机器学习在制造业的意义:ISM方法

IF 3.6 Q2 MANAGEMENT
Alisha Lakra, Shubhkirti Gupta, Ravi Ranjan, S. Tripathy, D. Singhal
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引用次数: 2

摘要

背景:我们的日常商品确实依赖于工业部门,随着人口的增长,工业部门正在快速扩张。商品的生产需要准确快速。因此,在本研究中,我们将机器学习(ML)技术纳入了制造业(MS)。方法:通过一项包容性研究,我们确定了研究背景下的11个因素,这些因素对制造业的机器学习具有重要意义。使用解释结构建模(ISM)方法,并应用专家的输入来建立关系。结果:ISM模型的结果表明,“订单履行因素”是长期关注点,“市场需求”因素是短期关注点。研究结果表明了影响制造业机器学习发展的关键因素。结论:我们的研究有助于制造业将机器学习纳入其中。使用ISM模型,行业可以直接指出它们的奇怪之处,并对其进行改进以获得更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Significance of Machine Learning in the Manufacturing Sector: An ISM Approach
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.
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来源期刊
Logistics-Basel
Logistics-Basel Multiple-
CiteScore
6.60
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11 weeks
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