Markov analysis, like decision analysis, is a probabilistic technique. However, Markov analysis is different in that it does not provide a recommended decision. Instead, Markov analysis provides probabilistic information about a decision situation that can aid the decision maker in making a decision. In other words, Markov analysis is not an optimization technique; it is a descriptive technique that results in probabilistic information.
Markov analysis is specifically applicable to systems that exhibit probabilistic movement from one state (or condition) to another, over time. For example, Markov analysis can be used to determine the probability that a machine will be running one day and broken down the next or that a customer will change brands of cereal from one month to the next . This latter type of examplereferred to as the "brand-switching" problemwill be used to demonstrate the principles of Markov analysis in the following discussion.