RSR Research Managing Partner Brian Kilcourse explains why the time is right for AI-enabled demand planning in retail

Retailers today are challenged by the increasing unpredictability of consumer demand and its inevitable result, causing forecasting and replenishment to become their primary focus, second only to improved customer analytics. This increased focus is due to two changes in the retail landscape: First, of course, is the fickle nature of consumer demand and, secondly, where that demand is triggered and where it is fulfilled may not be in the same place. These changes make using traditional forecasting methods a risky business.

But there have also been vast improvements promised by next generation forecasting systems. These improvements take advantage of valuable information coming from external sources such as competitive data, trade area data, consumer sentiment data, and even weather data.

That’s where artificial intelligence comes into the picture. AI can identify trends, extrapolate and learn from patterns in data and help retailers respond more nimbly to external factors that ultimately effect where inventory is positioned. Today’s faster and more powerful analytics powered by AI algorithms can help retailers understand in previously unheard-of ways the relationships between their products and customers.

Viewpoint: Demand Forecasting and AI

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    Hi, this is Brian Kilcourse with RSR Research. Retailers today are challenged by the increasing unpredictability of consumer demand and its inevitable result. Too much of the wrong inventory and too little of the right inventory in the wrong places. That concern has caused forecasting and replenishment to bubble near the top of retailers very important technologies, second only to improved customer analytics.

    The focus on forecasting is an outcome of two changes in the retail landscape. First, of course, is the fickle nature of consumer demand, and secondly, where that demand is triggered, and where it is fulfilled may not be in the same place, making use of same stores sales is the primary variable in calculating the new forecast to be a risky business.

    But there have also been vast improvements promised by next generation forecasting systems. Retailers seek to be able to take advantage of these new technologies to perform in-season demand re-forecasting. Retailers are also assigning a high value to new types of data coming from external sources. These are sources outside the operational databases that retailers have used in the past. Examples of this external data include such things as competitive data, trade area data, consumer sentiment data, and even weather data. All indications are that retailers want to improve their forecasts by understanding the patterns within that data that can affect demand.

    That’s where AI comes into the picture. Artificial intelligence has the ability to identify trends and extrapolate and learn from patterns and data that can help retailers to understand in previously unheard of ways the relationships between products that will improve their forecasts.

    Today’s faster and more powerful analytics powered by AI algorithms can improve the demand forecast, but the innovative technology can also help retailers respond more nimbly to external factors that ultimately effect where inventory is positioned. That of course is ultimately how retailers succeed, by having the right inventory in the right place, at the right time, and in the right quantity.