What is Analytical Hierarchy Process?

The Analytical Hierarchy Process is a mathematic tool for solving problems that became popular in management during the 1990s and 2000s. AHP was developed after analyzing the problem’s structure and identifying the obstacles that hinder managers from solving the issue. In this article, the structure of a problem is explained.

AHP: The logic behind it

Three parts are involved in the AHP approach.

• First, you need to resolve the problem.

• The second part is the alternative solutions available for solving the problem.

• Criteria used to assess alternative solutions is the third most important aspect of the AHP.

AHP understands, that even though there may be several criteria to consider, their importance is not always equal. If you are choosing between two restaurants and have to consider both the wait time and taste, they may not be equally important in your mind.

Taste may be more important that waiting time, etc. If you give 2 points to the taste of the food and 1 point to wait time, it is more likely that you will find a restaurant which meets your needs.

To ensure the right conclusion, it is important to assign weight to each of the criteria when evaluating alternatives. It may appear obvious. Management scientists struggled with the issue of weighting until recently.

Our weighting was arbitrarily assigned in the example above. The example also had two criteria. The assignments get more random as the criteria (factors), or factors, multiply.

The AHP Method has built-in checks and balances. The checks and balances ensure logical consistency when comparing the relative importances of criteria during the weighting process.

AHP has become one of today’s most popular management techniques. It is used by managers in large corporations such as General Electric, Ford Motors and Motorola in six-sigma projects.

Six Sigma and AHP: The Relationship

AHP is an independent technique. This technique is separate from the Six Sigma standard methodology. It was actually developed years after Six Sigma was created.

It has been widely used in Six Sigma projects. AHP is used by managers to give numerical weights for factors.

The factors can be used either by customers to assess a product, or by management when evaluating alternative solutions.

The downsides of AHP

It has some issues. This method requires higher-level mathematics. The method is based upon the concept of Eigen vectors. This is why performing AHP calculations on Excel can be a pain.

In recent years, software has been developed to perform calculations. Managers only need to know about the AHP, as the calculations will be automated.

The Analytical Hierarchy process (AHP).

The AHP, although one of the best methods in management science and operations analysis available today, is difficult to use due to its complexity. Software tools automates the math intensive parts.

To get results, the user must follow a straightforward methodology for collecting data.

This is how you can do it:

Step 1: Identify Alternatives

AHP begins with the definition of alternatives to evaluate. The alternatives may be different criteria against which solutions are to be assessed. These alternatives could be different product features that must be weighed to understand better the perception of the customer. After completing step 1, you should have a list with all of the alternatives.

Step 2: Define your problem and criteria

Next, model the problem. A problem, according to AHP method is an interrelated set of sub-problems. AHP relies therefore on breaking down the problem into smaller problems. The sub-problems are broken down and criteria for evaluating the solution emerge. Like root cause analysis, one can continue to dig deeper into the problem. It is up to the individual when they decide how many sub-problems are too small.

For example, a firm must decide which investment is the most profitable among stocks, bonds and real estate. Using the AHP method, you can break down the best investment problem into smaller issues such as protection against downfall, maximising the chance of appreciation, and liquidity of the market. The sub-problems can be further broken down into smaller ones until the manager feels the criteria is met.

Step 3: Prioritize Criteria by Pairwise Comparison

AHP uses pairwise comparisons to generate a matrix. The firm is asked, for example, to compare the relative importance between protection against downfall and liquidity. In the following matrix there will be pairwise comparisons between chance of appreciation, liquidity, and other factors. Managers will have to enter the data according to the consumer’s expectations or those who use the system.

Step 4: Verify consistency

Most software programs that solve AHP issues include this step. If I, for example, say that the importance of liquidity in a matrix is double that of protection against downfall while the same is true when I state that the protection from downfall has half the significance as the chance of appreciation then I get the following:

Protection from the downfall = Liquidity

Protection from fall = (Chance for appreciation) / 2 Liquidity, therefore, must be equal to chance for appreciation.

If I give a greater or lesser weight in the pairwise analysis of liquidity and probability of appreciation than 1 then, my data are inconsistent. Data that is inconsistent can lead to inconsistencies, so prevention over cure.

The Relative weights

Software will calculate the equation based on data, and then assign weightings. After the weighted criterion is created, the user can compare the different solutions to find the one that best suits their needs.