Terminal Volume Forecasting

August 12, 2020

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.
•  

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.