Deciding which R&D ideas to invest in and how to prioritize product portfolio opportunities is difficult.  CTOs of several large companies have actually reported that R&D performance increases with cuts in budgets (TI, Pfizer).  However, we have often discussed the problems with reliance on gut feelings or irrationality of decision making.  Here is an interesting article from the Harvard Business Review about The Big Idea: Before You Make That Big Decision…:

Thanks to a slew of popular new books, many executives today realize how biases can distort reasoning in business. Confirmation bias, for instance, leads people to ignore evidence that contradicts their preconceived notions. Anchoring causes them to weigh one piece of information too heavily in making decisions; loss aversion makes them too cautious. In our experience, however, awareness of the effects of biases has done little to improve the quality of business decisions at either the individual or the organizational level.

Clearly, some intuition will always be necessary to make decisions about the future. However, should we try to minimize the impact of biases in our decision making?

Though there may now be far more talk of biases among managers, talk alone will not eliminate them. But it is possible to take steps to counteract them. A recent McKinsey study of more than 1,000 major business investments showed that when organizations worked at reducing the effect of bias in their decision-making processes, they achieved returns up to seven percentage points higher.

Cost of questioning and examining decision-making can be large. The article has some great pointers about when and how to dig into decisions.  Here is my version of a checklist based on the article to eliminate decision bias. (as you know, I love checklists):

1. Is the decision sufficiently large to warrant an evaluation of bias? (do not question all decisions)
2. Is there a  reason to suspect the self-interest bias in the team making the recommendation? (do a thorough review)
3. Has the team has clearly fallen in love with its proposal and not evaluated other options? (Are credible alternatives included along with the recommendation?
4. If there seems to be Groupthink because dissenting views were not solicited or explored.  (Solicit dissenting views, discreetly if necessary).
5. Are the people making the recommendation overly attached to past decisions?
1. Is the team is relying mainly on a memorable success?
2. Is the team assuming that a person, organization, or approach that is successful in one area will be just as successful in another?
6. Do you know where the numbers came from? Can you get better numbers / results from other models?
1. If you had to make this decision again in a year’s time, what information would you want, and can you get more of it now? 
2. Is the base case overly optimistic?
3. Is the worst case bad enough?
4. Is the recommending team overly cautious?