MACHINE LEARNING AND
The Supply Chain
Separate from the unique process of creating medications, big data can have a major impact on the pharmaceutical industry in much the same way that it can bolster any other business: by helping companies better understand who their customers are, where they are, and how to reach them.
Identifying the most efficient supply system will become even more important as drugs are increasingly customized to small populations of patients with certain genetic profiles. Figuring out how to efficiently distribute a medication that is only relevant to 1,000 patients around the world is very different than, say, distributing ibuprofen to pharmacies across the world. As one expert put it at the 2017 LogiPharma US Conference, “Instead of executing one supply chain a thousand times, we should get ready to execute a thousand supply chains, one at a time.”
Shipping drugs is expensive business. Many of the most expensive drugs demand very specific conditions and need to be transported by air. In 2012, the total value of pharma freight was $269 billion, with air freight alone accounting for $213 billion. More advanced analytics allow pharma companies to better-forecast demand and to distribute products more efficiently. They will also allow many key decisions to be automated, thereby allowing pharma companies to cut down on labor costs as well as the variety of bottlenecks that occur when decisions are dependent on a complex human chain-of-command.