Preference Learning Based Framework to Sustainable Supplier Segmentation and Development. Application to UK Automotive Industry

  • Farok Wanes Bin Iqdara

    Student thesis: Doctoral Thesis

    Abstract

    Abstract
    Incorporating sustainability aspects to supplier segmentation and development poses several theoretical and practical challenges. Based on the analysis of relevant literature on sustainable supplier segmentation and development, six important issues that char- acterise most existing research have been identified. The first issue concerns the limited consideration of social aspects in supplier segmentation and development. The second issue is due to the lack of comprehensive list of sustainability criteria and lack of experts involvement in the specification of this list. The third issue results from the lack of full support of multicriteria aspects of sustainable supplier segmentation and development operations. The fourth issue is related to the considerable cognitive effort needed to specify the preference parameters used by most of existing multicriteria analysis based approaches. The fifth issue arises from the explicit separation between segmentation and development operations and the lack of formal approaches to design development strategies. The sixth gap is linked to lack of post-classification negotiation, which is often confused with development. An integrated sustainable supplier segmentation and development framework (I3SDF) to handle all these issues is proposed in this report. The I3SDF is structured into four phases. The first phase is devoted to identify and validate a comprehensive and advanced list of sustainability criteria. The second phase relies on a preference learning method, namely Dominance based Rough Set Approach (DRSA), to classify suppliers’ firms based on the identified sustainability criteria. The third phase uses the outputs of DRSA to extract a collection of knowledge about the relative importance and role played by the sustainability criteria. The fourth phase relies on extracted knowledge to support post-classification negotiation and design sustain- able development strategies, where three innovative procedures are proposed. These procedures are based on Pareto Analysis, Dominance Analysis and Rank Aggregation, respectively. The proposed I3SDF has been successfully applied and validated using a collection of data about the UK Automotive Industry.
    Keywords: Supplier Segmentation, Supplier Development, Sustainability, Dominance based Rough Set Approach, Preference Learning, UK Automotive Industry
    Date of Award3 Jun 2024
    Original languageEnglish
    Awarding Institution
    • University of Portsmouth
    SupervisorSalem Chakhar (Supervisor), Menelaos Tasiou (Supervisor) & Jana Ries (Supervisor)

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