Commodity Intelligence - Resins (Fortune 500 Technology Company)

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In this case study learn how BRIDGEi2i helped a Fortune 500 Technology company to develop a Price Forecasting algorithm for Resins as a commodity to help understand forward-looking trends in the market and to use these forecasts to create more accurate budgets for the commodity.

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Commodity Intelligence - Resins (Fortune 500 Technology Company)

  1. 1. A Case Study in Commodity Intelligence - Resins A Fortune 500 Technology Company Quick Context Objective a. Resins are highly price volatile commodities with wide industry applications b. Their prices depend on Crude Oil and various other industry factors • 85% forecast accuracy 6 months out Impact • BRIDGEi2i has developed well- researched and proven frameworks for price risk management • Our understanding of chief commodities and its drivers helps clients realize quick value Key Success Elements Our Approach 2 Years 3 Years Client Project length Length of relationship with client • Resins are derivatives of crude and depend on several feedstock materials • Historical Buy Prices were accessed from Cleint Cost Management Systems • Price History of ABS (resins) and the feedstock was acquired from the spot market • Demand and Supply outlook was ascertained from market reports and social media • 4 families of statistical models were used to resolve the lead-lag relationship between Crude oil, feedstock and ABS resin prices • The models were ensembled using BRIDGEi2i’s REACT** framework • Finally, an accurate 12 month rolling forecast is generated by the model along with accuracy evaluation metrics • Resin prices are driven by several macro-economic factors simultaneously – not just crude oil • However, Crude Oil prices are a good 4-5month leading indicator of ABS prices • Butadiene leads by 2 months, Styrene by 3 months • The World Business Confidence Index is a good trend indicator for Resins • The Chinese Consumer Confidence Index and Industrial production Index are good indicators for movements up to $100/T Data Management Algorithmic Play Operationalization a. To develop a Price Forecasting algorithm for Resins as a commodity to help understand forward-looking trends in the market b. To use these forecasts to create more accurate budgets for the commodity * REACT – REcursive ACcuracy Testing

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