Lean Six Sigma 4.0 Application in the Food & Beverage Industry: A Case Study

This work investigates the integration of Predictive Lean Six Sigma (PLSS) and Machine Learning (ML) methodologies to optimise Overall Equipment Effectiveness (OEE) in a beverage industry. The combined DMAIC framework with ML tools, such as Random Forest Regression, form a detailed monitoring of changeover times and Clean-In-Place (CIP) processes. Additional data cleaning and integration are performed to ensure high-quality input for the ML models. This leads to the improvement of OEE from 44.4 to 50%, which is partially supported by the integration of inline specific gravity meters and conductivity meters.Using the current case study outcome, the beverage plant is planned to expand with the support of predictive analytics and exploration of ML tools integration across other areas of operations.