Analytic Hierarchy Process (AHP) is a decision making technique that was developed in 1970s. Since then, our knowledge on good decision-making increased significantly and scientists have developed lots of new decision making methodologies.
So why do we use AHP, a 30-year-old decision making methodology, in TransparentChoice? This article contains a few reasons why AHP is still a good choice for making collaborative decisions.
For the last 30-35 years AHP has been thoroughly tested by thousands of organizations from around the globe… and it seems to work!
There are lots of case studies that describe how large organizations used AHP for their strategic decisions to achieve better outcomes.
2. Broad set of applications
When you look into the case studies you will notice that it was used for variety of decision making problems.
There are AHP applications related to project prioritization, vendor selection, technology selection, site selection, hiring decision and more...
Having one good approach lets good decision making become part of your everyday processes; part of your culture.
3. Intuitive and easy to use
People don’t feel comfortable taking recommendations from software if they don’t understand how it works. They don’t need to know the details but they want to understand the idea behind it.
For AHP, you break a complex decision into explicit goals, alternatives and criteria. You prioritize criteria and evaluate alternatives in light of those criteria. People get it.
In contrast, some more recently developed methodologies have a process and underlying math that is so complex that the software becomes a “mathematical black box”. It takes your input and returns recommendations. What happens in between is a mystery (at least for a regular human being) and that undermines trust. And trust is really important when it comes to the final outcome.
Using Analytic Hierarchy Process for your collaborative decision making works because, with AHP, you can explain how it works.
4. Designed for multi-criteria
When you make important decisions, there are always conflicts between criteria. For example, “minimizing price” and “maximizing quality” are often contradictory goals. This is made worse when you're working in a team. Collaborative decision making, by definition, means people have different views and priorities.
Decision making best practice involves taking into account all important criteria. However, this "best practice" is often ignored as multi-criteria analysis is much more challenging than, say, making a decision based on just the price.
Analytic Hierarchy Process allows you to take into account all important criteria and to organize them into a hierarchy.
5. Builds alignment around criteria priorities
Some decision-making methodologies ask participants to assign weights to criteria. The question is, "How should I set those weights?" Well, however Fred in Finance does it, Sally in Sales will disagree.
Participants always have different priorities and without a supporting process, it is hard to reach consensus. Some methodologies are missing this key mechanism. It leaves your group with an unresolved problem.
And then the very smart quants dive in. They've developed another group of methods where weight of criterion is not a value but function. From theoretical point of view this might be a good concept but it is usually very hard to use for real collaboration.
If it is hard to agree on a value, and people need sensitivity analysis to check different scenarios, it will be much harder to agree on a function with a weird shape. How was the function developed? Shouldn't it be a bit different? Remember what I wrote about “mathematical black boxes”? This is another. And it's a Pandora's box. I wouldn't recommend you open it unless you want to spend a lot of time with equations and whiteboards.
In AHP, setting priorities is resolved with pairwise comparisons.
Each participant is individually asked, "Is A or B more important, and by how much?"
After that, you review comparisons together, discuss and try to reach consensus on their values.
If consensus is not possible you can use average value and move forward.
Then, your collaboratively-agreed comparisons are translated into weights by an algorithm.
This is a process that is centered on collaboration and which has a mechanism that helps to eliminate deadlocks.
6. Validates consistency
We are all humans and it's our prerogative to be inconsistent sometimes. We make mistakes. When we make collaborative decisions there are multiple people that can make mistakes and be inconsistent. Is there any wonder so many group decisions end in deadlock and confusion?
Fortunately, AHP can eliminate some of these problems. You deliver redundant data (more than needed) and an algorithm checks to see if your input is consistent.
Good supporting software can then identify these inconsistencies and help you address the ones that need to be addressed.
Good concepts stand the test of time...
The ancient Greeks invented democracy, and it's still the most successful system of government. But nobody today would implement it the way they did 2,000 years ago. So it is with AHP.
Analytic Hierarchy Process is not the latest methodology but it is a well-proven process for collaborative decision making. It is one that is accessible to everyone and is affordable so that it can be used for decisions large-and-small.
Even though AHP is simple in concept, the math is time consuming, so good software will help you roll it out with confidence. AHP software can also help the consensus-building process and generates graphs and data that help get that crucial executive sign-off.