Product Demand Forecast for Off-Patent Drugs Using Machine Learning at PUREN Pharma
Tuesday, May 12, 2020
The German generic industry faces a big challenge: They need to deliver drugs to patients immediately, but due to high price pressure, they have very long lead times from suppliers. Therefore an accurate demand forecast in the long run is essential. Fortunately the data basis of the whole market is very good. This means one can very good identify what factors drive demand. To solve this issue at PUREN Pharma we implemented more than one year ago a new demand forecast process based on time series algorithms (which are used to determine the market size) and regression algorithms (for determining the market share of PUREN Pharma in the future). The process also includes a web-front end for planning responsibles. Based on this new process, the demand forecast accuracy and the error rates could be reduced significantly. Technologies we haved used for this project were R for time series, Python for regression, AZURE SQL for storage, GAPTEQ Forms for web front end.