  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.
