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STATISTICAL PROCESS CONTROL
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Introduction
Statistical process control (SPC) was pioneered by Walter A. Shewhart in the early 1920s. Dr Deming applied Statistical process control methods in the United States during World War II, to improve quality in the manufacture of munitions and other strategically important products. Dr Deming was a statistician who gained fame by helping Japanese companies to improve quality after the Second World War. His basic philosophy was that quality and productivity increase as variability decreases and, because all things vary; statistical methods of quality control must be used to measure and gain understanding of the causes of the variation. Many companies, particularly those in manufacturing industry or its suppliers, have adopted the Deming philosophy and approach to quality. In these companies, attention has been focused on performance improvement through the use of quality management systems and SPC. Deming was also instrumental in introducing SPC methods to Japanese industry after the war had ended. Shewhart developed the "control chart" and the concept of a state of statistical control determined by carefully designed experiments.
Statistical process control is a method of quality control which uses statistical methods. Statistical process control is applied in order to monitor and control a process. Monitoring and controlling the process ensures that it operates at its full potential. At its full potential, the process can make as much conforming product as possible with a minimum (if not an elimination) of waste (rework or trash).Key tools used in SPC include control charts; a focus on continuous improvement; and the design of experiments. An example of a process where Statistical process control is applied is manufacturing lines. Statistical process control (SPC) is a group of tools and techniques used to determine the stability and predictability of a process. Statistical process control is also used to monitor, control, and improve processes.
Review of Literature
SPC is one of the techniques used in quality assurance programs and/or in total quality management, abbreviated as TQM [1]. SPC and TQM are generally associated respectively by each other.TQM is a philosophy to nurture continuous improvement activities in an organization. Meanwhile, SPC is a technique found in TQM, which stresses primarily on satisfying the customers through the adoption of continuous improvement during product manufacturing stage [2].
SPC is a powerful problem-solving technique used for monitoring, controlling, analyzing, managing and improving a process using statistical methods. SPC is a type of feedback system in which information about a process is used to maintain and improve the process. The main goal of a SPC system is to make economically efficient decisions concerning the types of actions to take on a process and who should initiate the action [3]. SPC is a statistical technique commonly used to control processes and reduce variation in order to improve quality. At present, there is hardly any descriptions on the approaches used to implement SPC can be found in the literature. If descriptions are given, they often focus on the methodological aspects, i.e. SPC tools, Berger and Hart [4] and Chaudry and Higbie [5].
Wetherill and Brown [6] found that SPC sampling technique and quality control have been developed since the 1920s. Modern control chart and SPC developed by Walter A. Shewart were widely used during the 2nd World War in Britain and United States of America. Japanese companies had demonstrated that by applying SPC, a company would be able to minimize cost and as well as satisfying more customers.
Concept of Statistical process control (SPC)
Statistical process control (SPC) is not only a tool kit. It is a strategy for reducing variability, the cause of most quality problems; variation in products, in times of deliveries, in ways of doing things, in materials, in people’s attitudes, in equipment and its use, in maintenance practices, in everything. Control by itself is not sufficient; SPC requires that the process should be improved continually by reducing its variability. This is brought about by studying all aspects of the process using the basic question: ‘Could we do the job more consistently and on target (i.e. better)?’, the answering of which drives the search for improvements. This significant feature of SPC means that it is not constrained to measuring conformance, and that it is intended to lead to action on processes which are operating within the ‘specification’ to minimize variability. There must be a willingness to implement changes, even in the ways in which an organization does business, in order to achieve continuous improvement. Innovation and resources will be required to satisfy the long-term requirements of the customer and the organization, and these must be placed before or alongside short-term profitability. Process control is vital and SPC should form a vital part of the overall corporate strategy. Incapable and inconsistent processes render the best designs impotent and make supplier quality assurance irrelevant. Whatever process is being operated, it must be reliable and consistent. SPC can be used to achieve this objective.
Application of SPC
The application of SPC involves three main sets of activities. The first is, understanding of the process. This is achieved by business process mapping. The second is measuring the sources of variation assisted by the use of control charts and the third is eliminating assignable (special) sources of variation.
Control charts:
The data from measurements of variations at points on the process map is monitored using control charts. Control charts can differentiate "assignable" ("special") sources of variation from "common" sources. "Common" sources, because they are an expected part of the process, are of much less concern to the manufacturer than "assignable" sources. Using control charts is a continuous activity, ongoing over time.
Stable process:
When the process does not trigger any of the control chart "detection rules" for the control chart, it is said to be "stable". A process capability analysis may be performed on a stable process to predict the ability of the process to produce "conforming product" in the future.
Excessive variation:
When the process triggers any of the control chart "detection rules", (or alternatively, the process capability is low), other activities may be performed to identify the source of the excessive variation. The tools used in these extra activities include: Ishikawa diagrams, designed experiments and Pareto charts. Designed experiments are critical to this phase of the application of SPC. They are the only means of objectively quantifying the relative importance (strength) of sources of variation. Once the sources of variation have been quantified, those sources that are both statistically and practically significant can be eliminated. (A source that is statistically significant may not be considered cost effect to eliminate. A source that is not statistically significant would not be considered significant in practical terms). Methods of eliminating a source of variation might include: development of standards; staff training; error-proofing and changes to the process itself.
Variability in Process
In nature two extremely similar things are very difficult to obtain. If at all we come across exactly similar things, it must be only by chance. This fact holds good for production processes as well. No production process is good enough to produce all items exactly the alike. Most industrial and administrative situations involve a combination of materials, men and machines. Each of these elements of combination has some inherent or natural variability, the causes of which cannot be isolated plus the unnatural variability or variability due to assignable causes which can be isolated and therefore controlled and reduced to economic minimum.
For example, suppose drilling operation is to be performed on casting. Now, what are the possible sources of variation? First the material of which the casting is made will have some variability form unit to unit. Some units will be harder than others. If the operation is being done on mass production by number of workers on different similar machines.
Chance variations (Random Variations)
Variations due to chance are inevitable in any process or product. They are difficult to trace and difficult to control even under best conditions of production. Since these variations may be due to some inherent characteristics of the process or machine which functions at random. For example, a little play between nut and screw at random may lead to back-lash error and may cause a change in a dimension of machined part. The chance factors affect each component in separate manner. It has been established that if variations are due to chance factors alone, the observations will follow a ‘normal curve’. Knowledge of the behavior of chance variations is foundation on which control chart analysis rests.
If after random selection, observations are made under the same condition and if the distribution
Of observation follows a standard curve (normal curve) then it is assumed that the variations are due to chance causes and no assignable causes of error are present. The conditions which produced these variations are according to said to be “under control”. On the other hand, if the variations in the data do not conform to a pattern that might reasonably be produced by chance causes, then it is concluded that one or more assignable causes are at work. In this case conditions producing the variations are said to be “out of control”.
History of control charts
The control chart was invented by Walter A. Shewhart while working for Bell Labs in the 1920s.The company's engineers had been seeking to improve the reliability of their telephony transmission systems. Because amplifiers and other equipment had to be buried underground, there was a business need to reduce the frequency of failures and repairs. By 1920 the engineers had already realized the importance of reducing variation in a manufacturing process. Moreover, they had realized that continual process-adjustment in reaction to non-conformance actually increased variation and degraded quality. Shewhart framed the problem in terms of Common- and special-causes of variation and, on May 16, 1924, wrote an internal memo introducing the control chart as a tool for distinguishing between the two.