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Quality control refers to the check or control of a product with respect to its "goodness" or "excellence". In todays world of constantly changing pattern of making and production of things, if the quality of a product is not maintained survival of the product becomes difficult. The entire chain of functions from selection of raw materials to the assessment of finished product is linked by means of quality control only effectively. It is not at all possible to get a completed product of good quality unless the job is properly, effectively and continuously checked for quality. A significant output increase and reduction in breakdown can be achieved by quality control. Conformance to the specified standards of performance, utility and reliability are important parameters that quality control ensures in any industry. Without any proper standards being set for the purpose of achieving a desired quality in the operation of a product, it is impossible for a product to get a rating of proper quality. Industry demands are such that the product quality has to be according to a particular standard which has been predetermined by the industry. Otherwise no such product can face the competition of the market where there is increasing competition day by day. Producers are taking all possible measures to ensure that product quality is given priority as almost equal to the raw materials so that the good or commodity( tangible) or services (intangible) that is produced faces the test of competition from other goods. With global competition and the number of new producers coming up increasing on a steady basis, sustaining the product without proper quality is all the more difficult for a company.
Quality has several definitions, these days satisfying requirements of customers, whether the product is fit for use and finally how it confirms to the requirements. Satisfying customers must be the primary goal of any business and hence the definition of quality should include customers in itself. There is enough proof for this. Quality is a vital factor for the success and growth of a business as suggested by professionals who had experienced it during the last two decades in the U.S. and world markets has also proved the same.
Hence, in any field today, quality control has become an indispensable tool of modern management. In the following sections we will see in detail about quality control which we all are used to as "Statistical Quality Control".
2. Literature Survey: History of Quality Control
Â 1900: F. W. Taylor, by including Product Inspection into what he was working in his list of areas of manufacturing management emphasized on quality.Â
Radford'sÂ was in the product design stage and used to connect-together Quality, Productivity and Costs by involving quality consideration in these aspects.
1924,Â Walter ShewhartÂ introduced 'Statistical Process Control (SPC)' through 'Control Charts' for control in production.
1930:Â Dodge & Romig introduced the Acceptance Sampling Inspection Tables which are well known as Dodge-Romig Tables. Till 1940s SPC found the Manufacturing Industry to have little acceptance.
The importance of quality control increased with the second world war only.
1940:Â W. EdwardDemingÂ introduced SQC in Japanese Industry. Japan experienced creation of high quality manufacturing facilities due to this and this led to a revolution of quality control in Japan.
The country that was devastated in the Second World War became a huge competitor to the global market mainly the American Manufacturing Firms. Japan became a tough competitor soon the area of manufacturing to other leading nations.
1950:After the second world war, professionals and engineers in the industry in terms of training in quality control hugely benefited through the American Universities. This led to the emergence of 'Quality Assurance' due to the development around the concept of 'Quality Control'.
Â Joseph JuranÂ started his `Cost of Quality' approach, which laid emphasis on the accurate and complete identification and measurement of Costs of Quality.
Also, Armand Fiegen BaumÂ proposed Total Quality Control which included Product Design in quality control and enlarged its focus from manufacturing alone.
1960s "Zero-defects" gained importance.Â Philip Crosby focused on employee motivation and awareness, and came to be known as the champion of "Zero defects" concept.
The decade from 1950 to 1960 was one in which quality control and management aligned along with the Industrial Revolution in Japan.
1970s, In services such as government operations, health care, banking Quality Assurance methods were used. Importing heavily increased from Japan including America and European countries during this period in the world.
Late 1970s, a dramatic shift occurred from quality assurance to that of a strategic approach to quality. A pro-active' approach that basically focused on the preventing of defects from recurring emerged from the `reactive' approach of finding and correcting defectives in products manufactured.
Simultaneously, 'British Standards' (BS 5750) came into existence along withÂ ISO 9000Â Standards of Quality.
In late 1980s, Total Quality Management (TQM) became very popular not just in but even outside Japan and became the most important dimension revolving around Quality Control.
The concept of quality is getting a total or gross approach In the twenty first century in terms of what we call as 'Business Excellence'.
As we can see quality control has undergone a lot of changes from its inception way back in 1900. This trend is expected to continue in the industry as well, not just manufacturing but all other industries as well. SO it is mandatory for the industries to focus on this concept, keep updating their standards with respect to this concept of quality control.
In the following section we will see what is basically Statistical quality control.
3.The Eight Dimensions of Quality
Conformance to Standards
Performance: Customers generally evaluate a product to determine if it can perform certain specified functions and the degree to which performs them. A simple example is that, evaluating a spreadsheet software to a PC to determine data manipulation operations that they can perform.
Reliability: Products which are complex, such as automobiles, or airplanes, generally need some repair over their entire life duration. Example, we know that an automobile will require to be repaired occasionally, but if it needs frequent repair, it is judged as unreliable.
Durability: The effective service life of the product. Customers need products that perform as per satisfaction for a long period of time. The automobile and the appliance industries are major examples.
Serviceability: In Several industries where the customer's view of quality is influenced by how quickly and economically a repair or routine maintenance activity can be accomplished. Major examples are the automobile and appliance industries, several types of the service industries.
Aesthetics: The visual appeal of the product, along with several other factors such as packaging alternatives, tactile characteristics, style, color, shape, and other sensory features. As an example, soft-drink beverage manufacturers often rely on the visual appeal of their products in order to have a better market.
Features: Customers tend to associate a high quality of products with those that have added features; to the products which have features beyond the standard performance of surviving the competition.
Perceived Quality: Customers tend to rely on the past brand of the company concerning quality of its products in several cases.
Conformance to Standards: The product which exactly meets the requirements placed on it is usually perceived as a high-quality product. Simple example, how well does the hood fit on a new car.
Quality Engineering Terminology:
The parameters/ elements that every product possess describe what the customer thinks of as quality. These parameters are often called quality characteristics. These are also called critical-to-quality (CTQ) characteristics sometimes. Quality characteristics may be of several types:
1. Physical: length, weight, voltage, viscosity
2. Sensory: taste, appearance, color
3. Time Orientation: reliability, durability, serviceability
Quality engineering can be defined as the set of engineering, operational and managerial activities that any company may use to ensure that the quality parameters of a product are at the required or nominal levels and the variability is kept to minimum around the desired levels.
Specifications- These are used to evaluate quality characteristics based on a relative measurement.
The desirable values corresponding to the quality are called as a target or nominal value. There is a particular range or domain within which these values exist. There are certain limits for these ranges.
1. Lower specification limit- smallest allowable value for a quality characteristic
2. Upper specification limit- largest allowable value for a quality characteristic
3. Target or nominal values- The desirable values corresponding to the quality
4. Defective or nonconforming product- Products that fail to meet one or more of its specifications.
5. Defect or nonconformity- nonconformities which are sufficient to affect the safe or effective use of the product.
6. Not all products containing a defect are necessarily defective
The emphasis that has been placed recently on concurrent engineering has resulted in a team kind of an approach to the design, with specialists in quality engineering, manufacturing and other respects who are currently working together with the product designers at the earliest stages of the product design process.
Statistical Quality Control techniques can be broadly divided into two categories: (i) Statistical Process Control (SPC, in short) techniques; and (ii) Acceptance Sampling.
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4. Statistical Process Control
SPC techniques are used frequently in almost any manufacturing process and are really very useful in achieving process stability, solving real situation problems and making continuous improvements in product quality.
SPC techniques have wide applications in non-manufacturing processes as well.
SPC is one of the greatest technological developments of the twentieth century as it is based on very strong principles, is very easy to use, produces a significant impact on production, and can be applied to any process not just manufacturing.
The seven major tools of SPC are
1. Histogram or stem-and-leaf plot
2. Check sheet
3. Pareto chart
4. Cause-and-effect diagram
5. Defect concentration diagram
6. Scatter diagram
7. Control chart
The concept of Variation
Even if the process is maintained so well also, a particular amount of variation is unavoidable. But, when the process can be operated under stability i.e., quality of the materials and the machine settings are same, the operators are well experienced, then the quality characteristics exhibit particular patterns of variation. This is nothing but statistical distribution.
There are two types of causes for these variations:
Small causes: The amount of variation in the quality characteristic in a process operating under stable conditions is very small and is due to several small causes which are known as chance causes and cannot be avoided usually.
Assignable causes: The process becomes unstable when such causes are present in a process and we can see a reflection of it in the behavior of the quality characteristic. Frequent changes in the distribution can be observed.
The following is an example:
Till time t1 the process is kept under control and only chance causes are present.
As a result, both the mean and standard deviation of the process are at their in-control
values. At time t1 an assignable cause occurs and the process mean is shifted to a new value. Similarly another assigned cause results in further increase in standard deviation. Finally at t3, the cause that occurs is sufficient enough to move the process to an out of control state.
5. Control Charts
These are powerful tools used for on-line process control and monitoring of processes.
A typical control chart is a two-dimensional graph in which x-axis is the sample numbers and y-axis the quality characteristic. It has a solid center line (CL), two dotted lines called upper control limit (UCL) and lower control limit (LCL).
An example of a control chart is as follows:
How is the graph plotted?
Samples are collected periodically from the process, computing the quality characteristic is computed for each sample and is plotted against the sample number. The consecutive points are joined by lines and this gives the chart.
UCL = Âµw+ LÏƒw
Center line= Âµw
LCL= Âµw- LÏƒw
where L refers to the distance of the upper and lower control limits from the center or fixed line, and is expressed in standard deviation units.
w is the sample under consideration.
Process improvement using control charts:
The control chart has a vital function of improving the process as well.
1. Most of the processes never tend to operate in
2. If the causes are possible to be eliminated from the process, the degree of variability will be reduced
and the process will be improved.
3. The control chart is capable of only detecting the assignable causes.
The responsibility is from the management, operators, and engineering
will usually be necessary to eliminate the assignable causes. This is an example.
This is an example of a process improvement control chart.
An important component of the corrective action process in control charts is the out-of-control-action plan (OCAP). An OCAP is a flow chart of the order of the activities that should take place after the occurrence of an activating event. This is nothing but the out-of-control signals that are coming from the control chart.
Types of Control Charts:
Two main categories exist for Control Charts, the charts which display attributes,
and the charts which display variables.
Attribute Data: This type of Control Chart will display data that are resulting from the counting of the number of occurrences or the items that exist in a single category of similar occurrences or items. These data may be expressed in the form of pass or fail, yes or no, or presence or absence of a defect in an item.
Variables Data: This category of Control Chart displays values which result by the measure of a continuous variable. Examples of variables data are time, radiations and temperature.
While the above two are the categories which encompass quite a number of different types of Control Charts, there are major three types of charts that work for the majority parts of the data analysis cases we see today.
These three types of Control Charts are:
X-Bar and R Chart
Individual X and Moving Range Chart for Variables Data
Individual X and Moving Range Chart for Attribute Data
In several cases we might tend to have a confusion as to which type of chart we might use.
So in such a case we use the following decision tree diagram to find the type of charts wherever possible:
Process stability can be reflected by the relatively constant variation that is exhibited in the Control Charts.
The rules for interpretation of control charts:
Control Charts are based on control limits which are 3 standard deviations (3 sigma)
away from the centerline. There is already enough evidence that just considering the plus and minus 3 sigma for limits will lead to several false assumptions about the special causes which are operating in the process.
The three standard deviation limits maybe identified by zones. Each zone's separation line is exactly equal to one-third of the distance from the center line to that either the upper control limit or the lower control limit .
Zone A maybe defined as the area between the region of 2 and 3 standard deviations from the
centerline on both the positive and negative sides of the centerline.
Zone B may be defined as the area between the region 1 and 2 standard deviations from the
centerline on both the positive and negative sides of the centerline.
Zone C may be defined as the area between the centerline and the 1 standard deviation from the centerline, on both the positive and negative sides of the centerline.
Two sets of rules are in use for interpreting the Control Charts:
Rules for X-Bar and R Control Charts.
A similar, but a separate, set of rules for the interpretation of XmR Control Charts.
Typical Out-of-Control Patterns
Point outside control limits
Sudden shift in process average
Hugging the center line
Hugging the control limits
6. Acceptance Sampling:
Inspection is a means of monitoring the quality of the processes.
It is a method that is used in order to make a decision so as to accept or to reject the samples based on inspection. The objective of the sampling is not to control or estimate the quality of
lots, only to pass a judgment on samples. Using sampling instead of 100% inspection of the samples will bring risks to the consumers and also to the producer, which are called the consumer's and the producer's risks, respectively. There is a chance to encounter making decisions on sampling in daily affairs.
The above flowchart shows the operation of the single sampling plan.
A single sampling plan is sampling made out of a single sample.
A Single Sampling plan is characterized by the sample size which is n and drawn from the total sample and inspected for defects. The number of defects which are found are checked against the acceptance number and the procedure works as follows:
These risks can be written as:
a : This is the producer's risk, and is the probability that a lot with AQL will mostly be rejected.
b : The consumer's risk, and is the probability that a lot with LTPD will mostly be accepted.
here note that,
Acceptable Quality Level (AQL) = The quality level acceptable to the consumer
Lot Tolerance Percent Defective (LTPD) = The level to which the customer will accept the errors in sampling.
This is how an acceptance sampling curve looks:
7. PUBLICATIONS ON STATISTICAL QUALITY CONTROL:
1. Statistical quality control in infection control and hospital epidemiology at hospitals:
This application shows us how statistical quality control can be used for effective infection control in hospitals. But we have to note that the SPC that is used must be updated enough to adapt to any type of information of the epidemic that is available, which might vary from the levels that are present.
A few recommendations are suggested for this by the experts. Individual charts must be avoided, mainly when the sizes of the subgroups differ in the sample size that we consider. There are also improper use of quality control charts in few places due to technical issues.
Just passively generating charts are not sufficient to identify the out of control stages in the infection spread as well.
hence more training programs for employees for additional types of monitoring and generating types of flow charts need to be introduced.
This graph shows the infection control chart, but the data available is limited to months only. This must not be an a limitation.
This graph shows the corresponding number of occurrences of diseases and the time duration between the infections
2. A double neural network approach for the automated detection of quality control chart patterns
An artificial neural network algorithm has been proposed in order to detect and identify any of the five control chart patterns; which are , natural, upward shift, downward shift, upward trend, and downward trend. This kind of identification is made in addition to the traditional method of statistical detection of sequential data runs.
The performance of this algorithm has been a great success. This is also as per the latest trend in the algorithms and above all it conforms to the standards of quality that is set.
The results of this algorithm is compared to the algorithm which was proposed earlier and was the best till date.
8.CONCLUSIONS AND FUTURE TRENDS IN STATISTICAL QUALITY CONTROL:
It is not possible for quality to be inspected into the products. The processes must be designed to operate in order to achieve quality conformance; quality control is used for achieving this. Statistical quality control charts are mainly used to provide feedback about quality performance.
Quality control in the future has been planned for operation in the basis of risk management.
Any organization that is faced with a risk situation has to decide an alternative quality control for such a situation.