Design of Experiments (DOE)
What is Design of Experiments (DOE)?
Design of Experiments (DOE) is a systematic and statistical approach. Specifically, researchers use DOE to plan, conduct, and analyze experiments. In DOE, the goal is to gain insights into the relationships between input factors (also known as independent variables) and output responses (dependent variables). Primarily, the goal of DOE is to optimize processes, products, or systems and improve their performance while minimizing resources, time, and costs.
In the Design of Experiments, researchers deliberately manipulate the input factors. In particular, they do these at different levels and observe the corresponding changes in the output responses. By doing so, they can identify which factors have the most significant impact on the response. Consequently, they determine the optimal combination of factor levels that result in the desired outcome.
The key components of Design of Experiments:

Factors:
These are the variables that researchers choose to investigate during the experiment. These variables are either controllable (e.g., temperature, pressure, dosage) or uncontrollable (e.g., ambient humidity) and can have different levels (values or settings). 
Responses:
These are the outcomes or results of the experiment that researchers measure and analyze. Responses are either quantitative (e.g., product yield, processing time) or qualitative (e.g., customer satisfaction ratings, pass/fail). 
Levels:
At different levels, researchers test each factor with different values or settings. Based on prior knowledge, they select these levels. Alternatively, researchers explore and determine the potential range of values or settings that each factor can take. 
Experimental Design:
This involves planning the sequence and arrangement of experiments. They strategically choose a combination of factor levels to test. Eventually, researchers aim for efficient data collection and maximum information gain. 
Replication:
In DOE, replication involves repeating the experiment at the same factor level combinations multiple times to account for random variability and increase the reliability of the results. 
Randomization:
Randomization is the process of assigning the order of experiments randomly to minimize the influence of confounding variables and biases. 
Data Analysis:
In order to analyze the data collected during the experiments, researchers employ statistical methods such as Analysis of Variance (ANOVA), Regression Analysis, and hypothesis testing. As a result, these analyses help identify significant factors, quantify their effects, and detect interactions between factors.
Benefits of Design of Experiments (DOE)
1. Efficient Resource Utilization:
DOE enables organizations to identify the most influential factors in process performance. Thereby, it focuses efforts and resources on improving those factors instead of wasting time on nonsignificant factors.
2. Cost Reduction:
By optimizing process settings through DOE, organizations can identify costeffective solutions while minimizing waste, rework, and unnecessary expenses.
3. Speedy Problem Solving:
DOE enables organizations to systematically explore multiple factors and their interactions simultaneously. Consequently, this leads to faster identification of the root causes of process issues and the formulation of effective solutions.
4. DataDriven Decision Making:
DOE provides organizations with reliable and objective data. It empowers them to make informed decisions based on statistical evidence rather than intuition or guesswork.
Example of DOE in a Service Industry
In this example, we’ll consider a customer support center that handles customer inquiries and complaints.
Objective: The objective of the project is to reduce the average resolution time for customer inquiries while maintaining or improving customer satisfaction.
Step 1: Define the Problem and Factors
First, we need to define the problem and identify the factors that could potentially impact the resolution time. Factors may include the number of support agents, the training level of agents, the complexity of the inquiries, and the availability of relevant resources.
Step 2: Identify Levels for Each Factor
For each factor, identify the different levels or settings that can be tested. For example:
 Number of support agents: 5, 7, 9
 Training level of agents: Basic, Intermediate, Advanced
 Complexity of inquiries: Low, Medium, High
 Availability of relevant resources: Limited, Adequate, Abundant
Step 3: Construct the Experimental Design Matrix
Using the identified factors and levels, create an experimental design matrix that represents different combinations of the factors. In this case, a fractional factorial design or a Taguchi design could be used to reduce the number of experiments required.
Step 4: Conduct the Experiments
Assign the different combinations from the experimental design matrix to different support teams. Each team will handle inquiries based on the assigned factor levels. Record the resolution time for each inquiry.
Step 5: Analyze the Data
Collect and analyze the data from the experiments. Use statistical tools to identify the significant factors that have the most impact on the resolution time. Also, determine the best factor settings that result in the shortest resolution time and highest customer satisfaction.
Step 6: Optimize the Process
Based on the analysis, identify the optimal factor settings that lead to the shortest resolution time and highest customer satisfaction. Implement these settings as the new standard operating procedures for the support center.
Step 7: Verify the Results
Monitor the performance of the improved process over time to ensure that the changes have a sustainable impact on reducing resolution time while maintaining or improving customer satisfaction.
Example 2: Enhancing Website Conversion Rate
An ecommerce company is struggling with a low conversion rate on its website. To address this issue, they employ DOE to identify the key factors that affect the conversion rate. They experiment with variables like website layout, color scheme, calltoaction placement, and pricing strategy. By systematically varying these factors and measuring the impact on user behavior, they identify the optimal combination that leads to the highest conversion rate. This datadriven approach allows them to revamp their website effectively, resulting in increased sales and customer satisfaction.
Common statistical methods used in Design of Experiments (DOE):
Analysis of Variance (ANOVA):
ANOVA is a widely used statistical technique in DOE. Especially, it is used when there are multiple factors or independent variables being studied. Primarily, the purpose is to compare the means of two or more groups to determine if there are any statistically significant differences among them.
In the context of DOE, ANOVA helps identify which factors have a significant impact on the response variable (the output or outcome being measured). By comparing the variation between groups (caused by different factor levels) to the variation within groups (caused by random error), ANOVA determines if there is a strong association between the factors and the response variable.
If the pvalue obtained from ANOVA is below a predetermined significance level (commonly 0.05), it suggests that at least one factor has a significant effect on the response. Posthoc tests may then be performed to identify which specific factor levels lead to statistically different responses.
Regression Analysis:
Regression analysis is another crucial statistical method in DOE. Especially, this method is used when the goal is to establish a mathematical relationship between the input factors and the output response variable. The two main types of regression analysis commonly used in DOE are:
 Simple Linear Regression: This is used when there is one independent variable (factor) and one dependent variable (response). It models the relationship between the predictor variable and the response using a straight line. The equation of the line helps predict the response based on different values of the factor.
 Multiple Linear Regression: When there are multiple independent variables (factors) influencing a single dependent variable (response), multiple linear regression is used. Further, it extends the concept of simple linear regression to accommodate multiple factors. Also, it allows for a more complex relationship between the factors and the response.
Regression analysis helps quantify the strength and direction of the relationships between the factors and the response. Consequently, it provides valuable insights into how changes in the factors affect the output.
Factorial Designs:
In DOE, factorial designs are a specific type of experimental design to study the effects of multiple factors simultaneously. The main advantage of factorial designs is that they allow researchers to examine the main effects of each factor, as well as the interactions between factors.
Most commonly, a 2level factorial design is one of the simplest setups used. Here, each factor is studied at two levels (e.g., high and low). By systematically combining these levels, researchers can efficiently explore how each factor and its interactions contribute to the response variable.
In addition, factorial designs provide comprehensive information about the effects of multiple factors on the response. Particularly, they are valuable when trying to optimize a process with limited experimental resources.
In summary, these are just a few of the essential statistical methods used in the Design of Experiments. Depending on the complexity of the study and the research objectives, other advanced techniques, such as Response Surface Methodology (RSM) and Fractional Factorial Designs, may also be employed to further enhance the understanding of the relationships between factors and responses. Therefore, a solid grasp of these statistical methods is essential for effectively conducting and interpreting DOE studies.
Conclusion
DOE is a powerful technique that empowers organizations to uncover vital insights about their processes and make datadriven decisions. By identifying critical factors and optimizing their settings, organizations can improve efficiency, reduce costs, and achieve higher levels of performance. The realworld examples we explored demonstrate how DOE can solve complex problems and drive continuous improvement. Incorporating DOE into the Improve phase of Six Sigma equips organizations with a valuable tool for achieving operational excellence.