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The Significance of Machine Learning in the Manufacturing Sector: An ISM Approach

Author

Listed:
  • Alisha Lakra

    (School of Mechanical Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Orissa, India)

  • Shubhkirti Gupta

    (Department of Information Technology, KIIT Deemed to be University, Bhubaneswar 751024, Orissa, India)

  • Ravi Ranjan

    (School of Electronics Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Orissa, India)

  • Sushanta Tripathy

    (School of Mechanical Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Orissa, India)

  • Deepak Singhal

    (School of Mechanical Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Orissa, India)

Abstract

Background: Our day-to-day commodities truly depend on the industrial sector, which is expanding at a rapid rate along with the growing population. The production of goods needs to be accurate and rapid. Thus, for the present research, we have incorporated machine-learning (ML) technology in the manufacturing sector (MS). Methods: Through an inclusive study, we identify 11 factors within the research background that could be seen as holding significance for machine learning in the manufacturing sector. An interpretive structural modeling (ISM) method is used, and inputs from experts are applied to establish the relationships. Results: The findings from the ISM model show the ‘order fulfillment factor as the long-term focus and the ‘market demand’ factor as the short-term focus. The results indicate the critical factors that impact the development of machine learning in the manufacturing sector. Conclusions: Our research contributes to the manufacturing sector which aims to incorporate machine learning. Using the ISM model, industries can directly point out their oddities and improve on them for better performance.

Suggested Citation

  • Alisha Lakra & Shubhkirti Gupta & Ravi Ranjan & Sushanta Tripathy & Deepak Singhal, 2022. "The Significance of Machine Learning in the Manufacturing Sector: An ISM Approach," Logistics, MDPI, vol. 6(4), pages 1-15, October.
    • Handle: RePEc:gam:jlogis:v:6:y:2022:i:4:p:76-:d:956189
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    References listed on IDEAS

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    1. Claudia Aparecida de Mattos & Fernanda Caveiro Correia & Kumiko Oshio Kissimoto, 2024. "Artificial Intelligence Capabilities for Demand Planning Process," Logistics, MDPI, vol. 8(2), pages 1-16, May.

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