| Author: | Pun, Kim Ping |
| Title: | Hybrid-fuzzy multi-criteria decision supporting model for vendor evaluation process to building facility maintenance and renovation in Hong Kong |
| Advisors: | Law, Wing-cheung (ISE) Choy, King Lun (ISE) |
| Degree: | Eng.D. |
| Year: | 2022 |
| Subject: | Decision support systems Facility management -- China -- Hong Kong Building management -- China -- Hong Kong Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Industrial and Systems Engineering |
| Pages: | xiv, 170 pages : color illustrations |
| Language: | English |
| Abstract: | Building facilities management, repairs, maintenance, alterations and additions play a vital role in the sustainable development of buildings, covering safety, technical, economic and environmental aspects. At the same time, procurement is a critical process in identifying the best-value vendors, improving resource allocation efficiency, maintaining equipment reliability and availability, and thus enhancing facility conditions, thereby reducing business risk, attracting tenants, increasing assets and creating value. Therefore, the payback and return on investment (ROI) can be incredibly beneficial by enabling relevant decisions to achieve their strategic goals in a cost-effective, efficient and effective manner. Even though technological advancements have been considered to simplify internal processes by digitizing the transfer of information into a digital format, the procurement process is still managed manually without any decision support system. Maintenance knowledge and expertise are usually very subjective in the current process, and traditional methods are insufficient to systematically interpret judgment and evaluation criteria. In addition, decision-making in most organizations today is still in a centralized direction. In many cases, decisions made "at the top" cascade down the organizational chart, and frontline employees are expected to execute those orders neatly. Group decision support is less explored in the supplier and contractor selection literature despite its benefits. It leads to the lack of validation of the importance and applicability level of criteria and sub-criteria faced by frontline maintenance personnel even though they have a lot of understanding of the products, processes, machines, customers and clients in real-life applications. Additionally, there is a lack of quantitative measures to incorporate linguistic selection criteria for maintenance personnel and domain experts into the decision-making process to improve the current supply base's competitive advantage, competitiveness, and capability assessment. Quantitative and qualitative multi-criteria analysis and associated performance feedback also lack decision-making to support a strategic approach. Therefore, in this study, a hybrid fuzzy multi-criteria decision support model (HFMCDSM) is developed for the vendor selection process by integrating Fuzzy Set Theory, Analytic Hierarchy Process (AHP) and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) to provide decision support under a consensus approach. The model can provide a framework to capture the uncertainty and imprecision associated with human cognitive processes, such as inherent knowledge, experience, and reasoning. A case study was conducted showing how (i) Aggregated decision preferences can be applied at different levels of stakeholders to set criteria weights and (ii) Incorporate expert and management decisions into the procurement process for contract award analysis, and (iii) Make reliable decisions using objective tools to identify the best-value vendors to improve analytical performance. This approach enables establishing a value-driven procurement strategy rather than the traditional lowest price selection strategy. Purchasing personnel can develop adaptation plans based on the recommended results. |
| Rights: | All rights reserved |
| Access: | restricted access |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 6343.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 6.25 MB | Adobe PDF | View/Open |
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