Demand forecasting in supermarkets (part. 1)

The Problem

Striking a good balance between supply and demand has always caused massive headaches for retailers. How much inventory is enough to have on hand to accommodate future sales? Accurate demand forecasting removes the uncertainty and puts retailers one step ahead of the curve. Some organizations are antifragile, whereby they thrive from uncertainty. This is the case of information security companies that become more resilient as they face hackers. However, less agile companies, which is the case for retailers, and in particular supermarkets, are fragile to the uncertainty of future demand. This prevents them from being proactive, worsens profits and hurts the cohesion of their business. We highlight here some of the negative implications of lack of accurate forecasts.


By far the most negative one is stockouts, that is, empty shelves. On average, at any point across the world, around 8% of supermarket shelves are empty. Missing sales leads to lower profit margins, while brands take a hit as well in terms of customer preferences.  At the other end of the spectrum, there is overstocking. Too much inventory means blocked stock and for perishable items this often leads to waste. In business language, it means higher operational costs and additional pressure on cash flows. The end result is similar to stockouts: lower margins.

Food waste, apart from eating away a yearly sum of roughly 200 million USD from supermarkets, it’s a major source of environmental damage. So much so, that in 2015, the United Nations General Assembly made it one of its goals to address irresponsible food consumption. Some countries (France, Italy, UK) were quick in passing laws against food waste by supermarkets, adding additional pressure on supermarkets to stock adequately (compare that to the 19th century, when there was almost no food waste since everything was produced and consumed locally).


Traditionally, supermarkets rely solely on past history, human input, and gut-feeling (heuristics) for future demand projections. At most, traditional statistical models are employed in this process. However, the forecast accuracy remains disappointing. That’s mainly because so many things change from year to year. Over-reliance on past sales gives on to repeating past mistakes. Subtle things such as the effect of promotions on demand, the effect of new products on the sales of other products or the importance of external data (geography, competition, economy, social activity etc.) fail to be captured by traditional approaches. To make things even worse, demand forecasting needs to be done at store level, which further increases the complexity of the problem.

This is where Neurolabs’ solution comes in, by leveraging the power of specialized machine learning models and deep learning to integrate all the above factors (and many more) and increase the accuracy of demand forecasting.