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

Alisha Lakra, Shubhkirti Gupta, Ravi Ranjan, Sushanta Tripathy () and Deepak Singhal ()
Additional contact information
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

Logistics, 2022, vol. 6, issue 4, 1-15

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) (search for similar items in EconPapers)
JEL-codes: L8 L80 L81 L86 L87 L9 L90 L91 L92 L93 L98 L99 M1 M10 M11 M16 M19 R4 R40 R41 R49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2305-6290/6/4/76/pdf (application/pdf)
https://www.mdpi.com/2305-6290/6/4/76/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jlogis:v:6:y:2022:i:4:p:76-:d:956189

Access Statistics for this article

Logistics is currently edited by Ms. Mavis Li

More articles in Logistics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().