Online Training 2016 - Pre-Control charts: A precursor to Statistical Process Control (SPC), this talks about how to develop and implement pre-control charting - By AtoZ Compliance

Key Take Away
Get motivated with a plan to reduce Quality Management System complexity while improving upon efficiency and effectiveness using Statistical Process Control (SPC) to develop and implement pre-control charting.
Overview
Pre-Control is easier than (SPC) Statistical Process Control training! With no control limits to calculate and “go/no go” colored zones, this control tool can rapidly help to reduce process variation. Participants will observe a modeled webinar that will develop and run Pre-Control charting.
Measurement System Analysis (MSA): High level overview of MSA will be conducted to help participants understand the necessity of an effective measuring system in advance to implementing Pre-Control
Cpk Overview: High level Cpk overview will be conducted to help participants understand baseline capability in advance of implementing pre-control
Normal Distribution: High level overview of how the normal distribution's cumulative distribution function is used to estimate and establish the zones on a Pre-Control Chart
Pre-Control Chart: Participants will see the application of the concepts above along with the development of a mock Pre-Control Chart where the process will be “run” based upon the “go/no go” zones that are established.
Why Should You Attend
Formal Statistical Process Control chart requires the calculation of limits and constant assessment for control. If a business is not yet “on-line” electronically with their charts, SPC charts can be difficult to efficiently execute in a process environment. Pre-Control is a color-coded format which uses specification limits which operators can relate to. They use simple levels of “go and no go” based on where the data falls.
Areas Covered In This Webinar
Participants will see how to draft and create a Pre-Control chart. A sample process will be “run” in which the next step decisions will be modelled. This webinar will leave people motivated with a plan of how to reduce system complexity while improving upon the efficiency and effectiveness of their quality management system.
Learning Objectives
Reduce process complexity and minimize risk
Increase affectivity of a Core Tool
Increase personnel compliance in proactive process management
Who Will Benefit
Quality, Lean and Engineering System Practitioners
Directors
Vice-Presidents
Engineers
Analysts
Managers
Speakers Profile
Jd Marhevko
Jd Marhevko has served as Vice President of Quality and Lean for Accuride Corporation since April 2012, having joined the company as Vice President, Quality in January 2012. She has been involved in Operations and Quality/Lean/Six Sigma efforts across a variety of industries for more than 25 years. Extensively experienced in developing cost-effective and value-added quality and lean systems enterprise-wide, Marhevko has an impressive track record of success in aligning goals to drive process improvement and productivity.
Marhevko came to Accuride Corp. from Global Productivity Solutions and JQLC, Inc., where she served as Business Excellence Consultant. She previously held quality, performance and operations positions at SPX Test & Measurement, Eaton Global-Hose Division, Cincinnati Milacron, Cherry Automotive, and Robert Bosch Corporation. Marhevko is an American Society of Quality (ASQ) Fellow, ASQ Certified Six Sigma Black Belt (CSSBB), ASQ Certified Quality Engineer (CQE), ASQ Certified Manager of Quality/Organizational Excellence (CMQ/OE), an ASQ Certified Trainer for a variety of Quality Tools including Hoshin Kanri and Strategic Planning. She is the Immediate Past-Chair of the ASQ Quality Management Division (QMD) which has more than 23,000 global members.
Additionally, she is a certified Master Black Belt (MBB) from Allied Signal; has held various Malcolm Baldrige National Quality Award (MBNQA) Senior Assessor roles; and held a Quality Systems Auditor (QSA) certification from the International Register of Certified Auditors (London, England) for more than 10 years.
Marhevko earned a Bachelor of Science in Mechanical Engineering degree from Oakland University, and a Master of Science in Administration in Manufacturing Plant Management from Central Michigan University.
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