Sometimes it’s possible to predict a single outcome with reasonable certainty, as when a company has made similar decisions many times before. More often, decision makers can identify a range of possible outcomes, both for specific success factors and for the decision as a whole. Often they can also predict the probability of those outcomes. However, under conditions of uncertainty, it’s common for executives not to be able to specify the range of possible outcomes or their probability of occurring with any real precision (even in instances where they understand critical success factors and the model for success).
Ask Yourself: Can you define the range of outcomes that could result from your decision, both in the aggregate and for each critical success factor?
Can you gauge the probability of each outcome?
Choosing the Right Tools: Five Contexts
As the exhibit “Diagnosing Your Decision” suggests, the answers to the questions above will point you to the best decision-support tools. (For brief definitions of each, see “Decision Support Tools: A Glossary.”) In some cases you’ll need just one tool; in others you’ll need a combination. Many of these tools will be familiar. However, the tool we advocate using most, case-based decision analysis, is not yet widely used, partly because the more formal, rigorous versions of it are relatively new and partly because executives typically underestimate the degree of uncertainty they face. (For more on case-based analysis, see the sidebar “Developing Rigorous Analogies: An Underutilized Tool.”)
Diagnosing Your Decision
When choosing a decision support tool for a major investment, executives need to answer three questions:
• Do I know what it will take to succeed? (or, Do I have a full causal model?)
• Can I predict the range of possible outcomes?
• Do I need to aggregate information?
The answers will point to the best decision-support tools.