Statistical process control tools

W.J. Arbegast, Using Process Forces as a Statistical Process Control Tool for Frichon Stir Welds, Friction Stir Welding and Processing III, K.V. Jata, et al., Ed., TMS (The Minerals, Metals and Materials Society), 2005... 

Quality in Japan. Japanese economic prowess has been attributed variously to such quahty improvement activities as quahty circles, statistical process control (SPG), just-in-time dehvery (JIT), and zero defects (ZD). However, the real key to success hes in the apphcation of numerous quahty improvement tools as part of a management philosophy called Kaizen, which means continuous improvement (10). 

The PAT guidance facilitates introduction of new measurement and control tools in conjunction with well-established statistical methods such as design of experiments and statistical process control. It, therefore, can provide more effective means for product and process design and control, alternate efficient approaches for quality assurance, and a means for moving away from the corrective action to a continuous improvement paradigm. 

Statistical process control (SPC), also called statistical quality control and process validation (PV), represents two sides of the same coin. SPC comprises the various mathematical tools (histogram, scatter diagram run chart, and control chart) used to monitor a manufacturing process and to keep it within in-process and final product specification limits. Lord Kelvin once said, When you can measure what you are speaking about and express it in numbers, then you know something about it. Such a thought provides the necessary link between the two concepts. Thus, SPC represents the tools to be used, while PV represents the procedural environment in which those tools are used. 

The intent in this chapter is not to present in great detail the mathematics behind the statistical methods discussed. An excellent reference manual assembled by the Automotive Industry Action Group (AIAG), Fundamental Statistical Process Control, details process control systems, variation, action on special or common causes, process control and capability, process improvement, control charting, and benefits derived from using each of these tools. Reprinted with permission from the Fundamental Statistacal Process Control Reference Manual (Chrysler, Ford, General Motors Supplier uality Requirements Task Force , Measurement Systems Analysis, MSA Second Edition, 1995, ASQC Press. 

Statistical analysis such as Statistical Process Control (SPC) may be used to derive performance parameters as well as track and trend for alert/alarm conditions. Automated moifitoring tools may be available to assist in the collection of relevant data. A record of any such tools used should be maintained and any validation requirements considered. 

Statistical process control (SPC) provides a statistical approach for evaluating processes and for improving the quality of these processes through elimination of special causes. When SPC is effectively implemented within a company, benefits can be derived through a reduced cost of manufacture, improved quality, fewer troubleshooting crises, and improved relationships with customers. Process capability is a companion tool—one that can be used once a state of statistical control is achieved—to assess the performance of a process relative to its product specifications. Process capability can be used to determine whether processes are capable of continually operating within their stated specification limits. 

In general, there should be more use for SPC (statistical process control) in the laboratory. With the current commercial software programs it is easy to get the data directly from the equipment. SPC is a simple universal useful tool for monitoring data, enabling early recognition of errors and failures and observing trends. The point is that you get much more information, besides just statistical data such as relative standard deviations and mean values. 

What we have presented here is only a small portion, and very simplified at that, of the extensive array of concepts and techniques that constitute statistical process control. It is not our aim to exhaust this subject, but only to discuss it a little as an application of the normal distribution. Deeper treatments can be found in any of many books entirely dedicated to quality or statistical process control. To learn more about these important tools you can consult, for example, Oakland and Followell (1990) or Montgomery (1997). 

Furthermore, it is also plain from the definition of validation above that documentary evidence is the lodestar of validation and the tool by which the assurance is derived. In respect of software, the assurance needed will be in the form of documentary evidence, not of the product directly (though this may be included) but, as we have shown, from an examination of the development process that produced it. This strongly implies that the product is inseparable from the process, and the fitness for use of the product can only be secured through the rigorous control of the process that produced it. This ought to come as no surprise, since it is precisely this principle, the essence of the science of statistical process control, that determines the quality of any manufactured or fabricated product. In other words, the quality (of fitness for use) of an item is entirely determined by the process that produced it, where quality is defined according to the ISO 8402 [3] ... 

Well-known and documented techniques exist to monitor product variation while it is within the producer s environment. Most of the techniques require the observations or data to be statistically independent. That is, the data for a specific performance measurement are assumed to have no relationship to prior or successive observations. It is assumed that no correlation exists between data collected prior to or following a specific observation. The techniques used to monitor such data are collectively called statistical process control (SPC). These techniques are utilized in consumer-oriented industries. Some of the more prominent or useful techniques are presented in this chapter. Specifically, seven tools for SPC are reviewed, and their applicability is examined. Furthermore, common and improved approaches for process capability analysis are presented. 

Virtual Instrumentation allows organizations to effectively harness the power of the PC to access, analyze, and share information throughout the organization. With vast amount of data available from increasingly sophisticated enterprise-level data sources, potentially useful information is often left hidden due to a lack of useful tools. Virtual instruments can employ a wide array of technologies such as multidimensional analyses and Statistical Process Control (SPC) tools to detect patterns, trends, causalities, and discontinuities to derive knowledge and make informed decisions. 

Note Although the terms statistical process control (SPC) and statistical quality control (SQC) are often used interchangeably, there are various differences between these terms. SQC is a broader concept including descriptive statistical methods, acceptance sampling, and SPC as commonly adopted tools. Ishikawa (Ishikawa 1976) points out that statistical process control and statistical quality control use the same set of tools to control respectively the input of a process (independent variables) and the output of the process (dependent variables). Other SPC/ SQC advocates further elaborate this concept by differentiating these terms according to the type of data elaborated by the tools SPC is based on process signal data analysis, while SQC is based on product feature-related data. 

Control charts are a fundamental tool in statistical process control (SPC) methods, which has become the foundation of quality control. Quality control is an essential issue throughout every aspect of a polymer s life - ranging from polymerization, to polymer finishing operations, to compounding and processing. Industrial processes are inherently unstable. The concept of an unstable process is illustrated conceptually in Figure 1. In such a process there are three types of variations (1) Drift - the gradual shift of the process mean overtime (2) Fluctuations - the short term variations about the current process mean and (3) Interruptions - random shocks to the process. Since product quality is directly related to process consistency, in order to improve quality we must reduce all these three types of variations. Improvements can be made in both the short term and in... 

Gauge repeatability and reproducibility study Brainstorming and the traditional seven tools of quality Problem-solving methods The seven tools of management Failure mode and effect analysis Statistical process control Control charting... 

Statistical Process Control (SPC) A set of techniques and tools that help characterize patterns of variation. By understanding these patterns, a business can determine sources of variation and minimize them, resulting in a more consistent product or service. Many customers are demanding consistency as a measure of high quality. The proper use of SPC provides a powerful way to ensure that the customer gets the desired consistency time after time. 

Principles of Quality—course covering the background and application of quality concepts. Topics include team skills, quality tools, statistics, economics, and continuous improvement. Focuses on the application of statistics, statistical process control, math, and quality tools to process systems and operations. 

In the Principles of Quality course, students use advanced statistics and mathematics to work with operational data. Process technicians collect, organize, and analyze data during routine operations. The statistical approach works well with statistical process control and control charts. A variety of processes can easily be adapted to fit these quality tools. Examples of these include equipment and quality variables process variables include pressure, temperature, flow, level, and analytical parameters. 

Statistics, Statistical Process Control, and Quality Tools... 

Statistical process control (SPC) is a quality tool based on the principles of statistical mathematics and applied to a process to control product quality. The theory of SPC is based on some complex mathematics, but you need not be a mathematician to understand how to use the system. 

Petersen (2005) included an 18-page appendix on Statistical Process Control in Measurement of Safety Performance. Janicak (2010) has a 30-page chapter on Run Charts and Control Charts in Safety Metrics Tools and Techniques for Measuring Safety Performance. Other authors in years past have suggested the use of control charts to track safety performance. Yet, the subject does not appear in the current safety-related literature. 

These technologies, when apphed properly, can reduce catastrophic failure, and thus maintenance cost. One other apphcation is statistical process control (SPC). This predictive tool can be used to predict failures, but a plan must be in place first, for the data collection process is critical. If a CMMS is used, then the proper system architecture must be developed, along with associated processes and procedures that allow for accurate data collection. 

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