Putting Science into the Art of Decision Making

One of the most difficult aspects of being at the top of any business organization is making the hard decisions. How can you choose between two business opportunities that both sound attractive? Should you spend scarce resources to develop a potential product, or are you unlikely to earn back your investment? These are the kinds of decisions that don’t lend themselves to, say, simply drawing a line down the middle of a page and listing the pros and cons of a pending decision on either side of the line.

The good news is that there are more sophisticated techniques that will help you put some science into the art of decision making. You don’t always need to rely on “entrepreneurial instinct” or your “gut” when trying to decide what to do.

Academic disciplines such as operations research and management science have examined and developed different decision-making methods. We’re going to take a look at one of the better-known methods, the decision tree method.

Basically a decision tree uses a graph or a model of decisions, their consequences (cost, time, etc.) and their possible outcomes. It’s a useful method because it’s fairly straightforward and easy to do, and gives the user a graphic representation of the risks and rewards of following various options. However, to use the decision tree method requires business owners to 1) be clear about the various alternative paths that are open to them, 2) estimate what are the possible outcomes that can result from each of the alternative paths they choose, including a probability for each, and 3) estimate a dollar value for each possible outcome.

The real payoff of a decision tree comes from calculating the Expected Value for each of the possible decisions, and then comparing the possibilities to see which is the most lucrative. As a simple example, if a friend tells you he will give you one dollar if a coin flip results in “heads,” and five dollars if it results in “tails,” your expected value is three dollars, i.e.,

$3 = ($1 x 50%) + ($5 x 50%)

It’s important to remember that decisionmaking techniques, including this one, are simply exercises to help you analyze a situation and make a decision. They shouldn’t make the decision for you. Probably the best way to show how this would be used by business owners is to look at a few situations where the decision tree method could be employed.

Software Upgrade Decision

Imagine a small business owner who has to upgrade the enterprise software used to run his manufacturing business. He does not have a full-time in-house information technology department to use as a resource, and he is aware from speaking to other users who have attempted the upgrade that it is not an easy task.

The new version of the enterprise software has added functionality that will allow the business owner to more efficiently order raw material and schedule production jobs, for an annual savings of $160,000. Hiring an outside IT consultant to implement the software upgrade would cost the business owner $50,000, and the consultant guarantees that he will successfully complete the upgrade, given that he has had a 100% track record with other clients on this particular upgrade. But the business owner finds this galling, as he feels it would let the software manufacturer off the hook for the after-sales support it should be providing. However, the IT consultant could complete this project in three months, which would result in nine months’ worth of year one savings, or $120,000.

If he does not hire the outside IT consultant, the business owner would rely on his engineering manager, who has some systems experience, to implement the upgrade. The engineering manager can clear time on his schedule by delegating other projects to his assistant and an intern, but he is not at all sure of the ultimate likelihood of success if he were to manage the upgrade. If he is successful in implementing the upgrade, it would take him six months — twice as long as the outside IT consultant is promising — given his need to first attend some training sessions on the software provided by the manufacturer. As a result, there would be only six months of savings from the project in year one, or $80,000.

If the engineering manager is asked to try the upgrade and is unsuccessful, that outcome would not be known until the end of the six months, at which time the business owner would turn to the outside IT consultant to do the upgrade, which would require an additional three months. In this scenario, there would be only three months’ savings from the software upgrade in year one, or $40,000.