employees are focused on the customers’ needs, servers work as a team, and transactions are processed hassle-free with every employee satisfied 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; delivering excellent service by serving every customer efficiently, effectively and with a personal touch.

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 characteristics in line with being a good leader: integrity, confidence, knowledge of the industry and cognitive ability, but lacks in enforcing, persisting and demanding 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 management, or even recommendations of this report. The only way anything can change, is if it is allowed to.

6.4

Further Recommendations

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

6.4 Further Recommendations

• 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. Reluctance 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 Department 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 Enquiries, 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.

Chapter 7

Closing Summary Previous chapters contain, amongst others, literature studies, suggested queuing models, 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 transactions 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 operations of three local traffic departments in comparison to the Stellenbosch department. This was used to suggest alternative queue models to be simulated â&#x20AC;&#x201C; 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.

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 implementation of the recommendations made in this report.

7.3

Contribution to Society

Many traffic departments across South Africa deliver unsatisfactory service. The Stellenbosch 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.

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.

References Baker, J. & Baker, M. (1996). The effects of the service environment on affect and consumer perception of waiting time: An integrative review and research prepositions. Journal of the Academy of Marketing Science, 24, 338–349. 24 Bekker, J. (2012a). Simulation 442: Short notes on aspects of discrete-event simulation. 15, 18 Bekker, J. (2012b). Final-year project meetings. Meeting Minutes. 16, 22, 46, 85 Bennis, W. (2003). Leaders: Strategies for Taking Charge. Collins Business Essentials. 27 Dube, L. & Schmidt, B. (1996). The temporal dimension of socia episodes: Position effect in time judgement of unfilled intervals. Journal of Applied Social Psychology, 26, 1816–1826. 24 Edas, N. (2012). Interview. 36 Eikestad News (2012). “vrot diens skaad ekonomie”, “dienste bestaan nie”, “beamptes reageer so op klagtes”. Newspaper. 70, 71, 72 Fitzsimmons, J. & Fitzsimmons, M. (2000). Service Management: Operations, Strategy, and Information Technology. McGraw Hill. 41 Freivalds, A. & Niebel, B. (2009). Niebel’s Methods, Standards, and Work Design. Mc Graw Hill, 12th edn. 16, 17 Frigge, M., Hoaglin, D. & Iglewicz, B. (1989). Some implementations of the boxplot. The American Statistician. 22

REFERENCES

Gross, D., Shortle, J., Thompson, J. & Harris, C. (2008). Fundamentals of Queuing Theory. Wiley, 4th edn. 4, 11, 77 Gryna, F., Chue, R. & DeFeo, J. (2007). Juran’s Quality Planning and Analysis. McGraw Hill. 59, 61 Hiriyappa, B. (2011). Management of Motivation and its Theories. CreateSpace Independant Publishing Platform. 26 Hitt, M., Miller, C. & Colella, A. (2011). Organisational Behaviour . Wiley, 3rd edn. 25, 26, 27, 58, 59 Hoover, S. & Perry, R. (1989). Simulation: A Problem Solving Approach. AddisonWesley. 13 Hui, M. & Dube, L. (1997). The impact of music on consumers’ reactions to waiting for services. Journal of Retailing, 73, 87–104. 25 Hui, M. & Zhou, L. (1996). How does waiting duration information influence customers’ reaction to waiting for services? Journal of Applied Social Psychology, 26, 1702–1717. 24 Jahanshahloo, G., Lotfi, F. & Izadikhah, M. (2006). An algorithmic method to extend topsis for decision-making problems with interval data. Applied Mathematics and Computation, 175, 1375–1384. 19, 20, 21 Katz, K., Larson, B. & Larson, R. (1991). Prescription for the waiting-in-line blues: Entertain, enlighten, and engage. Sloan Management Review , 32, 44–53. 24 Kelton, W., Sadowski, R. & Sturrock, D. (2007). Simulation with Arena. McGrawHill, 4th edn. 15, 18 Kelton, W., Smith, J., Sturrock, D. & Verbraeck, A. (2010). Simio & Simulation: Modeling, analysis, apllications. McGrawHill. 5, 9, 10, 11, 12, 13, 14, 15, 16, 18, 19, 51, 82 Kim, S. & Nelson, B. (2001). A fully sequential procedure for indifference-zone selection in simulation. ACM Transaction on Modeling and Computer Simulation, 11. 20, 22

REFERENCES

Law, A. (2005). How to build valid and credible simulation models. Tech. rep., Averill M. Law and Associates. 51 Luo, W., Liberatore, M., Nydick, R., Chung, Q. & Sloane, E. (2003). Impact of process change on customer perception of waiting time: A field study. The International Journal of Management Science, 77–83. 24 Maister, D. (1985). The Psychology of Waiting in Lines. Heath. 24 Management Study Guide (2012). Herzberg’s two-factor theory of motivation. Online: http://www.managementstudyguide.com/herzbergs-theory-motivation.htm. 26 Miner, J. (2005). Organisational Behaviour 1: Essential Theories of Motivation and Leadership. ME. Sharpe, Inc. 27 Murdick, R., Render, B. & Russel, R. (1990). Service Operations Management. Allyn and Bacon. 23 Nayland College Mathematics (2012). Comparing box plots. Online : http : //maths.nayland.school.nz/Y ear1 1/AS1.10M ultivard ata/11C omparingB oxplots.htm. 22 Nieuwoudt, A. (2012). Interview. 36 Pieterson, B. (2012). Interview. 32, 34 Proctor, R. (1994). Queues and the Power of Simulation, vol. 32, 50–55. MCB University Press Limited. 13 Ramaswamy, R. (1996). Design and Management of Service Processes. Addison Wesley. 41 Reid, T. (2012). Interview. 34 Ross, S. (1970). Applied Probability Models with Optimization Applications. HoldenDay. 11 Royi, A. (2012). Informal meetings. Meeting Minutes and Author Memory. 2, 29, 30, 39

REFERENCES

Sheu, C. & Babbar, S. (1996). A managerial assessment of the waiting-time performance for alternative service process designs. Omega, 24, 689â&#x20AC;&#x201C;703. 23, 42 Sheu, S., McHaney, R. & Babbar, S. (2003). Service process design flexibility and customer waiting time. International Journal of Operations and Production Management, 7, 901â&#x20AC;&#x201C;917. 23 Stats SA (2007). Community survey, 2007 basic results: Municipalities, statistical release p0301.1. Tech. rep., Statistics South Africa. 1 Swanepoel, E. (2012). Qmatic queueing solutions. Email Correspondence. 56 University of Stellenbosch (2012). Prospectus. 2 Winston, W. (2004). Opeartions Research: Applications and Algorithms. Brooks/Cole, 4th edn. 76, 77, 78, 79

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: â&#x20AC;&#x153;Rotten service detrimental to the economy.â&#x20AC;? (Eikestad News, 2012)

A.1 Newspaper Articles

Figure A.2: “Service doesn’t exist.” (Eikestad News, 2012)

A.1 Newspaper Articles

Figure A.3: â&#x20AC;&#x153;Officers react to complaints.â&#x20AC;? (Eikestad News, 2012)

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 â&#x20AC;&#x153;nearest due dateâ&#x20AC;?. 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.

Figure A.4: Planned Tasks and Deadlines for the Project.

Project: SkripsiePlanning Date: October 20

Project Summary External Tasks

Summary

Milestone

Finish

Split

Task

Start

Topic Registration 1 day February 24 February 24 Project Proposal Literature Research: Simulation & 4 days? February 24 February 29 Arena Library Research: Related topics 8 days? March 07 March 16 Planning & Study Leader Meetings 3 days? March 15 March 19 Project Plan 1 day? March 19 March 19 Problem Statement from Mr. Royi 1 day? March 14 March 14 Report Writing 2 days March 20 March 21 Progress Report Literature Research: Queuing & 96 days April 24 August 30 Simulation Literature Research : Perf Measures, 132 days April 24 October 18 Fac Layout Literature: LaTeX 132 days? April 25 October 19 Activity Charting 3 days? April 26 April 30 Queuing Theory Analysis 5 days August 31September 06 Report Writing 2 days? October 22 October 23 Exams 15 days? May 21 June 08 Holiday Work Short Course on Simio/Simulation 1 day June 11 June 11 Time Study at Department 21 days June 11 July 06 Benchmarking 5 days July 02 July 06 Simulation 6 days? July 23 July 30 Update Report 5 days? October 19 October 25 70% Draft Further simulation 2 days? August 18 August 20 Additional data required 5 days? August 20 August 24 Advice from Language Centre 1 day? August 24 August 24 Improve 70% Report 3 days August 23 August 27 Peer Reviews 5 days? August 27 August 31 Preliminary Exam Copy Feedback adjustments 15 days?September 11September 30 Benchmarking 1 day?September 17September 17 Add. Simulation 3 days?September 19September 22 Facility Design 2 days?September 23September 24 TOPSIS 2 days?September 27September 30 Edit Report 1 day? October 30 October 30 Final Report Simulation Stats Analysis 8 days? October 01 October 10 TOPSIS 75th %ile 6 days? October 08 October 15 Edit Report 16 days? October 16 November 06 Exams 15 days? October 29November 16 Summary Slide 2 days?November 16November 19

Duration

Task Name

ID Nov

Inactive Milestone

Inactive Task

External Milestone

1st Quarter Jan

Page 1

Duration-only

Manual Task

Inactive Summary

Mar

May

Jul

Start-only

Manual Summary

Manual Summary Rollup

3rd Quarter

Deadline

Progress

Finish-only

Sep

Nov

A.2 Project Plan

Section

75 1 1 1

11am - 12am

12am - 1pm - 2pm - 3pm - 3.30pm

1pm

2pm

3pm

y Mon/Tues/Wed/Thurs/Fri/Sat

2 Fines

3 Licences

4 Registration & Licensing

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Total Number of transactions requiring AUTHORISATION

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Number of Customers Total 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Day

1 Authorisation

Figure A.5: Time Study Template for Servers

This information will only be used to determine the capacity of the traffic department, NOT to monitor your performance - we know that you work extremely hard:)

Please tick the appropriate box each time a customer is served by you as a teller. Your name does not appear on the form, and will remain anonymous.

Please complete the form stating the appropriate section (Roadworthy/Fines/Licensing/Licence&Registration) with the date and day.

Instructions

Comments: Feel free to convey any suggestions you have to improve the service and flow of people at the Stellenbosch Traffic Department here. Your comments will remain anonymous!

Number of ROADWORTHIES

- 10am

10am - 11am

9am

- 9am

8am

d d m m y

Date

Section number 1 or 2 or 3 or 4 -->

(Roadworthy/Fines/Licences/Registration&Licensing)

A.3

Time Study: Hourly Customer Volume at Tellers (Service Time) for Luguen Gass

A.3 Time Study Template

Time Study Template

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, dependant 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 interarrival 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.

B.1 Alternative Queuing Models

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)

Average service rate of clients (clients per time unit)

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 probability 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 :

= 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 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 reasonably assumed to be exponentially distributed (Winston, 2004). There are three separate queues handling the transactions.

B.1 Alternative Queuing Models

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 interarrival and service times are assumed to be exponential and there are S servers serving the queue. Again, this is reasonably assumed (Winston, 2004).

B.1 Alternative Queuing Models

Table B.1: P (j ≥ S) for the M/M/s Queueing System ρ .10 .20 .30 .40 .50 .55 .60 .65 .70 .75 .80 .85 .90 .95

S=2 .02 .07 .14 .23 .33 .39 .45 .51 .57 .64 .71 .78 .85 .92

S = 3 S=4 S=5 .00 .00 .00 .02 .00 .00 .17 .04 .02 .14 .09 .06 .24 .17 .13 .29 .23 .18 .35 .29 .24 .42 .35 .30 .51 .43 .38 .57 .51 .46 .65 .60 .55 .73 .69 .65 .83 .79 .76 .91 .89 .88 (Winston, 2004)

S=6 .00 .00 .01 .04 .10 .14 .20 .26 .34 .42 .52 .61 .74 .87

S=7 .00 .00 .00 .03 .08 .11 .17 .21 .30 .39 .49 .60 .72 .85

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)i π0 /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

B.1 Alternative Queuing Models

only two entities in the queue, that they will be served by the other two servers. This is illustrated mathematically as follows: 2 1 P (idle) = π0 + π1 + π2 3 3 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 × =

38 225

π1 = (Sρ)1 π0 /1! 45 38 = 3( )( ) 60 225 19 = 50 π2 = (Sρ)2 π0 /2! 45 38 = [(3 × )2 ( )]/2 60 225 171 = 400

45 2 ) 60

B.1 Alternative Queuing Models

Therefore, 2 1 P (idle) = π0 + π1 + π2 3 3 38 2 19 1 171 = + ( )( ) + ( )( ) 225 3 50 3 400 = 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 45 45 = (0.57)( )/1 − ( ) 60 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

45 60 = 2.46 customers = 1.71 +

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.

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.

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 transaction 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 â&#x20AC;&#x201C; 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.

C.2

Input and Output Data

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:

C.2 Input and Output Data

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 operational 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 7 12 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 determine 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 distributions 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.

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) • Time in system (TIS) • Server processing time In order to make an informed decision on which design (1, 2, 3, or 4) is best, the following is required: • Time in system (TIS) • 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 narrowed 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.

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.

C.4

Model Experiments

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 section 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 Department, 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 Multiple Server

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

C.4 Model Experiments

obligated to enter the queue at the Fines section, and to renew a driversâ&#x20AC;&#x2122; 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 â&#x20AC;&#x201D; 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 â&#x20AC;&#x201D; 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

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 â&#x20AC;&#x201D; 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 â&#x20AC;&#x201C; 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.

C.5

Data Distributions Summary

A summary of all distributions fitted, using ARENA, to data of the physical time studies, are shown in Table C.1 (overleaf).

22/141 10/130

15/133 6/93

3.9000 3.3111 3.1972 2.9045 2.9530

3.9640

2.4962 0.7474 0.7211

3.5378 3.6583

3.7288 3.7701

3.2330

Mon 18 June Tue 19 June Wed 20 June Thurs 21 June Fri 22 June

Tue 26 June

Application Payment Issuance

Mon 2 July Tue 3 July Wed 4 July Thurs 5 July Fri 6 July

Thurs 28 June

31/142 56/192 47/190 45/203 30/180

0.9615 0.6156 0.3656

Application Payment Issuance

6/31 1/32 12/79 7/53 3/46

1.9427

15.4583 12.5806 7.4211 9.6889 10.0476

Mon 18 June Tue 19 June Wed 20 June Thurs 21 June Fri 22 June

Bulk Arrivals

Fri 29 June

Mean

Day/Segment

89 Drivers Licences Interarrival Time 0.5+11*Beta(0.926, 2.43) 0.5+Weibull(3.38, 1.22)** Learners Licences Only (Wednesdays) 0.5+Weibull(3.44, 1.19)** 0.5+Exponential(3.27) Service Time 1.16+Gamma(0.409, 5.07)**

Licence & Registration Interarrival Time 0.5+Weibull(3.61, 1.18)** 0.5+Gamma(1.75, 1.61)** 0.5+Exponential(2.7) 0.5+11*Beta(0.926, 3.31) 0.5+Weibull(2.62, 1.19)** Service Time Weibull(4.27, 1.17)** Segmented Service Time 11*Beta(0.574, 1.96) Exponential (0.747) Exponential (0.721)

Fines Interarrival Time 0.5+Gamma(12.7, 1.18)** 0.5+Exponential(12.1) 0.5+Gamma(5.67,1.22)** 0.5+Gamma(8.09, 1.14)** 0.5+Weibull(9.39,0.964)** Service Time Gamma(0.93,2.09)** Segmented Service Time Exponential(0.961) Weibull(0.601,0.953)** 0.01+Gamma(0.169, 2.1)**

Distribution (Arena)

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

0.699

0.297 0.11

0.745 0.542

0.136 0.655 0.0712

0.44

> 0.75 0.051 0.075 0.536 0.641

> 0.15 > 0.15 > 0.15

> 0.15

0.05 0.125 0.536 0.455 0.169

p-value

3.2336

3.7400 3.7700

3.5352 3.6600

2.4917 0.7470 0.7210

4.0400

3.9100 3.3175 3.2000 2.9046 2.9600

0.9610 0.6140 0.3649

1.9437

15.4860 12.6000 7.4174 9.7226 10.0400

Expected Value

C.5 Data Distributions Summary

1.0750

8.2917

2.2460

Issuance

Wed 4 July

Wed 27 June

35/83

Bulk Arrivals

Authorisation Interarrival Time Assumption — 10% of Licence & Registration arrivals Service Time Not Suitable. Constant service time ensures fair comparison of models

9.1800

Learners Licences Interarrival Time 0.5+Weibull(5.73,0.678)** 0.172 7.9800 Service Time Empirical since¡0.005 Not Suitable (53/30,1/35,791/360,17/35,473/180,31/35,367/120,34/35,157/45,34/35,1411/360,34/35,87/21,1)

Distribution (Arena) p-value Expected Value Segmented Service Time 0.32+Gamma(0.295,5.32)** 0.357 1.8894 Empirical since¡0.005 Not Suitable (1/12,3/77,13/30,10/11,47/60,74/77,17/15,75/77,89/60,76/77,11/6,76/77,131/60,76/77,38/15,1) Gamma(0.491,2.19)** 0.0931 1.0753

** Indicates that the parameter must be inverted for use in Simio

Fri 29 June

9.18

1.8900 0.2660

Application Payment

June

Mean

Day/Segment

C.5 Data Distributions Summary

C.6 TOPSIS Calculations

C.6

TOPSIS Calculations Table C.2: TOPSIS Analysis of 75th Percentile Results

vj Design 1 Design 2 Design 3 Design 4

TIS (Avg, Minutes) 24.9133 4.5860 75.9809 4.4400

Performance Measures Matrix CIS (Avg, Number) Utilisation (%) 21.5247 68.5459 3.3588 62.9562 55.0176 59.9900 3.2747 63.0218

Customers Not Served (%) 15.5488 1.2739 31.4465 1.2903

Pmax (vj ) rij =

75.9809 0.3279 0.0604 1.0000 0.0584

Normalised Matrix 55.0176 68.5459 0.3912 1.0000 0.0610 0.9185 1.0000 0.8752 0.0595 0.9194

31.4465 0.4945 0.0405 1.0000 0.0410

Weighting Tij =

0.7500 0.2459 0.0453 0.7500 0.0438

Weighted Normalised Matrix 0.0833 0.0833 0.0326 0.0833 0.0051 0.0765 0.0833 0.0729 0.0050 0.0766

0.0833 0.0412 0.0034 0.0833 0.0034

Ab = Aw =

TIS (Avg, Minutes) (Cost) 0.0438 0.7500

CIS (Avg, Number) (Cost) 0.0050 0.0833

Utilisation (%) (Benefit) 0.0729 0.0833

Customers Not Served (%) (Cost) 0.0034 0.0833

Table C.3: TOPSIS Analysis of 75th Percentile Results (continued) Model Design 1 Design 2 Design 3 Design 4

dib 0.20771 0.00389 0.71499 0.00369

diw 0.50838 0.71359 0.01040 0.71502

Sib 0.7099 0.9946 0.0143 0.9949

Appendix D

Administration of the Final 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 authorâ&#x20AC;&#x2122;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.

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.

Dissertation Time Sheet | SUMMARY Name Student Number

Luguen Gass 15771598

Total Hours Hours Per Month Hours Per Term Hours (1st Semester) June/July Holiday Hours (2nd Semester)

319.8333333 35.53703704 79.95833333 62.5 108 149.3333333

Figure D.2: Summary Time Sheet as on 21 October 2012.