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postgraduate thesis: Decision support system for evaluating strategic suppliers in sustainable supply chain

TitleDecision support system for evaluating strategic suppliers in sustainable supply chain
Authors
Advisors
Advisor(s):Wong, TN
Issue Date2013
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Sun, Z. [孙哲]. (2013). Decision support system for evaluating strategic suppliers in sustainable supply chain. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5177346
AbstractAs a crucial factor for forming a competitive supply chain, the supplier selection problem has gained a lot of attentions from both researchers and practitioners. To fulfil the increasingly stringent environmental regulations and meet the NGOs’ and customers’ call for environmental conscious and socially responsible business, companies find it necessary to incorporate environmental and social dimensions in the supplier selection process. This thesis presents a supplier selection-model incorporating the sustainability issues. Firstly, a comprehensive set of sustainable supplier selection criteria are identified. Then, a hybrid multi-criteria decision making (MCDM) model comprising the fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and the two-level DEA (Data Envelopment Analysis) algorithms is developed to screen out less qualified suppliers. To begin with, twenty one supplier selection criteria are defined in the categories of economic performance, environmental impacts, and corporate social responsibility (CSR) impacts. The criteria were identified from literature review and a small-scale consultation with industrial practitioners. Regarding the economic criteria, quality, delivery, production capacity, cost, technical capability, financial position, management & organization and reputation are the more important aspects. For environmental impacts, most frequently adopted criteria are targeted at measuring supplier’s current environmental performances, i.e. negative impacts of products and production process on the environment. For CSR impacts, labour, health and safety criteria are received the most attention. A hybrid supplier pre-selection model consists of the fuzzy TOPSIS and the two-level DEA is implemented to shortlist suppliers. The fuzzy TOPSIS is used to measure the supplier’s outcome based performance, while the two-level DEA is used to measure the supplier’s efficiency, that is, how efficiently the supplier uses various resources to achieve that level of performance. With the combination of the performance score and the efficiency score, suppliers are grouped in to four clusters, that is, high performance and efficient (HE), high performance and inefficient (HI), low performance and efficient (LE) and low performance and inefficient (LI). Improvement targets for suppliers in HI, LE and LI clusters are identified. In general, conventional DEA is deficient in the discriminating power when there are a large number of criteria. The two-level DEA is established in this thesis to enhance the discriminating power of the standard DEA. In the two-level DEA, the supplier selection criteria for the same aspect are grouped together and form the hierarchical structure composes of two levels. Thereby, the two-level DEA model is able to handle quite a large number of input and output criteria. A case study has been conducted in the context of automobile industry to validate the feasibility and usefulness of the proposed methodology. The results indicate that the proposed methodology is able to screen out less qualified suppliers considering a comprehensive set of sustainability criteria.
DegreeMaster of Philosophy
SubjectBusiness logistics
Dept/ProgramIndustrial and Manufacturing Systems Engineering
Persistent Identifierhttp://hdl.handle.net/10722/196470

 

DC FieldValueLanguage
dc.contributor.advisorWong, TN-
dc.contributor.authorSun, Zhe-
dc.contributor.author孙哲-
dc.date.accessioned2014-04-11T23:14:28Z-
dc.date.available2014-04-11T23:14:28Z-
dc.date.issued2013-
dc.identifier.citationSun, Z. [孙哲]. (2013). Decision support system for evaluating strategic suppliers in sustainable supply chain. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5177346-
dc.identifier.urihttp://hdl.handle.net/10722/196470-
dc.description.abstractAs a crucial factor for forming a competitive supply chain, the supplier selection problem has gained a lot of attentions from both researchers and practitioners. To fulfil the increasingly stringent environmental regulations and meet the NGOs’ and customers’ call for environmental conscious and socially responsible business, companies find it necessary to incorporate environmental and social dimensions in the supplier selection process. This thesis presents a supplier selection-model incorporating the sustainability issues. Firstly, a comprehensive set of sustainable supplier selection criteria are identified. Then, a hybrid multi-criteria decision making (MCDM) model comprising the fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) and the two-level DEA (Data Envelopment Analysis) algorithms is developed to screen out less qualified suppliers. To begin with, twenty one supplier selection criteria are defined in the categories of economic performance, environmental impacts, and corporate social responsibility (CSR) impacts. The criteria were identified from literature review and a small-scale consultation with industrial practitioners. Regarding the economic criteria, quality, delivery, production capacity, cost, technical capability, financial position, management & organization and reputation are the more important aspects. For environmental impacts, most frequently adopted criteria are targeted at measuring supplier’s current environmental performances, i.e. negative impacts of products and production process on the environment. For CSR impacts, labour, health and safety criteria are received the most attention. A hybrid supplier pre-selection model consists of the fuzzy TOPSIS and the two-level DEA is implemented to shortlist suppliers. The fuzzy TOPSIS is used to measure the supplier’s outcome based performance, while the two-level DEA is used to measure the supplier’s efficiency, that is, how efficiently the supplier uses various resources to achieve that level of performance. With the combination of the performance score and the efficiency score, suppliers are grouped in to four clusters, that is, high performance and efficient (HE), high performance and inefficient (HI), low performance and efficient (LE) and low performance and inefficient (LI). Improvement targets for suppliers in HI, LE and LI clusters are identified. In general, conventional DEA is deficient in the discriminating power when there are a large number of criteria. The two-level DEA is established in this thesis to enhance the discriminating power of the standard DEA. In the two-level DEA, the supplier selection criteria for the same aspect are grouped together and form the hierarchical structure composes of two levels. Thereby, the two-level DEA model is able to handle quite a large number of input and output criteria. A case study has been conducted in the context of automobile industry to validate the feasibility and usefulness of the proposed methodology. The results indicate that the proposed methodology is able to screen out less qualified suppliers considering a comprehensive set of sustainability criteria.-
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subject.lcshBusiness logistics-
dc.titleDecision support system for evaluating strategic suppliers in sustainable supply chain-
dc.typePG_Thesis-
dc.identifier.hkulb5177346-
dc.description.thesisnameMaster of Philosophy-
dc.description.thesislevelMaster-
dc.description.thesisdisciplineIndustrial and Manufacturing Systems Engineering-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.5353/th_b5177346-

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