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How do you apply design of experiments (DOE) to optimize plant operations?

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Design of experiments (DOE) is a powerful tool for improving plant operations by testing and optimizing different factors that affect the output quality, efficiency, or cost. DOE can help you identify the best settings for your process variables, reduce variation, and enhance performance. In this article, you will learn how to apply DOE to optimize your plant operations in six steps.

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Gary Hemphill

Semi-Retired; self-employed
Mohd Azfar bin Md Jaafar

Chemical Engineer | Investigator in Technical Solutions, Project Optimization, and Organizational Synergy | Aligning…

1 Define your objective

The first step is to define your objective and scope for the DOE. What are you trying to achieve or improve in your plant operations? For example, you may want to increase the yield, reduce the defect rate, or lower the energy consumption. You also need to specify the criteria for measuring your success, such as the target values, the tolerance limits, or the cost savings.

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Gary Hemphill

Semi-Retired; self-employed
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5-Whys are usually (but not always) enough to drill down and define the true problem. It is essential to define the root cause or all efforts will be wasted.

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Mohd Azfar bin Md Jaafar

Chemical Engineer | Investigator in Technical Solutions, Project Optimization, and Organizational Synergy | Aligning Strategies with Value Creation. The first step to problem-solving is acknowledging the problem
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Objective is the tether, the root, the main hypothesis that drives your solution. It is the lens from which you view the problem and derive the solution. A clear objective must have a measurable target and concise aim in order not to derail the design. Hence, defining a clear objective is key. However, certain objectives can be met without the proper quality. I believe the objective must be achieved with best intent and effort. Therefore, it is best to have an objective that also defines quality requirements as indication objective is fully met. This ensures the final product itself is not compromised. In conclusion, a well-defined objective must be measurable, specific, and with quality requirements to ensure proper DOE.

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Edward Jaeck

Principal at SINE18, LLC ? The keystone between strategy and tactical execution | Strategic Planning | Manufacturing | Data-Based Quality Decisions
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Plant level DOEs by nature are optimization DOEs with multiple outputs to balance and optimize simultaneously. Higher output, while sustaining quality. Higher quality, while not hurting output or on time delivery. With this, a good DOE for a plant has these equations modeled before the DOE is planned and the team can really discuss tradeoffs. At the end of the day, the factory manager is optimizing many variables at the same time with happy customers and profit in the top three.

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2 Select your factors and levels

The next step is to select the factors and levels that you want to test in your DOE. Factors are the process variables that you can control or manipulate, such as temperature, pressure, speed, or concentration. Levels are the different values or settings that you assign to each factor. For example, you may test three levels of temperature: low, medium, and high. You should choose the factors and levels that are relevant, feasible, and significant for your objective.

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3 Choose your design

The third step is to choose your design for the DOE. The design is the plan or method that you use to arrange and conduct the experiments. There are different types of designs, such as factorial, fractional factorial, response surface, or Taguchi. The choice of design depends on your objective, the number of factors and levels, the interactions between factors, and the resources available. You should choose the design that can answer your questions with the least number of experiments.

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4 Conduct the experiments

The fourth step is to conduct the experiments according to your design. You should follow the instructions and procedures for setting up and running the experiments. You should also collect and record the data for the output or response variable that you are measuring. For example, you may measure the yield, the defect rate, or the energy consumption for each combination of factors and levels. You should ensure that the data is accurate, reliable, and consistent.

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5 Analyze the results

The fifth step is to analyze the results of your experiments using statistical tools and techniques. You should use graphs, tables, and charts to visualize and summarize the data. You should also use tests, models, and equations to identify and quantify the effects of the factors and levels on the output or response variable. For example, you may use ANOVA, regression, or optimization to determine which factors are significant, how they interact, and what are the optimal settings.

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6 Implement and verify

The final step is to implement and verify the findings and recommendations from your analysis. You should apply the optimal settings or solutions to your plant operations and monitor the performance and improvement. You should also verify that the results are valid, robust, and consistent under different conditions or scenarios. You should document and report the outcomes and benefits of your DOE.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you apply design of experiments (DOE) to optimize plant operations?

1

2

3

4

5

6

7

1 Define your objective

2 Select your factors and levels

3 Choose your design

4 Conduct the experiments

5 Analyze the results

6 Implement and verify

7 Here’s what else to consider

Plant Operations

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How do you apply design of experiments (DOE) to optimize plant operations?

1

2

3

4

5

6

7

1 Define your objective

2 Select your factors and levels

3 Choose your design

4 Conduct the experiments

5 Analyze the results

6 Implement and verify

7 Here’s what else to consider

Plant Operations

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