Authors:
Amber Lin, Anirudh Narula
MIT Supply Chain Management Program
66
Summary:
The pharmaceutical industry faces long lead times. Delays in one batch can affect subsequent shipments and jeopardize the health outcomes of patients relying on these medications. Our project aims to design an early warning system by using a machine learning model to predict sequential delays when a batch faces disruptions at a certain location.
Looking forward for more details!
Good start Rudy and Amber, I liked the framing you did.
You can see the tyranny of linear supply chains as they they often have latency in information flow. Operationally you may not be able to move away from that linear model, but perhaps the information flow can be established as if it was a meshed network. The machine learning could perhaps send alerts, or the data, in the chain based on collaboration agreements between the chain participants up and down the stream and thus allowing the linear process to have feedback in a networked manner.
Identifying the important early indicators of delay (at least task completion but might include other results) is one of the challenges and then not over reacting or under reacting to that data. Machine learning might unburden human from that effort and apply consistent rules to amplify or demote a notification.
I wish you the best with you project and look forward to seeing the findings from your capstone work.
Excellent
Look forward to an operational model like for India or elsewhere
As each area specificities will impact the model
Wishing the best