Aiag spc manual pdf free download. AIAG – Statistical Process Control (SPC) 2nd Edition
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Comparison of Loss Functions Process Alignment to Requirements We must constantly seek more efficient ways to produce products and services. These products and services must continue to improve in value. We must focus upon our customers, both internal and external, and make customer satisfaction a primary business goal. To accomplish this, eveiyone in our organizations must be committed to improvement and to the use of effective methods.
This manual describes several basic statistical methods that can be used to make our efforts at improvement more effective. Different levels of understanding are needed to perfom different tasks. This manual is aimed at practitioners and managers beginning the application of statistical methods.
It will also serve as a refresher on these basic methods for those who are now using more advanced techniques. Not all basic methods are included here.
Coverage of other basic methods such as check sheets, flowcharts, Pareto charts, cause and effect diagrams and some advanced methods such as other control charts, designed experiments, quality fiinction deployment, etc. The basic statistical methods addressed in this manual include those associated with statistical process control and process capability analysis. Chapter I provides background for process control, explains several important concepts such as special and common causes of variation.
It also introduces the control chart, which can be a very effective tool for analyzing and monitoring processes. Chapter I1 describes the construction and use of control charts for both variables1 data and attributes data. Chapter I11 describes other types of control charts that can be used for specialized situations - probability based charts, short-sun charts, chasts for detecting small changes, non-normal, multivariate and other charts. Chapter IV addresses process capability analysis.
The Appendices address sampling, over-adjustment, a process for selecting control charts, table of constants and formulae, the normal table, a glossary of terms and symbols, and references. The overall aim should be increased understanding of the reader's processes. It is very easy to become technique experts without realizing any improvements. Increased knowledge should become a basis for action. Measurement systems are critical to proper data analysis and they should be well understood before process data are collected.
When such systems lack statistical control or their variation accounts for a substantial portion of the total variation in process data, inappropriate decisions may be made.
For the purposes of this manual, it will be assumed that this system is under control and is not a significant contributor to total variation in the data. The basic concept of studying variation and using statistical signals to improve performance can be applied to any area. Such areas can be on the shop floor or in the office. Some examples are machines performance characteristics , bookkeeping error rates , gross sales, waste analysis scrap rates , computer systems performance characteristics and materials management transit times.
This manual focuses upon shop floor applications. The reader is encouraged to consult the references in Appendix H for administrative and service applications.
Historically, statistical methods have been routinely applied to parts, rather than processes. Application of statistical techniques to control output such as parts should be only the first step. Until the processes that generate the output become the focus of our efforts, the fhll power of these methods to improve quality, increase productivity and reduce cost may not be fully realized. Although each point in the text is illustrated with a worked-out example, real understanding of the subject involves deeper contact with process control situations.
The study of actual cases from the reader's own job location or from similar activities would be an important supplement to the text. There is no substitute for hands-on experience. This manual should be considered a first step toward the use of statistical methods.
It provides generally accepted approaches, which work in many instances. However, there exist exceptions where it is improper to blindly use these approaches.
This manual does not replace the need for practitioners to increase their knowledge of statistical methods and theory. Readers are encouraged to pursue formal statistical education.
Where the reader's processes and application of statistical methods have CHAPTER I Continual Improvement and Statistical Process Control advanced beyond the material covered here, the reader is also encouraged to consult with persons who have the proper knowledge and practice in statistical theory as to the appropriateness of other techniques. In any event, the procedures used must satisfy the customer's requirements.
In administrative situations, work is often checked and rechecked in efforts to catch errors. Both cases involve a strategy of detection, which is wasteful, because it allows time and materials to be invested in products or services that are not always usable.
It is much more effective to avoid waste by not producing unusable output in the first place - a strategy of prevention. A prevention strategy sounds sensible - even obvious - to most people. It is easily captured in such slogans as, "Do it right the first time". However, slogans are not enough. What is required is an understanding of the elements of a statistical process control system.
The remaining seven subsections of this introduction cover these elements and can be viewed as answers to the following questions: What is meant by a process control system? How does variation affect process output? How can statistical techniques tell whether a problem is local in nature or involves broader systems? What is meant by a process being in statistical control? What is meant by a process being capable? What is a continual improvement cycle, and what part can process control play in it?
What are control charts, and how are they used? What benefits can be expected from using control charts? As this material is being studied, the reader may wish to refer to the Glossary in Appendix G for brief definitions of key terms and symbols. SPC is one type of feedbaclc system.
Other such systems, which are not statistical, also exist. The total perfomance of tlie process depends upon communication between supplier and customer, tlie way the process is designed and implemented, and on the way it is operated and managed.
The rest of the process control system is useful only if it contributes either to maintaining a level of excellence or to improving the total performance of the process. Information About Perfor ance - Much information about the actual performance of the process can be learned by studying the process output. The most helpful infomation about the perfomance of a process comes, however, from understanding the process itself and its internal variability. Process characteristics such as temperatures, cycle times, feed rates, absenteeisill, turnover, tardiness, or number of intemlptions should be the ultimate focus of our efforts.
We need to deteimine the target values for those characteristics that result in the most productive operation of the process, and then monitor how near to or far from those target values we are. If this information is gathered and interpreted correctly, it can show whether the process is acting in a usual or unusual manner.
Proper actions can then be taken, if needed, to correct the process or the just-produced otltput. When action is needed it must be timely and appropriate, or the information-gathering effort is wasted.
Action on the Process - Action on the process is frequently most economical when taken to prevent the important characteristics process or output from varying too far from their target values. This ensures the stability and the variation of the process output is maintained within acceptable limits. Such action might consist of: a, Changes in the operations J operator training J changes to the incoming materials Changes in the more basic elements of the process itself J the equipment J how people communicate and relate J the design of the process as a whole - which may be vulnerable to changes in shop temperature or humidity The effect of actions should be monitored, with further analysis and action taken if necessary.
Unfortunately, if current output does not consistently meet customer requirements, it may be necessary to sort all products and to scrap or rework any nonconforming items. This must continue until the necessary corrective action on the process has been taken and verified.
It is obvious that inspection followed by action on only the output is a poor substitute for effective process management. Action on only the output should be used strictly as an interim measure for unstable or incapable processes see Chapter I, Section E. Therefore, the discussions that follow focus on gathering process information and analyzing it so that action can be taken to correct the process itself.
Remember, the focus should be on prevention not detection. Continuous improvement is vital to prospering in today's economy. This guide provides several basic and advanced statistical methods that can be used to make your manufacturing improvements more effective, resulting in products and services that improve value to both you and your customer.
Developed to address common issues in the industry, this guideline defines the minimum quality-related requirements for Sub-Tier suppliers and provides explicit guidance on effective identification and control of Pass Through Characteristics PTC. It al AIAG has released a common supplier management process developed by tier 1 automotive suppliers for use with tier 2 suppliers CQI It focuses on current automaker concerns, e. A comprehensive set of reliability tools and process to manage product development and supplier assessment.
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Version: 1 Publication Date: Oct, Version: 1 Publication Date: Jun, Version: 1 Publication Date: Aug, Version: Publication Date: Mar, Version: 1 Publication Date: Jan, D Downloadable File - English. Version: 2 Publication Date: Aug, When beneficial, they should be understood and made a permanent part of the process. With some mature processes 2, the customer may give special allowance to run a process with a consistently occurring special cause. Such allowances will usually require that the process control plans can assure conformance to customer requirements and protect the process from other special causes see Chapter I, Section E.
Juran, and have been borne out in Dr. Deming's experience. There is an important connection between the two types of variation just discussed and the types of action necessary to reduce them. Discovering a special cause of variation and taking the proper action is usually the responsibility of someone who is directly connected with the operation. Although management can sometimes be involved to correct the condition, the resolution of a special cause of variation usually requires local action, i.
This is especially true during the early process improvement efforts. As one succeeds in taking the proper action on special causes, those that remain will often require management action, rather than local action.
These same simple statistical techniques can also indicate the extent of common causes of variation, but the causes themselves need more detailed analysis to isolate. The correction of these common causes of variation is usually the responsibility of management. Sometimes people directly connected with the operation will be in a better position to identify them and pass them on to management for action.
Overall, the resolution of common causes of variation usually requires action on the system. Confusion about the type of action to take is very costly to the organization, in terms of wasted effort, delayed resolution of trouble, and aggravating problems. It may be wrong, for example, to take local action e. The process control system is an integral part of the overall business management system. This leads to economically sound decisions about actions affecting the process.
These decisions require balancing the risk of taking action when action is not necessary over-control or "tampering" versus failing to take action when action is necessary under-control. A process is said to be operating in statistical control when the only sources of variation are common causes. One function of a process control system, then, is to provide a statistical signal when special causes of variation are present, and to avoid giving false signals when they are not present.
This allows appropriate action s to be taken upon those special causes either removing them or, if they are beneficial, making them permanent. Process capability is determined by the variation that comes from common causes. It generally represents the best performance of the process itself. This is demonstrated when the process is being operated in a state of statistical control regardless of the specifications. Customers, internal or external, are however more typically concerned with the process performance; that is, the overall output of the process and how it relates to their requirements defined by specifications , irrespective of the process variation.
In general, since a process in statistical control can be described by a predictable distribution, the proportion of in-specification parts can be estimated from this distribution. As long as the process remains in statistical control and does not undergo a change in location, spread or shape, it will continue to produce the same distribution of in-specification parts. Once the process is in statistical control the first action on the process should be to locate the process on the target.
If the process spread is unacceptable, this strategy allows the minimum number of out-ofspecification parts to be produced. Actions on the system to reduce the variation from common causes are usually required to improve the ability of the process and its output to meet specifications consistently. For a more detailed discussion of process capability, process performance and the associated assumptions, refer to Chapter IV. The process must first be brought into statistical control by detecting and acting upon special causes of variation.
Then its performance is predictable, and its capability to meet customer expectations can be assessed. This is a basis for continual improvement. Every process is subject to classification based on capability and control. A process can be classified into 1 of 4 cases, as illustrated by the following chart: Statistical Control. To be acceptable, the process must be in a state of statistical control and the capability common cause variation must be less than the tolerance.
The ideal situation is to have a Case 1 process where the process is in statistical control and the ability to meet tolerance requirements is acceptable. A Case 2 process is in control but has excessive common cause variation, which must be reduced.
A Case 3 process meets tolerance requirements but is not in statistical control; special causes of variation should be identified and acted upon. In Case 4, the process is not in control nor is it acceptable.
Both common and special cause variation must be reduced. Under certain circumstances, the customer may allow a producer to run a process even though it is a Case 3 process. These circumstances may include: o The customer is insensitive to variation within specifications see. The economics involved in acting upon the special cause exceed the benefit to any and all customers. Economically allowable special causes may include tool wear, tool regrind, cyclical seasonal variation, etc.
In these situations, the customer may require the following: The process is mature. The special cause to be allowed has been shown to act in a. A process control plan is in effect which will assure conformance to specification of all process output and protection from other special causes or inconsistency in the allowed-special cause. The accepted practice in the automotive industry is to calculate the capability common cause variation only after a process has been demonstrated to be in a state of statistical control.
These results are used as a basis for prediction of how the process will perform. There is little value in making predictions based on data collected from a process that is not stable and not repeatable over time. Special causes are responsible for changes in the shape, spread, or location of a process distribution, and thus can rapidly invalidate prediction about the process.
That is, in order for the various process indices and ratios to be used as predictive tools, the requirement is that the data used to calculate them are gathered from processes that are in a state of statistical control. Process indices can be divided into two categories: those that are calculated using within-subgroup estimates of variation and those using total variation when estimating a given index see also chapter IV.
Several different indices have been developed because: 1 No single index can be universally applied to all processes, and 2 No given process can be completely described by a single index. For example, it is recommended that Cp and Cpk both be used see Chapter IV , and further that they be combined with graphical techniques to better understand the relationship between the estimated distribution and the specification limits.
In one sense, this amounts to comparing and trying to align the "voice of the process" with the "voice of the customer" see also Sherkenbach All indices have weaknesses and can be misleading. Any inferences drawn from computed indices should be driven by appropriate interpretation of the data from which the indices were computed. Automotive companies have set requirements for process capability. It is the reader's responsibility to communicate with their customer and determine which indices to use.
In some cases, it might be best to use no index at all. It is important to remember that most capability indices include the product specification in the formula. If the specification is inappropriate, or not based upon customer requirements, much time and effort may be wasted in trying to force the process to conform.
Chapter IV deals with selected capability and performance indices and contains advice on the application of those indices. In applying the concept of continual improvement to processes, there is a three-stage cycle that can be useful see Figure L4. Every process is in one of the three stages of the Improvement Cycle. Analyze the Process A basic understanding of the process is a must when considering process improvement.
Among the questions to be answered in order to achieve a better understanding of the process are:. What should the process be doing? What can go wrong? What is the process doing? Many techniques discussed in the APQP Manual 7 may be applied to gain a better understanding of the process.
Control charts explained in this manual are powerful tools that should be used during the Process Improvement Cycle. These simple statistical methods help differentiate between common and special causes of variation. The special causes of variation must be addressed. When a state of statistical control has been reached, the process' current level of long-term capability can be assessed see Chapter IV.
Maintain Control the Process Once a better understanding of the process has been achieved, the process must be maintained at an appropriate level of capability.
Processes are dynamic and will change. The performance of the process should be monitored so effective measures to prevent undesirable change can be taken. Desirable change also should be understood and institutionalized. Again, the simple statistical methods explained in this manual can assist. Construction and use of control charts and other tools will allow for efficient monitoring of the process.
When the tool signals that the process has changed, quick and efficient measures can be taken to isolate the cause s and act upon them. It is too easy to stop at this stage of the Process Improvement Cycle. It is important to realize that there is a limit to any company's resources. Some, perhaps many, processes should be at this stage.
However, failure to proceed to the next stage in this cycle can result in a significant competitive disadvantage. The attainment of "world class" requires a steady and planned effort to move into the next stage of the Cycle. Improve the Process Up to this point, the effort has been to stabilize the processes and maintain them. However, for some processes, the customer will be sensitive even to variation within engineering specifications see Chapter IV.
In these instances, the value of continual improvement will not be realized until variation is reduced. At this point, additional process analysis tools, including more advanced statistical methods such as designed experiments and advanced control charts may be useful. Appendix H lists some helpful references for further study. Process improvement through variation reduction typically involves purposefully introducing changes into the process and measuring the effects.
The goal is a better understanding of the process, so that the common cause variation can be further reduced. The intent of this reduction is improved quality at lower cost. When new process parameters have been determined, the Cycle shifts back to Analyze the Process. Since changes have been made, process stability will need to be reconfirmed. The process then continues to move around the Process Improvement Cycle.
In his books 8, Dr. Deming identifies two mistakes frequently made in process control: Mistake 1. Ascribe a variation or a mistake to a special cause, when in fact the cause belongs to the system common causes.
Mistake 2. Ascribe a variation or a mistake to a system common causes , when in fact the cause was special. Over adjustment [tampering] is a common example of mistake No. Never doing anything to try to find a special cause is a common example of mistake No. There is a common misconception that histograms can be used for this purpose.
Histograms are the graphical representation of the distributional form of the process variation. The distributional form is studied to verify that the process variation is symmetric and unimodal and that it follows a normal distribution. Unfortunately normality does not guarantee that there are no special causes acting on the process. That is, some special causes may change the process without destroying its symmetry or unimodality. Once stable, the process can be analyzed to determine if it is capable of producing what the customer desires.
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SPC Statistical Process Control is the use of statistical techniques such as control charts to analyze a process or its output so as to приведу ссылку appropriate actions to achieve and maintain a state of statistical control and to improve нажмите чтобы узнать больше process capability.
Establish a foundational knowledge-base to analyze your manufacturing system and enhance its effectiveness. Gain a basic understanding of how to establish, aiag spc manual pdf free download and implement a statistical process aiag spc manual pdf free download SPC system in a manufacturing environment.
Improve your understanding of the integration of statistical process control SPC and measurement systems analysis MSA into IATF and discover how to develop a download free minecraft 1.8.1 quality process control system by selecting and applying the appropriate statistical tools. Examine methods for implementing frer applying the principles of statistical process control to manufacturing processes.
Want to make sure that the learning читать больше The manual covers the majority of situations that occur in early planning, design, aiag spc manual pdf free download process analysis phases. Program Management: Quality Team - Loading Changes. Please wait. There are two phases in statistical process control studies. The first is identifying and eliminating the special causes of variation in the frse.
The objective /31105.php to stabilize the process. A stable, predictable process is said to be in statistical control. The second phase is concerned with predicting future measurements thus verifying ongoing process stability. During this phase, data analysis and reaction to special causes is done in real time. Once stable, the process downlad be analyzed to determine if it is capable of producing what the customer desires. Sign Up For Training Now. Publications - Order Today.
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