Supply Chain Optimization through the Implementation of Lean Manufacturing in the Manufacturing Industry

Semuel Pajala; Rusdiaman Rauf; Eric Alan Jones

doi:10.12928/si.v24i1.517

Authors

Semuel Pajala Department of Industrial Engineering, STITEK Dharma Yadi, Makassar, 90231, Indonesia
Rusdiaman Rauf Department of Enterpreneurship, Universitas Handayani Makassar, Makassar, 90231, Indonesia
Eric Alan Jones Department of History, Northern Illinois University, 60115, United States of America

DOI:

https://doi.org/10.12928/si.v24i1.517

Keywords:

Lean manufacturing, Machine learning, Manufacturing industry, Productivity prediction, Supply chain performance

Abstract

Manufacturing companies in emerging economies are increasingly challenged to improve supply chain performance amid rising competition, cost pressures, and demand uncertainty. Lean manufacturing improves operational efficiency, but studies integrating performance evaluation and predictive analytics in Indonesian manufacturing remain limited. This study aims to analyze the impact of lean manufacturing implementation on supply chain performance and to develop machine learning–based models for predicting productivity improvement. An explanatory research design using a before, after (pre–post) approach was employed. Data were collected from ten manufacturing firms in South Sulawesi, Indonesia. Supply chain performance indicators, including lead time, productivity, defect rate, and customer satisfaction, were evaluated using paired sample t-tests. In addition, several machine learning algorithms, namely Linear Regression, Support Vector Regression, Random Forest, and Gradient Boosting, were applied to predict productivity outcomes. The findings show that lean manufacturing implementation significantly improves supply chain performance, particularly in reducing lead time and defect rates while increasing productivity. Among the tested models, ensemble-based algorithms, especially Random Forest and Gradient Boosting, exhibited superior predictive accuracy and robustness compared to single-model approaches. The study concludes that lean manufacturing operates as an integrated management system rather than merely a set of operational tools. Theoretically, this research integrates Lean Theory and the Resource-Based View (RBV) with predictive analytics, enriching the lean manufacturing literature. Practically, the results contribute to provide managers with data-driven insights to support sustainable performance improvement and informed decision-making in manufacturing supply chains.

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