TY - JOUR
T1 - An integrated Delphi-MCDM-Bayesian Network framework for production system selection
AU - Dohale, Vishwas
AU - Gunasekaran, Angappa
AU - Akarte, Milind
AU - Verma, Priyanka
N1 - Funding Information:
Dr. Milind Akarte is a Professor of Industrial Engineering at the NITIE, Mumbai, India. He has earned his master’s degree in IEOR and Ph.D. in Mechanical Engineering from IIT, Bombay. His research areas include Manufacturing Strategy, Additive Manufacturing, Smart Manufacturing, and Operations and Supply Chain Management. He is a recipient of a research grant from the SERC, DST for the project “Benchmarking of hybrid tooling methods (Rapid Prototyping & Tooling) for metal casting.” He has published more than 40 technical papers and guided 6 Ph.D. candidates and over 50+ master’s students. He has also carried out industry consulting and executive training programs.
Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2021/12
Y1 - 2021/12
N2 - 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.
AB - 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.
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U2 - 10.1016/j.ijpe.2021.108296
DO - 10.1016/j.ijpe.2021.108296
M3 - Article
AN - SCOPUS:85114948077
SN - 0925-5273
VL - 242
JO - International Journal of Production Economics
JF - International Journal of Production Economics
M1 - 108296
ER -