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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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