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A decision making model based on fuzzy inference to predict the impact of SCOR® indicators on customer perceived value

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  • Zanon, Lucas Gabriel
  • Munhoz Arantes, Rafael Ferro
  • Calache, Lucas Daniel Del Rosso
  • Carpinetti, Luiz Cesar Ribeiro

Abstract

Customer perceived value (CPV) is critical for supply chain management, due to its link with satisfaction and market share. In addition, value perception is a consequence of several factors including operational performance. Hence, analyzing the cause and effect relationship between CPV and supply chain performance can help decision makers to identify attributes of performance that need improvement efforts so as to enhance CPV. However, modeling this relationship is very dependent of cognitive judgments associated with incomplete or imprecise information. To overcome this, fuzzy inference has been largely used in supply chain management. However, no study was found that applies this soft computing technique with natural language processing to investigate the impact of supply chain performance on CPV. Therefore, this article proposes a decision making model based on fuzzy inference to help predicting the impact on CPV of the performance indicators of the SCOR® (Supply Chain Operations Reference) model. The SCOR® level 1 indicators were applied as a mean to assess CPV in a multidimensional way, to enable benchmarking with other supply chains and to facilitate the communication with stakeholders. It is an axiomatic prescriptive model-based research that includes an illustrative application based on the distribution of beverages to final customers. Analysis of the response surfaces of both Fuzzy Inference Systems allowed identification of the attributes of performance that most impact CPV, therefore providing the possibility of anticipation and prioritization. The model is adaptable to various supply chain configurations. Also, it provides the possibility of internalizing CPV as a driver for supply chain continuous improvement initiatives.

Suggested Citation

  • Zanon, Lucas Gabriel & Munhoz Arantes, Rafael Ferro & Calache, Lucas Daniel Del Rosso & Carpinetti, Luiz Cesar Ribeiro, 2020. "A decision making model based on fuzzy inference to predict the impact of SCOR® indicators on customer perceived value," International Journal of Production Economics, Elsevier, vol. 223(C).
    • Handle: RePEc:eee:proeco:v:223:y:2020:i:c:s0925527319303329
    DOI: 10.1016/j.ijpe.2019.107520
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    2. Arpan Kumar Kar & P. S. Varsha & Shivakami Rajan, 2023. "Unravelling the Impact of Generative Artificial Intelligence (GAI) in Industrial Applications: A Review of Scientific and Grey Literature," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(4), pages 659-689, December.

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