Drug Delivery Optimization

DataRobot helps optimize supply chain delivery processes by predicting which orders can likely to be consolidated.
To increase product adoption, pharmaceutical firms ship millions of drug samples to doctors and hospitals. The orders can be consolidated when the same location requests two or more drug samples. DataRobot can predict which drug samples should wait for consolidation, reducing the overall cost of delivery.

Problem/Pain

Pharmaceutical firms spend significant money on producing and shipping drug samples to medical practitioners in an effort to acquire new adopters. To avoid unnecessary costs, the delivery and supply chain processes must be optimized using models based on real-time requests.

Solution

Using DataRobot model automation and historical drug delivery data, a business analyst can build a model that accurately predicts whether a given drug sample order could be consolidated with another upcoming order to the same location or department. You can then minimize shipping costs on those orders predicted to be consolidated by retaining them for a few days at the warehouse.

Why DataRobot

Factors that help predict whether a given drug order can be consolidated are very heterogeneous: time, drug characteristics, geographical information, doctor characteristics, etc. DataRobot’s algorithms and feature engineering naturally handle these complex datasets to generate the most accurate machine learning models in minutes.