Fuzzy Logic Optimizes Global Inventory Management

Abstract

Inventory control is crucial for balancing customer demand and inventory levels, especially in make-to-order production systems like those in refrigeration manufacturing. This study addresses the challenges faced by a company producing sandwich panels, where inefficiencies in inventory processing led to overstock and outstock issues. By applying the fuzzy inventory control method using Python, the study aimed to optimize inventory levels. The results showed a 6% reduction in inventory, from 191,307 m² to 182,619.4627 m², demonstrating the method's effectiveness. This approach can improve inventory management in similar industrial settings, aligning production with demand and reducing excess inventory. Highlight: Efficiency Boost: Fuzzy logic optimizes inventory, minimizing overstock and outstock. Precise Management: Python aids accurate inventory analysis for informed decisions. Cost Savings: Aligning inventory with demand reduces excess, enhancing profitability. Keywoard: Inventory control, Fuzzy logic, Optimization, Production management, Industrial engineering