Client Profile: Global hardware giant in Bangalore, with massive supply chain across the globe (2017-18)

Business Results: Savings of around $ 30 million annually, on purchase of various material(OEM/commodity) and with large variation in purchase rate, across and within vendors.

Technical Areas: Data collation, Regression, Decision Tree (didn’t succeed), Random forest(succeeded).

Main Solution steps:

  1. Analysing data pivoted on < Given vendor, given product, given data/time range, country, unit price, cost of purchase> to examine the average cost of purchase per unit – min unit cost within six months
  2. Predictive aspects: Predicting sale price offered by various suppliers on products against their supplied minimum price /unit.
  3. Interventions: Arranging alternatives <confidential>