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Overview:

This course teaches participants the fundamental concepts and methods needed to establish effective control charts and estimate process capability. In addition to learning traditional control charts (e.g. xbar, r/s), participants will be exposed to other useful charts for handling multiple sources of variation (within/between) and short production runs. Practical aspects of implementing SPC on the shop floor are also discussed.

Estimating process capability for both normal and non-normal data is discussed. The meaning and limitations of popular capability are presented in detail.

This highly interactive course will allow participants the opportunity to practice applying SPC techniques with various data sets. The objective is to provide participants with the key tools and knowledge to be able to apply the methods effectively for their own processes.

Why should you attend?

  • Understand SPC terminology and basic concepts
  • Implement traditional control charts for variable and attribute data
  • Diagnose control charts to detect process instability
  • Ensure appropriate sampling strategies and sample sizes
  • Choose the correct type of chart for various applications
  • Estimate Process Capability (For Normal and Non-Normal Data)
  • Understand limitations of popular capability indices (Cp, Cpk)
  • Develop awareness of specialized charting techniques and when to use them
  • Be able to make immediate improvements in the implementation of SPC in your organization

Who will benefit:

  • Quality & Process Engineers
  • Quality Technicians
  • SPC Supervisors
  • Production Supervisors
  • Personnel involved in SPC techniques Laboratory Personnel
  • Manufacturing/Operations Personnel
  • Process Improvement Personnel
  • Supplier Quality Personnel

 Agenda:

Day 1 Schedule:

Lecture 1:

Variation Fundamentals

  • Concept of Variation
  • Data Types
  • The Normal Distribution
  • Control Chart Concepts/Examples
  • Control Limits vs. Specification Limits
  • Definition of Control/Stability

Lecture 2:

Variation Fundamentals (cont'd)

  • Definition of Quality
  • Sources of Variation
  • Process Capability Concepts
  • Quality Control vs. Process Control
  • Basic Statistics
  • Individuals, Averages, and the Central Limit Theorem

Lecture 3:

Traditional Control Charts

  • Constructing Charts (Variable Data)
  • Xbar/R, Xbar/S, I-MR Charts
  • Chart Signals for Special Causes
  • Interpreting Charts
  • Type I and Type II Errors

Lecture 4:

Traditional Control Charts

  • Re-computing Control Limits
  • Sampling Procedures and Frequency
  • Rational Samples
  • Chart Sensitivity / Sample Size
  • Determining Appropriate Sample Sizes
  • Control Chart Selection

Day 2 Schedule:

Lecture 1:

Process Capability

  • Stability vs. Capability
  • Types of Capability Assessments
  • Estimating PPM / Proportion Defective
  • The Standard Normal (Z Values)

Lecture 2:

Process Capability (cont'd)

  • Capability Indices: Cp, Cpk, Pp, Ppk
  • Interpreting Indices
  • Process Capability for Non-Normal Data

Lecture 3:

Other Control Charts

  • Charts for Attribute Data
  • Charts for Short Production Runs

Lecture 4:

Other Control Charts

  • Within/Between Charts (Multiple Locations)
  • CUSUM Charts

 

Speaker:

Steven Wachs

Principal Statistician, Integral Concepts, Inc.

Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.

Steve is currently a Principal Statistician at Integral Concepts, Inc. where he assists manufacturers in the application of statistical methods to reduce variation and improve quality and productivity. He also possesses expertise in the application of reliability methods to achieve robust and reliable products as well as estimate and reduce warranty.

Education

M.A., Applied Statistics, University of Michigan, 2002

M.B.A, Katz Graduate School of Business, University of Pittsburgh, 1992

B.S., Mechanical Engineering, University of Michigan, 1986

Location: Baltimore, MD Date: December 1st & 2nd, 2016 and Time: 9:00 AM to 6:00 PM

Venue: The DoubleTree Baltimore-BWI Airport

Address: 890 Elkridge Landing Road - Linthicum, MD 21090

Register now and save $200. (Early Bird)

Price:  $1,295.00 (Seminar Fee for One Delegate)

Until October 20, Early Bird Price: $1,295.00 from October 21 to November 29, Regular Price: $1,495.00

Register for 5 attendees   Price: $3,885.00   $6,475.00 You Save: $2,590.00 (40%)*

Quick Contact:

NetZealous DBA as GlobalCompliancePanel

Phone: 1-800-447-9407

Fax: 302-288-6884

Email: support@globalcompliancepanel.com      

Website: http://www.globalcompliancepanel.com

Registration Link - http://bit.ly/2dVoMyu-Statistical-Process-Control

Follow us on LinkedIn: https://www.linkedin.com/company/globalcompliancepanel  

If the Statistical Process Control & Process Capability - GlobalCompliancePanel 2016 is important to your business, act now and make the appropriate connections. See the contact information below.

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Website: http://www.globalcompliancepanel.com/...
Conference/Event Dates: 12/01/2016 - 12/02/2016
Conference/Event Hours: 9:00 AM to 6:00 PM
Classification: B2B
Primary Industry: Clinical Pharmacology
Other Industries: Clinical Pharmacology, Professional Healthcare
Estimated Attendance: 50
Audience: Quality & Process Engineers
Quality Technicians
SPC Supervisors
Production Supervisors
Personnel involved in SPC techniques Laboratory Personnel
Manufacturing/Operations Personnel
Process Improvement Personnel
Supplier Quality Personnel
Venue: The DoubleTree Baltimore-BWI Airport
Venue Phone: (800) 447-9407
Venue Type: Indoor - Hotel

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Steven Wachs has 25 years of wide-ranging industry experience in both technical and management positions. Steve has worked as a statistician at Ford Motor Company where he has extensive experience in the development of statistical models, reliability analysis, designed experimentation, and statistical process control.

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