Prediction of production facility priorities using Back Propagation Neural Network for bus body building industries: a post pandemic research article

A. Sivakumar (), N. Bagath Singh (), D. Arulkirubakaran () and P. Praveen Vijaya Raj ()
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A. Sivakumar: Kongu Engineering College
N. Bagath Singh: Kurinji College of Engineering and Technology
D. Arulkirubakaran: Karunya Institute of Technology and Sciences, Karunya Nagar
P. Praveen Vijaya Raj: Indian Institute of Management Raipur

Quality & Quantity: International Journal of Methodology, 2023, vol. 57, issue 1, No 26, 585 pages

Abstract: Abstract The pandemic recession has caused enormous disturbances in many industrialized countries. The massive disruption of the supply chain of production is affecting manufacturing companies operating in and around India. Particularly the medium-sized bus body building works have been reduced, due to its compound anomalies. The integrated view of the production facility priorities is not an easy task. Since it is difficult for available labour to conduct an entire project, the completion of a production process is delayed. But still, the dilemma remains as to how production managers can correctly interpret the priorities of the facility. Indeed, this is a problem missing from the previous study. Fortunately, in the current competitive environment, it is essentially needed. This study has been used Back Propagation Neural Network (BPNN) approach for predicting production facility priorities. The experimental results confirm the suitability of the model for predicting priorities. A real-world problem is taken into account in making use of the model output. In this sense, this total solution facilitates production managers in assessing and enhancing the production facilities. The findings emphasize the priority of “equipment effectiveness, labour scheduling and communication” in order to strengthen the post-pandemic production facility.

Keywords: Production facility priorities; BPNN; Bus bodybuilding; Competitiveness; Flexibility (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s11135-022-01365-1

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