3 reasons AI and retail demand forecasting are just meant to be

Artificial intelligence enables comprehensive, responsive, future-looking demand forecasts for retail

AI has a long way to go in terms of uniform and consistent adoption. But it’s gaining ground in supply chain management, and in particular demand forecasting, where an endless loop of forecasting continually adjusts inventory levels. Applied correctly, AI alleviates inconsistent inventory buys, overstocking, understocking and consequent margin erosion. It also helps to create happy, loyal customers who keep returning due to more relevant assortments and new products, ultimately driving overall shopper satisfaction. In a recent interview, leading US wholesaler SpartanNash shared with us their experiences and lessons learned with AI for demand forecasting.

Here are three ways that AI can take retailer demand forecasting to the next level:

  1. Those who only pay attention to history are doomed to repeat it. Historical sales data, even when combined with seasonal data, is no longer enough. AI technology predicts sales and drives enhanced forecasts based on real-time data using internal and external influencers including demographics, weather, performance of similar items and even online reviews and social media.
  2. Put an end to the garbage-in-garbage-out syndrome. Using progressive analysis, AI and machine learning can identify and correct data errors and risks in the supply chain, elevate insights from devices in the field and plan logistics as well as notify users of potential pit falls. Organizations can then optimize delivery of merchandise while balancing supply and demand and keep the need for human analysis to a minimum.
  3. Know when, where and how it needs to be fulfilled. Issues stemming from inventory planning can have a profound effect on logistics operations. Multi-channel strategies beget struggles to manage the complex interactions between demand forecasting, orders, channel allocation and logistics as they attempt to respond to customer demand. Adoption of AI to analyze varied demand patterns and scenarios can provide an end-to-end view of the supply chain and become an important step in timely replenishment and efficient logistics.

AI is gaining an important place in retail and, driven by an increase in customer-centric initiatives, shows no sign of slowing down.

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