Predicting the Readiness of Indonesia Manufacturing Companies toward Industry 4.0

A Machine Learning Approach

Authors

  • Sean Yonathan Tanjung Petra Christian University
  • Kresnayana Yahya
  • Siana Halim Petra Christian University

DOI:

https://doi.org/10.9744/jti.23.1.1-10

Keywords:

Industry 4.0, Clustering, Classification, Decision Tree

Abstract

This research discusses Indonesia's readiness to implement industry 4.0. We classified the Indonesia manufacturing companies' readiness, which is listed in the Indonesia Stock Exchange, in industry 4.0 based on the 2018 annual reports. We considered 38 variables from those reports and reduced them using principal component analysis into 11 variables. Using clustering analysis on the reduced dataset, we found three clusters representing the readiness level in implementing industry 4.0.  Finally, we used the decision tree for analysing the classification rules. As the finding of this study, Total book value of the machine is the variable that defined the readiness of a company in industry 4.0. The bigger those values are, the more ready a company to compete in industry 4.0. The other measures, i.e., Total cost of revenue by total revenue; Direct labor cost; Total revenue/Total employee and Transportation cost/Total revenue, will define the readiness of a manufacturing company to transform into industry 4.0. or not ready to transform into industry 4.0.

Author Biography

Siana Halim, Petra Christian University

Industrial Engineering Department

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Published

2021-05-31

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