A System-Dynamic Based Simulation and Bayesian Optimization for Inventory Management

TL;DR

This paper introduces a novel approach combining system-dynamic Monte-Carlo simulation and Bayesian optimization to improve inventory management under unpredictable demand, demonstrating significant performance improvements.

Contribution

It presents a new robust method integrating simulation and Bayesian optimization for inventory control in dynamic, uncertain supply chain environments.

Findings

Significant improvement in inventory policy performance.

Effective handling of demand unpredictability.

Enhanced decision-making in supply chain management.

Abstract

Inventory management is a fundamental challenge in supply chain management. The challenge is compounded when the associated products have unpredictable demands. This study proposes an innovative optimization approach combining system-dynamic Monte-Carlo simulation and Bayesian optimization. The proposed algorithm is tested with a real-life, unpredictable demand dataset to find the optimal stock to meet the business objective. The findings show a considerable improvement in inventory policy. This information is helpful for supply chain analytics decision-making, which increases productivity and profitability. This study further adds sensitivity analysis, considering the variation in demand and expected output in profit percentage. This paper makes a substantial contribution by presenting a simple yet robust approach to addressing the fundamental difficulty of inventory management in a dynamic business environment.