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Grid Analysis

Making a Decision By Weighing Up Different Factors
Also known as Decision Matrix Analysis, Pugh Matrix Analysis, and Multi-Attribute Utility Theory (MAUT)

Imagine that your boss has put you in charge of taking on a new outsourced IT supplier. You've already identified several different suppliers, and you now need to decide which one to use.

You could decide to go with the low-cost option. But you don't want to make your decision on cost alone - factors such as contract length, underlying technology, and service levels need to be taken into consideration. So how can you make sure you make the best decision, while taking all of these different factors into account?

Grid Analysis is a useful technique to use for making a decision. It's 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

Grid Analysis 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 by the relative importance of the factor, and add these scores up to give an overall score for each option.

While this sounds complex, this technique is actually quite easy to use. Here's a step-by-step guide with an example. Start by downloading our free worksheet. Then work through these steps:

1. 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. For example, if you were buying a new laptop computer, factors to consider might be cost, dimensions, and hard disk size.
   
   
2. Next, 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.
   
   
3. The next step is to 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.)
   
   

   Tip: These values may be obvious. If they are not, then use a technique such as Paired Comparison Analysis to estimate them.




4. Now multiply each of your scores from step 2 by the values for relative importance of the factor that you calculated in step 3. This will give you weighted scores for each option/factor combination.
   
   
5. Finally, add up these weighted scores for each of your options. The option that scores the highest wins!

Example:

A windsurfing enthusiast is about to replace his car. He needs one that not only carries a board and sails, but also one that will be good for business travel. He has always loved open-topped sports cars, but no car he can find is good for all three things.

His options are:

• An SUV/4x4, hard topped vehicle.
• A comfortable "'family car."
• A station wagon/estate car.
• A convertible sports car.

Factors that he wants to consider are:

• Cost.
• Ability to carry a sail board safely.
• Ability to store sails and equipment securely.
• Comfort over long distances.
• Fun!
• Look, and build quality.

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

Factors:	Cost	Board	Storage	Comfort	Fun	Look	Total
Weights:
Sports Car	1	0	0	1	3	3
SUV/4x4	0	3	2	2	1	1
Family Car	2	2	1	3	0	0
Station Wagon	2	3	3	3	0	1

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

Factors:	Cost	Board	Storage	Comfort	Fun	Look	Total
Weights:	4	5	1	2	3	4
Sports Car	4	0	0	2	9	12	27
SUV/4x4	0	15	2	4	3	4	28
Family Car	8	10	1	6	0	0	25
Station Wagon	8	15	3	6	0	4	36

This gives an interesting result: Despite its lack of fun, a station wagon is the best choice. Windsurfing really matters to him!

Key Points

Grid Analysis helps you to decide between several options, where you need to take many different factors into account.

To use the tool, lay out your options as rows on a table. Set up the columns to show the factors you need to consider. Score each choice for each factor using numbers from 0 (poor) to 5 (very good), and then allocate weights to show the importance of each of these factors. 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. The highest scoring option will be the best option.

Note: Grid Analysis is the simplest form of Multiple Criteria Decision Analysis (MCDA), also known as Multiple Criteria Decision Aid or Multiple Criteria Decision Management (MCDM). Sophisticated MCDA can involve highly complex modelling of different potential scenarios, using advanced mathematics. A lot of business decision making, however, is based on approximate or subjective data. Where this is the case, Grid Analysis may be all that’s needed.

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Extension Resources (Not included in the Mind Tools E-book.)

* Shows articles available in full only to Career Excellence Club members.

Analytic Hierarchy Process (AHP) - Choosing by weighing up many subjective factors
Reactive Decision Making - Making good decisions under pressure*
Critical Thinking - Developing the skills for successful thinking*
The Ladder of Inference - Avoiding "jumping to conclusions"*
Blindspot Analysis - Avoiding common "fatal flaws" in decision making*
Multi-Voting - Choosing fairly between many options*
Monte Carlo Analysis - Bringing uncertainty and risk into forecasting*
The Kepner-Tregoe Matrix - Making unbiased, risk assessed decisions*
Impact Analysis - Identifying the "unexpected" consequences of a decision*
The Vroom-Yetton-Jago Decision Model - Deciding how to decide*

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