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Exploring the relations between supply chain performance and organizational culture: A fuzzy grey group decision model

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  • Zanon, Lucas Gabriel
  • Marcelloni, Francesco
  • Gerolamo, Mateus Cecílio
  • Ribeiro Carpinetti, Luiz Cesar

Abstract

Assessing the relationship between supply chain performance and organizational culture can help to predict scenarios and improve decision-making. However, this relationship is rarely explored due to the complexity of quantitatively addressing its natural subjectivity. Although soft computing techniques would have the potential to overcome this limitation, they have been rarely applied to this context. This paper aims to introduce a decision model to analyze and quantify the causal relationship between organizational culture and supply chain performance based on the combination of fuzzy grey cognitive maps, grey clustering and multiple fuzzy inference systems. Such model is novel in the literature and can provide new theoretical and practical perspectives. The development of this study is based on the SCOR® (Supply Chain Operations Reference) model attributes (SCC, 2017) and Hofstede's (2001) organizational practices, following the quantitative axiomatic prescriptive model-based research. The main contribution is the introduction of a decision-making model that promotes the alignment between organizational culture and supply chain management, internalizing culture as a driver for performance improvement efforts. By conducting two real application cases in companies from different industrial sectors, results show that the model is able to identify crucial elements regarding cultural profile and performance for both organizations, aiding prioritization, anticipation and enabling the development of guidelines for action plans.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:proeco:v:233:y:2021:i:c:s0925527320303728
    DOI: 10.1016/j.ijpe.2020.108023
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    2. 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).
    3. Dubey, Rameshwar & Bryde, David J. & Dwivedi, Yogesh K. & Graham, Gary & Foropon, Cyril, 2022. "Impact of artificial intelligence-driven big data analytics culture on agility and resilience in humanitarian supply chain: A practice-based view," International Journal of Production Economics, Elsevier, vol. 250(C).
    4. Jiachen Sun & Haiyan Wang & Zhimin Cui, 2023. "Alleviating the Bauxite Maritime Supply Chain Risks through Resilient Strategies: QFD-MCDM with Intuitionistic Fuzzy Decision Approach," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
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