Demand forecasting in supermarkets (part. 2)
The techie bit
This is where machine learning and, in particular, a branch of machine learning, called deep learning, comes in. Specialised artificial intelligence models, they are designed to deal with vast amounts of data. As opposed to the static traditional methods, these algorithms respond dynamically to changes in the data and get better the more data becomes available. Properly built, they can find meaningful insights in an ocean of data.
At Neurolabs we focus extensively on developing cutting edge deep learning algorithms to predict future demand for retailers. Our edge consists of augmenting customer’s internal data with vast amounts of relevant external data. Specifically, we look at more than 35 external factors that can influence future demand. Amongst these, there are some obvious ones (e.g. seasonality, weather) but also some less obvious ones that are more difficult to adjust for via traditional methods (e.g. competitor proximity, consumer trends, competitor prices/promotions, social activity etc.). Technically, we managed to show significant additional improvement in terms of the accuracy of the predictions when relevant external data was taken into account.
Our technical expertise, combined with our business acumen, allows us to meet and surpass our clients’ expectations in terms of delivered results.
We tested our algorithms working with a top supermarket chain in Southern Europe. Running across a large and diverse set of different stores (81 stores) and products (14,000 unique SKUs), our solution delivered massive improvements in terms of forecasting accuracy compared to the supermarket chain’s existing baseline method. More importantly, we managed to bring the average stockout rate down by 3% and reduce the average amount of overstocking by up to 40%. Were the supermarket to rely fully on our predictions for a period of 3 months and always order as much inventory as predicted by our solution, the profit margins would have increased by up to 19% for most products. Additionally, the food waste bill would have been 7 figure lower during the same period of time.
In practice, however, there are various barriers to fully unlock this margin potential, such as a disconnected or constrained supply chain. Regardless, we managed to show the asymmetrical business impact of our solution: a small investment in the existing inventory management system can pay off handsomely. Technically, we succeeded in showing the importance of incorporating external data in demand forecasting, as can be seen in our demo page. Moreover, such results can be achieved while always aligning with supermarkets’ top priorities: security and control of data. The future will always be uncertain, but for supermarkets partnering with Neurolabs it can be less uncertain and less costly, unleashing enormous benefits.