Pharmaceutical distribution

Supply Chain Optimization Application

A pharmaceutical distribution player had to rethink its entire supply chain: pharmacy assortment, inventory management and delivery channels. Operational research alone was no longer enough. We built the application that transformed its operations.

Problem

Each pharmacy in the network received a standard assortment, without taking into account its location or the profile of its population. Inventory was managed without a predictive model, shortages and overstocks coexisted and delivery channels followed historical patterns that had never been questioned.

The result: millions stuck in useless stock, sales lost due to lack of availability and constantly rising logistics costs, without teams having the tools to act.

Vue rapprochée d’une coupe transversale colorée d’une géode montrant des couches concentriques de minéraux en jaune, marron, rouge et vert.

Solution

What we built

We deployed a team of 2 Data Scientists, 1 Data Engineer and 1 operational research consultant to design and industrialize a complete Supply Chain Optimization application.

Step 1 — Data collection and structuring. Retrieving, cleaning and processing all necessary data: pharmacy sales history, location data, demographic profiles of catchment areas, stock and delivery data.

Step 2 — Algorithm development. Combination of machine learning and operational research to deal with three dimensions simultaneously: prediction of demand by pharmacy with a very fine level of granularity, optimization of the assortment according to the local context, and optimization of delivery circuits.

Step 3 — Continuous improvement of the models. The learning algorithms were designed to improve continuously as new data entered the system. The performance of the predictions improved with each cycle.

Step 4 — Start of production. Development using agile methodology, test and acceptance phases, then deployment in production on the customer's Azure infrastructure.

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