Applying ML to the pharmaceutical supply chain and sales
The evolution of the global pharmaceutical industry has introduced new challenges and complexities in the supply chain process. It needs to cater to new distribution mechanisms for therapeutics, adhere to new regulations around supply chain transparency, cater to storage requirements of specialized biologics, and also be flexible enough to handle changes. Moreover, the supply chain should be resilient enough to avoid disruptions. These disruptions can be caused by a variety of reasons such as natural disasters, equipment failures, shortage of raw materials, or even cyber-attacks on IT infrastructure.
ML can help reduce supply chain disruptions and optimize them to reduce waste and increase efficiency. Let us now look at some of the common ways in which we can apply ML to the pharmaceutical supply chain and sales workflows.
Targeting providers and patients
As described earlier, pharmaceutical sales involves visiting providers...
