Technology & Engineering
Statistical Process Control (SQC)
Statistical Process Control (SQC) is a method used to monitor and control processes to ensure they operate efficiently and produce high-quality products. It involves using statistical techniques to analyze and improve processes, identify variations, and make data-driven decisions. By continuously monitoring and adjusting processes, SQC helps organizations maintain consistency and meet quality standards.
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12 Key excerpts on "Statistical Process Control (SQC)"
eBook - PDF- Christopher J. Biermann(Author)
- 1996(Publication Date)
- Academic Press(Publisher)
13, Make education and self-improvement a goal. People who are challenged and see growth in themselves will enjoy their jobs more and perform better in the long run. 14. Everyone is involved in the transforma-tion of the company. Accomplishments are the result of team effort. This reduces duplication of effort and allows different departments to interact more successfully. 20.2 STATISTICAL PROCESS (QUALITY) CONTROL, SPC, SQC Introduction Statistical process control (SPC) or statistical quality control (SQC) involves data collection and analysis, modeling of systems, problem solving, and design of experiments. I like to summarize SPC as the application of elementary statistical analysis to control a process. It is the scientific method applied to manufacturing. Shewhart (1939) made the following comparison: Mass Production Specification Production Inspection Scientific Method Hypothesis Experiment Test of Hypothesis Statistical analysis Statistical analysis of the process is a key part of SPC since it is crucial to determine what is random variation and what is nonrandom variation that can be controlled. Anyone who wants to implement SPC must understand elementary statistics, experimental design, and sampling tech-niques. There are many good books on statistical analysis so there is little point including all this information here, but some basic statistical equa-tions are included later. When using statistical analysis, the underlining assumption is that all of the variation is random. SPC tools must be devel-oped when the process is in control and there are no trends in the data. (This does not mean all of the product will be satisfactory, only that the operator is doing the best that he/she can with the equipment.) This is not always easily done since an apparent trend in the short run could be due to statistical fluctuation. Steps can be taken to de-crease the random variation so that actual changes can be observed more easily.
- Johnson Edosomwan(Author)
- 1995(Publication Date)
- CRC Press(Publisher)
Chapter 5 Productivity and Quality Improvement Through Statistical Process Control This chapter presents the concepts and techniques of statistical process control (SPC) and its usefulness in improving productivity and quality in the business environment. A step-by-step methodology for implementing SPC and the design of the experiment are presented. A case study that shows the application of the SPC technique in a group technology production environment is presented. A procedure for designing an experiment is also offered. Statistical tools for data analysis are described. 5.1 PROCESS CONTROL DEFINITION Process control is a state whereby statistical inference techniques are used to m onitor and control a specified process in order to achieve improved quality and gains in productivity. The control concept utilizes both historical and present technical knowledge of the process in understand ing cause-and-effect relationships combined with statistical techniques to control and minimize defects. The implementation of process control concepts and techniques is achieved by providing a control system for defect and error detection, a control system for defect and error analysis, and a control system for defect and error correction. 139 140 5 Statistical Process Control 5.2 A PROCESS CONTROL SYSTEM DEFINITION A process control system is a feedback m echanism that provides inform a tion about the process characteristics and variables, process performance, action on the process inputs, transformation process, and action on the output The major components o f a process control system are presented in Figure 5.1. Figure 5.1 Components of a process control system. 5.3 Requirements of Process Management 141 5.3 THE KEY REQUIREMENTS OF PROCESS MANAGEMENT In order to improve the quality of products or services from a process,the following basic requirements are necessary.
eBook - ePub- Robin Kent(Author)
- 2016(Publication Date)
- William Andrew(Publisher)
Chapter 5Statistical process control (SPC)
Statistical process control is one of the most important components of effective quality management. It acts as a ‘feed forward’ control to allow a processor to actually control quality rather than the typical inspection-based approach which is reactive and after the event.The sad thing is that SPC has been around for many years and yet has never really been taken seriously by the plastics processing industry. Individual companies have used SPC to great effect and have gained control of their process but the industry as a whole is still remarkably reluctant to use the methods and they are not often seen at processors.This is, in part, due to widespread misunderstanding of how the process works and the very quick benefits that it delivers. Most of the standard texts on SPC are written by statisticians who move very quickly over the basics and get very involved in the mathematics and formulas without explaining how SPC works in practice. Many people have a fear of mathematics and their eyes glaze over at the first sight of an equation. In reality, operating SPC requires nothing more than a basic grasp of arithmetic and the ability to see patterns in the results that are plotted as a result of this. Setting up a basic SPC system does need some higher skills but most of it is very simple and still only needs basic arithmetic.Anybody who can operate Excel can set up an SPC system and reap the benefits almost immediately.SPC naturally involves numbers and many off-the-shelf and bespoke systems have been developed (even I developed one in the dim and distant past). Many of these are excellent (sadly, not mine) and when the amount of data becomes large they can be useful to improve data handling. Their fundamental problem is that they take away all of the basics and present the users simply with numbers and little background information. This can divorce the user from the actual process and, by reducing interaction with the actual process, can reduce the value of the SPC system.
eBook - ePub- Larry Webber, Michael Wallace(Authors)
- 2011(Publication Date)
- For Dummies(Publisher)
Chapter 10 Assessing Quality with Statistical Process Control In This Chapter Digesting the basics of Statistical Process Control Becoming familiar with the role of the control chart in SPC Building control charts to evaluate and correct a process Determining whether a process is capable T he following quote is from the “father” of Statistical Process Control (SPC), Walter Shewhart. Shewhart pioneered the idea that all manufacturing processes produce some variation that’s natural to the processes and that quality problems arise when abnormal variation occurs. Shewhart was the first to use statistics to measure the natural variation of a process and detect the occurrence of unnatural variation. Bringing a production process into a state of ‘statistical control,’ where there is only chance-cause variation, and keeping it in control, is necessary to predict future output and to manage a process economically. — Walter Shewhart In this chapter, you find out how to use simple statistical techniques to, as Shewhart said, bring your production processes into a state of “statistical control” by eliminating variation. With this control and variance elimination, you can produce a quality product or service for your customers. Grasping the Basics of Statistical Process Control Statistical Process Control (SPC) is defined as the use of statistical tools and techniques to measure a production process in order to detect change. To produce a quality product, you must have a process that’s consistent. SPC helps you detect any changes in the process, because change is more times than not a bad thing. SPC is a philosophy that embraces the idea of continuous improvement brought on by using an assortment of statistical tools. The basis of the philosophy is that problems within a system cause most of your process issues, not problems with individual people
- Robert Zorich(Author)
- 2012(Publication Date)
- Academic Press(Publisher)
C H A P T E R 1 2 S T A T I S T I C A L QUALITY C O N T R O L The term Statistical Quality Control has been used in a wide variety of ways in the semiconductor manufacturing industry. The term is typi-cally synonymous with the term Statistical Process Control, and they are both usually referred to by the acronyms SPC or SQC. Although there are various methods of implementation, I will be describing the ones that have seen the most service in the semiconductor industry. It should be noted that there are a number of differing viewpoints on the exact makeup of SQC, which is a topic covered quite well in the published literature. Technically speaking, Statistical Quality Control is the act of observation of a process and is more of a reactive or passive term. The term Statistical Process Control implies that an active role is being taken in the control of the process. This action is taken in accordance within the guidelines laid out by the Statistical Quality Control procedure. However, there is little strict adherence to these terms so they are often used interchangeably. There is probably no right method for implementation of the SQC or SPC methods; in-stead, it is probably more important to be consistent in the methodol-ogy used so that the repeatability of the process is maintained. The effectiveness of the program has varied drastically from com-pany to company and from fab to fab. There are many reasons for this inconsistency, but the main reason, I believe, that there has been a problem is the lack of consistency in methodology and lack of stan-dardization in the area. A significant amount of work has been per-formed by AT&T and Grant and Leavenworth. This work forms the basis of many of the SQC programs that have been implemented. Note that there are terminology differences from place to place in the literature and in the real world, especially with regard to limit names, and the phrases defect and defective. We will not get into 464
eBook - ePubStatistical Process Control for the Food Industry
A Guide for Practitioners and Managers
- Sarina A. Lim, Jiju Antony(Authors)
- 2019(Publication Date)
- Wiley(Publisher)
4 An Introduction of SPC in the Food Industry: Past, Present and Future4.1 Statistical Process Control: A Brief Overview
Understanding the meaning of Statistical Process Control (SPC ) is vital in operating SPC in the food industry. There have been attempts to expand the concept of SPC, beyond the process monitoring technique.SPC has been categorised into several types of definitions such as:- technological innovation (Bushe 1988 ; Roberts, Watson, and Oliver 1989 );
- process management technique (Bissell 1994 );
- control algorithm (Hryniewicz 1997 );
- a component of total quality management (TQM ) (Barker 1990 );
- one of the quality management system in the food industry (Caswell, Bredahl, and Hooker 1998 ).
- Wallace et al. (2012 ) and Davis and Ryan (2005 ) viewed SPC as a participatory management system – teamwork efforts, employee involvement and enable real‐time decision‐making to be made (Deming 1986 ; Elg, Olsson, and Dahlgaard 2008 ).
SPC is a powerful collection of problem‐solving tools useful in achieving process stability and improving capability through the reduction of variability(Montgomery 2012 )The focus of SPC is for the users to understand the variation in values of quality characteristics (Woodall 2000 ). The primary indicator of an effective SPC application is a stable process. The process stability refers to the stability of the underlying probability distribution of a process over time, and these very often can be described as the stability of the distribution parameters overtime (Mahalik and Nambiar 2010 ). The process stability is extremely crucial as it is one of the pre‐requirement to assess the process capability indices determination (Brannstrom‐Stenberg and Deleryd 1999 ; Motorcu and Gullu 2006 ; Sharma and Kharub 2014 ).
Prior to the assessment of process capability, the process must be ensured to be stable. Process capability indices developed from an unstable process are not reliable.
eBook - PDF- Douglas C. Montgomery, Cheryl L. Jennings, Michele E. Pfund(Authors)
- 2015(Publication Date)
- Wiley(Publisher)
The third objective is to discuss and illustrate some practical issues in the implementation of control charts and their associated process improvement tools. Collectively, these tools are often called statistical process control (SPC). After careful study of this chapter you should be able to do the following: 1. Understand chance and assignable causes of variability in a process 2. Explain the statistical basis of the Shewhart control chart 3. Understand the basic process improvement tools of SPC: the histogram or stem-and-leaf plot, the check sheet, the Pareto chart, the cause-and-effect diagram, the defect concentration diagram, the scatter diagram, and the control chart 4. Explain how sensitizing rules and pattern recognition are used in conjunction with control charts 3.1 Introduction If a product is to meet or exceed customer expectations, generally it should be produced by a process that is stable or repeatable. More pre- cisely, the process must be capable of operating with little variability Control: Finally, the team fully documented the process changes and trained all personnel on new procedures and expectations. They also imple- mented control charts to detect process changes that could cause air bubbles. By following these steps, the company saved £290,000 and reduced its scrap rate from 1 in every 5 cough drops to 1 in 10,000 or more. More importantly the organization now has a much better understanding of the impact of variation. around the target or nominal dimensions of the product’s quality char- acteristics. SPC is a powerful collection of problem-solving tools useful in achieving process stability and improving capability through the reduction of variability. A process is an organized sequence of activities that produces an output (product or service) that adds value to the organization. All work is performed in a process, and any process can be improved.
eBook - ePub- Joseph Berk, Susan Berk(Authors)
- 2000(Publication Date)
- Butterworth-Heinemann(Publisher)
Figure 10-2 . Each step is further explained below.Figure 10-2 A Six-Step Process for Statistical Process Control Implementation. These steps assure critical parameters are identified and selected for SPC applications.Selecting Processes for Statistical Control
One of the first steps in implementing statistical process control is realizing that the technique is not appropriate for all processes.In our experience, processes ideally suited for statistical process control are repetitive (in the sense that they produce quantities of similar items), have a high inspection content, have higher than desired reject rates, and create items with dimensions or other characteristics that are fairly straightforward to measure. Processes with high inspection content are potential candidates because statistical process control greatly reduces or eliminates inspection. Processes producing parts with high reject rates are candidates because statistical process control, when properly implemented, frequently eliminates defects. Processes that are not producing rejects should also be considered for statistical control, but processes with high reject rates should be considered first.Processes that produce items with dimensions or other characteristics that are fairly straightforward to measure are good statistical control candidates because straightforward measurement techniques simplify statistical process control training and acceptance.Attributes Versus Variables Data
The last characteristic listed above for identifying statistical process control candidates (processes producing parts with dimensions or other characteristics that are fairly straightforward to measure) brings us to another issue. This issue concerns attributes and variables data. Variables data are related to measurements with quantifiable values (for example, shaft diameters are measured and recorded with specific values, as shown in Figure 1). Attributes data only reflect a yes or no decision, such as whether an item passed or failed a test. Attributes data are recorded in such terms as pass or fail, go or no go, yes or no, true or false, accept or reject, etc. There are no quantifiable values included with attributes data.
eBook - PDF- Roberta S. Russell, Bernard W. Taylor(Authors)
- 2023(Publication Date)
- Wiley(Publisher)
Process control is achieved by taking period samples from the process and plotting these sample points on a chart to see if the process is within statistical control limits. Quality-focused companies provide extensive training in SPC methods for all employees at all levels. In this environment, employees have more responsibility for their own operation or process. Employees rec- ognize the need for SPC for accomplishing a major part of their job: product quality. When employees are provided with adequate training and understand what is expected of them, they have little difficulty using statistical process control methods. Learning Objective 3.2 Utilize attribute control charts to determine if a process is in control. The quality measures used in attribute control charts are discrete values reflecting a simple decision criterion such as good or bad. A p-chart uses the proportion of defective items in a sample as the sample statistic; a c-chart uses the actual number of defects per item in a sample. A p-chart can be used when it is possible to dis- tinguish between defective and non-defective items and to state the number of defectives as a percentage of the whole. A c-chart is used when it is not possible to calculate a proportion defective and the actual number of defective items must be used. Learning Objective 3.3 Utilize variable control charts to determine if a process is in control. Variable control charts are used for continuous variables that can be measured, such as weight or volume. Two commonly used variable control charts are the range chart, or R-chart, and the mean chart, or _ x-chart. A range (R-) chart reflects the amount of disper- sion present in each sample; a mean ( _ x-) chart indicates how sample results relate to the process average or mean. Companies normally use these charts together to determine whether a process is in control.
eBook - PDFStatistical Quality Control
A Modern Introduction
- Douglas C. Montgomery(Author)
- 2014(Publication Date)
- Wiley(Publisher)
Figure 5.32 presents a high-level flowchart of this planning process. After plans are produced, they are sent to a checker who tries to identify obvious errors and defects in the plans. The plans are also reviewed by a quality-assurance organization to ensure that process specifications are being met and that the final product will conform to engineering standards. Then the plans are sent to the shop, where a liaison engineering organization deals with any 5.7 Applications of Statistical Process Control and Quality Improvement Tools 223 To copier Sort by # copies Make 3 copies To desk Deposit copies To word processing Copy files Sort copies Cut vouchers Staple & cover Straight? Mailing labels available? Type labels Take labels Typing box To desk Get transmittal and/or request labels File firm copy Sort/ discard copies Staple returns Cut vouchers Labels on envelopes To word processing Return cover Cover O.K.? To desk Retrieve transmittal C Attach envelopes Revise transmittal Transmittal O.K.? Any open items? To fix Put copy in file Return sequence? To signer B B C ■ F I G U R E 5 . 3 1 Flowchart of the assembly portion of the Form 1040 tax return process. 224 Chapter 5 ■ How SPC Works errors in the plan encountered by manufacturing. This flowchart is useful in presenting an overall picture of the planning system, but it is not particularly helpful in uncovering non- value-added activities, as there is insufficient detail in each of the major blocks. However, each block, such as the planner, checker, and quality-assurance block, could be broken down into a more detailed sequence of work activities and steps. The step-down approach is frequently helpful in constructing flowcharts for complex processes. However, even at the relatively high level shown, it is possible to identify at least three areas in which SPC methods could be use- fully applied in the planning process.
eBook - PDF- Roberta S. Russell, Bernard W. Taylor(Authors)
- 2019(Publication Date)
- Wiley(Publisher)
Harley-Davidson uses a statistical operator control (SOC) quality-improvement program to reduce parts variability to only a fraction of design tolerances. SOC ensures precise tolerances during each manufacturing step and predicts out-of-control com- ponents before they occur. SOC is especially important when deal- ing with complex components such as transmission gears. The tolerances for Harley-Davidson cam gears are extremely close, and the machinery is especially complex. CNC machinery allows the manufacturing of gear centers time after time with tol- erances as close as 0.0005 inch. SOC ensures the quality necessary to turn the famous Harley-Davidson Evolution engine shift after shift, mile after mile, year after year. Prior to machining and fabrication, some of the parts for cer- tain newer Harley-Davidson models are 3D printed in order to val- idate the design tolerances, proportions, and other attributes. The 3D printed parts used for validation are full-sized 1:1 scale parts. This prototyping process allows everyone involved in the design and manufacturing process to test, fit, hold, and see parts of the bike in proper proportion. Once a design is completed, the com- pany’s design team uses computer-aided manufacturing (CAM) software to manufacture it. Discuss how reducing “parts variability to only a fraction of design tolerances” is related to a goal of achieving Six Sigma quality. EXHIBIT 3.3 formula for C pk in cell D16 =(D6–D7)/(6*D8) Excel Key Terms 133 Summary Statistical process control is one of the main quantitative tools used in most quality-management systems. Quality-focused companies pro- vide extensive training in SPC methods for all employees at all levels. In this environment employees have more responsibility for their own operation or process. Employees recognize the need for SPC for accom- plishing a major part of their job, product quality.
- James D. Meadows(Author)
- 1998(Publication Date)
- CRC Press(Publisher)
process capability. Since stability and accuracy are not one and the same, if the process is producing features that are beyond the product's specifications, the process must be improved. The X and R chart can show a graphical display of a relationship between process average and process standard deviation. Normal Curve Chart The process depicted in Figure 18-9 will yield acceptable parts. Were the process to shift, out-of-spec parts could be produced. Process Shifted from Mean The process depicted in Figure 18-10 has shifted so as to produce features that fall outside of the lower specification limit. Capability Analysis Using Variable Data Capability analysis predicts the variation one should expect from the process or portion of the process under analysis. The variation is then compared to a speci-fied tolerance to determine the process's ability to meet the specification. The three types of variation studies conducted in SPC are: 472 Chapter 18 Lower Spec Limit Upper Spec Limit -3cr x X FIGURE 18-9 Normal curve describing a capable process. Lower Spec Limit Upper Spec Limit FIGURE 18-10 Normal curves showing shift of process from specification mean. 1. Process capability 2. Process potential/machine capability 3. Measurement system capability. Let's examine three common process capability indices. All these calcula-tions assume a stable, in-control process. These will quantify the degree of process capability. 1. The process capability ratio (PCR) 2. C index 3. C pk index Statistical Process Control 413 The PCR is calculated as follows: Process spread 60^ PCR = = Total tolerance USL -LSL If the PCR is multiplied by 100, it can be interpreted as the percent of the tolerance the process is capable of holding. However, although capable, the PCR doesn't use the process average's location. The process could be stable and have a small PCR value (which is good) but still produce a large percentage of out-of-tolerance parts.
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