Online Webinar 2019 - 60 Minutes Live Webinar on Statistical Process Control – Keys to Assess Process Variation and Ensure Quality

Overview
Statistical process control (SPC) is a form of feedback process control that tells us when a process needs intervention due to special or assignable cause variation. SPC charts are visual controls that tell the user immediately that a special or assignable cause is present. A point outside a control limit tells us that (1) the process variation has increased or (2) the process mean has shifted from the nominal, either of which makes the production of nonconforming parts more likely.
Session Highlights
1. Role of SPC in quality as a form of feedback process control
2. Effect of variation (spread) on quality, and also the effect of changes in the process mean that we want to be centered on the nominal (halfway between the specification limits) for processes that follow the normal or bell curve distribution.
3. Use of simulated gun targets to explain variation and accuracy to a workforce, and how to relate SPC charts to the gun targets on a side-by-side basis to easily generate a clear understanding.
4. Points outside control limits suggest beyond a quantifiable reasonable doubt that an undesirable process change has occurred.
5. The rational subgroup is a sample that is affected by all variation sources from a process. This includes long term as well as short term variation. The webinar will discuss symptoms—such as control charts that keep telling us that the process mean has shifted both up and down—that the rational subgroup has not been defined correctly. This may answer any questions about control charts that do not seem to function properly in your workplace.
6. Normal distributions (bell curves) are far more common in textbooks than they are in real factories. A special talking point of this webinar is that control charts that keep giving out of control signals for no apparent reason may well involve non-normal distributions, and also that off the shelf methods are readily available to deal with these situations.
Why one should attend the training
SPC is a form of feedback process control that can be part of the control plan for a manufacturing process. It allows the user to detect abnormal conditions, such as an increase in process variation or a shift in the process mean, before these undesirable changes result in the generation of rework or scrap. This webinar will equip the attendee to understand the basics of SPC, as well as the effect of variation on quality and the difference between random or common cause variation, and special or assignable cause variation.
key learning objectives
1. Know that SPC is a form of feedback process control that tells us when to adjust the process and when to leave it alone. Overadjustment or tampering (adjusting the process in response to random variation) makes quality worse.
2. Know the roles of variation and accuracy in manufacturing quality, and the implications of a Six Sigma process.
3. Know how control charts reflect undesirable changes in variation and accuracy (centering of the process on the nominal).
4. Know how to describe SPC as a statistical hypothesis test, the principles of which relate to everything we do in industrial statistics.
5. Know the concept of a rational subgroup, or a sample that reflects all variation sources from a process.
6. Know that special control charts are available for processes that do not follow the normal or bell curve distribution.
7. Know the basics of how to set up a control chart.
8. Recognize the role of the control chart in an ISO 9001 (or IATF 16949) quality management system.
Who WIll Benefit
Manufacturing
Quality engineer
Manager
Technician
Instructor Profile
William Levinson
Principal Consultant at Levinson Productivity, Years of Experience: 30+ years
Areas of Expertise: Statistical Process Control, Lean Manufacturing, Quality, ISO 9001, Design Of Experiments, Non-Normal Distributions, Quality Management Systems
William Levinson is the principal of Levinson Productivity Systems, P.C. He is an ASQ Fellow, Certified Quality Engineer, Quality Auditor, Quality Manager, Reliability Engineer, and Six Sigma Black Belt. He holds degrees in chemistry and chemical engineering from Penn State and Cornell Universities, and night school degrees in business administration and applied statistics from Union College, and he has given presentations at the ASQ World Conference, ISO/Lean Six Sigma World Conference, and others.