We employed our state-of-the-art Deep Learning based Lummetry LENS+ solution to predict sales at Store-SKU level and offer preventive out-of-stock (OOS) alerts taking into account the lead time.
Context
Studies say that retailers face losses up to $1 trillion due to out-of-stock issue. Current statistical based solutions fail to deliver up to the expectations and state-of-the-art research papers prove that Artificial Intelligence and Deep Learning in particular already have a great positive impact on improving the sales forecasting and OOS predictive alerts.
Reckitt Benckiser is a world-wide leader in the home and hygiene sector with a huge base of retail sellers (large and small) and a significant number of SKUs. Thus, they are negatively impacted by the OOS too, consumers choosing a product from the competition in case they do not find the product from RB that they were looking for.
Challenge
Improve the current accuracy of sales forecasting at the Store-SKU level and correlate the predictions with the initial stock and planned replenishment in order to provide better OOS preventive alerts to the merchandisers and distributors.
Solution
Lummetry LENS+ has complex Deep Learning models capable of analyzing all time-series based buying patterns of each SKU at a store level. Once the predictions are validated with the business team, by either the backtesting method or deliver predictions for the next X days and wait for the reality to happen in order to calculate the prediction error, we then add information about initial stock level at a day level and information about the planned replenishment for each SKU for the next X days. Next, Lummetry LENS+ processes all the info and triggers preventive OOS alerts according to the lead time for each SKU.
Results
Our solution managed to achieve better accuracy than internal benchmark, depending on the SKUs predictability and data availability for each timeseries