Optimizing Powertrain Disassembly Efficiency via Machine Learning -Based Lean Six Sigma at PT. TU Surabaya Branch
DOI:
https://doi.org/10.30871/jaba.10209Keywords:
Decision Tree Regression, Lean Six Sigma, DMAIC, Process Cycle Efficiency, Operational EfficiencyAbstract
Operational efficiency is vital in mining and construction, were equipment availability drives productivity. This study assesses reconditioning effectiveness for Powertrain components at PT. TU Surabaya, focusing on the Disassembly stage the primary bottleneck in the maintenance cycle. Lean Six Sigma is applied using the DMAIC (Define, Measure, Analyze, Improve, Control) framework to identify, measure, and regulate service duration factors. Machine Learning, via Decision Tree Regression in KNIME, analyzes historical data to predict optimal Disassembly timeframes. Efficiency improvement is implemented using the 5S method, while a Decision Matrix prioritizes solutions to enhance overall system performance. Results from initial implementation show a reduction in average process duration from 26.37 days to 15.33 days. Predictive analysis also reflects an increase in Process Cycle Efficiency (PCE) from 46.49% to 53.20%. These findings affirm the effectiveness of a structured, data-driven operational strategy that combines Lean Six Sigma and predictive analytics to resolve service bottlenecks and improve industrial process outcomes.
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