Get the Most Out of Supply Chain Analytics-- Five Tips I gave at a GE Research Conference
I gave a short talk at a GE Research conference yesterday. I am always excited to visit GE. I did an internship at GE Appliances, had a great mentor from that experience, and have enjoyed keeping up with them throughout the Welch, Immelt, and now Culp days. GE provides a good window into Corporate America.
We had five speakers in our session (pictured above), and I learned something from them (I’ll share those learnings later). Here are the five tips I gave.
One, don’t over-engineer the solution.
Said another way, I don’t think AI is just deep learning. I always like to remind audiences that people tend to use AI in two ways: AGI and Practical AI. It is important to make sure everyone is talking about the same thing. See this blog for more details.
Two, be creative.
The book Prediction Machines taught me to think creatively about reframing many business problems as prediction problems. The book suggests that most business problems are prediction problems. I’ve seen this advice used effectively over the years.
For example, we worked with a company that was having problems understanding why they weren’t hitting 100% complete orders. Instead of just diagnosing the issue, our team realized that predicting next week’s order could help get out in front of the problem. Forecasting demand is common, but using a prediction algorithm to reverse engineer what the customer might order is creative.
In another example, we used prediction models to help determine risks within the supplier base and overall supply chain. Companies also use prediction models to help prevent worker injuries in industrial settings.
Three, don’t forget about optimization.
Prediction without optimization can be useless. You need the optimization algorithm to decide what to do with the prediction.
DoorDash wrote a blog on exactly this topic. They needed to predict how orders would play out and then feed that into an optimization to help make decisions.
Four, your traditional supply chain modeling tool can help with risk analysis.
The other speakers gave me some new ways to think about risk.
But, your supply chain modeling tool should still be used. Modeling tools are great for understanding how product flows, where facilities should be, and where to make products. Here is a typical view of a solution:
Running different scenarios can help you understand risk and build a more resilient supply chain.
I worked with a paint company that used their network model to understand the impact of a fire at each of their plants. They took a plant out of the model to see how much costs would go up and how much demand would go unmet.
In another case, we were building a 5-year plan for a chemical company. Something happened that caused a plant to close. The CEO came to our team and said: “Forget your 5-year plan; I need you to take your model and focus on the next five weeks. Figure out what other plants can help and what customers will be impacted.”
If you keep an active model (sometimes called a digital twin), you can react and quickly mitigate problems.
Five, keep an eye out for new trends.
The Practical AI space moves fast. There are probably hundreds of trends. Here are a few I like:
In my time at Coupa, I came to love their strategy on community data. I see the idea as an extension of the Big Data movement from 10 years ago. Yet, instead of data from one company, can you combine your data with other companies to do things that weren’t possible before. The CEOs of Coupa and Accenture talked about using community data to identify child labor in the supply chain—easier to do with community data. The WSJ published an article about banks sharing data to counter cyberattacks- more effective with community data.
Natural Language Processing (NLP) is moving fast.
A lot of supply chain research is looking into Reinforcement Learning. It seems like we are a few years out, but worth keeping an eye on.
And although I knocked Deep Learning earlier in the post, use it and keep an eye on it. Things like AlphaFold (for folding proteins) and DALL-E (for converting text to a picture) are worth watching.