Analytical Hierarchy Process
In the late 1990s and early 2000s, the Analytical Hierarchy Process (AHP) became popular among management employees as a quantitative method for issue resolution.
The AHP approach was developed after researchers studied the structure of an issue and the genuine challenges that managers confront while trying to solve it. Today we’ll discuss this in a detailed context.
The analytic hierarchy process (AHP), sometimes known as the analytical hierarchy process, is a systematic methodology based on mathematics and psychology for organizing and evaluating complicated choices. It was created in the 1970s by Thomas L. Saaty, who collaborated with Ernest Forman to create Expert Choice software in 1983. Since then, AHP has been widely investigated and enhanced.
It is a precise method of measuring the weights of choice criteria. Pair-wise comparisons are used to evaluate the relative magnitudes of variables based on individual experts’ experiences. Using a carefully constructed questionnaire, each responder compares the relative value of each pair of elements.
How Does the Analytical Hierarchy Process Work?
Analytical Hierarchy Process (AHP) is a technique that helps decision-makers identify the best alternative from many possible options. The method places all of the other options along with a series of scales. Decision-makers compare the strengths and weaknesses of each alternative as they move up and down this series of scales. This process can help organize any information to be presented, such as rankings or reviews. The AHP can also help decision-makers classify the quality of the alternatives compared.
The AHP approach divides the problem into three halves. The issue that has to be fixed is the first portion, and the different solutions that are accessible to fix the problem are the second portion. The criteria used to assess the various solutions are the third and most significant component of the AHP technique.
Although there are multiple criteria, the AHP technique recognizes that the size of each criterion may not be equal. If you have to select between two restaurants, the flavour and the wait time are two things to consider, but they may not be equally important to you.
The flavour of the food may be significantly more essential than the time it takes to prepare it. As a result, if you give flavour 2 points and waiting time 1 point, you’ll be more likely to find a restaurant that meets your needs.
As a result, weights must be assigned to the criteria while weighing various solutions to reach the proper conclusion. This may seem self-evident. On the other hand, management scientists have had difficulty assigning weights until recently. In the preceding example, we assigned weights at random. In addition, the example only contained two conditions. The allocations get increasingly arbitrary as the number of criteria (factors) increases.
What is the Connection Between AHP and Six Sigma?
AHP is a distinct method. It isn’t part of the normal Six Sigma approach. It was really created several years after the Six Sigma approach was created. It has, nonetheless, found widespread use in six sigma initiatives. Managers use AHP to assign numerical weights to various criteria. These variables might be ones that customers consider when assessing a product, or they may be ones that management considers when assessing alternative alternatives.
The disadvantage of Using AHP
The AHP approach has its own set of problems. The procedure necessitates the use of advanced mathematics. It is built upon the idea of eigenvectors. As a result, conducting AHP calculations on an Excel sheet is a nightmare. However, software tools that can conduct computations have recently been developed. Managers just need to be aware of the AHP process; the calculations are done automatically.
How to use Analytical Hierarchy Process (AHP)?
Even though the AHP is one of the most sophisticated methodologies available in management science and operations research, its complexity makes it challenging to employ. Thankfully, software tools have been developed to automate the math-intensive component of the process. The user must follow a basic data gathering process, then be put into the programme to obtain the desired results.
This is the way to achieve the same thing:
Step 1: Define Alternatives
The AHP process starts with the definition of the options to be assessed. These possibilities may be the many criteria to be evaluated for solutions. The many aspects of a product might also be the ones to weigh to grasp buyers’ perceptions better. A complete list of all the possibilities accessible must be ready after step 1.
Step 2: Define the Problem and Criteria
The next step is the issue. A linked number of sub-problems are an issue under the AHP technique. Therefore, the AHP technique divides the problem into a hierarchy of the minor problems. Criteria for the evaluation of the solutions come in breaking down the subproblem. However, a person might go deeper inside the problem, like the root cause analysis. A subjective judgement is when the issue is stopped in smaller subproblems.
Example: A company has to pick among stocks, bonds, real property and gold on the best investment choice. The best investment problem will be divided into more minor issues using the AHP technique, such as downturn protection, maximum appreciation, market liquidity, etc. Each sub-problem may then be divided into more minor issues until the management believes the requirements are met.
Step 3: Establish Priority amongst Criteria Using Pairwise Comparison
The AHP technique compares the matrix in pairs. For instance, the company should assess the relative value of safeguarding against downfall vs Liquidity. There will then be a comparison of Liquidity and appreciation probability on a pair basis in the following matrix. Managers should put up this data according to end users’ expectations or those who will utilize the procedure.
Step 4: Check Consistency
Most software products that assist with AHP difficulties include this step. For example, if I claim that Liquidity is twice as significant as downside protection and that downfall protection is half as significant as the probability of appreciation in the next matrix, the following situation emerges:
Liquidity = 2 (Protection from downfall)
Protection from downfall = ½ (Chance of appreciation)
As a result, Liquidity must have an equal probability of increasing in value.
However, if I have assigned a weight of more or less than 1 in the pair-wise comparison of Liquidity and possibility of appreciation, then my data is inconsistent. Because conflicting data yields inconsistent results, prevention is preferable to cure.
Step 5: Get the Relative Weights
The software programme will use the data to do a mathematical computation and give relative weights to the criterion. Once the equation with weighted criteria is complete, one may assess the options to choose the best answer for their needs.
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