AI in Demand Forecasting: How Retailers Improve Real-World Demand Forecasting Using AI

In recent blogs about Demand Forecasting AI, my Product Management Director colleague Guillaume Hochard wrote about Extending Science for Highly Accurate Demand Planning and The Rise of Data-Centric Models. I want to bring it full circle now with insights on how retailers have successfully adopted and applied Demand Forecasting AI to create more accurate, agile forecasts to reduce stock-outs, enhance shopper experience, and more consistently meet business objectives.

First, successful use of Demand Forecasting AI means leveraging to the full a broader array of rich data sources. A huge part of the value of AI-driven forecasting is that we can leverage much more in the way of contextual data than traditional statistical models could ever accommodate – including geographic location, calendar events, weather, detailed promotion activity, etc. This not only yields better, more accurate forecasts than traditional approaches, but it also enables much more meaningful scenario planning. Users can quickly vary the input scenarios to game out different approaches to be best prepared for curveballs. This is particularly valuable during a time of rapidly evolving shopper behaviors and significant supply chain havoc, such as we’ve seen in the last two years. While you can’t rely on history for any meaningful guide, you can confidently rely on real-time science to give you agile, responsive forecasting amid the chaos.

Retailers and their Demand Forecasting AI vendor partners have also worked to successfully overcome cultural barriers to adoption, such as being reluctant to give up traditional “gut feeling” approaches to forecasting. Teams can build broader organizational trust in the science’s accuracy by providing visibility into successful application of the solution and showing the impact of various features. And the best solutions leverage human expertise as well, so users can modify or adjust inputs based on domain knowledge. Adopting a philosophy of “explainable AI” helps users understand the power and logic of AI predictions. Google Cloud tells us that “Explainable AI is a set of tools and frameworks to help you understand and interpret predictions made by your machine learning models,” and I think that sets the right tone.

Back in the days of reliance on statistical models, users had to invest significant cycles tuning these model predictions to compensate for their limited, simplistic approaches. Today, we see a powerful and profound shift to a data-centric mindset. In the most successful scenarios, supply chain teams partner with their DFAI vendor partners and their IT teams to ensure accurate, clean data from all contextual data sources. The upfront effort to ensure access to well-prepared, properly stored data pays significant dividends downstream – indeed, it’s the foundation on which to build success data-centric forecasting.

Retailers who intelligently adopt Demand Forecasting AI find that their demand forecasters are freed up from repetitive manual work to focus where they have more strategic impact, on the exception-driven situations or particularly problematic areas. They experience enhanced workflows and greater efficiency, particularly as users gain familiarity with and confidence in increased forecast accuracy. And shoppers enjoy more reliable access to the items they want most, generating the holy grail of good retail technology: a win for both the retailer and for the shopper.

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