Advanced Product Quality Planning - Core Tools

APQP (Advanced Product Quality Planning) deliverables are discussed in conjunction with the PPAP (Part Approval Process) requirements. Course content discusses at high level the core tools requirements as later course content deep dives into the Core Tools:

• PFMEA (Process Failure Mode Effects Analysis)
• Control Plans
• Measurement Systems Analysis (MSA)
• PPAP documentation requirements

All content is based on the AIAG (Automotive Industry Action Group) standards and meets the IATF16949 requirements.

P-FMEA Overview

Process Failure Mode Effects Analysis (PFMEA) is a structured analytical tool used by an organization, business unit, or cross-functional team to identify and evaluate the potential failures of a process. PFMEA helps to establish the impact of the failure and identify and prioritize the action items with the goal of alleviating risk. It is a living document that should be initiated prior commercializing production and maintained through the life cycle of the product. PFMEA evaluates each process step and assigns a score on a scale of 1 to 10 for the following variables:

• Severity
• Occurrence
• Detection
• RPN – Risk priority number

Control Plans Overview

A control plan is a written summary that describes what is needed to keep an improved process at its current level. This includes human resources and training requirements, actions that should be taken if measures are outside the specified range, and reactions needed to ensure process owners sustain the gains of process improvements. The purpose of the control plan is to ensure that performance improvements made by the project team are sustained over time.

Control Plan Benefits:

• Provides a summary of process knowledge
• Keeps process requirements and expectations visible for all functional area involved
• Describes data used to assess process performance (process and/or quality indicators)
• Describes when action needs to occur, how frequently to check performance, who is responsible for checks, and the contingency plans
• Describes who is involved and what their roles are in the process

Measurement System Analysis (MSA), is a method to identify the components of variation in the measurement. It is used to quantify the impact of measurement errors and to ensure the integrity of data used for analysis. MSA is an important pre-requisite for data analysis because a measurement system that is not accurate and/or precise may lead to misguided decision making.