“Once you make a decision, the universe conspires to make it happen.” – Ralph Waldo Emerson
There are times when an FMEA facilitator has difficulty arriving at consensus with the FMEA team. This sometimes happens when there are two or more competing ideas or solutions and members of the team feel strongly about their personal idea or solution. One tool that can be used to solve this problem is called Pugh Analysis, a type of decision matrix.
The Oxford English Dictionary defines “decision” as “a conclusion or resolution reached after consideration” and “matrix” as “a rectangular array of quantities or expressions in rows and columns that is treated as a single entity and manipulated according to particular rules.”
What is a Pugh Analysis?
A Pugh Analysis is a decision matrix where alternatives or solutions are listed on one axis, and evaluation criteria are listed on the other axis. The objective is to evaluate and prioritize the alternatives or solutions. The team first establishes and weights the evaluation criteria and then evaluates each option against those criteria. It’s an iterative process for narrowing down a list of potential product concepts to a concept that best meets the criteria.
Pugh Analysis was invented by Stuart Pugh, a professor from the University of Strathclyde, in Glasgow, Scotland.
How is Pugh Analysis performed?
The steps of a Pugh Analysis are:
- Document the short list of ideas or alternatives that are being evaluated.
- Develop the list of criteria that will be used to evaluate alternatives.
- Assign a relative weight to each criterion based on how important that criterion is to the subject being evaluated.
- Using a simple matrix, list the evaluation criteria on the left and the alternatives across the top.
- Establish a baseline, which may be one of the alternatives or some other solution or alternative the team agrees is a baseline.
- For each of the criteria, evaluate each of the alternatives against the baseline, using scores of worse (-1), same (0), or better (+1).
- Multiply each rating by the criterion weighting. Sum the results and identify the alternative with the highest score.
- If one of the alternatives has not emerged as the clear best idea with team consensus, look for a hybrid idea that captures the best features of the competing alternatives, and rescore.
What is an example of Pugh Analysis?
The following is an example, taken from chapter 10 of the book Effective FMEAs. It is taken from a fictitious Design FMEA for an all-terrain bicycle brake cable. One of the failure modes being analyzed is “cable binds” and a cause is “inadequate or wrong lubrication between cable and sheath.” The team is working on finding the best solution for this cause (recommended action) and has narrowed down to two possible lubricants.
The first alternative is a lubricant gel that is lower cost and easier to maintain, but has less performance over temperature and humidity extremes, and provides slightly less friction reduction. The supplier has an excellent record of quality.
The second alternative is a high-performance liquid lubricant that provides better friction reduction over a wide variety of operating extremes, but is harder to apply and costlier, and the supplier has a spotty quality record.
For the purposes of the Pugh Analysis, the current lubricant is called “baseline.”
As can be seen from the decision matrix, the gel lubricant slightly outperformed the liquid lubricant. The team looked for a hybrid solution and found a supplier for the liquid lubricant with a better-quality record that was easier to apply. The hybrid column shows the results, and the decision was made.
Of course, the actionable results from the Pugh Analysis will need to be added to the FMEA recommended actions.
When you have competing ideas in an FMEA meeting, first use the facilitation skills outlined in the “Inside FMEA” series. Many competing ideas can be resolved with good facilitation. However, if the use of these tools does not resolve the issue, consider using Pugh Analysis. Above all, it is important that the FMEA team agree on the most effective solutions to reduce risk.
Nothing can frustrate a team more than getting to consensus on one or more of the topics being analyzed. The next article summarizes effective approaches for bringing the team to consensus.