Intel completed a study of several generations of products to learn how product forecasts and plans are managed, how demand risks manifest themselves, and how business processes contend with, and sometimes contribute to, demand risk. The study identified one critical area prone to breakdown: the aggregation of market insight from customers. Information collected from customers and then rolled up through sales, marketing, and business planning teams is often biased, and it can lead to inaccurate forecasts, as evidenced by historical results.

A research effort launched in 2005 sought to introduce new methodologies that might help crack the bias in demand signals. We worked with our academic partners to develop a new application, a form of prediction market, integrated with Intel's regular short-term forecasting processes. The process enables product and market experts to dynamically negotiate product forecasts in an environment offering anonymity and performance-based incentives. To the extent these conditions curb bias and motivate improved performance, the system should alleviate demand miscalls that have resulted in inventory surpluses or shortages in the past. Results of early experiments suggest that market-developed forecasts are meeting or beating traditional forecasts in terms of increased accuracy and decreased volatility, while responding well to demand shifts. In addition, the new process is training Intel's experts to improve their use and interpretation of information.