Grid Analysis (also known as Decision Matrix Analysis, Pugh Matrix Analysis or MAUT, which stands for Multi-Attribute Utility Theory) is a useful technique to use for making a decision. It is particularly powerful where you have a number of good alternatives to choose from, and many different factors to take into account. This makes it a great technique to use in almost any important decision where there isn’t a clear and obvious preferred option. Being able to use Grid Analysis means that you can take decisions confidently and rationally, at a time when other people might be struggling to make a decision.
How to Use the Tool:
The technique works by getting you to list your options as rows on a table, and the factors you need consider as columns. You then score each option/factor combination, weight this score, and add these scores up to give an overall score for the option.
While this sounds complex, in reality the technique is quite easy to use. Here’s a step-by-step guide with an example.
- The first step is to list all of your options as the row labels on the table, and list the factors that you need to consider as the column headings.
- Next, work out the relative importance of the factors in your decision. Show these as numbers from, say, 0 to 5, where 0 means that the factor is absolutely unimportant in the final decision, and 5 means that it is very important. (It’s perfectly acceptable to have factors with the same importance.) We will use these to weight your preferences by the importance of the factor. These values may be obvious. If they are not, then use a technique such as Paired Comparison Analysis to estimate them.
- The next step is to work your way down the columns of your table, scoring each option for each of the factors in your decision. Score each option from 0 (poor) to 5 (very good). Note that you do not have to have a different score for each option – if none of them are good for a particular factor in your decision, then all options should score 0.
- Now multiply each of your scores from step 3 by the values for relative importance you calculated in step 2. This will give you weighted scores for each option/factor combination.
- Finally, add up these weighted scores for each of your options. The option that scores the highest wins!
A windsurfing enthusiast is about to replace his car. He needs one that not only carries a board and sails, but also that will be good for business travel. He has always loved open-topped sports cars. No car he can find is good for all three things.
His options are:
- An SUV/4×4, hard topped vehicle.
- A comfortable ‘family car’.
- A station wagon/estate car.
- A convertible sports car.
Criteria that he wants to consider are:
- Ability to carry a sail board safely.
- Ability to store sails and equipment securely.
- Comfort over long distances.
- Nice look and build quality to car.
Firstly he draws up the table shown in Figure 1, and scores each option by how well it satisfies each factor:
Figure 1: Example Grid Analysis Showing Unweighted Assessment of How Each Type of Car Satisfies Each Factor
Next he decides the relative weights for each of the factors. He multiplies these by the scores already entered, and totals them. This is shown in Figure 2:
Figure 2: Example Grid Analysis Showing Weighted Assessment of How Each Type of Car Satisfies Each Factor
This gives an interesting result: Despite its lack of fun, a station wagon may be the best choice.
If the wind-surfer still feels unhappy with the decision, maybe he has underestimated the importance of one of the factors. Perhaps he should give ‘fun’ a weight of 7, and buy an old station wagon to carry his board!
Grid Analysis helps you to decide between several options, while taking many different factors into account.
To use the tool, lay out your options as rows on a table. Set up the columns to show your factors. Allocate weights to show the importance of each of these factors. Score each choice for each factor using numbers from 0 (poor) to 5 (very good). Multiply each score by the weight of the factor, to show its contribution to the overall selection. Finally add up the total scores for each option. Select the highest scoring option.