An Approved Covid-19 Vaccine is Ready. Is the Supply Chain?
John Hennen | 12/17/2020
Unless you have been living under a rock, you heard the news – a Covid-19 vaccine has been approved for immediate distribution and use! When announced, my first thought was “finally!”. My second thought was, “how are they going to possibly distribute this to the entire world in a safe and timely way?” Apparently, I was not the only person to ask this question because there is an in-depth and well-written article that goes into detail:
One of the main points – and indeed the headline – of the article is “everything has to come together.” There are so many moving parts to this supply chain that the chance of delays, issues, spoiled vaccines, missed deliveries, and more is very high. An interesting discussion to have is this supply chain could benefit from the application of Machine Learning. It may be too late to adjust the current process, but as other countries worldwide and further distribution occurs in the United States, it’s time to see how Machine Learning could assist.
So, what data could we collect and feed into machine learning models to produce insights? Some ideas that occurred to me are:
- Route optimization
- Failure analysis (chain of custody/cold chain)
- Identification of most likely failure causes (delivery sites, truck types, distance from the airport, etc.)
Route optimization is interesting – what is the most efficient path from the vaccine warehouse to a destination? How about to ALL destinations? The amount of data that will be produced by this rollout of the vaccine will be immense. We will know how long it takes the vaccines to get to the airport from the warehouse, how long it is on the plane, how long it sits on the plane after it has landed, how long it is in a truck from the airport, and on and on until it’s used. Feeding this data into a properly configured model can produce all sorts of useful insights.
Failure analysis and identification of the most likely failure causes are related but distinct. In cases where the chain of custody or cold chain broke – what happened? Are there any commonalities? Can we identify specific parts of the supply chain that “break” more often than others? All of this data provides a vast opportunity to enhance the processes. These opportunities range from a different configuration of the vaccine storage case to the other type of truck transporting them. The data will be there – ripe for analysis and insights.
I hope that the organizations that oversee this supply chain will use the data available from this rollout to enhance their processes. Distribution is just starting, and lessons learned can be implemented to speed the process up, with a higher success rate of delivery. I think leveraging machine learning will be critical to that approach. Here is another article that shows some of these ideas in action:
Does your organization have a lot of data just waiting for some machine learning love? Book a 30-minute meeting with our team to discuss how we can help your organization.
Director of Business Technology
John Hennen is the Director of Business Technology of Tuatara Consulting. He is responsible for the development and execution of Tuatara’s technology vision. He brings his deep customer-oriented business perspective to the table combined with the technical expertise required to guide his customers to their desired outcomes.