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Article

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

by
Alisha Lakra
1,
Shubhkirti Gupta
2,
Ravi Ranjan
3,
Sushanta Tripathy
1,* and
Deepak Singhal
1,*
1
School of Mechanical Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Orissa, India
2
Department of Information Technology, KIIT Deemed to be University, Bhubaneswar 751024, Orissa, India
3
School of Electronics Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Orissa, India
*
Authors to whom correspondence should be addressed.
Logistics 2022, 6(4), 76; https://doi.org/10.3390/logistics6040076
Submission received: 11 September 2022 / Revised: 24 September 2022 / Accepted: 12 October 2022 / Published: 28 October 2022
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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.
Keywords: machine learning; Industry 4.0; manufacturing industry; smart manufacturing; interpretive structural modeling (ISM) machine learning; Industry 4.0; manufacturing industry; smart manufacturing; interpretive structural modeling (ISM)

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MDPI and ACS Style

Lakra, A.; Gupta, S.; Ranjan, R.; Tripathy, S.; Singhal, D. The Significance of Machine Learning in the Manufacturing Sector: An ISM Approach. Logistics 2022, 6, 76. https://doi.org/10.3390/logistics6040076

AMA Style

Lakra A, Gupta S, Ranjan R, Tripathy S, Singhal D. The Significance of Machine Learning in the Manufacturing Sector: An ISM Approach. Logistics. 2022; 6(4):76. https://doi.org/10.3390/logistics6040076

Chicago/Turabian Style

Lakra, Alisha, Shubhkirti Gupta, Ravi Ranjan, Sushanta Tripathy, and Deepak Singhal. 2022. "The Significance of Machine Learning in the Manufacturing Sector: An ISM Approach" Logistics 6, no. 4: 76. https://doi.org/10.3390/logistics6040076

APA Style

Lakra, A., Gupta, S., Ranjan, R., Tripathy, S., & Singhal, D. (2022). The Significance of Machine Learning in the Manufacturing Sector: An ISM Approach. Logistics, 6(4), 76. https://doi.org/10.3390/logistics6040076

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