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A fuzzy rule-based industry 4.0 maturity model for operations and supply chain management

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  • Caiado, Rodrigo Goyannes Gusmão
  • Scavarda, Luiz Felipe
  • Gavião, Luiz Octávio
  • Ivson, Paulo
  • Nascimento, Daniel Luiz de Mattos
  • Garza-Reyes, Jose Arturo

Abstract

Industry 4.0 (I4.0) aims to link disruptive technologies to manufacturing systems, combining smart operations and supply chain management (OSCM). Maturity models (MMs) are valuable methodologies to assist manufacturing organizations to track the progress of their I4.0 initiatives and guide digitalization. However, there is a lack of empirical work on the development of I4.0 MMs with clear guidelines for OSCM digitalization. There is no I4.0 MM with an assessment tool that addresses the imprecision brought by human judgment and the uncertainty and ambiguity inherent to OSCM evaluation. Here we develop a fuzzy logic-based I4.0 MM for OSCM, through a transparent and rigorous procedure, built on a multi-method approach comprising a literature review, interviews, focus groups and case study, from model design to model evaluation. To provide a more realistic evaluation, fuzzy logic and Monte Carlo simulation are incorporated into an I4.0 self-assessment readiness-tool, which is connected with the model architecture. The proposed model has been validated through a real application in a multinational manufacturing organization. The results indicate that the approach provides a robust and practical diagnostic tool, based on a set of OSCM indicators to measure digital readiness of manufacturing industries. It supports the transition towards I4.0 in OSCM domain, by holistically analyzing gaps and prescribing actions that can be taken to increase their OSCM4.0 maturity level.

Suggested Citation

  • Caiado, Rodrigo Goyannes Gusmão & Scavarda, Luiz Felipe & Gavião, Luiz Octávio & Ivson, Paulo & Nascimento, Daniel Luiz de Mattos & Garza-Reyes, Jose Arturo, 2021. "A fuzzy rule-based industry 4.0 maturity model for operations and supply chain management," International Journal of Production Economics, Elsevier, vol. 231(C).
    • Handle: RePEc:eee:proeco:v:231:y:2021:i:c:s0925527320302401
    DOI: 10.1016/j.ijpe.2020.107883
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    1. Huaiqing Wang & Kun Chen & Dongming Xu, 2016. "A maturity model for blockchain adoption," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-5, December.
    2. Wang, Yingli & Singgih, Meita & Wang, Jingyao & Rit, Mihaela, 2019. "Making sense of blockchain technology: How will it transform supply chains?," International Journal of Production Economics, Elsevier, vol. 211(C), pages 221-236.
    3. Osiro, Lauro & Lima-Junior, Francisco R. & Carpinetti, Luiz Cesar R., 2014. "A fuzzy logic approach to supplier evaluation for development," International Journal of Production Economics, Elsevier, vol. 153(C), pages 95-112.
    4. Angelica Cuylen & Lubov Kosch & Michael H. Breitner, 2016. "Development of a maturity model for electronic invoice processes," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(2), pages 115-127, May.
    5. Yuquan Meng & Yuhang Yang & Haseung Chung & Pil-Ho Lee & Chenhui Shao, 2018. "Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review," Sustainability, MDPI, vol. 10(12), pages 1-28, December.
    6. Dalenogare, Lucas Santos & Benitez, Guilherme Brittes & Ayala, Néstor Fabián & Frank, Alejandro Germán, 2018. "The expected contribution of Industry 4.0 technologies for industrial performance," International Journal of Production Economics, Elsevier, vol. 204(C), pages 383-394.
    7. Mendes, Paulo & Leal, José Eugênio & Thomé, Antônio Márcio Tavares, 2016. "A maturity model for demand-driven supply chains in the consumer product goods industry," International Journal of Production Economics, Elsevier, vol. 179(C), pages 153-165.
    8. Frank, Alejandro Germán & Dalenogare, Lucas Santos & Ayala, Néstor Fabián, 2019. "Industry 4.0 technologies: Implementation patterns in manufacturing companies," International Journal of Production Economics, Elsevier, vol. 210(C), pages 15-26.
    9. Azadegan, Arash & Porobic, Lejla & Ghazinoory, Sepehr & Samouei, Parvaneh & Saman Kheirkhah, Amir, 2011. "Fuzzy logic in manufacturing: A review of literature and a specialized application," International Journal of Production Economics, Elsevier, vol. 132(2), pages 258-270, August.
    10. Wong, Bo K. & Lai, Vincent S., 2011. "A survey of the application of fuzzy set theory in production and operations management: 1998-2009," International Journal of Production Economics, Elsevier, vol. 129(1), pages 157-168, January.
    11. Jörg Becker & Ralf Knackstedt & Jens Pöppelbuß, 2009. "Developing Maturity Models for IT Management," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 1(3), pages 213-222, June.
    12. Dimitris Mourtzis & Sophia Fotia & Nikoletta Boli & Ekaterini Vlachou, 2019. "Modelling and quantification of industry 4.0 manufacturing complexity based on information theory: a robotics case study," International Journal of Production Research, Taylor & Francis Journals, vol. 57(22), pages 6908-6921, November.
    13. Aqlan, Faisal & Lam, Sarah S., 2015. "A fuzzy-based integrated framework for supply chain risk assessment," International Journal of Production Economics, Elsevier, vol. 161(C), pages 54-63.
    14. Ding, Li & Lam, Hugo K.S. & Cheng, T.C.E. & Zhou, Honggeng, 2018. "A review of short-term event studies in operations and supply chain management," International Journal of Production Economics, Elsevier, vol. 200(C), pages 329-342.
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