FOLLOWING THE BASICS ENHANCES FORECASTING
In 1997, Centocor had a promising drug in Phase II trials and a pharmaceutical company, Schering-Plough, interested in a deal to market it internationally. The only problem was that the drug, a biologic aimed at Crohn's disease and rheumatoid arthritis, would be first in the space and thus no historical data existed about the size of the potential market. That number, of course, was key to the company's future.
"The historical research wasn't very useful, so we needed new information," says James Schoeneck, then general manager of the immunology business unit at Centocor and a key negotiator in the deal. Enter forecasting. Centocor did its information gathering and modeling, and entered negotiations projecting an approximate $1 billion international market by 2007. Schering-Plough, however, predicted annual sales of around $300 million.
While the two companies were far apart in their forecasts, Schoeneck's team was confident that it had done a thorough job getting at the assumptions, and it stuck with its prediction. Rather than negotiating based on a compromised number, or walking away from the deal, Centocor structured the deal to reflect its team's confidence. The company took a lower percentage on sales up to the number Schering-Plough was predicting, but would then get a percentage bump above it. "We knew if we were right, we would have a lot of money coming in," says Schoeneck. "Yes, we would lose something" if Centorcor's number was wrong, he says, but Schoneck and his colleagues focused on the upside.
The company's confidence was eventually validated: In 1999, Johnson & Johnson acquired Centocor, and Remicade, the monoclonal antibody that had been the subject of the earlier negotiations, reached annual sales of just under $942 million outside the United States in 2005. More than using complex models to get an accurate forecast, Centocor depended on following some forecasting fundamentals.
Five Steps To Good Forecasts
1. Anchor your assumptions.
Gathering information is the most important part of building a forecast. Before entering the meeting room with their pharmaceutical suitor Schering-Plough, Schoeneck, who is now CEO of San Diego-based BrainCells, and his team searched for data that they could confidently put into their forecast model. On the treatment side, they hired a marketing firm to conduct a survey of 500 rheumatologists and gastroenterologists. To help predict how much they could charge for their drug, the team commissioned a similar survey of payers and managed-care organizations.
2. Offer a clear link to sources of data.
Sources of data should be clear and made visible to every key member of your company. Then, everybody on the team can probe the assumptions. "The forecast is only as good as your assumptions," says Schoeneck. In negotiations, offer up this transparency. "Both sides should have a transparent forecast structure and [should] share; otherwise you have a ‘my dad is bigger than your dad' argument, and you need a meeting of the minds," says Martin Joseph, head of information management and forecasting for global operations at AstraZeneca in Chesire, UK.
3. Focus on the process.
A forecast's true value can come from how it's created. "The emphasis should be what you can learn from the process," says James Stutz, director of corporate development at InterMune in Brisbane, Calif. Gaining consensus throughout the organization makes up the heart of a forecast. Bring in different parts of the organization, have them contribute their assumptions, and then drill down on all the assumptions, says Stutz. Some companies, such as Aspreva Pharmaceuticals in Victoria, BC, have a dedicated forecaster to lead this effort. Others rely on the CEO and other executives to do the work.
4. Use the right person to build the forecast.
The job of the person leading the consensus meetings for data review is to "keep everybody honest," says Daniel Kiely, senior manager of strategic forecasting and market analysis for Celgene in Summit, NJ. To do so, says Kiely and others, the forecaster needs technical skills in statistical modeling and the ability to facilitate meetings and gain consensus among various functional areas. At larger companies, different product teams compete for resources; they know a bigger number is better, says Joseph. It is the leader's job to ferret out bias and remove it.
5. Don't stop.
When events change, underlying assumptions driving a forecast will change, and the numbers will need to be updated. However, the process must not be merely events-driven, say forecasters. Rather, it needs to be ongoing; some experts suggest monthly meetings to review the forecast.
