The Six Sigma methodology – Define, Measure, Analyze, Improve, Control, or DMAIC – is known for its ability to eliminate problems resulting from variability in manufacturing, engineering and transactional processes. There will be those times, though, when no improvement will enable an existing process to meet customer expectations.
When this happens, a new process is required to replace the existing one, and the best way of developing that process is through DMAIC’s companion methodology known as Design for Six Sigma (DFSS).
Though DFSS doesn’t actually design a new part or process (every organization has a unique design process tailored specifically to its own service or products) it can make processes more robust, less wasteful and less costly. While DMAIC is often defined as the “find and fix” methodology, DFSS can be defined as a preventative and proactive one. Its ability to ‘predict’ potential issues can save organization time, prototypes and validation tests, which translates into a less expensive launch.
Here are some key aspects to designing for Six Sigma:
Understand Your Customers and the Capability of the Processes
One of the first things a DFSS plan must do is compare customer requirements and process capability. In order to do this, DFSS users must understand the customer’s expectations of their product or service and identify which will be the focus of the DFSS effort. This will involve listening to the voice of the customer (VOC), prioritizing customer responses and, most important, identifying a measurable target and range for these requirements.
Once you understand customer expectations you need to make sure you understand the capability of the processes. In order to do this you’ll need to answer this question for each key process: “How often will this process cause us to fail to meet customer requirements?” Comparing customer requirements and process capability will allow you to predict the level at which you’ll be able to meet customer expectations.
Prediction Equations
Another key principle of DFSS is to understand the relationship of inputs to outputs, also known as ‘prediction equation.’ Prediction equations can be determined using various methods: by obtaining data from a process map or product drawings, using principles inherent in a design’s physics, chemistry or geometry, or using a Design of Experiments to describe the relationship between inputs and outputs.
Regardless of how the prediction equation is obtained, knowing it will allow you to predict whether a design will meet customer expectations and, if not, what needs to be changed.
Prediction equations create robust design, meaning they allow you to fully understand what effect each input will have on an output so you may adjust a design to hit a target and to choose settings that will reduce variability.
Determining Success
There are a few critical factors that will determine your success at implementing DFSS. The first and most important factor is the stability in the critical processes. Prediction is at the very heart of DFSS, and prediction relies on understanding process capability.
The second factor of DFSS success is ensuring cross-functionality, meaning process stability must interact with customer requirements, which should be communicated in quantitative and measurable terms.
And finally, it’s very important that DFSS is implemented in an environment where there are clear targets set for performance, quality, cost and delivery, and where rewards for the design team are based on measurable achievement at the end.
In order for DFSS implementations to be successful, design teams should start by focusing on a single pilot project to get a handle on the tools and disciplines involved. And, as with any Six Sigma endeavor, it is recommended that teams use a knowledgeable and experienced practitioner who can guide them every step of the way.
