Terminal Volume Forecasting

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Leading Player in Oil Processing Industry

Problem Statement

  • In-house forecasting of products was not giving accurate information; client wanted an accurate forecasting model.
  • Organization wanted assistance in understanding the appropriate KPI’s / metrics that have a direct influencing impact on the petroleum sales
  • Organization also wanted an forecasting model which will forecast at a Customer / Channel / Terminal level; to plan the inventory.

Analytics Led Approach

  • Selection of Response variables based on Statistical Tests.
  • Understand and identify the key KPI’s for each segment
  • SPSS to analyze data using Statistical techniques, user friendly (Multiple Regression Analysis)
  • Correlation Analysis to identify the relationship.
  • Regression Analysis to predict / forecast.
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Business Impact

  • Pricing – Based on the existing / recent demand, pricing can be done without any intuition (By an Account Manager)
  • Trend Analysis. And structured steps to understand the relationships between multiple fields in the datasets.

Critical Success Factors

  • Channel Behavior – Depicts the prevailing fluctuation and also helps us in tracking the product at a channel level.
  • Pantry Loading – Over loading of products can be avoided and Inventory can be planned accurately to meet demand.

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