A Data science & RPA based process to gain substancial savings on raw material purchase

  • Practice
  • Team

1 architect, 1 Data engineer, 2 Datascientists, 2 Business experts

  • Technical environment
10 M€
savings expected per year.
7 M€
savings measured on 1st year
other business cases to be delivered on the same framework
  • Sources around 700-800M€ of plastic yearly, mainly polyethylene  (as P.E.T.)
  • Plastic price is volatile, strongly correlated with oil price, leading to high impact on P&L and product margin
  • Current process to determine purchase policy is long, manual, lacks of efficiency
  • Objectives : take better buying & hedging decisions .
  • Busines Case Estimated gain at 10M€/yr
  • Automate market data collection on referent sites (+20)
  • Standardize and cleanse data for compliance and consistency
  • Build a Datawarehouse for all these values
  • Select and parameterize the datascience algorithms best suited to determining prices and forecasting trends
  • Have the approach and calculations validated to gain the confidence of the business lines
  • Systematically present the results in forms adapted to the culture and needs of the business lines.
  • On-time and trustable insight on the raw material market
  • Capability to quickly extend the model to new sources for PET, but to other raw materials as well
  • Strong Machine Learning capabilities through XGBoost algorithm
  • Historical and comparable database for pricing knowledge and tracking
  • UX oriented presentations for a common and trustable signe view of prices truth
  • Estimated gain of 7M€ in the first year