How predictive analytics saves millions on logistics

Traditional supply planning systems lose out on the speed and accuracy of software demand forecasts based on Big Data. What has changed in the digitalization of logistics management and how do neural networks predict changing demand?

About the expert: Evgeny Nepeyvoda, co-founder and CEO of Novo BI, Skolkovo resident. Business is critically dependent on the exact timing of deliveries. OOS (out of stock) sounds almost like SOS. After all, the missed deadlines are: lost profits from lost sales; direct losses on penalties; damage to the company's reputation; loss of market share. The supplier is more likely to be forgiven for slightly higher prices and even problems with the quality of the goods than for the slightest violation of deadlines. And to observe them in the present conditions became even more difficult, than before. Due to coronavirus restrictions, the fall in the European freight market was 40%. Of course, such force majeure can not be predicted and taken into account in advance. But many other factors are subject to analysis, and thanks to modern IT solutions, the accuracy of demand forecasts has increased. The difference in the performance of old and new analysis technologies is too large to calculate the return on average gross values. Innovators are already getting superprivileged...