Design of Experiments: Definition, Importance, and Types

Any process is a complex web of variables. What if you could have a surefire technique to untangle this web and gain exceptional clarity on the relationships between these variables? Time to step into the world of Design of Experiments (DOE). This structured methodology is a powerful ally for Scientists, Researchers, and Engineers to conduct experiments with precision and ease.  

Whether it's the field of Agriculture and Manufacturing or Marketing and the food industry, DoE empowers you to refine processes like never before. This blog will take you on a journey through the nuances of DoE, diving into its types, phases, benefits and more. So read on and take the next big leap on your next big project! 

Table of Contents

1) What is Design of Experiments (DOE)? 

2) Why use DOE? 

3) Types of Experimental Design  

4) Phases of Experimental Design  

5) Examples of Design of Experiments (DoE) 

6) Benefits of Implementing DoE 

7) Conclusion 

What is Design of Experiments (DOE)? 

Design of Experiments (DOE) is a method employed in Applied Statistics to evaluate the possible alternatives in one or more design variables. It enables the manipulation of various input variables to determine their effect to get the desired output or improve the result.
 

1) DoE is used to discover unknown outcomes, test theories, or demonstrate known effects. 

2) Scientists, Engineers, and other professionals use DoE to analyse system behaviours. 

3) It helps in identifying key input factors that impact desired outcomes. 

4) DoE determines the best input conditions to achieve a specific output. 

5) DoE takes a data-driven approach and collects information during experiments to pinpoint factors and processes leading to desired results.
 

 

Why use DOE? 

These are the reasons why professionals use DoE: 

Types of Design of Experiments 

Depending on your objectives, assumptions, available data, and other factors, you can have your pick from various designs at any stage of your DoE process. However, for those new to DoE, the range of options can feel overwhelming. The main types of DoE include the following: 

Factorial Designs  

In full factorial designs, you can test every possible combination of component levels. This allows for a comprehensive analysis of interactions between key factors and their effect on the measured responses. Remember the following points: 

a) Full factorial experiments need numerous runs when testing multiple components at various levels. 

b) Fractional factorial designs go with the assumption that higher-order interactions (three or more factors) are not significant. 

c) These designs originate from full factorial matrices by adding new factors and interactions. 

d) While fractional factorials retain major factor effects, it leads to trade-offs during analysing interactions. 

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Space-filling Designs  

These designs aim to cover the experimental space as uniformly as possible, ensuring that the entire range of input variables is explored. One of the defining features of space-filling DoE is its consistency in distributing points across the design space.  

a) Space-filling designs are useful when there is little prior knowledge about the system. 

b) They allow for broad exploration of the system or serve as a starting point for future optimisation. 

c) These designs examine factors at multiple levels without assuming the structure of the space or model type. 

d) Unlike classical DoE designs, space-filling designs sacrifice efficiency and some statistical properties, such as those in factorial designs. 

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Response Surface Methodology  

Response Surface Methodology (RSM) is used to analyse multiple components, though typically only two are examined at a time. By utilising a series of full factorial DoEs, RSM maps responses and formulates equations to describe factor influences. 

Once a key main effect is identified through experiments like Plackett-Burman, RSM helps refine processes. Factor parameters can then be adjusted to achieve the desired outcome. 

a) RSM designs are most effective during the optimisation and robustness stages. 

b) They can be applied to various types of factors. 

c) RSM designs are generally not used for categorical and discrete factors due to the high experimental cost and number of runs required. 

Phases of Experimental Design  

There are five phases or steps of experimental design, namely planning, screening, modelling, optimisation and verification. Let’s explore these phases in detail 

Planning 

a) Careful planning and attention to detail can help avoid potential pitfalls. 

b) Limited resources mean conducting experiments with the minimum number of runs. 

c) Begin with a clear understanding of the problem and a well-defined experimental purpose. 

d) Identify key factors (independent variables) that significantly affect the response using past experience and expert knowledge. 

e) Ensure the process being analysed is under Statistical Process Control (SPC). 

f) Verify that the measurement system variation is within acceptable limits. 

Screening  

a) When studying a large number of factors (more than five), begin with screening experiments to reduce them. 

b) The number of factors directly affects the required number of experimental runs. 

c) For example, studying 10 factors in a full factorial design would require 2¹⁰  or 1024 runs, which is often impractical. 

d) Screening experiments help narrow down key factors before further analysis. 

e) Common designs used in the screening phase include: 

i) Fractional Factorial Design 

ii) Plackett-Burman Design 

iii) Definitive Screening Designs 

Modelling 

After determining the significant factors through screening experiments, the next step involves modelling their relationship with the response. This is done using Regression Analysis. Common designs used in the modelling phase include: 

a) Fractional Factorial Design 

b) Full Factorial Design 

Optimisation

a) Once significant factors are identified and modelled, the next step is optimising process conditions to achieve the desired outcome. 

b) Optimisation focuses on finding the best combination of factors and levels for optimal results. 

c) Common designs used during the optimisation phase include: 

i) Central Composite Design 

ii) Box-Behnken Design
 

Verification  

a) Verification is the final phase conducted after achieving the optimised condition. 

b) It confirms whether the optimised condition truly delivers the expected results. 

c) If the results are not optimal, the experimental plan or design is adjusted accordingly. 

d) Verification involves follow-up experiments under anticipated ideal conditions to validate optimisation outcomes. 

e) Outcomes can also be verified by estimating the best settings for each factor and testing them multiple times. 

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Examples of Design of Experiments (DoE) 

There are many prominent application of Design of Experiments in industries such as Food, Agriculture, Manufacturing, Marketing and more. Here we explore a few of them: 

In the Food Industry  

a) DoE is used in this industry to increase flavour and texture by optimising key factors. 

b) It helps businesses develop consumer-preferred products by understanding the influences of taste and texture. 

c) It leads to increased sales by improving product appeal. 

d) It makes brand reputation stronger through better-quality offerings. 

In Agriculture  

a) In agriculture, DOE helps increase crop yields and decrease the use of pesticides and fertilisers. 

b) It helps in optimising plant development conditions in controlled environments. 

c) It helps in finding the optimal combination of fertiliser and irrigation rate to maximise crop yields. 

In Six Sigma  

a) Six Sigma focuses on achieving process excellence and reducing variance. 

b) Design of Experiments (DoE) is a key component in Six Sigma approaches. 

c) Minimising defects and variances leads to improved overall quality. 

d) Strategies are implemented to reach optimal performance levels. 

e) DoE helps identify critical process parameters essential for improvement. 

In Marketing  

1) In the field of Marketing, DoE can test and optimise advertisement elements such as: 

a) Graphic design 

b) Headline 

c) Wording 

d) Call-to-action 

2) It can compare various pricing methods and their effects on:  

a) Consumer behaviour 

b) Buy intent 

c) Profitability 

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In Manufacturing  

a) DoE helps uncover reasons for the differences and flaws in manufacturing processes. 

b) Quality Engineers can conduct experiments to identify the cause of problems and develop solutions. 

c) It minimises process variability, enhancing quality measurement. 

d) DoE aids in identifying sources of quality issues. 

e) It is useful for optimising manufacturing processes for parts. 

Benefits of Implementing DoE 

DoE has been utilised in every industry owing to the following benefits of it brings: 

Conclusion 

Design of Experiments plays a fundamental role in scientific and industrial research. Researchers can minimise variability and gain meaningful insights through this technique by meticulously planning, executing, and analysing experiments. This helps them accelerate innovation and decision-making like never before. The various experimental types, as outlined in this blog, such as space-filling, factorial, and response surface, highlight this approach's versatility in addressing diverse research questions. 

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