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An integrated Delphi-MCDM-Bayesian Network framework for production system selection

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  • Dohale, Vishwas
  • Gunasekaran, Angappa
  • Akarte, Milind
  • Verma, Priyanka

Abstract

Several attempts are needed to choose the most compatible production system for achieving the desired manufacturing outputs. The significant role of manufacturing strategy deployment is selecting the production system best suited for a manufacturing firm. The appropriately chosen production system (strategic process choice) facilitates a firm to produce “order winning” outputs and provides a production competence to achieve business success. This research presents a novel framework to determine the compatible production system for a manufacturing firm. An integrated three-stage Delphi-MCDM-Bayesian Network (BN) framework has been proposed. The process choice criteria (PCC) considered for deciding production systems are identified through an in-depth literature review and then validated by experts through a Delphi method in the first stage. It resulted in the determination of twenty-six PCC. In the second stage, the multi-criteria decision-making (MCDM) based voting analytical hierarchy process (VAHP) method is adopted to determine each criterion's relative importance for a firm. The relative weights obtained are then used as input for the machine learning (ML) technique- Bayesian network (BN) in the third stage. The BN model quantifies the selection probability of production systems. The proposed Delphi-MCDM-BN framework is demonstrated using a real-life case of a “hydraulic and pneumatic valve” manufacturing firm to select a suitable production system. The three-stage framework is a novel contribution to the literature, which can be used by researchers, practitioners, and manufacturing strategists to choose an appropriate production system for any manufacturing firm.

Suggested Citation

  • Dohale, Vishwas & Gunasekaran, Angappa & Akarte, Milind & Verma, Priyanka, 2021. "An integrated Delphi-MCDM-Bayesian Network framework for production system selection," International Journal of Production Economics, Elsevier, vol. 242(C).
    • Handle: RePEc:eee:proeco:v:242:y:2021:i:c:s0925527321002723
    DOI: 10.1016/j.ijpe.2021.108296
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    1. Liu, Fuh-Hwa Franklin & Hai, Hui Lin, 2005. "The voting analytic hierarchy process method for selecting supplier," International Journal of Production Economics, Elsevier, vol. 97(3), pages 308-317, September.
    2. S. G. Deshmukh & Abid Haleem, 2020. "Framework for Manufacturing in Post-COVID-19 World Order: An Indian Perspective," International Journal of Global Business and Competitiveness, Springer, vol. 15(1), pages 49-60, June.
    3. Alejandro Bello-Pintado & Teresa García Marco & Ferdaous Zouaghi, 2019. "Product/process definition, technology adoption and workforce qualification: impact on performance," International Journal of Production Research, Taylor & Francis Journals, vol. 57(1), pages 200-215, January.
    4. Parhizkar, Tarannom & Vinnem, Jan Erik & Utne, Ingrid Bouwer & Mosleh, Ali, 2021. "Supervised Dynamic Probabilistic Risk Assessment of Complex Systems, Part 1: General Overview," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    5. Bokrantz, Jon & Skoogh, Anders & Berlin, Cecilia & Stahre, Johan, 2017. "Maintenance in digitalised manufacturing: Delphi-based scenarios for 2030," International Journal of Production Economics, Elsevier, vol. 191(C), pages 154-169.
    6. Pishchulov, Grigory & Trautrims, Alexander & Chesney, Thomas & Gold, Stefan & Schwab, Leila, 2019. "The Voting Analytic Hierarchy Process revisited: A revised method with application to sustainable supplier selection," International Journal of Production Economics, Elsevier, vol. 211(C), pages 166-179.
    7. Jan Olhager & Andreas Feldmann, 2018. "Distribution of manufacturing strategy decision-making in multi-plant networks," International Journal of Production Research, Taylor & Francis Journals, vol. 56(1-2), pages 692-708, January.
    8. Hallgren, Mattias & Olhager, Jan, 2006. "Quantification in manufacturing strategy: A methodology and illustration," International Journal of Production Economics, Elsevier, vol. 104(1), pages 113-124, November.
    9. M. Hossein Safizadeh & Larry P. Ritzman & Deven Sharma & Craig Wood, 1996. "An Empirical Analysis of the Product-Process Matrix," Management Science, INFORMS, vol. 42(11), pages 1576-1591, November.
    10. Miltenburg, John, 2008. "Setting manufacturing strategy for a factory-within-a-factory," International Journal of Production Economics, Elsevier, vol. 113(1), pages 307-323, May.
    11. Partovi, Fariborz Y., 2007. "An analytical model of process choice in the chemical industry," International Journal of Production Economics, Elsevier, vol. 105(1), pages 213-227, January.
    12. Terry J. Hill & Rafael Menda & David M. Dilts, 1998. "Using Product Profiling to Illustrate Manufacturing-Marketing Misalignment," Interfaces, INFORMS, vol. 28(4), pages 47-63, August.
    13. Ikuobase Emovon & Rosemary A. Norman & Alan J. Murphy, 2018. "Hybrid MCDM based methodology for selecting the optimum maintenance strategy for ship machinery systems," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 519-531, March.
    14. Hosseini, Seyedmohsen & Barker, Kash, 2016. "A Bayesian network model for resilience-based supplier selection," International Journal of Production Economics, Elsevier, vol. 180(C), pages 68-87.
    15. von der Gracht, Heiko A., 2012. "Consensus measurement in Delphi studies," Technological Forecasting and Social Change, Elsevier, vol. 79(8), pages 1525-1536.
    16. S. G. Deshmukh & Abid Haleem, 0. "Framework for Manufacturing in Post-COVID-19 World Order: An Indian Perspective," International Journal of Global Business and Competitiveness, Springer, vol. 0, pages 1-12.
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    Cited by:

    1. Saporiti, Nicolò & Cannas, Violetta Giada & Pozzi, Rossella & Rossi, Tommaso, 2023. "Challenges and countermeasures for digital twin implementation in manufacturing plants: A Delphi study," International Journal of Production Economics, Elsevier, vol. 261(C).
    2. Rukiye Kaya & Said Salhi & Virginia Spiegler, 2023. "A novel integration of MCDM methods and Bayesian networks: the case of incomplete expert knowledge," Annals of Operations Research, Springer, vol. 320(1), pages 205-234, January.

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