AUTOMOTIVE CORE TOOLS ; SPC, MSA, FMEA, APQP/CONTROL PLAN CQI Wessex - 11 December 2012

1. AUTOMOTIVE CORE TOOLS; • SPC, MSA, FMEA, APQP/CONTROL PLAN • CQI Wessex - 11 December 2012 • John Skinner rdaconsultancy.com

2. AUTOMOTIVE PROGRESS SINCE 1990 Best selling car in the UK in 1990 – Ford Fiesta – sold in EU 1.4, 1.6 CVH engines were very harsh and had early failures (valve guide wear, dropping valve seats damaging the engine). Rust was still a problem, though better than the 1970’s & 80’s. Radio-cassette, some speaker adjustments, maybe electric windows. No a/c and definitely no climate control. No airbags, little in the way of crumple zones. Central locking on higher end models. Door mirrors had ‘remote adjustment’ on top models. Security was very poor – cars like the XR2 were being stolen by joyriders on a regular basis (epidemic levels in some areas). Car insurance costs on ‘hot’ models went through the roof. No ABS, stability control etc. Cost – around £8500 – roughly equivalent to £15000 in today’s prices

3. WHAT CHANGED? • Improvements demanded by legislation (emissions, safety), safety (NCAP), competition (i.e. VW/Audi), consumer demand (phone compatibility, convenience features etc.) • A thorough focus on improved product quality achieved through; computer modelling/simulation, enhanced testing techniques (bench and road), consumer feedback (JD Power), supplier capability, effective corrective action (8D) • Increased use of electronics (particularly powertrain) • Need to improve product reliability (reducing warranty/ product recall costs) • Application of ISO/TS16949 and mandated use of Automotive Core Tools ?

4. ISO/TS16949 • Initiated as QS9000 in 1995, based on the requirements of ISO9001, but adds many automotive industry specific requirements (though only one additional documented procedure from ISO9001) • Produced and controlled by the International Automotive Task Force (IATF) • Members include; Ford, GM, VW/Audi, PSA, BMW, Chrysler, Renault, Daimler and major trade organisations across the globe • Recognised and required as a prerequisite for becoming an ‘approved supplier’ to the respective automotive customers

5. AUTOMOTIVE REQUIREMENTS • APQP; Advanced Product Quality Planning • FMEA; Failure Mode & Effect Analysis • Control Plan • SPC; Statistical Process Control • MSA; Measurement System Analysis • These require a team approach (cross functional teams with management commitment) • Automotive Industry Action Group (AIAG) manuals define basic requirements for application • There are other requirements, based on the customer

6. Advanced Product Quality Planning (APQP) ISO9001 requires Planning of Product Realisation; The Automotive industry goes further; ‘Some customers refer to project management or APQP as a means to achieve product realization. APQP embodies the concepts of error prevention and continual improvement as contrasted with error detection and is based on a multidisciplinary approach’. (extract from ISO/TS)

7. Advanced Product Quality Planning • Why plan? • What gets in the way of planning?

8. Product Quality Planning Timing Chart Concept Initiation /Approval Program Approval Prototype Pilot Launch Planning Planning Product design Process design Validation Production Feedback assessment & corrective action Product Design & Development Verification Product & Process Validation Plan & Define Programme Feedback Assessment & Corrective Action

9. APQP – Project Scope • 2011 model year Range Rover – minor changes

10. APQP – Project Scope • 2012/13 model year Range Rover - major project. • New design, technologies, material – aluminium, using self piercing rivets and aerospace sourced epoxy adhesive

11. Advanced Product Quality Planning (APQP) • Advantages; • Thorough planning and improved decision making • Shorter development timescales • Simultaneous engineering (design & manufacturing) • Early procurement of long lead time tooling/facilities • Defined objectives, measured as project stages are achieved • Defined project gateways, with key deliverables • Improved use of resources • Significant cost savings (known impact on company finances) • Effective feedback & corrective action (enhanced with use of computer systems)

12. Failure Mode & Effect Analysis (FMEA) • Typically used at design (DFMEA) and manufacturing process planning (PFMEA) stages • FMEA - a systematic set of activities intended to: • Recognise and evaluate the potential failure of a product/process and the consequential effects of failure (risk management) • Identify actions that could eliminate or reduce the chance of the potential failure occurring (improvement) • Document the entire process • Needs a ‘team approach’ to be successful

13. DFMEA

14. DFMEA

15. DFMEA

16. DFMEA • Ignores manufacturing issues; i.e. manufacturing producing/using parts that are to specification • Can direct design effort to critical/significant characteristics and improve design validation/ verification testing results, avoiding late design changes • Identifies special characteristics that need to be controlled in manufacturing to assure product quality • Provides a documented record of the analysis which can be used into the future (many vehicle recalls could have been prevented by effective DFMEA) • Needs to be maintained as a live document; continual improvement

17. PFMEA • A PFMEA will follow the stages defined for the manufacturing route from material receipt, through the manufacturing stages to despatch • Typically the manufacturing route will be defined on a process flow diagramme, including locations, machines, operation sequences etc.

18. PFMEA

19. PFMEA

20. PFMEA • Focuses on potential for non-conforming product (in use and impact on manufacturing process including employee safety) and mistake proofing techniques • Identification/prioritisation of potential failure modes and implementation of preventive/corrective action • Focus on special characteristics • Continual improvement • Assumes product as designed will meet intent • Should be extended to other areas; receiving, storage, transport, despatch etc. (complete process) • Provide feedback to design (mistake proofing features etc.)

21. Control Plan • A documented description of the systems and processes required for controlling product • This is a key output once the DFMEA and PFMEA analysis has been completed • Applies to distinct stages; Prototype, Pre-launch and Production. • Each part must have a control plan, but family control plans can be used where justified (e.g. a foundry producing many different castings)

22. Control Plan

23. Control Plan

24. Control Plan • Identifies the controls required to ensure product quality with a focus on special characteristics • Defines the reaction plans required to be implemented where non-conformance is identified (containment of product, 100% inspection to ensure process becomes stable and capable) • Is an output from the FMEA process

25. Statistical Process Control • Traditional inspection techniques (patrol inspection, batch sampling etc.) rely on detection, which is wasteful as time and resources are put into producing parts that are not always useable • Prevention verses detection; i.e. not producing the non-conforming parts in the first place is an obvious preferred situation • If we can predict the process output, then we may be able to ensure conforming parts • Statistical techniques can be used for process control

26. Process Control System Model with Feedback Statistical Process Control (SPC) People Equipment Materials Methods Environment Manufacturing Process Product Customers Identifying changing expectations; direct feedback on product quality Inputs Feedback from Customers

27. Goals of SPC • To achieve a state of statistical control (stability) • To maintain a state of statistical control (stability over time; prevention verses detection) • To improve process capability

28. Statistical Process Control • Taking action on the result of the output of a process (traditional inspection techniques) permits waste – rejected/reworked product, wasted resources, potential for rejects to ‘escape’ the process • Understanding the variation of the process and applying statistical techniques allows for predictable process output (capability) • To do this, we need to understand the types of variation present in the process

29. Statistical Process Control • Machining process; part dimensions vary from each other Size • If the output is stable (only common cause variation), the results form a pattern that can be described as distribution Size

30. Statistical Process Control • Common cause variation; inherent in the process – backlash/clearances, coolant feed, machine tolerances Special cause variation (or assignable cause) – machine set up, material change, environment

31. Special Cause Variation Target line Predictable ? Time Size If special causes of variation are present, the process output is not stable over time and is not generally predictable; we cannot always use sampling and SPC to control the process.

32. Target line Time Size Common Cause Variation If only common causes of variation are present, the output of a process forms a distribution that is stable over time and is predictable; we can then monitor the process using sampling and SPC charts

33. Statistical Process Control • _ • X – Range Control chart (AIAG SPC manual)

34. General Rules for Interpretation • A point outside a control limit • 7 points in a row on one side of the centreline • 6 points in a row steadily increasing or decreasing • 2 out of 3 points more than 2 standard deviations from the centreline (same side) • 4 out of 5 points less than one standard deviation from the centreline (same side)

35. Process Capability • Capability indices are able to summarise process performance as a number to reflect how well the process will meet customer requirements (specification). • They will indicate; - How variable the process is (i.e. spread of results) - Where the process output is in relation to the specification limits.

36. Process Capability Upper specification Lower specification Process variation or spread Tolerance

37. Capability Indices • Process capabilities are an index produced by comparing the observed process variation or spread against the required tolerance. Examples include; • Capability Index Cp = USL - LSL • 6 δ (process variation) • _ _ • Capability Index Cpk = USL – x OR x - LSL • (largest of) 3 δ 3 δ • i.e. Worst case result • Cpk will indicate the position of the process relative to the specification (i.e. centering)

38. Capability Indices • If the process data has a normal distribution, the following can be used to interpret Cpk: Cpk Approximate proportion out of spec 1.33 63/1,000,000 1.67 0.6/1,000,000 2.0 0.02/1,000,000 (Where the process is centred between the specification limits)

39. Attribute Charting • Attribute data is the result of inspection or testing that produce a fixed result and cannot be measured using measurement equipment e.g. number of paint defects on a door panel, number of rejected units from a functional test batch, number of weld failures on a floor-pan assembly • Attributes can also be monitored using control charts with control limits to determine long term stability

40. What are the Benefits of SPC? • Properly used, control charts and SPC can: • Distinguish special from the common causes of variation, as an aid to improvement in capability • Enable the process perform consistently and predictably • Provide a common language for discussing the performance of the process (capability indices) • Be used by employees for on-going control of a process

41. Measurement System Analysis (MSA) • As measurement data is often used to make decisions with regard to manufacturing (and test) activities then the ‘quality’ of this data needs to be assured • No measurement system is ‘perfect’ (i.e. measures exactly with reference to known standards each time); some variation will be evident in all systems, including human influences • A series of analytical techniques can be used to ensure that the inherent variation in measurement systems can be determined and the effects understood i.e. possibility for accepting ‘bad’ parts and rejecting ‘good’ parts • In essence, we need to understand the variation and limitations of the measurement systems we are using to enable confidence in those results (for equipment on the control plan)

42. Measurement System Analysis

43. Variable Gauge R&R study • Gauge repeatability and reproducibility data sheet used for the numerical analysis of the study data (MSA software is also available). • 10 parts used to represent variability of the process; 3 appraisers. • Typically 3 trials per appraiser • Complete the study using usual equipment and individuals (random presentation of parts to avoid influence on the results)

44. Gauge R&R • Extract from the AIAG MSA manual

45. Variable Gauge R&R study • The study estimates the variation and percentage of process variation for the measurement system (Gauge R&R) and its components - Repeatability - Reproducibility - Part to part variation (how representative the parts are; this will influence the results).

46. Gauge R&R • Acceptable Gauge R&R% results; • Up to 10% - generally acceptable • 10 to 30% - may be acceptable based on importance of measurement feature, cost of better equipment, cost of refurbishment/repair of equipment, OR the skill level of the appraisers • Over 30% - would be considered un-acceptable and improvement is required • Where results are not acceptable, check the data, calculations etc. to determine if there are any errors

47. Use of Graphical Techniques • There are many different methods for presenting data for analysis • ‘A picture paints a thousand words’. • The AIAG MSA manual strongly recommends the use of both calculated methods and graphical techniques. • Other methods are available for specific situations and where more detail is required – Analysis of Variance (ANOVA), gauge performance curves, regression analysis etc.

48. Attribute Studies • Go/No-go gauging systems (acceptance gauging, gap gauges etc.) • A series of comparative techniques are available to evaluate the effectiveness of attribute gauging systems, again using several appraisers and trials

49. Product Part Approval Process (PPAP) • The intent of PPAP is to validate that products made from production tools/processes meet engineering requirements (specifications), that the processes are capable (Cpk, SPC data) and are capable of producing acceptable product consistently over time. • The type and format of submission will depend on the customer requirements, but the AIAG PPAP manual has defined formats

50. PPAP • To support this, appropriate data has to be completed and be made available for review by the customer; • Part Submission Warrant (PSW), DFMEA, PFMEA, Control Plan, MSA data, initial process capability results, material certification, marked up drawing/ dimensional results, engineering change documents, material/performance test results, qualified laboratory documentation, appearance approval report (if applicable), sample product • Approval may require full or partial submission of the information (invariably depending on supplier approval status) reviewed at the customer location or supplier site