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.”
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 doses in a year?
The first step, according to Sarfraz, was simply identifying and cataloguing all the core platforms that are part of developing and distributing a vaccine. Once those core systems are identified, you then need to figure out how to tweak each component to serve an immediate need for that product. Of course, the supply chain behind this operation has to be done with the strictest health and safety protocols (which is itself a supply chain problem).
Improving decision making in the marketing analytics world
We also discussed one of Sarfraz previous roles developing a multi-touch marketing attribution platform. Those who work in marketing analytics can attest to the challenge of coalescing multiple data sources to properly give attribution to a marketing channel that drives conversion for a company. The space Sarfraz focused on was addressable media and how you can use historical data to predict future marketing spend.
Sarfraz talked about a model he built that incorporate spend data, user behavior, and other inputs that the marketing and analytics teams would use to optimize marketing spend. Large retailers like Macy’s and Neiman Marcus would feed their data into this model and the model would help figure out attribution. The output would show Macy’s that if they spent X dollars on paid search, email, and other channels, it would have Y effect on customer purchase behavior. Big retailers had direct relationships with platforms like Pinterest and Snap which allowed them to have more insight into how their marketing spend is leading to conversions.
Systems thinking and key takeaways
We ended the episode by talking about an episode from Hanselminutes with Inés Sombra, VP of Engineering at Fastly. Scott and Inés talk about what it takes to go fast in an organization. They also talk about systems thinking and viewing systems under the lens of technical infrastructure and people. We chatted a little about how Sarfraz and his team apply systems thinking at J&J.
In terms of key takeaways, Sarfraz highlights the importants of data, engineering, and STEM education and how its been transformational within various industries. As a proponent of digital transformation, Sarfraz talks about how this trend is just getting started.
Other Podcasts & Blog Posts
In the 2nd half of the episode, I talk about some episodes and blogs from other people I found interesting:
- Hanselminutes #831: What does it take to go fast? with Fastly’s Inés Sombra