Managing Software Development

Managing Software Development

Table of contents

Chapter 3: Discrete Event Simulation-based (DES) approaches3

Introduction3

Bank branch customer area layout6

The DEVS framework7

Model representation8

Atomic model specification8

Representing complex models9

Model manipulation10

Summary of DEVS framework12

Chapter 4 Research Methodology:14

Research methods14

Data Collection Method15

Unit of analysis and site selection15

Background16

Data collection strategy18

SES for WDV process20

Model parameters24

Process performance25

Research findings26

Model validation26

Face validation26

Estimation of parameter values27

Discrepancy resolution27

Empirical validation of model29

Managerial buy-in for validated model29

Generation of alternatives30

A radical option30

Simulation results33

Some interesting results36

Sensitivity analysis39

Increasing the number of initiators40

Reducing error analysis times41

Summary of results42

Discussion43

Model representation43

Model manipulation44

Chapter 5 Conclusions and Future:47

Limitations49

Future research49

Conclusion50

References52

Managing Software Development

Chapter 3: Discrete Event Simulation-based (DES) approaches

Introduction

The activity of banking industry has gone through significant changes in UK as well as in several other countries. The unfolding of technical progress within a changing competitive framework has paved the way to remarkable process and product innovation. As a result British financial institutions have long since been engaged with the systematic development of specific procedures to coordinate the progressively improved processing capacity with the organizational structure in place. Technical change in UK banking industry emerges as an incremental, adaptive process of coordination unfolding at several, complementary levels in which a twofold set of causal relations can be individuated. The first concerns the impulse provided by the implementation of GPT, the other involves the embedment of such technologies by means of the procedural changes that characterized the microdynamics of the system. The theoretical forerunner for this discussion is Hughes (1983) who illustrated the development of large technical systems in three phases: invention, technical transfer and growth. In this context, integration among the interacting components of a system is the result of the alternating emergence of contextual problems, or reverse salients, and of the solutions to them. We argue that, as foreseen by Hughes, coordination drives and shapes at any time the evolution of a technical system such as banking. Hence as interaction unfolds, innovation becomes an increasingly distributed process across the agents who participate in the implementation of such solutions.

DEA has been demonstrated to be effective for benchmarking in many service industries involving complex input-output relationships (Cooper et al. [24]; Zhu [27]). In the last two decades, there have been numerous published applications of DEA to measure the efficiency of banks and branch systems, which have further motivated the development and improvement of DEA techniques. However, due to the much easier availability of corporate data (typically from the regulator), the majority of the studies focusing on bank efficiency measurements are at the institutional level, rather than at the branch level. To the authors' knowledge, since 1997 there are 65 published papers on bank branches using DEA for efficiency measurements compared to 163 papers on bank efficiency analysis. The first published paper on a DEA application in a bank branch setting was by Sherman and Gold [34] examining a small sample of fourteen branches of a US bank. Since then many other DEA studies have been completed around the world, for instance, Vassiloglou and Giokas [35] on bank branches in Greece; Oral and Yolalan [36] in Turkey; ...