Demand risk is implicit to manufacturing businesses, but for high-tech firms it poses a particularly strong threat. As product lifecycles shrink and new generations of technology enter the market more quickly, achieving strong top- and bottom-line results hinges on estimating overall demand and product mix as accurately as possible. Products with manufacturing lead times of months or even quarters are all the more critical to forecast correctly because last-minute inventory adjustments are limited or sometimes just not feasible. Our study of multiple generations of product transitions discovered that producing high-quality demand forecasts is difficult to achieve consistently and that mistakes can be quite costly [1].

Managing demand risk is critical to Intel's success, but it is only one of many business challenges the company faces. Across the organization, teams grapple with questions such as how many units of products x, y, and z customers will demand at certain prices, how much factory capacity should be funded, which products should be brought to market, which features and technologies should be included in new products, and when new products will be ready for production and distribution. Interviews with employees trying to answer these questions reveal a common issue: belief that they do not have the best available information and insight to guide business decisions.

Tackling demand risk and other challenges requires moving information around decentralized organizations in new ways. If employees across Intel's many functional groups have information and insights that can help inform our planning and forecasting decisions, we need a way to aggregate that information and turn it into intelligence. Prediction markets are a potential solution to this problem and have been written about extensively for the past five to ten years. Our research discovered that, despite the buzz around prediction markets, the integration of prediction markets and similar Information Aggregation Mechanisms (IAMs) into organizational forecasting processes is still in its infancy. Popular stories on prediction markets still frame the potential as being greater than the demonstrated value, and reports of usage at companies such as Hewlett Packard, Microsoft, Google, Eli Lilly, and others suggest that application is often viewed as experimental and that markets are largely separate from other organizational forecasting processes [2, 3].

While our research of prediction markets is growing to explore more business problems over time, the area we first tackled at Intel is demand forecasting. We have developed and piloted an IAM that is integrated into our regular forecasting processes and, through this development, have considered many questions and ideas about designing markets real companies can use to address real problems. It will take extensive research and experimentation to answer these design questions, but we are encouraged that even trial solutions based on the experience of other researchers, feedback from our business partners, and our own intuition are producing good results.