Case Study

Reducing Out-of-stock Issues through Improved Forecasting
in an Omnichannel environment

Business Objective

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

Solution Methodology 


  • Conducted design sessions to help identify opportunities for tangible action based on insights
  • Collating and integrating data from multiple sources – POS, Inventory, Pricing, Promo, Seasonality
  • Exploring the data quality and understanding multiple factors (promotions, activations) that impact sales
  • Advanced analytics-based modeling approaches at varying granularity to forecast sales considering the complexity of cohorts.
  • Developed tools to drive activation by leveraging key KPIs like Inventory, OOS, lost $, safety Stock, Phantom

Business Impact

  • Total estimated ~$20 Mn lost $ historically, which can be avoided in future with necessary actions at Store Level over next 2 years for a given retailer
  • 1.5% revenue uplift
  • Proactive inventory planning specially during seasonal periods.
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