Process Optimization in a Condiment SME through Improved Lean Six Sigma with a Surface Tension Neural Network

Abstract

This study offers an innovative solution to address performance issues in the manufacturing process of garlic salt within a condiment-producing SME. A hybrid Lean/Six Sigma model utilizing a Surface Tension Neural Network (STNN) was implemented to control temperature and relative humidity in real-time. The model follows the Define, Measure, Analyze, Improve, Control (DMAIC) methodology to identify root causes and correlate them with waste. By integrating statistical tools, artificial intelligence, and engineering design principles, alternative solutions were evaluated to minimize waste. This document contributes to existing knowledge by demonstrating the integration of an STNN with the Lean/Six Sigma framework in condiment production, an area with limited empirical research. It underscores the benefits of advanced AI technologies in enhancing traditional process optimization methods. The STNN model achieved 97.31% accuracy for temperature classification and 97.37% for humidity, outperforming a Naive Bayes model, which attained 90% accuracy for both. The results showed a 3.15% increase in yield, saving 39.7 kg of waste per batch. Additionally, a 2.13-point improvement at the Six Sigma level was achieved, reducing defects per million opportunities by 551.722. These improvements resulted in significant cost savings, with a reduction in waste-related losses amounting to USD 1585 per batch. The study demonstrates that incorporating artificial intelligence into the Lean/Six Sigma methodology effectively addresses the limitations of traditional statistical methods. Significant improvements in yield and waste reduction highlight the potential of this approach, enhancing operational efficiency and profitability, and fostering sustainable manufacturing practices critical for SMEs’ competitiveness and sustainability in the global market.