Our client is a global CPG major. The client wanted to create omnichannel forecasts essential to meet the ever-changing demands online (Delivery and Pick Up in-store). The key step of the journey is to understand the root cause of common inventory-related issues in store and enable decision-making by identifying gaps.
The main objective was to-
- Perform robust forecasting & safety stock measurement to optimize the minimum stock levels
- Identify phantom issues & minimize loss
- Overlay service-level issues to identify the additional root causes of OOS
- Sparseness of data for low selling UPCs X Stores makes it hard to estimate the future sales
- Adjusting for Impact of Covid in the training period data by using external data like Mobility Index
- Scale of Training & data processing was extremely high (300K+ time series) which required creative ways to optimize & execute the experiments