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Inventory Forecasting and Improvement

About the Customer

North American Auto major

Business Problem

  • Lack of visibility into inventory and consumption patterns
  • Lack of item level forecast data, which lead to incorrect assessment of inventory
  • Low forecast accuracy and naïve forecasting methods for spare parts

The Solution

Our AI-based Inventory Forecasting model, which helped in analysing massive data sets for pattern identification, to better plan inventory forecasting and improve accuracy.

Solution Description

The solution helped in:

  • Identifying and analysing existing data sets for patterns in multiple aspects like item consumption and spare part cost, frequency of order, demand changes and lead time.
  • Designing and developing best fit model with lowest weightage average forecasted error
  • Identifying relevant qualitative measures
  • Performing judgmental/qualitative forecasting to improve accuracy

Value Delivered

  • Performed quantitative and qualitative forecast to improve accuracy for class A and class C items
  • Improved forecast accuracy by significant levels through the identification of relevant qualitative & quantitative parameters
  • More than 90% accuracy of 40% spare parts
  • More than 70% accuracy of 94% spare parts

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