Implementation of machine learning to increase productivity in the manufacturing industry: a literature review.

Yulio Agefa Purmala


Industry 4.0 is currently developing quite rapidly, one of the technologies that is currently very popular in the industry is artificial intelligence, where an event can be diagnosed and predicted more quickly and accurately. One of the branches of artificial intelligence that can do this is Machine Learning, and its application can now be found in daily activities. In the manufacturing industry, the application of Machine Learning is one of them is to increase productivity through the results of analysis and predictions given based on the experience gained. This study uses a systematic literature review method, in which several articles were collected from several journal databases such as Elsevier, IEE, Springer, Taylor & Francis and ACM, with the publication period of the articles from 2015 to 2020. A total of 100 articles were collected, then re-validated. suitability based on the main goals and objectives of the research. There were 36 articles that were validated and used as a reference for a more in-depth review and analysis of their boundaries, so that there was a gap for further research. In this literature review study, its application is very helpful in making decisions in improving the quality, efficiency, and performance of companies in the manufacturing industry. The most popular algorithms used in this study include random forest, support vector machine, neural network, linear regression, and k-nearest neighbor. Finally, in this study it was found that the application of Machine Learning in diagnosing or predicting an event is suggested by modeling more than one algorithm to find and determine which algorithm is the most accurate and suitable to be applied to the phenomenon that occurs.


machine learning; artificial intelligence; manufacturing industry; productivity, literature review

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Portal ISSNPrint ISSN: 2085-4293
Online ISSN: 2654-5799

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Operations Excellence: Journal of Applied Industrial Engineering

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Journal DOI: 10.22441/oe

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