Statistical Methods for Quality Improvement
Instructor:
Steven Wachs
Product ID: 705856
Training Level: Intermediate
- Duration: 90 Min
Description:
This webinar introduces important statistical concepts and methods for making objective decisions to ensure and improve product quality.
The methods have many applications including:
- Determining how well my process/product meets requirements
- Knowing when a process or system is behaving consistently or differently than before
- Uncovering which key inputs to my process affect product performance or customer satisfaction
- Ensuring that I can effectively measure what I need to
- Comparing groups of data when random (natural) variation is present?
- Predicting future outcomes using a predictive model
Why Should You Attend:
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.
Areas Covered in the Webinar:
- Variation & Quality
- Process Stability/Statistical Process Control
- Process Capability Assessment
- Predictive Models (Regression & Design of Experiments)
- Hypothesis Testing for Decision Making
- Measurement Systems Assessment
- Examples & Applications
Who Will Benefit:
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
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.
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Refund Policy
Our refund policy is governed by individual products and services refund policy mentioned against each of offerings. However in absence of specific refund policy of an offering below refund policy will be effective.
Registrants may cancel up to two working days prior to the course start date and will receive a letter of credit to be used towards a future course up to one year from date of issuance. ComplianceOnline would process/provide refund if the Live Webinar has been cancelled. The attendee could choose between the recorded version of the webinar or refund for any cancelled webinar. Refunds will not be given to participants who do not show up for the webinar. On-Demand Recordings can be requested in exchange. Webinar may be cancelled due to lack of enrolment or unavoidable factors. Registrants will be notified 24hours in advance if a cancellation occurs. Substitutions can happen any time. On-Demand Recording purchases will not be refunded as it is available for immediate streaming. However if you are not able to view the webinar or you have any concern about the content of the webinar please contact us at below email or by call mentioning your feedback for resolution of the matter. We respect feedback/opinions of our customers which enables us to improve our products and services. To contact us please email [email protected] call +1-888-717-2436 (Toll Free).
