Understanding Attribute Acceptance Sampling including Z1.4 and c=0 Plans

This webinar provides the tools needed to understand and implement acceptance sampling. It explains the basis for sampling plans, the binomial distribution, and uses it to understand the sampling plan’s performance using the operating characteristic (OC) curve.

Why Should You Attend:

Attribute acceptance sampling is a common tool for medical device manufacturers. Unfortunately, many people don’t understand the underlying concepts or the basis to make decisions. This presentation explains the underlying ideas of attribute sampling plans and how to use the two most common published plans, Z1.4 and c=0. In addition, the presentation describes the major differences between the approaches. Attribute sampling plans are characterized by the operating characteristic curve (OC curve) which provides the basis for risk and understanding sampling costs.

Participants gain a solid understanding of how the OC curve is built, how to use it, and how to identify some of the most important points on the curve, including the AQL and RQL points. The webinar also provides complete descriptions of three other important curves that help you understand a sampling plan. The average sample number (ASN) helps you predict the number of samples you will take. The average outgoing quality (AOQ) helps you foresee the results if you inspect rejected lots. The average total inspected (ATI) helps you calculate how many items you will inspect including rejected lots.

Users of Z1.4 learn how to set up sampling and select parameters such as AQL and Level. The webinar provides a complete description of Z1.4, showing the process from receiving the lot to selecting the sample size to making the accept/reject decision. The c=0 plans are very popular, since they are based on the notion that everything in the sample should pass inspection. The course examines these plans using the curves described above. The OC curve, in these plans, has a different shape that can lead to problems.

Bonus Material:

• Participants receive an Excel spreadsheet to calculate the important curves that characterize attribute acceptance plans

Learning Objectives:

• Understand why the binomial distribution applies
• Understand the operating characteristic, OC, curve and how it quantifies risk
• Use Excel to calculate the OC curve
• Use the sampling tables to determine the sampling plan
• Understand the difference between single, double, and multiple sampling plans
• Learn why double sampling plans are the most economical choice
• Understand when to switch between normal, reduced, and tightened inspection
• Use the switching rules to help improve your supplier management program
• Learn how the switching rules can help reduce inspection cost

Areas Covered in the Webinar:

• Sampling concepts.
• With or without replacement
• Simple or stratified sampling
• The binomial distribution.
• Possible outcomes and Bernoulli trials
• The binomial formula and what it means and The cumulative binomial
• Sampling plans.
• The AQL concept.
• The ideal OC curve, The practical OC curve, Reading risk off the OC curve
• Special points on the practical OC curve.
• The AQL point, The IQL point, The RQL point
• Characterizing sampling plans.
• Using rectifying inspection
• The four important curves
• The OC curve, The ASN curve, The AOQ curve, The ATI curve
• Z1.4 Plans.

Setting up the plan

• Selecting the Level, Selecting the AQL, Knowing the lot size
• Selecting single, double, or normal plans
• Cost analysis for single v. double plans
• Switching rules
• When to switch
• Why reduced inspection lowers cost
• How tightened inspection can help improve supplier performance

c=0 plans

• How they are matched to Z1.4 plans
• The RQL point is the key
• Making the curves cross at one point
• How to employ switching rules
• The OC curve
• Consequences of moving from Z1.4 to c=0
• Inventory management and stock-outs
• Supplier management and performance metrics

Who Will Benefit:

The presentation is valuable to anybody who uses attribute sampling plans including:

• Quality Engineers
• Production and Process Engineers
• Manufacturing Engineers
• Design Engineers
• Purchasing Managers
• Purchasing Agents
• Supplier Quality Engineers
• Quality Supervisors
• Quality Inspectors
• Quality Managers