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|Title:||Facility management benchmarking : measuring performances using multi-attribute decision tools||Authors:||Wong, Yat-lung Philip||Degree:||Ph.D.||Issue Date:||2007||Abstract:||The aim of this research is to develop and demonstrate the applicability of three different decision tools in facility management benchmarking. The three tools are Analytic Hierarchy Process (AHP), Data Envelopment Analysis (DEA) and regression analysis. Within this research, facility management is defined as a process of service operations which an organization should benchmark to improve the performance of its core business. There is a rich body of literature on performance measurement, facility management, benchmarking, and decision tools. However, there is a lack of understanding of how the most useful information and knowledge can be acquired through facility management benchmarking with the application of decision tools. Since the early 1990s, research in Facility Management and Benchmarking has stressed the importance of objective measurement based on objective and subjective data (Kincaid (1994)). This research presents methods which show how the data could be integrated for improvement execution. The performance of facility management is multi-dimensional and should cover both hard aspects such as operations costs, and soft aspects such as customer satisfaction measurements. Analysis of soft data was carried out with AHP and regression analysis. The relationships between various hard data and soft data were examined by DEA. As a process of service operations, facility management is interpreted as an input-output system which can be assessed by DEA in terms of productivity. As with many service industries, customer satisfaction is an important factor within the input-output system. This explains the need for applying decision tools for facility management benchmarking. The decision tools assist facility managers in analyzing customer satisfaction. AHP and regression analysis are identified as appropriate tools to analyze other soft data. The theoretical discussions are supported by two case studies. This research shows how the proposed tools can be applied to improve the optimization of resources of facility management units and thus improve their competitiveness. The proposed tools point out the relevance of some implications from collected data. Facility managers can identify not only the inefficiencies but are also given hints on the ways to catch up with their efficient peers. Based on the case studies, this research found that the tools could work with soft and hard data of facility management and clearly indicate need for improvements. Data collection was limited to facility management units in Hong Kong and the South Pacific region. Nevertheless, the tools have global applicability. This study reveals the inconsistency of the customers' perceptions on facility management quality. It also confirms the benefits of consistency test of AHP to the process of decision making in Facility Management planning. With reference to the conventional performance-gap analysis by comparison, significant improvements are made to the analysis methodology by introducing the concepts of matching between soft and hard data and correlating AHP and regression results. The major contributions to knowledge from this research are summarized as follows: 1. Integration of the knowledge of decision tools with that of facility management benchmarking. 2. Provision of comprehensive design principles for a facility management benchmarking framework targeted to the acquisition of a maximum amount of knowledge for business improvement. 3. Assistance to facility managers to develop a clear picture of their facility's operation and customers' demands with the proposed decision tools.||Subjects:||Hong Kong Polytechnic University -- Dissertations.
Decision making -- Mathematical models.
|Pages:||xiv, 148 leaves : ill. ; 31 cm.|
|Appears in Collections:||Thesis|
View full-text via https://theses.lib.polyu.edu.hk/handle/200/742
Citations as of Jun 4, 2023
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