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A fuzzy logic approach to supply chain performance management

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  • Ganga, Gilberto Miller Devós
  • Carpinetti, Luiz Cesar Ribeiro

Abstract

The aim of this paper is to propose a supply chain performance model based on fuzzy logic to predict performance based on causal relationships between metrics of the Supply Council Operations Reference model (SCOR) model. The main contribution and originality of this proposal relates to the application of Fuzzy logic to predict performance based on performance metrics levels 1 and 2 of the SCOR model. Fuzzy logic is a technique suitable for dealing with uncertainty and subjectivity, which becomes an interesting auxiliary approach to manage performance of supply chains. A descriptive quantitative approach was adopted as research method, based on the prediction model. Statistical analysis of the prediction model results confirmed the relevance of the causal relationships embedded in the model. The findings reinforce the proposition that the adoption of a prediction model based on fuzzylogic and on metrics of the SCOR model seems to be a feasible technique to help managers in the decision making process of managing performance of supply chains.

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  • Ganga, Gilberto Miller Devós & Carpinetti, Luiz Cesar Ribeiro, 2011. "A fuzzy logic approach to supply chain performance management," International Journal of Production Economics, Elsevier, vol. 134(1), pages 177-187, November.
    • Handle: RePEc:eee:proeco:v:134:y:2011:i:1:p:177-187
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    References listed on IDEAS

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    3. Jędrzej Charłampowicz, 2018. "Supply Chain Efficiency On The Maritime Container Shipping Markets – Selected Issues," Business Logistics in Modern Management, Josip Juraj Strossmayer University of Osijek, Faculty of Economics, Croatia, vol. 18, pages 357-368.
    4. Rajesh Katiyar & M. K. Barua & Purushottam L. Meena, 2018. "Analysing the Interactions Among the Barriers of Supply Chain Performance Measurement: An ISM with Fuzzy MICMAC Approach," Global Business Review, International Management Institute, vol. 19(1), pages 48-68, February.
    5. Ntabe, E.N. & LeBel, L. & Munson, A.D. & Santa-Eulalia, L.A., 2015. "A systematic literature review of the supply chain operations reference (SCOR) model application with special attention to environmental issues," International Journal of Production Economics, Elsevier, vol. 169(C), pages 310-332.
    6. Katiyar, Rajesh & Meena, Purushottam L. & Barua, Mukesh Kumar & Tibrewala, Rajen & Kumar, Gopal, 2018. "Impact of sustainability and manufacturing practices on supply chain performance: Findings from an emerging economy," International Journal of Production Economics, Elsevier, vol. 197(C), pages 303-316.
    7. 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).
    8. Lima-Junior, Francisco Rodrigues & Carpinetti, Luiz Cesar Ribeiro, 2019. "Predicting supply chain performance based on SCOR® metrics and multilayer perceptron neural networks," International Journal of Production Economics, Elsevier, vol. 212(C), pages 19-38.
    9. Lima-Junior, Francisco Rodrigues & Carpinetti, Luiz Cesar Ribeiro, 2016. "Combining SCOR® model and fuzzy TOPSIS for supplier evaluation and management," International Journal of Production Economics, Elsevier, vol. 174(C), pages 128-141.
    10. Mohamed Rafik Noor Mohamed Qureshi, 2022. "Evaluating and Prioritizing the Enablers of Supply Chain Performance Management System (SCPMS) for Sustainability," Sustainability, MDPI, vol. 14(18), pages 1-21, September.
    11. Alireza Goli & Hatam Mohammadi, 2022. "Developing a sustainable operational management system using hybrid Shapley value and Multimoora method: case study petrochemical supply chain," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(9), pages 10540-10569, September.
    12. Javier Parra-Domínguez & Maria Alonso-García & Juan Manuel Corchado, 2023. "Fuzzy Logic to Measure the Degree of Compliance with a Target in an SDG—The Case of SDG 11," Mathematics, MDPI, vol. 11(13), pages 1-16, July.
    13. Hald, Kim Sundtoft & Mouritsen, Jan, 2018. "The evolution of performance measurement systems in a supply chain: A longitudinal case study on the role of interorganisational factors," International Journal of Production Economics, Elsevier, vol. 205(C), pages 256-271.
    14. Maestrini, Vieri & Luzzini, Davide & Maccarrone, Paolo & Caniato, Federico, 2017. "Supply chain performance measurement systems: A systematic review and research agenda," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 299-315.
    15. Chen, Sihua & Du, Jiangze & He, Wei & Siponen, Mikko, 2022. "Supply chain finance platform evaluation based on acceptability analysis," International Journal of Production Economics, Elsevier, vol. 243(C).

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