Advanced Product Quality Planning

Advanced Product Quality Planning Advanced Product Quality Planning (or APQP) is a framework of procedures and techniques used to develop products in industry, particularly the automotive industry. It is quite similar to the concept of Design For Six Sigma (DFSS). It is a defined process for a product development system for General Motors, Ford, Chrysler 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 810-358-3003. History Advanced Product Quality Planning is a process developed in the late 1980's by a commission of experts gathered from the 'Big Three' US automobile manufacturers: Ford, GM and what was then Chrysler. This commission invested five years to analyse 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 utilised 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 basis for the make-up of a Process Control Plan is included in the APQP Manual. [1] These manuals include: The FMEA Manual The Statistical process control (SPC) Manual The Measurement Systems Analysis (MSA) Manual The Production Part Approval Process (PPAP) Manual The Automotive Industry Action Group (AIAG) is a non-profit association of automotive companies founded in 1982. Main content of APQP APQP serve as a guide in the development process and also a standard way to share results between suppliers and automotive companies. APQP specify three phases: Development, Industrialisation 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 aspects as: design robustness, design testing and specification compliance, production process design, quality inspection standards, process capability, production capacity, product packaging, product testing and operators training plan between other items.

APQP focuses on: o Up-front quality planning o Determining if customers are satisfied by evaluating the output and supporting continual improvement APQP consists of five phases: o Plan and Define Program o Product Design and Development Verification o Process Design and Development Verification o Product and Process Validation o Launch, Feedback, Assessment & Corrective Action There are five major activities: o Planning o Product Design and Development o Process Design and Development o Product and Process Validation o Production The APQP process has seven major elements: o Understanding the needs of the customer o Proactive feedback and corrective action o Designing within the process capabilities o Analyzing and mitigating failure modes o Verification and validation o Design reviews o Control special / critical characteristics

APQP 39 STEP PROCESS Plan & Define Programme •Design Goals •Reliability & Quality Goals •Preliminary Bill of Materials •Preliminary Process Flow •Preliminary Listing of Special Products and Process Characteristics •Product Assurance Plan

Product Design & Development Verification

Process Design & Development Verification

Product & Process Validation

•Design FMEA

•Packaging Standards

•Production Trial Run

•DFMA

•Product & process Quality System Review

•Design Verification

•Process Flow Chart

•Measurement Systems Evaluation •Preliminary Process Capability Study

•Design Reviews

•Floor Plan Layout

•Production Part Approval

•Prototype Build

•Characteristics Matrix

•Production Validation Testing

•Engineering Drawings

•Process FMEA

•Packaging Evaluation

•Material Specifications

•Pre-Launch Control Plan

•Production Control Plan

•Drawing & Specification Changes •New Equipment, Tooling & Facilities Requirements •Special Product and Process Characteristics •Prototype Control Plan •Gauges & Testing Equipment Requirements

•Process Instructions •Measurement Systems Analysis Plan •Preliminary Process Capability Study Plan •Packaging Specifications

•Quality Planning Sign-Off

FMEA (Failure Modes and Effects Analysis) Failure Modes and Effects Analysis, or FMEA, is a methodology for identifying the potential failure modes that a product or process may encounter, assessing the risks associated with these failure modes, prioritization of these failure modes according to their urgency, and prevention of the more urgent failure modes, i.e., the ones that are most likely to cause serious harm to the company. The output of an FMEA cycle is the FMEA Table, which documents how vulnerable a product or process is to its potential failure modes. The FMEA table also shows the level of risk attached to each potential failure mode, and the corrective actions needed (or already completed) to make the product or process more robust. The FMEA Table generally consists of 16 to 17 columns, with each column corresponding to a piece of information required by FMEA.

The FMEA is a proactive analysis tool, allowing engineers to anticipate failure modes even before they happen, or even before a new product or process is released. It also helps the engineer to prevent the negative effects of these failure modes from reaching the customer, primarily by eliminating their causes and increasing the chances of detecting them before they can do any damage. The actions generated by a good FMEA cycle will also translate to better yield, quality, reliability, and of course, greater customer satisfaction. There are many types of FMEA, but the most widely used are probably the following: 1) System FMEA, which is used for global systems; 2) Design or Product FMEA, which is used for components or subsystems; 3) Process FMEA, which is used for manufacturing and assembly processes; 4) Service FMEA, which is used for services; and 5) Software FMEA, which is used for software. In the semiconductor industry, the Design or Product FMEA and the Process FMEA are the most frequently-encountered FMEA versions. Despite the existence of many types of FMEA today, the basic structure and process for executing them remains the same. Any FMEA process must include the following steps, information details of which are documented in the FMEA Table: 1) Assembly of the team; 2) Understanding of the Product or Process to be subjected to FMEA; 3) Breaking down of the product or process into its components or steps (components and steps are also known as items); 4) Identification and assessment of the following for every item listed: function(s), potential failure mode(s), failure mode effect(s), failure mode cause(s), and controls for detecting or preventing the failure mode(s); 5) Evaluation of the risks associated with the failures modes and prioritizing them according to importance; 6) Implementation of corrective actions to minimize the occurrence of the more significant failure modes; 7) Reassessment of the product or process by another cycle of FMEA after the actions have been completed; and 8) Regular updating of the FMEA Table. The most critical information on the FMEA Table is the Risk Priority Number (RPN), which is the numerical rating given to the level of risk associated with a failure mode, and therefore denotes the urgency of addressing that failure mode. The RPN is actually the product of three (3) factors, namely, the severity of the effect of the failure mode (SEV), the probability of the occurrence of the cause of the failure mode (PF, for probability factor), and the effectiveness of the controls

for detecting and preventing the failure mode (DET). Thus, RPN = SEV x PF x DET. The SEV, PF, and DET are also documented in the FMEA Table. The FMEA Table is a living document, constantly changing from the time of its first release when the product or process is still being designed until its archiving after the product or process has been obsoleted. Critical times or events that require an update to the FMEA Table include the following: 1) when a new product or process is being designed or introduced; 2) when a critical change in the operating conditions of the product or process occurs; 3) when the product or process itself undergoes a change; 4) when a new regulation that affects the product or process is instituted; 5) when customer complaints about the product or process are received; and 6) when an error in the FMEA Table is discovered or new information that affects its contents come to light. . Table 1. Example of a Simplified FMEA Table

FMEA Methodology? RETURN

SPC (Statistical Process Control) Introduction: Statistical process control (SPC) involves using statistical techniques to measure and analyze the variation in processes. Most often used for manufacturing processes, the intent of SPC is to monitor product quality and maintain processes to fixed targets. Statistical quality control refers to using statistical techniques for measuring and improving the quality of processes and includes SPC in addition to other techniques, such as sampling plans, experimental design, variation reduction, process capability analysis, and process improvement plans. SPC is used to monitor the consistency of processes used to manufacture a product as designed. It aims to get and keep processes under control. No matter how good or bad the design, SPC can ensure that the product is being manufactured as designed and intended. Thus, SPC will not improve a poorly designed product's reliability, but can be used to maintain the consistency of how the product is made and, therefore, of the manufactured product itself and its asdesigned reliability.

A primary tool used for SPC is the control chart, a graphical representation of certain descriptive statistics for specific quantitative measurements of the manufacturing process. These descriptive statistics are displayed in the control chart in comparison to their "in-control" sampling distributions. The comparison detects any unusual variation in the manufacturing process, which could indicate a problem with the process. Several different descriptive statistics can be used in control charts and there are several different types of control charts that can test for different causes, such as how quickly major vs. minor shifts in process means are detected. Control charts are also used with product measurements to analyze process capability and for continuous process improvement efforts.

Typical charts and analyses used to monitor and improve manufacturing process consistency and capability (produced with Minitab statistical software). Benefits:

• • • • • • • •

Provides surveillance and feedback for keeping processes in control Signals when a problem with the process has occurred Detects assignable causes of variation Accomplishes process characterization Reduces need for inspection Monitors process quality Provides mechanism to make process changes and track effects of those changes Once a process is stable (assignable causes of variation have been eliminated), provides process capability analysis with comparison to the product tolerance

Capabilities: •

• • • • • • •

All forms of SPC control charts o Variable and attribute charts o Average (X— ), Range (R), standard deviation (s), Shewhart, CuSum, combined Shewhart-CuSum, exponentially weighted moving average (EWMA) Selection of measures for SPC Process and machine capability analysis (Cp and Cpk) Process characterization Variation reduction Experimental design Quality problem solving Cause and effect diagrams

More on SPC Charts

RETURN

MSA (Measurement Systems Analysis) 1. A MSA study is a method of validating a measurement system to ensure that it is fit for purpose. A measurement system consists of measuring devices, procedures, definitions and people. The idea of validating the measurement system is to minimise controllable factors that could exaggerate the amount of variation in data. 2. There are two types of measurement data collected by a measurement system. a. Variable data is data where an actual measurement is taken, e.g. length, temperature . b. Attribute data is data where the measurement result is OK or not OK. It does not give any information about the quality of the part, only whether the part is to be accepted or rejected, e.g. a Go/No-go gauge. 2. A MSA study typically consists of the completion of a Gauge Reproducibility and Repeatability Study (Gauge R&R Study), where multiple operators measure multiple parts, multiple times. The gauge R&R study is carried out blind, so that operators do not know which parts they are measuring. A MSA study may also include a study on gauge bias and linearity. Both types of measurement systems can be subjected to a gauge R&R study.

3. For variable data measurement systems, analysis of the results from the study using statistics (Minitab is commonly used to carry out gauge R&R calculations) is carried out and shows how much variation comes from differences in the operators, techniques, or the parts themselves. The gauge R&R makes up part of the study variation, and is made up from variation due to measuring equipment (repeatability) and variation due to operators (reproducibility). Typically, the variation due to gauge R&R should account for less than 30% of the study variation for a variable study, and should demonstrate adequate discrimination with the number of distinct categories (NDC) typically greater than 5. 4. Attribute studies are carried out out in a different way, and again it is common to use statistical software to analyse the results. The attribute study is looking for agreement between operators and commonly against a known standard. Typically, agreement should be greater than 95% for an acceptable attribute based measurement system. 5. Measurement Systems Analysis is a huge subject and the Automotive Industry Action Group (AIAG) has devoted an inordinate amount of time to the production of a ‘Measurement Systems Analysis’ manual for guidelines on techniques to be used in the validation of measurement systems for use in production processes. RETURN

SPC Charts SPC charts are essentially a sophisticated form of Time Series plot that enable the stability of the process, and the type of variation involved, to be understood.

What do SPC charts detect? …… Changes! Changes in process average A change in process average: The plot shows a very clear increase in the process average, but SPC charts may detect much smaller changes that are not obvious to the human eye. This might be the result of an uncontrolled change or a deliberate improvement to the process.

Dimension

•

Time Changes in process variation A change in process variation: The plot shows a clear increase in the amount of variation in the process. Again, this is a very marked change, but SPC charts can detect much smaller changes in variation which wouldn’t normally be obvious

Dimension

•

Time

One off events such as “special causes”

Dimension

•

Time

One off events (Special Causes): This plot shows a process which appears to be relatively stable and “in control”, with the exception of two points that are significantly higher than the rest. These points are known as special causes because they fall outside of the expected variation range of the process and are likely to be as a result of a special cause.

How Do SPC Charts Detect Changes? 1. The performance of the process is plotted as a time series plot 2. The level of variation in the process 3. Control limits (see below for an explanation) are drawn on the plot based on the variation method 4. Each data point on the chart is then assessed against a number of ‘tests’ 5. If the test are ‘broken’ then the process is not in control and needs investigation or adjustment

Control Limits: These are limits that are calculated using a statistical measure of variation, either the “standard deviation” (σ) or the “range” (R). They represent the limits of the process, such that all process data should fall within these bounds.

Control Limits

The ‘Tests’: A process is out of control if: o Any point is outside the control lines o There is a run of 7 points above or below the mean line o There is a run of 7 points going in the same direction

o Any obvious non-random pattern (e.g. saw tooth shape) There are more complex tests (carried out by software such as MiniTab) which are designed to find much finer process changes but are not of use on the shop floor.

SPC for In-Process Control This historical data is used to construct the control limits

Each future point will be plotted here as is occurs

Time

As each result occurs it is plotted on the SPC chart and assessed using the tests above to detect any change in the process. This gives real time information to the operator of the process who may make adjustments based on data rather than opinion or ‘gut feel’. RETURN

PPAP: Production Part Approval Process 1. The purpose of PPAP is to determine if all customer engineering design record and specification requirements are properly understood by the supplier and that the process has the potential to produce product consistently meeting these requirements during an actual production run at the quoted production rate. 2. Typically, a Customer will ask for PPAP in the following situations: a. A new part or product (i.e. a specific part, material, or colour not previously supplied to the customer). b. Correction of a discrepancy on a previously submitted part. c. Product modified by an engineering change to design records, specifications or materials. d. Significant process changes as detailed within AIAG PPAP Manual Fourth Edition (e.g. supplier change, new tooling, etc). 2. PPAP has 19 elements/requirements, and a typical submission would feature the production of a pack of documentation which provides objective evidence to show that the requirements detailed in paragraph 1 have been met. The customer would review the pack and master sample parts and either approve or reject the PPAP. If the PPAP is approved, this is permission to ship production parts to the customer facility. If the PPAP is rejected, dependent on the nature of the rejection, parts would either be supplied under waiver, or with a serious issue, the customer may not grant permission to ship product. Whenever a PPAP is rejected, a corrective action plan would need to be submitted to the customer to show how a supplier plans to achieve full approval. 3. When PPAP is called up on a purchase order, production parts may not be shipped to the customer without a customer approved PPAP or a waiver from the customer granting permission to ship in lieu of PPAP approval.

RETURN

FMEA Methodology Fundamental to a Process FMEA is the production of a process map that charts the constituent discrete steps or parts of the relevant process. In the case of a Design FMEA the Bill of Material is used (BOM) For each discrete step identified above, an analysis is made of all conceivable failure modes. For each failure mode the Root Cause(s) are then identified. This is undertaken in close association with the Process or Design Owner (Brainstorming and the 5Y’s techniques are usually applied.) For each Root Cause an assessment is then made of the risks involved in terms of: a. Its Occurrence b. Its Severity c. Its Detection d. Its Extent (environmental only) Each risk element is then ranked using the relevant tables to a ranking number between 1 and 10. (1 meaning Virtually Zero Risk, 10 meaning Extreme High Risk)

Each of these Rankings are then multiplied together to give an overall risk rating. This composite rating is called the Risk Priority Number or RPN. The RPN’s can then be prioritised (highest first) so as to ascertain the most significant areas for corrective action. By closely reviewing the individual RPN factors strong guidance is naturally given as to the actions necessary to reduce the overall RPN. The conclusions from this exercise (there may be more than one corrective action possible) are then assessed as to outcome on the overall RPN from which an actionable corrective action can be taken. The process can then be repeated for the next highest RPN and so on. Equally once a FMEA has been produced, investigations of any individual failure mode or root cause can be individually assessed and the effect of any specific corrective action evaluated.

Process FMEA Rankings

SEVERITY Rankin g

Description

10

Highly Hazardous

9

Hazardous

8

Very High

7

High

6

Moderate

5

Low

4

Very Low

3

Minor

2

Very Minor

1

None

Criteria Failure without Warning Product is hazardous for production personnel, Failure affects safe product operation or noncompliance with government regulations Failure with Warning Product is hazardous for production personnel, Failure affects safe product operation or noncompliance with government regulations Major production disruption, High rework or scrap potential, Loss of primary function Minor disruption to production Probable sort and scrap procedure. Primary function is impaired Minor disruption to production, A portion of the product may have to be scrapped (no sorting) Products primary function continues, Secondary function is impaired Minor disruption to production, High rework potential, Product’s primary function continues, Secondary function is impaired Minor disruption to production, Sort and rework potential Product is non-conformant, some Customer dissatisfaction expected Minor disruption to production, A portion of the product may be reworked Product is non-conformant, some Customer dissatisfaction may occur Minor disruption to production, A portion of the product may be reworked Product is non-conformant, Customer dissatisfaction is unlikely No Effect

RPN process = S x O x D

Process FMEA Rankings

OCCURANCE Rankin g

Description

10

Very High: Failure is almost inevitable

9 8 7

High: Frequent Failures

Moderate: Mild level of failure

2 1

10% Probability (100,000 PPM) 5% Probability (50,000 PPM) 2% Probability (20,000 PPM)

0.2% Probability (2,000 PPM) 0.04% Probability (400 PPM)

4 3

20% Probability (200,000 PPM)

1% Probability (10,000 PPM)

6 5

Failure Rate

Low: Isolated Failures Very Low: Infrequent Failures Remote: Failure is unlikely

0.0067% Probability (67 PPM) 0.0007% Probability (7 PPM) > 0.0007% Probability (> 7 PPM)

RPN process = S x O x D

Process FMEA Rankings

DETECTION Rankin g

Description

Criteria

10

NonDetectable

Current Control either does not exist or will not detect the Cause of Failure or its Failure Mode

9

Very Remote

Current Control is Very Unlikely to detect the Cause of Failure or its Failure Mode

8

Remote

Current Control is Unlikely to detect the Cause of Failure or its Failure Mode

7

Very Low

Current Control has a Very Low Capacity to detect the Cause of Failure or its Failure Mode

6

Low

Current Control has a Low Capacity to detect the Cause of Failure or its Failure Mode

5

Moderate

Current Control has a Moderate Capacity to detect the Cause of Failure or its Failure Mode

4

Moderately High

Current Control has a Moderately High Capacity to detect the Cause of Failure or its Failure Mode

3

High

Current Control has a High Capacity to detect the Cause of Failure or its Failure Mode

2

Very High

Current Control has a Very High Capacity to detect the Cause of Failure or its Failure Mode

1

Highly Detectable

The Design Control will almost certainly detect the potential Cause of the Failure or its Failure Mode

RPN process = S x O x D

Design FMEA Rankings

SEVERITY Rankin g

Description

Criteria Failure without Warning Product is unsafe for customer use, or violates government regulations Failure with Warning Product is unsafe for customer use, or violates government regulations

10

Highly Hazardous

9

Hazardous

8

Very High

Product’s primary function fails

7

High

Product’s primary function is impaired

6

Moderate

Product’s primary function is unaffected, Secondary function fails

5

Low

Product’s primary function is unaffected, Secondary function is impaired

4

Very Low

Product is non-conformant, some Customer dissatisfaction expected

3

Minor

Product is non-conformant, some Customer dissatisfaction may occur

2

Very Minor

Product is non-conformant, Customer dissatisfaction is unlikely

1

None

No Effect

RPN design = S x O x D

Design FMEA Rankings

OCCURANCE Rankin g

Description

10

Very High: Failure is a Continuous Problem

9 8 7

Failure Rate 20% Probability (200,000 PPM) 10% Probability (100,000 PPM) 5% Probability (50,000 PPM)

High: Frequent Failures

2% Probability (20,000 PPM) 1% Probability (10,000 PPM)

6 5

Moderate: Sporadic failures

0.2% Probability (2,000 PPM) 0.04% Probability (400 PPM)

4 3 2 1

Low: Relatively few Failures Remote: Failure is unlikely

0.0067% Probability (67 PPM) 0.0007% Probability (7 PPM) > 0.0007% Probability (> 7 PPM)

RPN design = S x O x D

Design FMEA Rankings

DETECTION Rankin g 10

Description

Criteria

NonDetectable

No Known Controls available Failure Mode or its Cause is undetectable

9

Very Remote

8

Remote

7

Very Low

6

Low

5

Moderate

4

Moderately High

3

High

2

Very High

1

Highly Detectable

Very Remote Likelihood that Current Controls will detect Failure Mode or its Cause Remote Likelihood That the Current Controls will detect Failure Mode or its Cause Very Low Likelihood That the Current Controls will detect Failure Mode or its Cause Low Likelihood That the Current Controls will detect Failure Mode or its Cause Moderate Likelihood That the Current Controls will detect Failure Mode or its Cause Moderately High Likelihood That the Current Controls will detect Failure Mode or its Cause High Likelihood That the Current Controls will detect Failure Mode or its Cause Very High Likelihood That the Current Controls will detect Failure Mode or its Cause The Design Control will almost certainly detect the potential Cause of the failure or its Failure Mode

RPN design = S x O x D RETURN