statistical-methods-for-quality-improvement

Statistical Methods for Quality Improvement

On Demand

  • 90 minutes
  • 0 Day Left
     May 20, 2019 - May 24, 2019
  •   08:00 AM - 09:30 AM ET
    05:00 AM - 06:30 AM PT

Many companies are swimming in data yet raw data is mostly useless without methods to turn this data into useful and actionable information. Those individuals and companies that make best use of the available data achieve a competitive advantage by optimizing their operations and making superior decisions. Companies that fail to take advantage of data are resigned to chasing rather than leading in this information age.

This webinar provides a solid introduction of important statistical concepts and methods that are essential for making objective decisions related to product quality. These methods are based on process data and allow optimal decisions to be made.

This session by expert speaker Steven Wachs, where participants will understand the purpose and value provided for each of the methods discussed and understand their place in a data driven quality system.

Session Highlights:

This webinar introduces important statistical concepts and methods for assessing and ensuring product quality. The methods include: Statistical Process Control, Process Capability Assessment, Regression Modeling, Design of Experiments, Hypothesis Testing, and Measurement Systems Assessment.

The methods have many applications including:

  • Ensuring that I can effectively measure key characteristics

  • Determining how well my process/product meets requirements

  • Knowing when a process or system is behaving consistently (stable) or differently (unstable) than before

  • Uncovering which key inputs to my process affect product performance or customer satisfaction

  • Predicting future outcomes using a predictive model

  • Comparing groups of data when random (natural) variation is present?

  • Ensuring adequate sample sizes for making decisions

This program will cover the following topics:

  • Variation & Quality

  • Measurement Systems Assessment

  • Process Stability/Statistical Process Control

  • Process Capability Assessment

  • Predictive Models (Design of Experiments & Regression Modeling)

  • Hypothesis Testing for Decision Making

  • Examples & Applications

Why You Should Attend:

In a relatively short session, participants will receive a solid overview of essential quantitative methods for developing process understanding, assessing current performance, and identifying methods for process improvement. Following this session, participants should be immediately be in a position to make improvements by identifying applications in their own operations.

Who Should Attend:

The target audience includes personnel involved in product/process development and manufacturing

  • Quality Personnel

  • Manufacturing Personnel

  • Operations / Production Managers

  • Production Supervisors

  • Supplier Quality personnel

  • Quality Engineering

  • Quality Assurance Managers, Engineers

  • Process or Manufacturing Engineers or Managers



*You may ask your Question directly to our expert during the Q&A session.

** You can buy On-Demand and view it as per your convenience.

Steven Wachs

Steven Wachs

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 to estimate and reduce warranty. In addition to providing consulting services, Steve regularly conducts workshops in industrial statistical methods for companies worldwide.

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