Abstract
In a supply chain scenario, companies have a stock of goods on hand. Companies face many challenges in making decisions under customer demand uncertainty. So, firms have to decide upon their inventory policy as part of the supply chain to compute average inventory levels, backorder levels, optimal order quantity, reduced loss of orders, and inventory cost, thus improving the profit. When traditional techniques such as Economic Order Quantity arenot providing the right solution concerning gaining optimal supply chain management application solutions, such as optimal inventory control under demand uncertainty, supply chain analytics employ inventory control policies that regulate the order to replenish stocks. For effective inventory control, two policies are used in the paper: periodic review policy and continuous review policy. With these policies, companies can get feasible inventory management solutions using mathematical, statistical modeling, and Simulation techniques. So, in this research,the Monte Carlo simulation technique has been applied and computed with visualizing charts using Python programming. The results show that managing inventory control with optimal values is possible using the simulation technique with negligible loss of orders. Profit distribution, safety stock, and order quantity values are determined and tabulated.