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Analytic Hierarchy Process

The Analytic Hierarchy Process (AHP) enables decision makers to structure decisions hierarchically with the overall goal of the decision at the top of the model, strategic objectives in the higher levels, evaluation criteria in the middle levels, and alternative choices at the bottom.
The AHP provides a structured framework for setting priorities on each level of the hierarchy using pair-wise comparisons, a process of evaluating each pair of decision factors at a given level on the model for their relative importance with respect to their parent.

The consistency of the judgments is tracked using the rigorous math analytics behind the AHP to validate the decision process. In cases where inconsistency is above 10% it is recommended that the criteria and judgments be revisited.
Decision makers are then able to create a model of their priorities where the weight of the decision is distributed from the goal downwards.

If a user increases the weight of a criterion, the alternatives that performed well on that criterion will always get higher scores. This sensitivity analysis is portrayed graphically in the Decision Lens software products and is extremely valuable for testing the impact of changing priorities on alternative business decision choices.

In Decision Lens, the AHP is integrated with a linear programming optimizer to enable decision makers to define business rules for budget allocation. They can then allocate resources across investment alternatives to maximize business value. Using this optimization capability, organizations can effectively manage investments in projects and people using a true portfolio-based framework rather than a project-based framework.

Over time, Dr. Thomas Saaty, the creator of the Analytic Hierarchy Process (AHP), developed a more advanced framework for setting priorities known as the Analytic Network Process (ANP) method of decision making.

The ANP differs from the AHP in that it generalizes the pair-wise comparison process so that decision models can be built as complex networks with the following interconnecting components:
Decision objectivesenvironmental factors

The key concept of the ANP is that influence does not necessarily have to flow only downwards, as is the case with the hierarchy in the AHP. Influence can flow between any factors in the network, causing non-linear networks of priorities and alternative choices.

The ANP is extremely useful for predictive modeling, and broad environmental influences can be factored into decisions.

The best applications of the ANP are in decisions where risks and threats are major factors in the decision process and when organizational success is highly dependent on a thorough understanding of the entire environment, rather than just that of business goals and objectives.

For more information about publications on AHP and ANP, please contact us.