This article may require to meet Wikipedia's. The specific problem is: Wikipedia is not a phone book. Please help if you can. ( November 2014) () () Advanced product quality planning ( APQP) is a framework of procedures and techniques used to develop in, particularly in the. It is similar to the concept of. According to the (AIAG), the purpose of APQP is 'to produce a product quality plan which will support development of a product or service that will satisfy the customer.' The chronicles of narnia 2005 in hindi download mp4 free.

It is the process employed by,, and their suppliers for their product development systems. Contents • • • • • History [ ] Advanced product quality planning is a process developed in the late 1980s by a commission of experts who gathered around the 'Big Three' of the US automobile industry: Ford, GM and Chrysler. Representatives from the three automotive original equipment manufacturers (OEMs) and the Automotive Division of American Society for Quality Control (ASQC)* created the Supplier Quality Requirement Task Force for developing a common understanding on topics of mutual interest within the automotive industry. This commission invested five years to analyze the then-current automotive development and production status in the US, Europe and especially in Japan.

At the time, the success of the Japanese automotive companies was starting to be remarkable in the US market. APQP is utilized today by these three companies and some affiliates.

Tier 1 suppliers are typically required to follow APQP procedures and techniques and are also typically required to be audited and registered to. This methodology is now being used in other manufacturing sectors as well.

The basis for the make-up of a process control plan is included in the APQP manual. The APQP process is defined in the AIAG's APQP manual, which is part of a series of interrelated documents that the controls and publishes. These manuals include: • The (FMEA) manual • The (SPC) manual • The (MSA) manual • The (PPAP) manual The (AIAG) is a non-profit association of automotive companies founded in 1982. Main content of APQP [ ] APQP serves as a guide in the development process and also a standard way to share results between suppliers and automotive companies. APQP specifies three phases: Development, Industrialization and Product Launch. Through these phases 23 main topics will be monitored.

These 23 topics will be all completed before the production is started. They cover such aspects as: design robustness, design testing and specification compliance, production process design, quality inspection standards, process capability, production capacity, product packaging, product testing and operator training plan, among other items.

February 2015 You have just completed a Gage R&R study on one of your critical to quality measurement systems. You want to find out how “good” that test method is.

Aiag Ppap Manual, free aiag ppap manual software downloads, Page 2.

You have done everything right. You carefully selected the parts to reflect the range of production. You carefully selected the operators to do the testing and randomized the run order for the parts. You ensured that the operators didn’t know what part they were testing. Each operator tested each part the required number of times. Now, you are ready to analyze the results. What method do you use?

You can analyze the Gage R&R study using one of the following analysis techniques: • Average and Range Method • ANOVA • EMP (Evaluating the Measurement Process) All three techniques have been covered in detail in past publications. This publication compares the output from the three techniques and attempts to decide which is best.

We will assume that we want to use the test method for process control. In this issue: • • • • • • • • You may download a pdf copy of this publication. The Data The data we will use is from the 4th edition of the Measurement Systems Analysis manual published by AIAG. In this Gage R&R study, there are three operators and ten parts. Each operator runs each part three times.

The data are shown in Table 1. For example, operator A ran part 1 three times with the following results: 0.29, 0.41, and 0.64. The data from this table are analyzed using each of the three Gage R&R analysis techniques using the SPC for Excel software. Before we start, we will quickly review the sources of variation in a Gage R&R study. There are four sources we primarily follow: repeatability, reproducibility, part, and total. Table 1: The Data Op. From reading Wheeler, my understanding is that the fault here lies with the AIAG (using% of total standard deviation) rather than anything about the Average & Range method of interpretation.Running your Gauge R&R example data in Minitab 17, I found that its two available methods (ANOVA and Average & Range) give practically indentical results, where for both the reported% of variance is useful and the reported% of standard deviation isn't (and adds up to well over 100% for both methods).