Design of experiments â€“ or DOE for short â€“ can be quite handy when you want to figure out exactly what components of your productâ€™s design are affecting its quality level, and are potential risk factors for failures and variation. Itâ€™s a commonly used technique in many companies nowadays, and mastering it can be very useful when dealing with products that are inherently prone to variation.
Itâ€™s hard to gain any useful knowledge about the defect rates of a product without experimenting with some of its variables, and this is the core idea behind DOE. Youâ€™ll find yourself tweaking with various variables during the production stage in order to see how they affect the final output, and sooner or later youâ€™re going to develop a strong intuition for predicting exactly how a certain change is going to affect your output. This is what DOE is all about, and it can lead to very effective product development if you apply it correctly.
The good thing about DOE is that it helps you narrow down the truly critical factors that impact the defect rate of your product, instead of working with the full list and trying to figure out where to direct your attention. Youâ€™ll find that a typical product is usually affected by a large number of factors that can all lead to defects, but some of them are very minor in comparison to others. Additionally, you may also find that some defects only manifest themselves when some of those factors are specifically combined in a certain way, a condition which may be false most of the time.
The point is, with the help of DOE you can figure out exactly what the critical factors are, and put the rest aside safely. When you want to optimize your production as much as possible, this is one of the best ways to go about that.
Constant Iteration Over the Results
Itâ€™s important to treat DOE as an ongoing process though, and not as something that you just do once and then forget about it. Itâ€™s supposed to be applied in a way that drives you towards continuous improvement, and itâ€™s very useful when combined with a good approach to data collection and retention. When you have access to the results of all past experiments, and you can easily compare them and analyze the differences, you can direct your efforts towards minimizing unwanted variation with a lot of efficiency.
Of course, this also assumes that you have good systems in place for verifying the results of each of those changes, and that youâ€™re able to roll back the design to a previous good state in case you mess something up. These are all factors that youâ€™ll need to take care of before even starting your DOE implementation, and you should take a long, hard look at the way your company is currently structured in order to figure out the best way to go about that.
Itâ€™s also important to ensure that your product doesnâ€™t lose any robustness over time with the changes youâ€™re making to its design. This is a common problem in organizations that push their development forward too fast, and it can lead to finding yourself in a situation where your product has even more defects than before but there is no good way to restore it to a proper working state.
Design of experiments can be an incredibly useful approach when you want to ensure that youâ€™re keeping defects and unwanted variation down to a minimum in your production. It can help you push the limits of your products in ways that youâ€™ve never even thought possible, and it doesnâ€™t take too much effort to implement in most organizations. You just need to be careful to ensure that youâ€™ve got all the important prerequisites in place that will help you prevent making some common mistakes.