Learning by doing: new New Product Planning...

Which of these are the most predictable? Biology, clinical trial outcomes, market performance (for a product in pre-launch), regulatory approval?

Most of our new product planning processes are built on the idea that each is, to an extent, predictable. However, none of us, for a moment, really considers that we have any degree of accuracy in our predictions (does anyone believe that the 'expected' NPV is expected?).

I suspect that we all believe that biology and clinical outcomes are the least predictable. However, as they are the basis for the other predictions, any error there is amplified through the system. And that is a huge error.

If we believed that the best AI, the best translational science minds, and the best Key External Experts could predict a molecule's effect in a disease with any validity, we would head straight to phase III to generate the requisite evidence. But we don't believe that. Our efforts to improve early phase are, at best, incremental improvements in probability, from astonishingly low to astonishingly low plus a few percent.

We believe in learning by doing, by laying out hypotheses and testing them. So, asymmetric learning applies - if you learn faster than your competition, you may establish opportunity. All of the ways to improve prediction of biology and clinical outcome in early phase should contribute to hypothesis generation, not conclusions.

So, a New Product Planning function that embraces Asymmetric Learning would generate market hypotheses, not forecasts. It is significantly more helpful to quickly and roughly indicate the attractiveness of a range of market positions than to pretend to accurately forecast the value of one. 95% of what is needed to be known about an opportunity should be at hand - a rating of attractiveness can be almost immediate given a handful of variables (addressable market, treatment days, price, market share). Far better to be helpful while alternative scenarios are being considered than to try to generate a number with any decimal places. Even better would be to plan to execute quickly in the presence of new, unexpected, information that shines a light on an opportunity (Viagra, Herceptin, Ocrevus...).

If we stop pretending that the TPP is accurate, and accept that the probabilities that surround it are of low validity, a New Product Planning process has to embrace the uncertainty as an opportunity, not as a problem. Unfortunately, this is the exact opposite of how it is implemented in 99% of companies. Those companies that understand this first will benefit from the asymmetry.