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Decision-making models for formulating and evaluating supplier development programs: A state-of-the-art review and research paths

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  • Resende, Carlos Henrique Lopes
  • Lima-Junior, Francisco Rodrigues
  • Carpinetti, Luiz Cesar Ribeiro

Abstract

Supplier development refers to the efforts of a purchasing company and its suppliers to improve supplier performance and its ability to fulfill the purchasing company’s supply needs. Recently, several quantitative models have appeared in the literature that support decision-making processes regarding supplier development programs (SDPs). To update the latest research related to this subject, as well as contribute towards the establishment of a research agenda, this study presents a review of the literature on decision-making models that assist the formulating and evaluating of SDPs. This review analyzes 65 articles published in relevant journals, which are characterized in terms of several factors which are inherent to decision making in supplier development. The results show that most models focus on formulating SDPs, whereas the most common decision-making methods are Data Envelopment Analysis and Rough Set Theory. The use of multicriteria models and Fuzzy Sets has increased. The research gaps identified allowed us to propose some future research directions on the topic, calling attention to the need of developing new models considering elements that have not yet been studied in the literature, as well as performing comparative studies between them. Our results here can help orient researchers and managers in developing their decision-making processes in terms of SDP formulation and evaluation.

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  • Resende, Carlos Henrique Lopes & Lima-Junior, Francisco Rodrigues & Carpinetti, Luiz Cesar Ribeiro, 2023. "Decision-making models for formulating and evaluating supplier development programs: A state-of-the-art review and research paths," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 180(C).
    • Handle: RePEc:eee:transe:v:180:y:2023:i:c:s1366554523003289
    DOI: 10.1016/j.tre.2023.103340
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