Product line demand forecasting stands as the proto-typical application for internal prediction markets. Internal prediction markets may have other uses, but the demand forecasting story is probably the most straightforward and has been most often discussed in articles on the topic.

When software vendors and consultants try to sell prediction market systems to business, both the successes and the failures of existing forecasting systems likely stand as barriers. If the company’s current system is seen as successful, the company will have little motivation to change. If the company’s most recent million dollar system is a disaster, they will hesitate to leap into something new. I guess that leaves the moderately dissatisfied and mildly happy folks as likely sales targets.

An article in CIO highlights a demand forecasting disaster at Nike and discusses ways in which companies have adapted demand forecasting systems into business plans. The article was first published in 2003, so it is a few years old, but it stresses an important point – that a good system involves both software and people.

It’s been more than two years since Nike Chairman Phil Knight owned up to the sneaker giant’s disastrous $400 million experiment with demand forecasting software. The headlines are well known: Nike went live with its much-vaunted i2 system in June 2000, and nine months later, its executives acknowledged that they would be taking a major inventory write-off because the forecasts from the automated system had been so inaccurate. With that announcement in February 2001, Nike’s stock value plummeted, along with its reputation as an innovative user of technology.

… Nike isn’t the only company with a forecasting horror story. Corporate America is littered with companies that invested heavily in demand software but have little or nothing to show for it.

… Yet vendors and academics are still pushing forecasting software. In 2002 alone, companies spent $19 billion on demand forecasting software and other supply chain solutions, according to IDC (a sister company to CIO’s publisher). And in a speech in February, Stanford University supply chain guru Hau Lee extolled the virtues of harnessing software to extract customer knowledge in order to forecast demand.

Many CIOs, however, remain skeptical. Privately, members of Lee’s audience complained to a reporter present that the ability to accurately forecast could hardly be taken for granted. And according to a recent Booz, Allen &amp- Hamilton survey of 196 senior executives, 45 percent said that supply chain technology in general had failed to meet their expectations. More than half—56 percent—blamed the shortcoming squarely on demand forecasting software.

Of course it is easier to blame the software than to blame the humans in the systems, but the article suggests that any good system will need both.

Even forecasts that are made with a limited number of variables and with accurate data will be off. They still make the fundamental assumption that what was true yesterday will be true tomorrow. But because the data about a change lags behind the change itself, it takes human market watchers to note business climate alterations.

… &#8220-When the future doesn’t resemble the past, none of this forecasting software works well,&#8221- [Vicor CIO Doug] Richardson says.

… The mishap taught Vicor the necessity of factoring human intelligence into its forecasts. In order to make sure that it isn’t caught off guard again, the company set up a dual forecasting process in which the sales department comes up with a forecast and the computer system, which was upgraded a year ago, makes another. The two are complementary- the sales department is too conservative with its forecasts (Richardson thinks the salespeople are merely cautious- a cynic might point out that they are compensated for selling above quota).

The article discusses several other cases as well.

(Vicor adopted a forecasting system from Smart Software, and is now featured as a customer testimonial on the Smart Software site. Presumably, they&#8217-re happier with their new system.)