The research presented in this Thesis is primarily concerned with the field of Geographical Information Systems (GIS) - specifically, the business applications of the technology. The empirical problem addressed is the selection of branch banks as candidates for closure using the network of branch banks of the Commonwealth Bank of Australia in the Sydney metropolitan region as the case study. Decisions to close branches are made by the Bank on the basis of performance indicators that are essentially financial. In this research, however, an alternative approach is adopted: the problem is addressed using a set of spatial criteria. Following the deregulation of the finance industry in the 1980's and the rapid introduction of new electronic channels for delivering financial services, the major banking institutions have been engaged in a process of reorganising their networks of branch banks. The most visible manifestation of this has been the ongoing and widespread closure of branches. Selecting branch banks for closure is a typical example of a complex semi-structured multi-dimensional, multi-criteria, decision-making problem. It has been well documented in previous research that Multi-Criteria Decision-Making (MCDM) models are the most appropriate ones for solving problems in this particular domain. The identification of branches for closure is also characterised by a significant spatial dimension. Decisions are based on a consideration of a number of geographical criteria and various forms of spatial analysis may be involved. An appropriate technology to assist with solving decision-making problems with a significant spatial dimension is a Spatial Decision Support System (SDSS). Most SDSS have been based on the integration of Geographical Information Systems (GIS) technology with analytical models that are proven to be best suited to specific decision-making problems and this is the approach adopted in this research. The prototype MCBC-SDSS (Multi-Criteria Branch Closure SDSS) developed here is based on the integration through the loose coupling of the ArcView GIS software with the Criterium DecisionPlus (CDP) software, which contains the suite of non-spatial analytical models that provide the analytical capability for solving multi-criteria problems. ArcView GIS is used as the engine that drives the system and to provide the analytical and display facilities to support the spatial data involved. Two MCDM models from the CDP software are used to support the decision-making analysis - the Analytical Hierarchy Process (AHP) and Simple Multi-Attribute Rating Technique (SMART). The integration of GIS with the MCDM models is based on a considerable amount of software enhancement, interface development, and computer programming. The development of the integrated system is designed to create an intelligent and user-friendly SDSS, the application of which, from the user's perspective, is a seamless operation. The success of the MCBC-SDSS is demonstrated by its application to identify candidates for closure among the 197 branches of the CBA in the Sydney metropolitan area in 2000 - the year when the building of the database for the research had been completed. The analysis is based purely on spatial considerations that have been gleaned from a major review of the literature that previous researchers have identified as affecting branch viability and performance. A set of 17 spatial variables was used as the criteria in the MCDM models. The criteria are organised in two blocks: the first includes 9 criteria relating to the characteristics of demand for branch service in the branch trade areas ('catchment area' specific criteria) while the second includes 8 criteria relating to aspects of supply provided by the existing branches in their location ('location specific' criteria). Using the developed approach, the MCBC-SDSS has been used directly to compare alternatives against criteria, not only spatial based but also financial ones, thus providing a basis for identifying the best choices regarding branch closure. The steps in the preparation of the data and the iterative procedure for implementing the MCDM models are explained and illustrated. This involves building the initial evaluation matrix, normalising the raw criteria scores, assigning weights to the criteria, and calculating priorities. Based on these, the AHP and SMART models then calculate a decision score for each branch that is used as the basis for creating the preference ranking of the branches. In this, branches with a high rank score based on the combined weighted contribution of the 17 criteria are considered to be operationally viable. On the other hand, branches with the lowest rank scores are considered as potential candidates for closure. The preference rankings generated by the models have been tested to examine their robustness in terms of the validity of criteria and their weights used in the decision analysis. Sensitivity analysis has been conducted, the results of which show that the preference rankings are stable. Different approaches have been used to validate the initial criteria, and analyse their contribution to the ranking of branch banks for closure. These help identify critical spatial variables among the 17 initial criteria selected, and suggest that some of the criteria initially selected could be deleted from the criteria list used to generate the preference rankings without substantially affecting the results. The reasonableness of the resulting preference ranking has been further demonstrated from analyses based on changing criteria weights and alternatives. The research successfully demonstrates one of the ways of enhancing the functionality of a GIS through its integration with non-spatial analytical models to develop a SDSS to aid solving decision-making problems in the selected domain. Given that to date there has been relatively few applications of SDSS similar to that developed in this research to real world decision-making problems, the procedure adopted makes it suitable for decision-making in a range of other service business applications characterised by a significant spatial dimension and multiple outlets including shopping centres, motor car dealerships, restaurant and supermarket chains. Instead of just providing solutions, however, the SDSS-based analysis in this research can better be thought of as adding value to spatial data that forms an important source of information required by decision-makers, providing insight about the situation, uncertainty, objectives, and trade-offs involved in reaching decisions, and being capable of generating alternative scenarios based on different inputs to the models that may be used to identify recommended courses of action. It can lead to better and more effective decision-making in institutions involving multi-outlet retail and service businesses and hence enables both integrated data analysis and modelling while taking multiple criteria and decision-makers' preferences into consideration.
| Identifer | oai:union.ndltd.org:ADTP/187648 |
| Date | January 2002 |
| Creators | Zhao, Lihua, Built Environment, Faculty of Built Environment, UNSW |
| Publisher | Awarded by:University of New South Wales. School of Built Environment |
| Source Sets | Australiasian Digital Theses Program |
| Language | English |
| Detected Language | English |
| Rights | Copyright Lihua Zhao, http://unsworks.unsw.edu.au/copyright |
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