Intelligent Predictive Analysis for Commodities (IPAC)

The Intelligent Predictive Analytics for Commodities (IPAC) Project will demonstrate a firstto-market prediction-based data analytics capability for the managing of procurementpurchases with insight on the source prices of commodities and raw materials. This will beachieved by applying novel data mining techniques to an extensive archive of commoditiesprices and contextual data to establish trend characteristics. A key capability will be the priceprediction for combinations of commodities and raw materials to identify the lowest, combined price.The innovation in the IPAC Project is based on unique:a) Trend-based data analytics to provide accurate and timely predictions on the expectedvariations of the prices of commodities and raw materials;b) ‘What If …’ analysis capability from the aggregation of information from a significant dataarchive with live information on prices combined with a probabilistic based predictioncapability;c) Visualisation algorithms that process the output from the predictive trend algorithms andrender the information in forms that can be easily manipulated by a user to enable useful‘What if…’ analyses.A recent state-of-the-art product evaluation has shown there is no other commercial solutionproviding a predictive-based pricing of commodities and raw materials. The benefits for userstherefore are:a) Improved understanding of commodities and raw materials prices enabling subscribers tosignificantly decrease their procurements costs;b) Subscribers will be able to undertake more sophisticated price analysis strategies byidentifying the consequences of combinations of aggregated commodity and raw materialscombinations. This will enable better planning of procurement activities.The key outcome will be a prototype demonstrator produced and evaluated to confirm theapproach and to ensure the required predictive functionality and performance is achieved.Economic benefit to both subscribers and consumers should follow.