What is Analytical Hierarchy Process (AHP)?

Picture the Analytical Hierarchy Process (AHP) as a bit like your GPS, guiding you through complex decisions. It's a system, grounded in maths and psychology, that helps us to weigh up options and make difficult 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.

Think back to the late 90s and early 2000s, the Analytical Hierarchy Process (AHP) became the go-to tool for managers. It was like their trusted Swiss Army Knife for solving problems. It was widely used in various industries, such as automobile manufacturing and healthcare, to aid decision-making.

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.

Imagine you're at a buffet and you can only choose a limited number of dishes. You'd likely compare each dish against the other, right? That's what AHP does. It compares options pair by pair based on the importance given by experts. So, it's like ranking your favourite dishes at a buffet.

analytical hierarchy process

How Does the Analytical Hierarchy Process Work?

Ever struggled to choose the best ice cream flavour at the local parlour? That's where the Analytical Hierarchy Process (AHP) comes in. It ranks all your choices on different scales, from tastiest to healthiest. So, decision-makers can compare each option's strengths and weaknesses. It's like having your ice cream line-up arranged from your top choice to your least favourite, making the choosing process much simpler.

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?

Let's think of AHP and Six Sigma like baking and cooking. They're different, but both are essential in a kitchen. AHP, created long after Six Sigma, has become very popular in Six Sigma initiatives. Just like you'd weigh ingredients for a recipe, managers use AHP to weigh various factors when making a decision.

The disadvantage of Using AHP

Now, AHP isn't a walk-in-the-park. It involves some rather complex maths, something akin to solving a cryptic crossword puzzle. Performing the AHP sums on the likes of an Excel sheet could give anyone a migraine. However, not to worry. Some whizzy software tools have been created to do all this number-crunching for us. So, bosses just need to know how AHP works; the tough sum-juggling is handled by the software.

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.

analytical hierarchy process example

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 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 the 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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Hi, My name is Kartik. I have expertise in Technical and Social Domains. I love to write articles that could benefit people and the community.

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