Design of experiments (DOE) is a systematic method to determine the relationship between factors affecting a process and the output of that process. In other words, it is used to find causeandeffect relationships. This information is needed to manage process inputs in order to optimize the output.
An understanding of DOE first requires knowledge of some statistical tools and experimentation concepts. Although a DOE can be analyzed in many software programs, it is important for practitioners to understand basic DOE concepts for proper application.
The most commonly used terms in the DOE methodology include: controllable and uncontrollable input factors, responses, hypothesis testing, blocking, replication and interaction.
The controllable input factors can be modified to optimize the output. The relationship between the factors and responses is shown in Figure 1.
The comparison of two or more levels in a factor can be done using an Ftest. This compares the variance of the means of different factor levels with the individual variances, using this equation:
F = ns^{2}_{Ybar} / s^{2}_{pooled}
where:
n = the sample size
s^{2}_{Ybar} = the variance of the means, which is calculated by dividing the sum of variances of the individual means by the degrees of freedom
s^{2}_{pooled} = pooled variance, or the average of the individual variances
This is similar to the signaltonoise ratio used in electronics. If the value of F (the test statistic) is greater than the Fcritical value, it means there is a significant difference between the levels, or one level is giving a response that is different from the others. Caution is also needed to ensure that s^{2}_{pooled} is kept to a minimum, as it is the noise or error term. If the F value is high, the probability (pvalue) will fall below 0.05, indicating that there is a significant difference between levels. The value of 0.05 is a typical accepted risk value.
If F = 1, it means the factor has no effect.
As an example of a onefactor experiment, data from an incoming shipment of a product is given in Table 1.
Table 1: Incoming Shipment Data
Lot  Data 
A  61, 61, 57, 56, 60, 52, 62, 59, 62, 67, 55, 56, 52, 60, 59, 59, 60, 59, 49, 42, 55, 67, 53, 66, 60 
B  56, 56, 61, 67, 58, 63, 56, 60, 55, 46, 62, 65, 63, 59, 60, 60, 59, 60, 65, 65, 62, 51, 62, 52, 58 
C  62, 62, 72, 63, 51, 65, 62, 59, 62, 63, 68, 64, 67, 60, 59, 59, 61, 58, 65, 64, 70, 63, 68, 62, 61 
D  70, 70, 50, 68, 71, 65, 70, 73, 70, 69, 64, 68, 65, 72, 73, 75, 72, 75, 64, 69, 60, 68, 66, 69, 72 
When a practitioner completes an analysis of variance (ANOVA), the following results are obtained:
Table 2: ANOVA Summary
Groups  Count  Sum  Average  Variance  
A  25  1,449  57.96  31.54  
B  25  1,483  59.32  23.14333  
C  25  1,570  62.80  18.5  
D  25  1,708  68.32  27.64333  
ANOVA  
Source of Variation  SS  df  MS  F  pvalue  Fcrit 
Between groups  1,601.16  3  533.72  21.17376  1.31 x 10^{10}  2.699394 
Within groups  2,419.84  96  25.20667  
Total  4,021  99 
Statistical software can provide hypothesis testing and give the actual value of F. If the value is below the critical F value, a value based on the accepted risk, then the null hypothesis is not rejected. Otherwise, the null hypothesis is rejected to confirm that there is a relationship between the factor and the response. Table 2 shows that the F is high, so there is a significant variation in the data. The practitioner can conclude that there is a difference in the lot means.
This is the most important design for experimentation. It is used in most experiments because it is simple, versatile and can be used for many factors. In this design, the factors are varied at two levels – low and high.
Twolevel designs have many advantages. Two are:
For an example of a twolevel factorial design, consider the cakebaking process. Three factors are studied: the brand of flour, the temperature of baking and the baking time. The associated lows and highs of these factors are listed in Table 3.
Table 3: Cakebaking Factors and Their Associated Levels
Factor  Name  Units  Low Level ()  High Level (+) 
A  Flour brand  Cost  Cheap  Costly 
B  Time  Minutes  10  15 
C  Temperature  Degrees (C)  70  80 
The output responses considered are “taste” and “crust formation.” Taste was determined by a panel of experts, who rated the cake on a scale of 1 (worst) to 10 (best). The ratings were averaged and multiplied by 10. Crust formation is measured by the weight of the crust, the lower the better.
The experiment design, with the responses, is shown in Table 4.
Table 4: Settings of Input Factors and the Resulting Responses
Run Order  A: Brand  B: Time (min)  C: Temp. (C)  Y_{1}: Taste (rating)  Y_{2}: Crust (grams) 
1  Costly(+)  10()  70()  75  0.3 
2  Cheap()  15(+)  70()  71  0.7 
3  Cheap()  10()  80(+)  81  1.2 
4  Costly(+)  15(+)  70()  80  0.7 
5  Costly(+)  10()  80(+)  77  0.9 
6  Costly(+)  15(+)  80(+)  32  0.3 
7  Cheap()  15(+)  80(+)  42  0.5 
8  Cheap()  10()  70()  74  3.1 
Analysis of the results is shown in Table 5. Figures 2 through 4 show the average taste scores for each factor as it changes from low to high levels. Figures 5 through 7 are interaction plots; they show the effect of the combined manipulation of the factors.
Table 5: ANOVA Table for the Taste Response
Factor  df  SS  MS  F  Effect  Contrast  p  Fcrit at 1% 
Brand  1  2.0  2.0  0.0816  1  4.00  0.82  16.47 
Time  1  840.5  840.5  34.306  20.5  82.00  0.11  
Brand x time  1  0.5  0.5  0.0204  0.5  2.00  0.91  
Temp  1  578.0  578.0  23.592  17  68.00  0.13  
Brand x temp  1  72.0  72.0  2.9388  6  24.00  0.34  
Time x temp  1  924.5  924.5  37.735  21.5  86.00  0.10  
Brand x time x temp  1  24.5  24.5  1  3.5  14.00  0.50  
Error  1  24.5  24.5  
Total  7  2442.0 
From reading an F table, the critical F value at 1 percent is 16.47. As the actual value of F for time and temperature exceed this value (time is at 34.306 and temperature is 23.592), it’s possible to conclude that both of them have a significant effect on the taste of the product. This is also evident from Figures 3 and 4, where the line is steep for the variation of these two factors. Figure 7 also shows that when the temperature is high, the taste sharply decreases with time (as charring takes place).
For the crust formation, the data analysis is shown in Table 6.
Table 6: ANOVA Table for the Crust Response
Factor  df  SS  MS  F  Effect  Contrast  Fcrit at 1% 
Brand  1  1.4  1.4  1.4938  0.825  3.30  16.47 
Time  1  1.4  1.4  1.4938  0.825  3.30  
Brand x time  1  1.1  1.1  1.1536  0.725  2.90  
Temp  1  0.5  0.5  0.4952  0.475  1.90  
Brand x temp  1  0.7  0.7  0.7257  0.575  2.30  
Time x temp  1  0.1  0.1  0.0672  0.175  0.70  
Brand x time x temp  1  0.9  0.9  1  0.675  2.70  
Error  1  0.9  0.9  
Total  7  5.9 
In this case the actual F value for the three factors (brand, time and temperature) are below the critical F value for 1 percent (16.47). This shows that these are not significant factors for the crust formation in the cake. If further optimization of the crust formation is needed, then other factors, such as the quantity of ingredients in the cake (eggs, sugar and so on), should be checked.
Design of experiments is a powerful tool in Six Sigma to manage the significant input factors in order to optimize the desired output. Factorial experiments are versatile because many factors can be modified and studied at once. The following resources can be helpful in learning more about DOEs:


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Comments
Realy informative!
Very good article on DOE. but I just got lost on how to get the Fcrit value. I dont seem to get it on my table, can some expand on how to do this, please!
Thanks to the author for the being concise on its explanations.
I would like an example of a good resolution 4 factor fractional
Good article for the beginner
Thanks a lot, very informative and helpful
Very interesting paper