Consumption Intelligence Engine Using Clustering And Time Series Modelling

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American multinational food, snack and beverage corporation

Problem Statement

  • Lack of visibility into future consumption resulting in sub-optimal inventory management, often leading to overstocks/ understocks
  • Low volume of flavor shot add-ons – lost dollar opportunity

Solution Approach

  • Data Input

  • Model Dataset

  • Dynamic K-means clustering to identify store clusters with similar consumption

  • Time series forecasting for predicting consumption

  • Item-Item Collaborative filtering based recommendations

Business Impact

  • Forecast Accuracy across millions of transactions

  • Increase in flavor shot add-ons, generating incremental revenue

Critical Success Factors

  • Identification of key product opportunities
  • Inventory Management

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