Reducing Variation in Manufacturing Processes
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When manufacturing variation is reduced, two good things happen. One is that the consumer’s second product experience is more like the first. This builds consumer confidence and, therefore, repeat sales. The second is fewer production line stoppages and a process flow that is more laminar, less turbulent. Both of these outcomes of Process Variation Reduction (PVR) enhance the financial bottom line.
PVR capitalizes on the fact that quality and productivity are positively correlated when efforts toward their improvement are carried out correctly. Data carefully and systematically derived from the process are used to quantify both process capability and process performance. Capability is the intrinsic, inherent variability of the process, while performance is a measure of the finished product variation received by the consumer. The difference between performance and capability can usually be translated into dollars, and the draw to capture those dollars provides the PVR motivation.
Why should you Attend: Demands for peak productivity and perfect quality are too high to permit manufacturing inefficiencies. Yet many manufacturing processes hemorrhage money, putting their directors at financial, competitive risk. This seminar guides practitioners in the approach to discovery and elimination of interfering sources of process variation so that manufacturing can proceed without interruption and with consistently high quality levels.
Areas Covered in the Session:
Who Will Benefit:
- Basic concepts and Statistical Thinking
- Motivation - why engage in PVR
- Simulated Example
- How to get started
- Process ownership
- Requirements for Success
- Understanding sources of variation
- Quantifying sources of variation
- Examples of application
- Useful tools
- Supply chain executives
- Production executives
- Plant managers
- Production managers & supervisors
- Quality executives
- Quality managers & supervisors
- Plant comptrollers
Lynne Hare is a consulting statistician emphasizing business process improvement in R&D, Manufacturing and other strategic functions. Serving a large client base, he has helped bring about culture change in Research by accelerating speed to the successful launch of new products and processes and in Manufacturing through the reduction of process variation.
His former positions include the Director of Applied Statistics at Kraft Foods, Chief of Statistical Engineering at the National Institute of Standards and Technology, Director of Technical Services at T.J. Lipton (a Unilever company), Manager of Statistical Applications there as well, Statistics Group Leader at Hunt-Wesson Foods and Visiting Professor at Rutgers University.
Lynne’s technical expertise includes experimental strategies and design of experiments for Research as well as quality and productivity improvement for Manufacturing. He holds M.S. and Ph.D. degrees from Rutgers University and an A.B. in mathematics from The Colorado College. Lynne is a Fellow of the American Statistical Association and former chairman of its Section on Quality and Productivity. He is also a Fellow of the American Society for Quality and former chairman of its Statistics Division.
The ASQ has awarded him the William G. Hunter and Ellis R. Ott Awards for excellence in quality management. Kraft Foods presented him with the Technology Leadership Award for career accomplishments. He writes a column for Quality Progress Magazine and has numerous publications in technical journals.