If you’re looking to integrate Design for Six Sigma (DFSS) in your organization, there are some fundamental points to keep in mind before you begin. DFSS can be a highly effective strategy for minimizing the variation in your processes and ensuring you’re in control of the final output as best as possible, but it also requires you to implement it on a low, fundamental level for the best results. It’s not something you can do as an additional treatment of your processes, which is why a strong understanding of how it works is important for any serious leader.
Adapting Your Design Procedures
When designing a new process for your organization, you’ll need to use DFSS to guide the whole procedure. Some inexperienced leaders treat DFSS as a method they can use to “verify” the correctness of their designs, but this is actually a very wrong approach that can lead to a lot of wasted time and effort.
When you think about it, the name of DFSS implies how it should be used – it doesn’t make much sense to treat it as a post-processing procedure when its very name says that you should design around it. Of course, your organization will likely already have some established design practices in place, and you will also need to adjust your work around them, so you can’t simply drop DFSS as a replacement for every process design system you already have in place. But combining it with your existing procedures and putting some careful thought into how you put them together can certainly change a lot in the way you’re running your organization.
Using Modern Technology to Prepare a Solid Implementation Plan
Having a lot of data to work with is a great plus when trying to develop a good plan for the implementation of DFSS in your company. The more statistics you have available, the better results you’ll be able to produce, and the more sensible your changes are going to be. Nowadays, you can achieve a lot in this regard through the use of modern technology like data mining systems.
You don’t necessarily need to work in the IT field in order to benefit from those techniques, and in fact, many other industries are adopting the use of data mining and predictive analysis in their standard procedures. There’s a lot to gain from obtaining a proper set of analytical data about your existing processes and working with it to develop new ones, but you also have to be careful. Too much data can sometimes be just as harmful as not having enough of it.
This means that you must be able to count on your analytical processes to produce good results, not just in general but also within the specific context of your own organization. And this, in turn, often translates to having a lot of domain knowledge. Previous data can help a lot too, so if you have proper collection practices in place you should definitely leverage your old data sets in order to get a better insight into your future developments.
DFSS can be a highly effective technique in multiple ways, and it’s simply one of the best options available when you have to reduce the variety in some process. However, you have to prepare for its implementation accordingly, and ensure that you have enough critical data in order to make the right decisions. Get that right, and the rest of the pieces will fall in place pretty much by themselves, especially if you already have some solid analytical methods in place and you know how to leverage their full potential.