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Better Decisions with Analytical Hierarchy Process

Advanced Analytics, Continuous Process Improvement, Data & Analytics, Strategy & Transformation

Government agencies make decisions every day. Like when deciding whether to relocate or downsize their current facilities, the organization must consider impacts on employees, costs, geographic requirements, space availability, and political impact. When decisions result in good outcomes, agencies increase capabilities that enhance their ability to accomplish their mission. Making the wrong decisions can lead to schedule delays, cost overruns, supportability issues, or even delivering the wrong capability to the field. 
When decisions have a broad impact, decision makers often require input from program stakeholders, subject matter experts (SME), and end users. LMI helps agencies make gathering and analyzing that input repeatable and defendable. 
One method we use is the Analytical Hierarchy Process (AHP). The main advantage of AHP is its ability to rank choices in order of their effectiveness in meeting conflicting or competing objectives. LMI’s decision support capability incorporates AHP to assist customers with understanding how to prioritize needs and discriminate between available alternatives. 

What is AHP? 

AHP is a versatile method of structured decision making that decomposes complex decisions into a hierarchy that considers the overarching focus, criteria, and alternatives of a decision. The overarching focus is the problem to be addressed or goal to be reached. The criteria are the decision-making variables that decision makers consider when determining how to solve the problem or reach the goal. This could include criteria such as performance, functionality, and technology. The alternatives are the possible solutions to address the organization’s needs. Once the decision is structured, surveys comparing criteria are sent out and analyzed.  

Why should decision makers consider using AHP?

Clients often must make complex decisions.  For example, when selecting an IT solution, the organization considers technical requirements, functional requirements, cost, cybersecurity, and other compliance requirements. Once the appropriate model is designed, the calculations are not complex and the decision participants do not need cumbersome calculations, as they can be automated either in open-source tools or commercial decision tools. While simple on its surface, surveying SMEs and users should avoid introducing biases. Experienced decision modelers can design models to ensure they obtain decision-specific data that is not skewed. 

While not the only structured decision-making process, AHP has the following advantages:

  • The hierarchical structure allows complex decisions to be broken down into simple sets of preferences and allows for as many levels of criteria and alternatives, as required.
  • When structured correctly, the AHP model injects minimal or no bias into results.
  • The preference (e.g., judgement) surveys are easy for users to complete; pairwise comparisons are intuitive and break down even the most complex decision into small digestible segments.
  • Decision analysts can verify consistency among a participant’s preferences and explore any issues if there is inconsistency that may skew the results.
  • Analysts can present sensitivity analysis to determine how robust a model is; sensitivity analysis that shows how much, or how little, change in respondents’ preferences would be required to alter the final recommendations.

All decision methods have possible drawbacks. For AHP, this includes potential difficulty eliciting meaningful responses from decision makers. LMI works with participants to understand the process, facilitate discussion, and analyze results for inconsistencies to minimize this issue. 

Another issue is when there are many criteria, and many alternatives, it results in the need for a large set of comparisons by each participant. One way to minimize this issue is to carefully design the model to highlight the most important criteria and to automate the workflow to focus on decisions versus processes.

How can organizations use AHP results to select the best alternative?

LMI works with customers to compare the possible benefit of implementing an alternative to its cost and risk. Without this consideration, the impacts of the alternative are not understood.

  • Cost: LMI’s experienced team of cost estimators work with organizations to develop timely, detailed, accurate, alternative lifecycle cost estimates. Consider that two alternatives may provide equal benefit in the AHP model, but one costs $1 million over three years while the other costs $8 million over three years. The first has a better return on investment.  
  • Risk: LMI works with customers to understand risk factors for each alternative. A high benefit alternative may have a risk so high it will not get approved and may not be recommended for implementation.

LMI empowers government decision-making ranging from vendor selections to setting strategic direction in business areas, from healthcare to logistics to acquisition support. We use the most appropriate model or tool in combination with trained decision support experts to understand objectives and priorities.
To learn more about how LMI’s decision support and cost analysis capabilities optimize program decisions and outcomes, contact Ryan Ferguson