Pricing Intelligence - DRAMs (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 Memory as a commodity to understand forward-looking trends in the market and to use these forecasts to create more accurate budgets for the commodity.

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Pricing Intelligence - DRAMs (Fortune 500 Technology Company)

  1. 1. A Case Study in Pricing Intelligence - DRAMs A Fortune 500 Technology Company Quick Context Objective a. Memory is a highly price-volatile and high-spend commodity for Technology companies b. It causes major PPV** in Procurement budgets due to its volatile prices • 93% price forecast accuracy 6 months out • Several million $$ saved due to a forward-buy decision based on these forecasts 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 • Data was securely accessed from client Cost Management Systems • Historical buy-price data for commodity • Spot market prices from DRAM Exchange • Market reports from multiple industry watchers – inSpectrum, Market View, Gartner etc. • Planned demand volumes • Set-up the multi-variate forecasting models for buy-price with identified drivers and an innovation effect due to spot market speculations • Develop price forecasting models using VAR, VECM and Bayesian models (available in SAS) • Profile price forecasting accuracy and track based on REACT (recursive accuracy testing) framework • Dashboard to track Track the drivers’ influence regularly to estimate model maintenance schedules • An accurate memory price forecasting model – especially to predict inflexion points in prices • ~93% accuracy 3 months out and >85% 6 months out • Low-touch, self-learning models Data Management Algorithmic Play Operationalization a. To develop a Price Forecasting algorithm for Memory 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

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