Manufacturing Reflections

If you are aware of the controversy of the title this week, you have been in conversations with two technology focused groups that really speak different languages.  I think the real challenge has always been vocabulary and to give my answer up front – we can be partners in Manufacturing Excellence and shouldn’t be competing.  The two technologies are completely complementary and I encourage you to educate yourself in Quality vocabulary with a visit to another blog at http://www.softwareadvice.com/articles/manufacturing/a-plain-english-guide-to-modern-manufacturing-methods-1071610/.

But there is a real controversy.  Quality black belts have shut the door on Model-Predictive Control Solutions even though they are constantly searching and driving for six-sigma (minimum variability) performance.  There are concerns about unfamiliar vocabulary and some feel threatened by the assumption that their processes are not “in control”.  But pull manufacturing systems and lean manufacturing concepts demand a flexible manufacturing environment and with the Production Center Solutions being delivered as part of our Rockwell Software Solutions, we as an organization doing Model-Predictive Control are becoming more and more familiar with QC vocabulary.  Production Center and many MES solutions facilitate a migration to a lean manufacturing environment directly and actively.  MPC has always delivered reduced variability, provided a stable solution for flexible processing and generally deliver where previously not possible ‘direct quality control’ that lets a processor drive directly to a quality target all the time.

There is only one area of tension, but not about delivery solutions, about traditional solution objectives. And ultimately our solution objectives are customized for each solution – so that the apparent difference is non-existent.  Traditionally MPC will reduce process variability, but we consider value delivery most easily and rapidly measured by then shifting that mean within process and quality limits to reduce costs, increase yields, increase energy efficiency or increase production capacity.  Thus where my specification range is currently 3-sigma from the current average (Cpk is 1) and I reduce variability in half (new standard deviation is half of the  previous standard deviation), then my Cpk becomes 2.

If you have justified a project on shifting the mean (a traditional MPC project objective) and running more efficiently, then you would shift the mean 1-2 sigma toward the limit, capture economic benefit, but your Cpk is back to 1.  This is where there is apparent (but only apparent) difference in QC and MPC philosophy.  When a customer can justify the value of reducing variability, in measuring a benefit and realizing value in reducing variability without shifting the mean – then you can deploy MPC, reduce variability (roughly in half) and reduce the current level of out-of-spec production.  The challenge customers have had in these discussions is described in the quality world as a traditional underestimation of the cost of non-performance.  What does it really cost your business if one out of a thousand customers have an unsatisfactory product delivered, how does it grow  your business if only one out of ten thousand or one out of a million have an unsatisfactory product delivered.  Where the value of reducing off-specification production assuming roughly Gaussian statistics by cutting standard deviation – roughly in half supports deploying MPC technology, MPC and QC become clear partners.