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Pharmaceutical Forecasting Fails

By APICS CEO Abe Eshkenazi, CSCP, CPA, CAE | 0 | 0 | October 18, 2013
In the scientific journal Nature, a study on the pharmaceutical industry showed that consensus forecasts were often substantially wrong. More than 60 percent of forecasts were over or under by 40 percent of the actual peak revenues. You might typically not think of the journal Nature, which reports on medical and scientific information, as a “go-to” publication for supply chain and operations professionals. At least, I don’t. But, an APICS staffer brought to my attention “Pharmaceutical Forecasting: Throwing Darts?” from the October issue. The article contains some essential APICS body of knowledge vocabulary: words such as forecasts, peak sales estimates, capital, and bias.

McKinsey analysts Myoung Cha, Bassel Rifai, and Pasha Sarraf studied how pharmaceutical forecasting incorporates the “vicissitudes of the external environment, uncertainty of drug development, and the unpredictable actions of competitors, weaving an array of scientific, clinical, regulators, and commercial data into a numerical representation of value for a drug.”

The answer: Not well. The study showed that consensus forecasts were often substantially wrong. More than 60 percent of the forecasts studied were over or under by 40 percent of the actual peak revenues. Common sense would suggest that drug forecasts improve over time once sales data are available. “Indeed, our analysis shows that there is a trend towards better forecasts once a drug is approved; however, there still appears to be a systemic variance in the forecasts, even years after the drug has been launched.”

The study’s other major finding was that large pharmaceutical manufacturers have better forecasts than smaller companies. “Although the average bias among estimates for drugs from large pharmaceutical companies is low, forecasts for drugs from small companies tend to overestimate peak sales by more than 30 percent.”

Of course, the message I hear often at APICS is “the forecast is always wrong.” That’s what the McKinsey researchers conclude too, but they declare that what is surprising is the magnitude and extent of the forecasting error. It is “striking and troubling as it suggests a large-scale and systemic misallocation of capital and destruction of value in the industry.”

The researchers offer the following suggestions based on their analysis:
  • Beware the wisdom of the crowd; the “consensus” means simply that more people are wrong.
  • Expand your ideas of what the future could hold, and learn to quickly adapt to new scenarios. 
  • Assess your capabilities and make honest evaluations.
Improving your forecast

As the Nature study demonstrates, forecasting can be a challenge, but there also is significant opportunity for companies to optimize it for competitive advantage. Consider the definition of forecast from the newly released APICS Dictionary, 14th edition: “An estimate of future demand. A forecast can be construed using quantitative methods, qualitative methods, or a combination of methods, and it can be based on extrinsic (external) or intrinsic (internal) factors.”

Is your company using its forecast to maximize competitive advantage? APICS is proud to work with the Institute of Business Forecasting and Planning to present the Best of the Best S&OP Conference Europe, May 15–16, 2014, in Amsterdam, Netherlands, and Best of the Best S&OP Conference, June 12–13, 2014, in Chicago, Illinois. Registration for both events opens in November. Go to apics.org for more information.

APICS members may now request their free copy of the APICS Dictionary, 14th edition, by visiting apics.org/freedictionary and completing the form. Dictionaries will become available for purchase and shipment on Monday, October 28.

QUESTIONS FOR DISCUSSION

What could account for the big differences between the forecast and actual sales in pharmaceuticals? Why might forecasts still be inaccurate years after the product has been released?
Is the forecast “always wrong?” How well do you trust your forecasts? What steps might you take to improve forecast accuracy in your supply chain?

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