What is Design of Experiments?

If you work in manufacturing, quality, or engineering, you’ve probably heard the term “Design of Experiments,” or DOE. Maybe you’ve even helped collect data for one without fully understanding what was going on behind the scenes. That’s okay. DOE is a sophisticated statistical method, but even if you’re not the one crunching the numbers, understanding the big ideas behind it can make you a more effective technician, engineer, or manager.

At its core, Design of Experiments is a structured statistical approach to testing. It helps us understand how different inputs (or factors) affect an outcome (or response). Whether you’re adjusting a process to improve tensile strength, dialing in machine settings to reduce defects, or figuring out which supplier provides the most consistent material, DOE helps answer one big question: What settings or conditions will give me the result I want?

Inputs, Outputs, and Why DOE Matters

Think of DOE as a smarter way to test. In manufacturing, we deal with a lot of variables—machine settings, material types, operator techniques, temperatures, speeds, and more. All of these inputs can influence the output of a process.

Traditionally, some teams might run “one-factor-at-a-time” experiments—changing just one thing while keeping everything else constant. While simple, this approach quickly breaks down in the real world. Processes usually involve interactions between multiple inputs, and you miss that when you isolate variables.

DOE is more efficient because it allows you to test multiple variables in a structured way. It’s also more effective because it reveals how combinations of inputs affect your outcomes—especially when those combinations create unexpected effects.

A Simple Example

Imagine you’re trying to control the temperature in a room using a small air conditioner. Your independent variables (factors) might include the fan setting (high, medium, low) and the temperature setting (cool, medium, warm). Your dependent variable (response) is the room temperature you measure with a thermometer.

A DOE would help you plan a set of experiments where you try different combinations of fan and temperature settings. Then you’d measure the results to see what combination gives you the best outcome—say, keeping the room at 22°C. More importantly, DOE helps you see how those two settings work together. Maybe the “high fan + warm setting” combination behaves differently than you’d expect just by looking at each variable on its own.

Key DOE Terminology

Let’s look at some essential terms you’ll hear when talking about DOE:

• Factor: A controllable input variable that might affect your result. Examples include material type, machine speed, or temperature setting.
• Level: A specific setting of a factor. For example, if “temperature” is your factor, the levels might be 120°C, 125°C, and 130°C.
• Response: The outcome you’re trying to understand or improve—like tensile strength, dimensional accuracy, or cycle time.
• Controlled Variable (or Nuisance Variable): A variable that you deliberately keep constant to prevent it from affecting your response. For instance, always running tests on day shift to avoid temperature swings between day and night.
• Interaction: When two or more factors combine to produce an effect that wouldn’t be apparent by changing just one of them. This is one of the most powerful insights you gain through DOE.
• Experimental Design: This refers to the plan for how you’ll carry out your experiments. It includes which factors you’ll test, how many levels each has, and how the tests will be arranged and sequenced.

Types of Experimental Designs

There are many flavors of DOE. You may hear about:

• Full factorial designs, where all possible combinations of all factors are tested.
• Fractional factorial designs, where only a carefully chosen subset of combinations is tested to save time and resources.
• Taguchi methods, which focus on robustness and noise reduction.

There are even more advanced DOE techniques out there, but at this level, it’s enough to understand that the right design depends on your goals, the number of variables, and the resources you have available.

Why It Matters to You

Even if you’re not the one setting up the experiment or running the stats, you might help collect data or run equipment during a DOE. Understanding what’s being tested, why certain variables are being changed, and what results matter will help you contribute more effectively.

More broadly, DOE reflects a mindset: Test intelligently. Learn efficiently. Improve continuously. Whether you’re solving a process issue or driving a long-term improvement, DOE is one of the most powerful tools in the quality and engineering toolbox.

Ray Harkins is the General Manager of Lexington Technologies in Lexington, North Carolina. He earned his Master of Science from Rochester Institute of Technology and his Master of Business Administration from Youngstown State University. He also teaches manufacturing and business-related skills such as Quality Engineering Statistics, Reliability Engineering Statistics, Failure Modes and Effects Analysis (FMEA), and Root Cause Analysis and the 8D Corrective Action Process through the online learning platform, Udemy.