IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v231y2021ics0925527320302401.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527320302401
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ijpe.2020.107883?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Petar Radanliev, 2023. "The Rise and Fall of Cryptocurrencies: Defining the Economic and Social Values of Blockchain Technologies, assessing the Opportunities, and defining the Financial and Cybersecurity Risks of the Metave," Papers 2309.12322, arXiv.org.
    2. Monshizadeh, Fatemeh & Sadeghi Moghadam, Mohammad Reza & Mansouri, Taha & Kumar, Maneesh, 2023. "Developing an industry 4.0 readiness model using fuzzy cognitive maps approach," International Journal of Production Economics, Elsevier, vol. 255(C).
    3. Behl, Abhishek & Singh, Ramandeep & Pereira, Vijay & Laker, Benjamin, 2023. "Analysis of Industry 4.0 and circular economy enablers: A step towards resilient sustainable operations management," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    4. Pinto, Marcelo Rezende & Salume, Paula Karina & Barbosa, Marcelo Werneck & de Sousa, Paulo Renato, 2023. "The path to digital maturity: A cluster analysis of the retail industry in an emerging economy," Technology in Society, Elsevier, vol. 72(C).
    5. Xiaozhu Yang, 2022. "Application of cloud computing technology in optimal design of decision support system under mass communication theory," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(3), pages 1177-1185, December.
    6. Bai, Chunguang & Sarkis, Joseph, 2022. "A critical review of formal analytical modeling for blockchain technology in production, operations, and supply chains: Harnessing progress for future potential," International Journal of Production Economics, Elsevier, vol. 250(C).
    7. Mastrocinque, Ernesto & Ramírez, F. Javier & Honrubia-Escribano, Andrés & Pham, Duc T., 2022. "Industry 4.0 enabling sustainable supply chain development in the renewable energy sector: A multi-criteria intelligent approach," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    8. Bodendorf, Frank & Sauter, Maximilian & Franke, Jörg, 2023. "A mixed methods approach to analyze and predict supply disruptions by combining causal inference and deep learning," International Journal of Production Economics, Elsevier, vol. 256(C).
    9. Bueno, Adauto & Goyannes Gusmão Caiado, Rodrigo & Guedes de Oliveira, Thaís Lopes & Scavarda, Luiz Felipe & Filho, Moacir Godinho & Tortorella, Guilherme Luz, 2023. "Lean 4.0 implementation framework: Proposition using a multi-method research approach," International Journal of Production Economics, Elsevier, vol. 264(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Culot, Giovanna & Orzes, Guido & Sartor, Marco & Nassimbeni, Guido, 2020. "The future of manufacturing: A Delphi-based scenario analysis on Industry 4.0," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    2. David Opresnik & Maurizio Fiasché & Marco Taisch & Manuel Hirsch, 0. "An evolving fuzzy inference system for extraction of rule set for planning a product–service strategy," Information Technology and Management, Springer, vol. 0, pages 1-17.
    3. 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).
    4. Tortorella, Guilherme Luz & Narayanamurthy, Gopalakrishnan & Thurer, Matthias, 2021. "Identifying pathways to a high-performing lean automation implementation: An empirical study in the manufacturing industry," International Journal of Production Economics, Elsevier, vol. 231(C).
    5. Li, Ying & Dai, Jing & Cui, Li, 2020. "The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model," International Journal of Production Economics, Elsevier, vol. 229(C).
    6. Bai, Chunguang & Dallasega, Patrick & Orzes, Guido & Sarkis, Joseph, 2020. "Industry 4.0 technologies assessment: A sustainability perspective," International Journal of Production Economics, Elsevier, vol. 229(C).
    7. Bianco, Débora & Bueno, Adauto & Godinho Filho, Moacir & Latan, Hengky & Miller Devós Ganga, Gilberto & Frank, Alejandro G. & Chiappetta Jabbour, Charbel Jose, 2023. "The role of Industry 4.0 in developing resilience for manufacturing companies during COVID-19," International Journal of Production Economics, Elsevier, vol. 256(C).
    8. Slavko Rakic & Marko Pavlovic & Ugljesa Marjanovic, 2021. "A Precondition of Sustainability: Industry 4.0 Readiness," Sustainability, MDPI, vol. 13(12), pages 1-12, June.
    9. Bokrantz, Jon & Skoogh, Anders & Berlin, Cecilia & Wuest, Thorsten & Stahre, Johan, 2020. "Smart Maintenance: a research agenda for industrial maintenance management," International Journal of Production Economics, Elsevier, vol. 224(C).
    10. Hausladen, Iris & Schosser, Maximilian, 2020. "Towards a maturity model for big data analytics in airline network planning," Journal of Air Transport Management, Elsevier, vol. 82(C).
    11. Kouhizadeh, Mahtab & Saberi, Sara & Sarkis, Joseph, 2021. "Blockchain technology and the sustainable supply chain: Theoretically exploring adoption barriers," International Journal of Production Economics, Elsevier, vol. 231(C).
    12. Calış Duman, Meral & Akdemir, Bunyamin, 2021. "A study to determine the effects of industry 4.0 technology components on organizational performance," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    13. Shet, Sateesh V. & Pereira, Vijay, 2021. "Proposed managerial competencies for Industry 4.0 – Implications for social sustainability," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    14. Cuesta-Valiño, Pedro & Gutiérrez-Rodríguez, Pablo & Núnez-Barriopedro, Estela & García-Henche, Blanca, 2023. "Strategic orientation towards digitization to improve supermarket loyalty in an omnichannel context," Journal of Business Research, Elsevier, vol. 156(C).
    15. Gillani, Fatima & Chatha, Kamran Ali & Sadiq Jajja, Muhammad Shakeel & Farooq, Sami, 2020. "Implementation of digital manufacturing technologies: Antecedents and consequences," International Journal of Production Economics, Elsevier, vol. 229(C).
    16. Barbieri, Paolo & Boffelli, Albachiara & Elia, Stefano & Fratocchi, Luciano & Kalchschmidt, Matteo, 2022. "How does Industry 4.0 affect international exposure? The interplay between firm innovation and home-country policies in post-offshoring relocation decisions," International Business Review, Elsevier, vol. 31(4).
    17. Culot, Giovanna & Nassimbeni, Guido & Orzes, Guido & Sartor, Marco, 2020. "Behind the definition of Industry 4.0: Analysis and open questions," International Journal of Production Economics, Elsevier, vol. 226(C).
    18. David Opresnik & Maurizio Fiasché & Marco Taisch & Manuel Hirsch, 2017. "An evolving fuzzy inference system for extraction of rule set for planning a product–service strategy," Information Technology and Management, Springer, vol. 18(2), pages 131-147, June.
    19. Raj, Alok & Dwivedi, Gourav & Sharma, Ankit & Lopes de Sousa Jabbour, Ana Beatriz & Rajak, Sonu, 2020. "Barriers to the adoption of industry 4.0 technologies in the manufacturing sector: An inter-country comparative perspective," International Journal of Production Economics, Elsevier, vol. 224(C).
    20. Cugno, Monica & Castagnoli, Rebecca & Büchi, Giacomo, 2021. "Openness to Industry 4.0 and performance: The impact of barriers and incentives," Technological Forecasting and Social Change, Elsevier, vol. 168(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:231:y:2021:i:c:s0925527320302401. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.