Development of an integrated FIS-DEA method for sustainable supplier selection in manufacturing / Atefeh Amindoust

Atefeh, Amindoust (2013) Development of an integrated FIS-DEA method for sustainable supplier selection in manufacturing / Atefeh Amindoust. PhD thesis, University of Malaya.

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    Supplier selection is an important area of decision making in manufacturing, especially for large and medium companies – either multinational (MNCs) or local. As sustainability in terms of preserving physical environment and developing long-term relationships between the partners in carrying out of manufacturing activities has gained world-wide focus, this dimension deserves due attention in selecting the competent suppliers in today’s companies. Literatures show that the past researches done in this area didn’t adequately discern and put the sustainable issues in a form of generic model. In real life applications, the importance of the various sustainable supplier selection criteria differ from one company to another and that depends on the circumstances where each organization may consider their relative importance for supplier selection criteria. The relative importance of the selection criteria and also the suppliers’ performance with respect to these given criteria is to be established by the pertinent decision makers. Decision makers, however, normally prefer to answer these two scenarios (the weights of criteria and the suppliers’ rating with respect to the criteria) in linguistic terms instead of being compared them numerically. So, the conventional supplier selection decision process involves a high degree of vagueness and ambiguity in practice. This research takes the aforesaid issues into account, proposes a conceptual sustainable supplier selection model, and develops an integrated method based on Fuzzy Inference System (FIS) and Data Envelopment Analysis (DEA) theories for such supplier selection under uncertainty considering the relative importance of the performance indicators. The FIS-DEA method is designed so that the shortcomings of the conventional DEA approach (not being able to handle imprecise data, decision makers can freely choose the weights to be assigned to each input and output in a way that maximizes the efficiency, limitation on the number of inputs and outputs (criteria) iii in accordance with the number of suppliers) could be eliminated. To handle the subjectivity of decision makers’ preferences, the related data including the relative importance of criteria and the suppliers’ performance with respect to these criteria are processed through fuzzy set theories. The processed data of suppliers’ performance are then passed into modular FIS system to achieve the sustainability affinity indices of suppliers. Moreover, to get the supplier ranking results, these indices are fed into a DEA approach. The applicability and feasibility of the proposed FIS-DEA method is tested through two test beds, which have been designed based on experts’ knowledge in two large companies from two different countries. The performance of the proposed FIS-DEA method is also assessed by comparing the results obtained with the existing supplier selection FIS-based method through error measurement criteria. The results show that the amounts of all error measurement criteria (such as mean squared error (MSE), root mean square error (RMSE), and mean absolute error (MAE)) are found to be very small. Among all, the biggest errors are found under RMSE calculations and these are 9.55 and 7.12 percent for the first and second test beds respectively. These are less than 10 percent (acceptable range is 0-10%) and that show the validity on acceptance of the proposed method. The proposed method is an open-ended approach to adapt any number of candidate suppliers as well as their selection criteria that might suit today’s flexible manufacturing needs.

    Item Type: Thesis (PhD)
    Additional Information: Thesis (PhD) - Faculty of Engineering, University of Malaya, 2013.
    Uncontrolled Keywords: Supplier selection; Manufacturing activities; World-wide focus; Industrial procurement; Decision making
    Subjects: T Technology > T Technology (General)
    T Technology > TJ Mechanical engineering and machinery
    Divisions: Faculty of Engineering
    Depositing User: Mr Prabhakaran Balachandran
    Date Deposited: 27 Apr 2018 15:17
    Last Modified: 27 Apr 2018 15:19

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