Decision analysis is about modeling the consequences or alternative paths that result in certain courses of action being followed for a particular business problem or opportunity. 

Decision analysis always includes the following key stages, regardless of which modeling technique is followed:

• Stage 1: Define the problem statement.
• Stage 2: Define the alternatives.
• Stage 3: Evaluate the alternatives.
• Stage 4: Choose an alternative to implement.
• Stage 5: Implement a choice.

The information about a problem, the alternatives, the evaluation criteria, and the recommended options to proceed with are depicted in multiple different types of models. Some of the key models to be familiar with for the exam include decision trees and decision matrices:

• Decision trees: More information about these is available in the Decision modeling section of this chapter. 
• Decision matrices: There are two primary types of decision matrices—simple and weighted. A simple decision matrix simply compares criteria between alternatives and there is no weighting or prioritization applied to any of the criteria options. In this case, the alternative with the most criteria met is the preferred option to proceed with. A weighted decision matrix expands on a simple decision matrix by adding a weighting against each criterion in the matrix. This means that criterion 1 may weigh more than criterion 2. 

So, to explain this concept further with an example, if alternative A meets criterion 2 but not criterion 1, and alternative B meets criterion 1 but not criterion 2, then alternative 1 will have a higher total score due to the weighting of criterion 2 being more than that of criterion 1. 

A weighted matrix is often used in vendor selection processes to ensure that criteria of higher importance are represented in a fair and just way when alternatives are considered.