- Demand Planning
Pharmaceuticals account for over hundreds of billions of dollars of the global annual healthcare expenditure. Inventory management is essential for the financial health of the retail pharmaceutical industry. The retail pharmacy studied in this research, faced a challenge managing high-performance inventory policies. This capstone project aims to determine how a set of replenishment policies can help maintain efficient inventory levels and minimize undesired effects of non-centralized discounts and stock-outs in the stores. Our analysis is based on descriptive analytics such as demand frequency, variability, level and profit, data mining and quantitative models such as inventory control, sensitivity analysis and scenario analysis on forecast horizon, stock-out penalty, and customer service level to determine the replenishment policies best suited for the group of prioritized SKUs analyzed. The analyzed policies demonstrate the tradeoff between leveraging supplier-pushed discounts and the increased costs of excess inventory. In addition, the tradeoff between reducing holding costs and controlling stock-out penalties is analyzed. From the SKUs analyzed the research suggests using the (Q, R) policy for high profit SKUs for an average of 33% cost reduction and (s, S) policy for low profit SKUs for an average of 37% cost reduction.