3 Examples of Design of Experiments (DOE) Methodology in Practice

Using the design of experiments method (DOE) is a great way to determine what factors are in control of the final output of your processes. It’s a good methodology for establishing all relevant relationships in your operation, and it can show you what variables you can change in order to push the final result in the right direction. Therefore, mastering its use as early as possible is strongly encouraged if you want to perfect the design of your product, provide a better service, etc. Let’s have a look at some examples of how DOE is used in practice, and what kinds of problems it can be used to address.

The Classic Weight Problem Example

A commonly given example that’s often used to explain how DOE works involves weighing objects in a pan, and attempting to identify the correct weight of each one. Different approaches to weighing are evaluated, with the result of each one recorded step by step for easy comparison. From there, it can be easily inferred which of the two approaches has a better potential for identifying the correct results more quickly, and you can additionally test how they change when you modify the size of the input. This is more of a thought experiment, but it has plenty of real-life implications and it’s commonly given as an example in case studies.

As you’ll see below, the way this experiment is carried out has some real-life applications and it can be used to study how certain other processes are carried out. If you’re looking for a good introduction to how DOE works and what kinds of effects it can have on your operations, you should start with this one and give it a good study.

Perfection in Silicon Wafer Design

The computer industry is a common adopter of DOE, and the methodology has seen plenty of uses in that area. Silicon wafers are very sensitive to tiny changes in many of the parameters they operate under, making it important to study their design very carefully, and ensure that there are no unexpected deviations. Unfortunately, this can sometimes be a huge challenge with the number of parameters typically involved in the standard wafer production facility, making DOE a good candidate for these types of studies.

Indeed, a correct application of DOE can help a facility quickly identify the exact variables that may cause the final product to fall out of line with regards to the intended safe parameter constraints, and it can help the facility operators to improve their overall work by focusing on those specific factors.

Optimizing the Yield of Chemical Processes

You’ll probably see DOE used frequently in the chemical industry, and for a good reason. It’s not rare to make attempts to optimize the outcome of a certain process when working with complex chemical reactions and multi-stage processes, and the sheer number of variables that can be tweaked can sometimes make it impossible to get the right balance by hand.

This is where design of experiments comes in, as it can help you identify the exact appropriate balance levels between the different variables involved in the process, and fine-tune them to perfection until you’re able to maximize the yield of each run. It’s worth noting that this requires you to have a very good underlying system for data collection, as otherwise you can’t expect to be able to adequately compare the results of different runs. On the other hand, once you’ve got that system in place, it’s trivial to keep it running and ensure that the outcome of each iteration is recorded and stored properly.