For example, it is natural to fixate on the analogous situation that best supports the action you would like to take, ignoring other cases that might provide a broader picture of possible strategies and their outcomes. Case-based decision making provides a structured framework for synthesizing information from multiple analogous experiences and examples. Even when decision makers don’t know the exact relationship between critical success factors and outcomes, they can use this method to learn from past successes and failures.
These methods require decision makers to collect a sample of analogous cases, determine the results achieved in those cases, and assess how similar each case is to the decision at hand. The best decision, then, is the one that maximizes the similarity-weighted average of results in the analogous cases. Sometimes the analogy is close to home: Movie producers can compare a project with similar projects from the past; serial acquirers can do the same. Decision makers on less familiar terrain must look to other industries for comparisons, and those comparisons will take more ingenuity. (Here it’s essential to use structured frameworks for comparison.) Consumer product industries facing digital disruption might look to the unbundling of music and books as an analogy. A company shifting from a product-based to a service-based business model might look at IT companies that have made this shift. When decision makers can’t build their own causal models of success, the best they can do is study the successes and failures of others in analogous situations, putting greater weight on the analogies that best fit their own situation.
To illustrate, let’s look at five scenarios that executives at McDonald’s might face. (These are oversimplified for the sake of clarity.)
Situation 1: You understand your causal model and can predict the outcome of your decision with reasonable certainty. Suppose McDonald’s executives must decide where to locate new U.S. restaurants. The company has or can get all the information it needs to be reasonably certain how a given location will perform. First, it knows the variables that matter for success: local demographics, traffic patterns, real estate availability and prices, and locations of competitive outlets. Second, it has or can obtain rich data sources on those variables. And third, it has well-calibrated restaurant revenue and cost models. Together that information constitutes a causal model. Decision makers can feed the information about traffic and other variables into standard discounted cash flow models to accurately predict (to a close-enough approximation) how the proposed location will perform and make a clear go/no-go decision.
Tools: Conventional capital-budgeting tools such as discounted cash flow and expected rate of return