How to Forecast During Unprecedented Uncertainty
By no means do I consider us “out of the woods” yet from the pandemic – or should I say the operational and economic instability pandemic that sent every organization in every industry into a tailspin. There are plenty of unexpected twists and turns ahead of us, which makes today the perfect day to start preparing for what's next. And, as retail and consumer packaged goods (CPG) planners will tell you, forecasting is key to proper preparation.
Now, I know that it may feel impossible to forecast anything anymore, but it’s not. In fact, our clients did not discard their forecasts when the pandemic started. Granted, the purpose and structure of forecasts have changed over the last three years. But their value has not. And those who took the time to manage and improve their forecasting results during this extended period of stress and uncertainty have experienced better outcomes. So, these are some of the key takeaways we’ve collectively garnered that I feel may be helpful for you:
1. Open, bilateral and collaborative communication is vital. Before the pandemic, we typically held weekly or bi-weekly meetings with our clients. As soon as Covid hit, we started having daily touchpoints where we'd all share what we knew, what we didn't know, and what we all were speculating, as well as what we were doing. These times require honesty and that can be brutal at times. But it has enabled us to be creative and come up with solutions to manage these situations. But back to my first point. We need each other.
2. Patterns continue to be strange. But that is why we need each other so much. Communication has been crucial since the beginning of the pandemic because none of us have seen these patterns in our forecasts before. We need to interpret “why they are happening,” “what is causing them,” and “what to do.” Since we (at Zebra/antuit.ai) work with a diverse client base, everyone we work with was reporting different operational experiences. Our apparel retail clients closed their stores, but not everyone shared the same surge in online business. Our food and beverage CPG clients experienced unexpected growth and stockouts. Other consumer products clients underwent massive channel sales shifts with some categories up, some down, and others not affected at all.
Here are a few simplistic examples, which with hindsight, are easy to explain:
1. We saw a massive increase in mascara sales and a drop in lipstick sales occurring in different states and times. The cause: mask mandates. People show their eyes, but not their mouths.
2. Personal grooming products and hair clippers immediately surged. The cause: hair salons and barbershops closed, so people started buying their own for home.
3. Forecasts were not discarded. As mentioned, contrary to many of the experts, our clients did not discard their forecasts. They didn't blindly follow them, either, because some forecast accuracies went to zero (Prior point: honesty). But not every forecast was inaccurate. Some retained their accuracy while others, to our surprise, were better. But all of them provided insights. The questions were, "What were they telling us? Why were they telling us this? What's the next action to take?" The best answers weren't by machine or man, but the combination of man and machine.
4. A lot of trials. Even more errors. Science is about experimentation, trial, and error. And during this period, there were many trials, and even more errors. We had these fantastic ideas, and then we would try it . . . and fail miserably. Those were humbling experiences. On the other hand, sometimes we’d throw stuff at the wall to see what would stick and, amazingly, some things worked brilliantly. But that is science, and sometimes progress happens that way: happy accidents.
5. Elegance in simplicity. For complicated situations, you think you need complicated solutions. We love trying out new techniques, the latest algorithms, and jumping into all the models. We are an AI company. But there were times where we gained incredible value from doing extensive data analysis and descriptive analytics. Why? It showed us what was going on and identified data issues. We didn't get caught up in making an existing solution solve the problem. We focused on finding a solution to the problem, whatever that solution was.
6. We became better. While this experience was extremely stressful, I take great satisfaction in how we responded. We proactively worked with our clients on a common goal, increased our communication, troubleshot problems, and employed a lot of experimentation. As a result, we've improved our ability to predict consumer demand. Our reporting and analytics have improved. And finally, we've enhanced our own AI and machine learning techniques to tag these types of events, consider leading market signals, and respond even more quickly to consumer demand changes. While we're never sure of what's next, we and our clients are better prepared.
If you’re still finding forecasting challenging – or practically impossible – let’s talk. You can contact me on LinkedIn or reach out to one of my colleagues for a consult here.
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Jasneet Kohli
Jasneet Kohli is head of antuit.ai’s Solution Consulting for Zebra and has more than 15 years of experience generating value for consumer products companies and retailers. He has held various leadership roles across business operations, solution strategy, and customer success. Previously, he served as head of procurement and logistics for Abbott in Singapore and Managing Consultant for IBM.
