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.
Reference61 articles.
1. Smart machining process using machine learning: A review and perspective on machining industry;Kim;Int. J. Precis. Eng. Manuf.-Green Technol.,2018
2. Amini, M., and Chang, S. (2018, January 19–22). A review of machine learning approaches for high dimensional process monitoring. Proceedings of the IISE Annual Conference and Expo, Orlando, FL, USA.
3. A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis;Ademujimi;Proceedings of the IFIP International Conference on Advances in Production Management Systems,2017
4. Researcher Bias: The Use of Machine Learning in Software Defect Prediction;Shepperd;IEEE Trans. Softw. Eng.,2014
5. Machine learning in manufacturing and industry 4.0 applications;Rai;Int. J. Prod. Res.,2021
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献