Why Should You Attend:
Many companies use attribute sampling plans at incoming, in-process, and final inspection. The common approach uses ANSI/ASQ Z1.4. There is an alternate, however, that may reduce cost. Sampling plans based on ANSI/ASQ Z1.9 can reduce cost by requiring smaller sample sizes.
Z1.9 is an effective and efficient method for acceptance sampling. Because it retains the methods required before calculators and spreadsheets, the standard appears overly complex.
This webinar explains the methods in Z1.9 and shows how to use it in a more modern setting. Participants will learn modern methods based on a simple calculator. The webinar uses the TI-30XA calculator, but any simple statistical calculator has the required functions.
Areas Covered in the Seminar:
Who Will Benefit:
This seminar is for people in manufacturing who are involved in setting product specifications, establishing acceptance plans, performing acceptance activities, and managing the supply chain. Attendees should have knowledge of manufacturing process that includes acceptance activities such as incoming, in-process, and final acceptance. Roles and jobs that will benefit include:
Dan is the President of Ombu Enterprises, LLC, a company offering training and execution in Operational Excellence, focused on analytic skills and a systems approach to operations management. Dan has more than 30 years experience in quality, operations, and program management in regulated industries including aviation, defense, medical devices, and clinical labs. He has a Masters Degree in Mathematics; is an ASQ certified Biomedical Auditor, Quality Auditor, Quality Engineer, Reliability Engineer, and Six Sigma Black Belt; and is certified by APICS in Resource Management.
Z1.9 uses measurements and their evaluation to make the decision on lot acceptance. Z1.9 looks complicated, but is quite simple. One problem is that the methods in Z1.9 use methods developed before the common calculator. Consequently, Z1.4 seems overly complex, but, with modern methods, it is straightforward.
Z1.9 uses information more effectively. When an inspection operation converts variables to attributes, it looses information. Basing the inspection on variables uses more information and allows smaller sample sizes.
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