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February 21st, 2019
Is Six Sigma a Pre-requisite to Design for Six Sigma?
No! It’s a common misconception though.
Design for Six Sigma (DfSS) is perceived to be more complex than Six Sigma and often ‘too hard’.
Before we address this, just what is DfSS?
DfSS is a methodology that revolves around the ability to be able to predict the capability of a process or product to meet its specifications.
If you can’t do this then you can’t really ‘do’ DfSS.
If one can make such predictions then this gives the opportunity for optimisation of the process or product design before the process or product is realised. Unfortunately this does not happen often enough, resulting in having to (over)use product/process improvement techniques like Lean Sigma!
Surely it makes more sense to put the effort into the design?
Once the process or product design is frozen it is very difficult to change it to such an extent that would give a step change in capability – most Six Sigma projects, for example, don’t get close to Six Sigma! Typically such projects give valuable, but incremental improvements relative to those that could be achieved at the design concept stage.
Process Design – In DfSS for process design we should have the ability to ‘play’ with different process designs in the virtual world. A discrete event simulation package is very useful for this and several exist on the market (Simul8 being one such package).
Using such a package one is able to predict lead times, staffing, work in progress, inventory levels and many more ‘lean’ metrics under as many different process design scenarios as you wish – quickly, and for free!
Product Design – In DfSS for product design we should have the ability to predict the design attributes of interest (e.g. lifetime, emissions, vibration levels, fluid flow characteristics etc.)
Some companies working in fields of advanced technology (automotive and aerospace being two such sectors) have sophisticated simulation tools that are routinely used during the design process for such calculations. It is but a short step to using these for optimising the ‘robustness’ of the design; making it sufficiently insensitive to uncontrollable factors.
In sectors with less analytical simulation capability DfSS requires the use of Designed Experiments (DoE) on existing (or preferably prototype) hardware to generate mathematical models of the outputs of interest. This sounds difficult but doesn’t need to be – although it will certainly be time-consuming, and this will certainly cause significant stress in the New Product/Process Introduction process if not adequately factored into the work breakdown structure.
DfSS requires very strong and disciplined project management to work effectively.
DfSS isn’t just about prediction and optimisation, however; it is a methodology, just as Six Sigma isn’t just about the Improve phase!
Unlike Six Sigma, however, DfSS does not (yet) have a standardised (e.g. ISO) methodology.
There are several common methodologies, however. Examples are:
- DCOV (Define, Characterize, Optimize, Verify)
- DMADV (Define, Measure, Analyze, Design, Verify)
- IDOV (Identify, Design, Optimize, Verify).
I have used these over the years but have found shortcomings in all of them.
This led me to invent my own methodological phases of Define, Design, Quantify, Optimize, Verify and Capture; DDQOVC, or D2QOVC (pronounced DEE SQUARED QUO VEE CEE).
Want to know more about D2QOVC? click here.
Admittedly, this doesn’t quite roll off the tongue but I find it a more appropriate sequence of phases.
The flowchart below shows these phases and the typical steps within each phase:
The importance of a strong Define phase is evident!
There is no point optimizing a process of product whose requirements are poorly understood or poorly articulated.
This phase is heavily reliant on the broader team, whilst the Design, Q, and O phases obviously place focus on the design community.
Quality Function Deployment (QFD) is a good vehicle for managing many of the activities in the Define phase but it is not essential. (Indeed QFD can be counter-productive if not handled carefully.)
The Capture phase is vital to DfSS. There is no doubt that adopting a DfSS approach will initially take rather longer than the new product/process introduction process usually allows.
This is a cause of tension if expectations are not managed from the beginning.
However, by capturing the predictive models and all other pertinent information to enable easy retrieval and contextualisation, the process of DfSS can be significantly ‘sped up’.
Indeed, if the Capture phase is done well you should find that adopting DfSS for process or product design takes up less time than traditional approaches, and gives more conformant processes and products, with less reliance on having to improve them after the fact.
Coming back to our earlier question: “Is DfSS hard?”
Well, yes it is – it requires a good deal of training and commitment by many people (not least of which senior management) over the whole design and realisation process.
DfSS is not simply a focused well-bounded stand-alone 3-6 month project like typical Six Sigma projects – the scope of DfSS is by definition much wider and some tools and techniques will be new to people, so it also requires experts in DfSS to help with training and application, at least initially.
The real question should be “Is DfSS beneficial?”. The answer is emphatically “Yes” – it can be a real game-changer.
Don’t wait until you’ve ‘done’ Six Sigma (or Lean Sigma), whatever that means!
Dr Phil Rowe is a Senior Consultant and Lean Six Sigma Master Black Belt with over 30 years’ industrial experience.
Phil was trained in Six Sigma by Dr. Mikel Harry, founder of the Six Sigma Academy. Trained in DFSS at GE, Phil became General Domestic Appliances’ DFSS Master Black Belt and programme manager, working with design engineers in applying DFSS tools to high investment new product programmes.
More recently Phil achieved recognition as a Chartered Statistician, the highest professional award of the Royal Statistical Society. This award provides formal recognition of his statistical qualifications, professional training and experience and follows on from his recent award of a 1st Class BA Hons in Mathematics and Statistics.
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