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ISSSP Announces August Webinar: SPC for Autocorrelated Data Using Automated Forecasting



AUSTIN, Texas - August 8, 2019 - (Newswire.com)

Statistical process control using automated forecasting will be explored in the upcoming webinar presented by the International Society of Six Sigma Professionals (ISSSP) Wednesday, August 21 at 2 p.m. CT.

The webinar will discuss how to obtain a more accurate forecast for an SPC time series leading to an improved control chart.

John Noguera, the presenter, is Co-founder and Chief Technology Officer of SigmaXL, Inc., a leading provider of user-friendly Excel add-ins for Lean Six Sigma tools, statistical and graphical analysis and Monte Carlo simulation. John leads the development of SigmaXL and DiscoverSim with a passion for ease-of-use, practical and powerful features and statistical accuracy.

John has provided consulting and training services since 1989 and specializes in teaching statistical methods and consulting in the implementation of Six Sigma Quality, with a focus on practical application, with return on investment in manufacturing, service and transactional areas. John began his involvement with Six Sigma being mentored by the doctrine’s originator, Bill Smith.

ISSSP is the leading Six Sigma non-profit organization, providing educational and networking resources for its members. Members can access past presentations, white papers, case studies and webinars by leading Six Sigma experts and renowned leaders.

Click here for more information about registering for the webinar. For more information about ISSSP, please visit isssp.org

About ISSSP

The International Society of Six Sigma Professionals (ISSSP) is the leading non-profit organization promoting the high standards and values that Lean Six Sigma operates by. ISSSP provides access to Lean Six Sigma resources, advice on choosing accredited training providers, and opportunities for members to submit webinars, presentations and white papers.


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Original Source: ISSSP Announces August Webinar: SPC for Autocorrelated Data Using Automated Forecasting
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