Advanced product quality planning (APQP) is a framework of procedures and techniques used to develop products, particularly the automotive industry. It is a defined process for a product development system for automobile manufacturers and their suppliers. According to the Automotive Industry Action Group (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 process is described in the AIAG manual.

 Advanced product quality planning is a process developed in the late 1980s by a group of experts gathered from the US automobile manufacturers: Ford, GM and Chrysler. This group 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 I suppliers are typically required to follow APQP procedures and techniques and are also typically required to be audited and registered to ISO/TS 16949. The APQP process is defined in the AIAG's APQP Manual, which is part of a series of interrelated documents that the AIAG controls and publishes.

The Core System Tools are defined and controlled by the following manuals published by AIAG.

·         The FMEA Manual

·         The Statistical process control (SPC) Manual

·         The Measurement Systems Analysis (MSA) Manual

·         The Production Part Approval Process (PPAP) Manual

APQP serves as a guide in the development process and also as a standard way to share results between suppliers and automotive companies.

Measurement System Analysis (MSA) is a specially designed to identify the components of variation in the measurement. Just as processes that produce a product may vary, the process of obtaining measurements and data could have variation and produce defects. A Measurement Systems Analysis evaluates the test method, measuring instruments, and the entire process of obtaining measurements to ensure the integrity of data used for analysis (usually quality analysis) and to understand the implications of measurement error for decisions made about a product or process.

MSA analyzes the effect of equipment, operations, procedures, software and personnel that affects the  measurement characteristic.

A Measurement Systems Analysis considers the following:

·         Selecting the correct measurement and approach

·         Assessing the measuring device

·         Assessing procedures and operators

·         Assessing any measurement interactions

·         Calculating the measurement uncertainty of individual measurement devices and/or measurement systems

Common tools and techniques of Measurement Systems Analysis include: calibration studies, fixed effect ANOVA, components of variance, Attribute Gage Study, Gage R&R, ANOVA Gage R&R, Destructive Testing Analysis and others. The tool selected is usually determined by characteristics of the measurement system itself.

MSA basically focuses on the following factors.

·         Measurement uncertainty

·         Accuracy and precision

·         Bias

·         Stability

·         Linearity

·         Repeatability and Reproducibility

·         Attribute study

·         Practical examples for calculating Bias, Stability, Linearity, Repeatability and reproducibility, Attribute study

Production Part Approval Process (PPAP) is another integral part of the System Tools used to ensure the product of a process is confirming to customer requirements. The result of this process is a series of documents gathered in one specific location (a binder or electronically) called the "PPAP Package".  The submission and approval of the PSW indicates that the supplier diligently gathered all required data, has reviewed all documents for accuracy and that the customer has not identified any issues that would prevent the release and use of the product(s) involved.

Suppliers are required to obtain PPAP approval from the vehicle manufacturers whenever a new or modified component is introduced to production, or the manufacturing process is changed. Obtaining approval requires the supplier to provide sample parts and documentary evidence showing that:

·         The clients requirements have been understood

·         The product supplied meets those requirements

·         The process (including sub suppliers) is capable of producing conforming product

·         The production control plan and quality management system will prevent non-conforming product reaching the client or compromising the safety and reliability of finished vehicles

PPAP may be required for all components and materials incorporated in the finished product, and may also be required if components are processed by external sub-contractors.

A completed PPAP package includes minimum the following items:

·         Design Records.

·         Authorized Engineering Change (note) Documents

·         Engineering Approval

·         Design Failure Mode and Effect Analysis

·         Process Flow Diagram

·         Process Failure Mode and Effect Analysis

·         Control Plan

·         Measurement System Analysis Studies (MSA)

·         Dimensional Results

·         Records of Material / Performance Tests

·         Initial Process Studies

·         Process Capability Study

·         Qualified Laboratory Documentation

·         Appearance Approval Report

·         Sample Production Parts

·         Master Sample

·         Checking Aids

·         Customer-Specific Requirements

·         Part Submission Warrant (PSW)

Statistical process control (SPC) is the application of statistical methods to the monitoring and control of a process to ensure that it operates at its full potential to produce conforming product. Under SPC, a process behaves predictably to produce as much conforming product as possible with the least possible waste. While SPC has been applied most frequently to controlling manufacturing lines, it applies equally well to any process with a measurable output. Key tools in SPC are control charts, a focus on continuous improvement and designed experiments.

Much of the power of SPC lies in the ability to examine a process and the sources of variation in that process using tools that give weight to objective analysis over subjective opinions and that allow the strength of each source to be determined numerically. Variations in the process that may affect the quality of the end product or service can be detected and corrected, thus reducing waste as well as the likelihood that problems will be passed on to the customer. With its emphasis on early detection and prevention of problems, SPC has a distinct advantage over other quality methods, such as inspection, that apply resources to detecting and correcting problems after they have occurred.

In addition to reducing waste, SPC can lead to a reduction in the time required to produce the product or service from end to end. This is partially due to a diminished likelihood that the final product will have to be reworked, but it may also result from using SPC data to identify bottlenecks, wait times, and other sources of delays within the process. Process cycle time reductions coupled with improvements in yield have made SPC a valuable tool from both a cost reduction and a customer satisfaction standpoint.

Two kinds of variation occur in all manufacturing processes: both these types of process variation cause subsequent variation in the final product. The first is known as natural or common cause variation and consists of the variation inherent in the process as it is designed. Common cause variation may include variations in temperature, properties of raw materials, strength of an electrical current etc. The second kind of variation is known as special cause variation, or assignable-cause variation, and happens less frequently than the first. With sufficient investigation, a specific cause, such as abnormal raw material or incorrect set-up parameters, can be found for special cause variations.

The effective implementation and utilization of the System Tools will help the organization to avoid waste and improve its process capabilities leading to improved bottom line.