Johnson & Johnson is one of the largest corporations in the world and they produce everything from medical devices to baby powder. They were also on the front lines of developing a vaccine during the pandemic. Internally, J&J is also at the forefront of digital transformation. Sarfraz Nawaz studied computer science and built data analytics and decision intelligence platforms inside companies across different industries. Sarfraz currently does product and digital management for J&J's supply chain team. In this episode, we discuss data analytics platforms, supply chain platforms, and optimization problems in the context of vaccine distribution. Building a supply chain platform at Johnson & Johnson Johnson & Johnson's supply chain supports 1.2 billion consumers every day. It's a staggering number. Building and optimizing a supply chain platform that supports so many people must be a huge problem, but could also be a fun one if you love to see things scale. Sarfraz discusses the one thing underpinning a successful supply chain: a data strategy. You normally may not think of a supply chain when it comes to data analytics. According to Sarfraz, J&J's data strategy lays the foundation for how other technologies at J&J are enabled. He's referencing things like cloud, governance, and machine learning. Increasing visibility into the supply chain is something Sarfraz works on a lot. For instance, one concept we talked about in this episode is Availability to Promise, or ATP. This concept basically ensures there is enough inventory available when a customer places an order, and that there isn't too much inventory sitting around either. There's a lot of esoteric software I've never heard of that helps corporations like J&J with ATP like Logiwa and Cogoport. Even SAP has an ATP platform showing how important this concept is for companies with big supply chains. Behind these ATP platforms are, of course, a ton of data. And more of that data is coming from customers. Competing with Amazon Demand forecasting and planning is a constant challenge for J&J. However, with J&J's digital transformation initiatives and data strategy in place, the corporation is getting better at forecasting every day. An important signal for demand forecasting is customer engagement. Sarfraz discusses the various inputs that go into this demand forecasting model. Imagine the model is an Excel file, and there are various inputs that go into the model (over-simplified analogy). There are data points coming from the manufacturing division, inventory levels, transportation, and many more inputs that go into the model. This is similar to the model Amazon Prime has built for its customers. You can go one level deeper and get data points from building control towers, temperature inside the vehicles that carry products, and the constant feedback look that arises from these data "producers." Source: Business Insider Optimizing COVID-19 vaccine distribution Sarfraz spoke a bit about the research and planning that went into developing and distributing the J&J vaccine. The key takeaway from this experience (like many problems related to big data) is scale. How does J&J ensure they have the right processes in place to produce over 500 million vaccine do...