Demand Forecasting Analysis for Pharma Retail

Abstract

Demand planning is the connection between marketing, finance, and operations. In an industry like pharma retail, products do not always behave according to a regular stable baseline. In addition, marketing enrichment like promotions or price fluctuations and the impactof government regulations and patient base characteristics increase operational complexity. Moreover, more than thirty percent of changes in the forecast from one cycle to another can lead to overstock or out-of-stock due to the high production and delivery lead times. The purpose of this project is to find a proper demand forecasting model for a selected group of stock-keeping units to improve supply processes of the most important stores of the sponsoring company, leading to further benefits such as budget purposes as a top-down analysis. Data analysis is needed for trends, seasonality, stockouts, and demand stability. Followed by the application of various forecasting models, including Machine Learning algorithms, this project provides a comparison of models to define the best baseline as a tool for the planning area to enrich to improve operational KPIs.