This is a 2 day workshop of the TS 16949 standard and the 5 core tools. It will enable you to understand the basic requirements of the ISO/TS 16949 Quality Management System Requirements, and the 5 Core Tools.
Advanced Product Quality Planning (APQP)
Failure Mode and Effects Analysis (FMEA)
Production Part Approval Process (PPAP)
Fundamental Statistical Process Control (SPC)
Measurement System Analysis (MSA)
This workshop has been specifically designed to provide an overview of the Core Tools for those personnel who do not use the tools as a part of their normal job, but need an understanding of what these Core Tools cover (e.g. internal auditors, managers, support staff)
Workshop Content
ISO/TS 16949 Requirements 2 hours
APQP 3 hours
FMEA 3 hours
PPAP 3 hours
SPC 2 hours
MSA 2 hours
You need to attend this workshop if:
You are an internal quality auditor and need to have a working knowledge of the core tools. You will find out what to look for in an audit. Attending this course will allow internal auditors to meet GM & Ford's Customer Specific Requirements for an internal auditor to have training in the core tools.
You are directly or indirectly supporting personnel who use the Core Tools on a daily basis and you need to understand what they are doing so you can assist them.
Statistical Process Control (SPC)
Statistical process control (SPC) procedures can help you monitor process behavior. Arguably the most successful SPC tool is the control chart, originally developed by Walter Shewhart in the early 1920s. A control chart helps you record data and lets you see when an unusual event, e.g., a very high or low observation compared with “typical” process performance, occurs.Control charts attempt to distinguish between two types of process variation:
Common cause variation, which is intrinsic to the process and will always be present.
Special cause variation, which stems from external sources and indicates that the process is out of statistical control.
Various tests can help determine when an out-of-control event has occurred. However, as more tests are employed, the probability of a false alarm also increases.
Background A marked increase in the use of control charts occurred during World War II in the United States to ensure the quality of munitions and other strategically important products. The use of SPC diminished somewhat after the war, though was subsequently taken up with great effect in Japan and continues to the present day. Many SPC techniques have been “rediscovered” by American firms in recent years, especially as a component of quality improvement initiatives likeSix Sigma. The widespread use of control charting procedures has been greatly assisted by statistical software packages and ever-more sophisticated data collection systems.Over time, other process-monitoring tools have been developed, including:
Cumulative Sum (CUSUM) charts: the ordinate of each plotted point represents the algebraic sum of the previous ordinate and the most recent deviations from the target.
Exponentially Weighted Moving Average (EWMA) charts: each chart point represents the weighted average of current and all previous subgroup values, giving more weight to recent process history and decreasing weights for older data.
More recently, others have advocated integrating SPC with Engineering Process Control (EPC) tools, which regularly change process inputs to improve performance.
Measurement System Analysis (MSA)
Measurement System Analysis (MSA) and Gage Management are important in controlling variation in measurement systems. Measusrement systems are used every day in manufacturing, research and development and sales and marketing. Attributes which are measured include distances, temperatures, strengths, resistance’s, and sales, just to name a few.
Often measurements are made with little regard for the quality of such measurements. Yet all too often, the measurements are not representative of the true value of the characteristic being measured. That might be because the measurement system is not accurate enough, not precise enough, introduces bias into the measurement, or is not properly being used by the operator.
The moral to the previous paragraph is that before you embark on using a new measurement system for a characteristic which has not been previously measured on it, you should perform a measurement capability analysis. Measurement capability analyses are critical to the success of every measurement and ensure that future measurements will be representative of the characteristic being measured.
MSA includes detailed tutorials on many measurement system analysis techniques including how to conduct and analyze GR&R (Gage Repeatability and Reproducibility) Studies. A GR&R is the accepted techniques for evaluating the level of variation in a measurement system and determining if the measurement system is acceptable for use. Measurement System Analysis covers techniques for analyzing the variation within a measurement system, determining its suitability for use, and ways to improve measurement systems. The GR&R analysis techniques used in MSA are in compliance with QS-9000/AIAG methods.
Once a measurement system is found to be acceptable, it is equally important to institute a formal system to manage the measurement system to ensure that it continues to be reliable and dependable. MSA explores approaches to managing measurement systems to ensure that they can be depended upon.
Gage R&R
Gage R&R (GageRepeatability andReproducibility) is the amount of measurement variation introduced by a measurement system, which consists of the measuring instrument itself and the individuals using the instrument. A Gage R&R study is a critical step in manufacturing Six Sigma projects, and it quantifies three things:
Repeatability – variation from the measurement instrument
Reproducibility – variation from the individuals using the instrument
Overall Gage R&R, which is the combined effect of (1) and (2)
The overall Gage R&R is normally expressed as a percentage of the tolerance for theCTQ being studied, and a value of 20% Gage R&R or less is considered acceptable in most cases. Example: for a 4.20mm to 4.22mm specification (0.02 total tolerance) on a shaft diameter, an acceptable Gage R&R value would be 20 percent of 0.02mm (0.004mm) or less.
The Difference Between Gage R&R and Accuracy
A Gage R&R study quantifies the inherent variation in the measurement system (the combination of items 1 and 2 noted above), but measurement system accuracy must be verified through a calibration process. For example, when reading an outdoor thermometer, we might find a total Gage R&R of five degrees, meaning that we will observe up to five degrees of temperature variation, independent of the actual temperature at a given time. However, the thermometer itself might also be calibrated ten degrees to the low side, meaning that, on average, the thermometer will read ten degrees below the actual temperature. The effects of poor accuracy and a high Gage R&R can render a measurement system useless if not addressed.
Measurement system variation is often a major contributor to the observed process variation, and in some cases it is found to be the number-one contributor. Remember, Six Sigma is all about reducing variation.
Think about the possible outcomes if a high-variation measurement system is not evaluated and corrected during theMeasurephase of aDMAICproject – there is a good chance that the team will be mystified by the variation they encounter in theAnalyzephase, as they search for variation causes outside the measurement system.
Measurement system variation is inherently built into the values we observe from a measuring instrument, and a high-variation measurement system can completely distort a process capability study (not to mention the effects of false accepts and false rejects from a quality perspective). The following graph shows how an otherwise capable process (Cpk= 2.0: this is aSix Sigmaprocess) is portrayed as marginal or poor as the Gage R&R percentage increases:
FMEA Failure Mode and Effects Analysis
What You Should Know About Failure Mode and Effects Analysis (FMEA)
All products or processes have failure modes. The effects are the impacts when the failures occur. A FMEA is a tool to:
Identify the relative risks designed into a product or process
Develop and take action to reduce the risks with the highest potential impact
Track the results of the action to determine risk reduction or elimination
Failure Mode and Effects Analysis helps you focus on and understand the impact of potential failures of process or product. A ranking methodology is used to prioritize the risks relative to each other. An RPN or Risk Priority Number is calculated for each failure mode and its resulting effect(s). The RPN is a function of three factors: The Severity of the effect, the frequency of Occurrence of the cause of the failure, and the ability to detect (or prevent) the failure or effect before it escapes to the customer.
RPN = Severity rating X Occurrence rating X Detection rating (S x O x D = RPN) The RPN can range from a low of 1 to a high of 1,000
Once the RPNs are determined, you need to develop an Corrective/Preventive Action Plan to reduce the risks of failure modes of high RPNs.
Next, use the completed FMEA as the basis for developing a Control Plan and work instructions. Control Plans are a summary of defect prevention and reactive detection techniques and must mirror the FMEA.
The Purpose of an FMEA
FMEAs help us focus on and understand the impact of potential process or product failures
A disciplined analysis is used to rank the risks relative to each other.
A Risk Priority Number, or RPN, is calculated for each failure mode and its resulting effect(s).
The RPN is a function of three factors: The Severity of the effect, the frequency of Occurrence of the cause of the failure, and the ability to Detect the failure or effect.
The RPN = The Severity ranking X the Occurrence ranking X the Detection ranking. The RPN can range from a low of 1 to a high of 1,000.
Develop a Corrective or Preventive Action Plan to reduce risks with unacceptable high RPNs.
Use FMEAs as the basis for Control Plans. Control Plans are a summary of proactive defect prevention and reactive detection techniques. Control Plans must mirror the FMEA.
APQP - Advanced Product Quality Planning
Advanced Product Quality Planning(orAPQP) is a framework of procedures and techniques used to developproductsinindustry, particularly theautomotive industry. It is quite similar to the concept of Design ForSix Sigma(DFSS).
It is a defined process for a product development system forGeneral Motors,Ford,Chryslerand their suppliers. According to theAutomotive 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.
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 specify 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 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:
Up-front quality planning
Determining if customers are satisfied by evaluating the output and supporting continual improvement
APQP consists of five phases:
Plan and Define Program
Product Design and Development Verification
Process Design and Development Verification
Product and Process Validation
Launch, Feedback, Assessment & Corrective Action
There are five major activities:
Planning
Product Design and Development
Process Design and Development
Product and Process Validation
Production
The APQP process has seven major elements:
Understanding the needs of the customer
Proactive feedback and corrective action
Designing within the process capabilities
Analyzing and mitigating failure modes
Verification and validation
Design reviews
Control special / critical characteristics
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