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Systematic mechanism for identifying the relative impact of supply chain performance areas on the overall supply chain performance using SCOR model and SEM

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  • Dissanayake, C. Kalpani
  • Cross, Jennifer A.

Abstract

This research aims to introduce a systematic mechanism for developing an organization specific supply chain performance measurement (SCPM) model and for generating a single regression relationship between the overall supply chain performance (SCP) and each of the SCPM areas using an organization's numerical data. The research uses a case study conducted in a small-scale asphalt manufacturing plant located in West Texas, USA with a sample of 218 data records collected over a period of one year. In this article, the systematic mechanism is demonstrated using the data from the case study organization. It provides guidance for developing a hierarchical SCPM model for the organization using the Supply Chain Operations Reference (SCOR) model, and then to obtain the final regression equation for the overall SCP using the Structural Equations Modeling (SEM) technique. The results obtained from the SCPM mechanism are then compared with the managerial input on the SCP that was analyzed using the Analytical Hierarchical Process (AHP). The results of the comparison are used to validate the SCPM results.

Suggested Citation

  • Dissanayake, C. Kalpani & Cross, Jennifer A., 2018. "Systematic mechanism for identifying the relative impact of supply chain performance areas on the overall supply chain performance using SCOR model and SEM," International Journal of Production Economics, Elsevier, vol. 201(C), pages 102-115.
    • Handle: RePEc:eee:proeco:v:201:y:2018:i:c:p:102-115
    DOI: 10.1016/j.ijpe.2018.04.027
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Thi Thuy Hanh Nguyen, 2024. "Measuring Supply Chain Performance Using the SCOR Model," SN Operations Research Forum, Springer, vol. 5(2), pages 1-28, June.
    2. 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).
    3. Zhang, Lu & Cui, Li & Chen, Lujie & Dai, Jing & Jin, Ziyi & Wu, Hao, 2023. "A hybrid approach to explore the critical criteria of online supply chain finance to improve supply chain performance," International Journal of Production Economics, Elsevier, vol. 255(C).
    4. Zanon, Lucas Gabriel & Marcelloni, Francesco & Gerolamo, Mateus Cecílio & Ribeiro Carpinetti, Luiz Cesar, 2021. "Exploring the relations between supply chain performance and organizational culture: A fuzzy grey group decision model," International Journal of Production Economics, Elsevier, vol. 233(C).
    5. Saoussane Srhir & Anicia Jaegler & Jairo R. Montoya‐Torres, 2023. "Uncovering Industry 4.0 technology attributes in sustainable supply chain 4.0: A systematic literature review," Business Strategy and the Environment, Wiley Blackwell, vol. 32(7), pages 4143-4166, November.
    6. Saba Pourreza & Misagh Faezipour & Miad Faezipour, 2022. "Eye-SCOR: A Supply Chain Operations Reference-Based Framework for Smart Eye Status Monitoring Using System Dynamics Modeling," Sustainability, MDPI, vol. 14(14), pages 1-17, July.
    7. Johnson, Andrew & Carnovale, Steven & Song, Ju Myung & Zhao, Yao, 2021. "Drivers of fulfillment performance in mission critical logistics systems: An empirical analysis," International Journal of Production Economics, Elsevier, vol. 237(C).
    8. Ahmad A. A. Khanfar & Mohammad Iranmanesh & Morteza Ghobakhloo & Madugoda Gunaratnege Senali & Masood Fathi, 2021. "Applications of Blockchain Technology in Sustainable Manufacturing and Supply Chain Management: A Systematic Review," Sustainability, MDPI, vol. 13(14), pages 1-20, July.

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