Prediction Markets and their Applications

September 19, 2008

Forecasting demand, especially for new products, has long been the weak spot for those of us interested in matching supply with demand. No matter the amount of market research a company does, forecasts are off target more frequently than we would like. While there exists a number of tools that support companies in planning for and absorbing this demand uncertainty (e.g. the Newsvendor model, models of risk pooling, reactive capacity), nothing solves this problem better than a good forecast.

Over the last decade, prediction markets have often been praised as the new way of creating demand forecasts. Prediction markets resemble betting on sports games: if you see that the betting return on $1 for one horse is $5 and on another horse is $50, you don’t need to be a jockey to figure out that the second horse is most likely a slower one. A fundamental advantage of a prediction market is that it implicitly puts a higher weight on those people’s opinion that have a stronger opinion. Ask 100 people how the weather will be tomorrow in Alaska. You could just take the average over the 100 forecasts that you get. But, chances are that many of the people you ask have little or no information about the weather in Alaska, while others are local experts. In a traditional forecasting approach, everyone is weighted equally. If, however, you ask people to place a bet about tomorrow’s weather in Alaska, you force them to “put their money to where their mouth is”. Those living in Florida, most likley, will not participate in this prediction market or place very small bets. In contrast, the meteoroligist living in Anchorage might be willing to bet a lot more. Since you, as the company who needs to forecast typically do not know who will have the best information for your forecasting problem, you should just let the (prediction) market decide.

Prediction markets have been used a lot for politics, including the US presidential elections (see e.g. However, as is discussed in a September 16 Wall Street Journal article, the number of corporate prediction markets is increasing. The article describes how Best Buy uses prediction markets to forecast the sales of new products and services. Every one of Best Buy’s 115,000 employees is allowed to participate. Yet, economic theory suggests that someone selling washing machines will be hesitant in placing bets on new lap-tops…

See WSJ, September 16: Best Buy Taps Prediction Market


Dell likely to shrink its network of factories

September 6, 2008

For years, Dell has been show-cased for its operational excellence. Because of its make-to-order production, the computer maker managed to work with very low inventory. And, because of its market power and close supply chain integration, it had an extremely short cash conversion cycle – in fact, for many of its products, Dell collected the money from its customers before paying its suppliers.

But, times are changing. In the computer industry, the make-to-order model works brilliantly for corporate customers who want their PCs built according to a very specific configuration. But the recent growth in the industry was fueled by consumers, who want cool notebook computers as part of their mobile lifestyle, most of them do not care if they run processor x123 or x124. In response to this change, Dell now sells a significant part of its computers through BestBuy and Wal-Mart.

However, if you purchase 500,000 computers to then ship them to Wal-Mart, the benefits of a flexible, US-based make-to-order production disappear. Cost is critical – customization is not. Hence, you might as well source the lap-tops in big batches from Taiwan.

Dell’s story reminds us that there exists no one-size-fits-all operation. As firms adjust their strategy in response to a changing world, the constantly have to rethink their operations. That’s what makes Operations Management interesting!

For more on Dell’s recent cost cutting, see: New York Times “Dell likely to shrink its network of factories” September 5, 2008