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.
Digitization has made the channel of doing the business easier and timeless. With the fast-growing economy with e-commerce, it becomes a priority to study the parameters that influence e-commerce. In this paper, the authors have recognized nine factors: customer satisfaction, user interface, gender-related approach to e-commerce platform, company's association with customers, data breach, digital literacy, localization of internet content, digital infrastructure, and government policy. This chapter presents a keen study on the contributions of the factors on the e-commerce structure. Interpretive structure modelling technique has been used to provide a systematic judgement on the influence of provided parameters. MICMAC analysis has been carried out to classify the critical factors as per their dependence and driving powers. Results from the study have shown that customer satisfaction is the most dependent factor and the long-term goal to achieve for an e-commerce success. Government policy remains lowest in the strata and derives all the other parameters.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.