STATISTICAL QUALITY CONTROL
Statistical quality control refers to the use of statistical methods in the monitoring and maintaining of the quality of products and services. One method, referred to as acceptance sampling, can be used when a decision must be made to accept or reject a group of parts or items based on the quality found in a sample. A second method, referred to as statistical process control, uses graphical displays known as control charts to determine whether a process should be continued or should be adjusted to achieve the desired quality.
All the tools of SQC are helpful in evaluating the quality of services. SQC uses different tools to analyze quality problem.
(1) Descriptive Statistics: – Descriptive statistics are brief descriptive coefficients that summarize a given data set, which can be either a representation of the entire population or a sample of it. Descriptive statistics are broken down into measures of central tendency and measures of variability, or spread. Measures of central tendency include the mean, median and mode, while measures of variability include the standard deviation or variance, the minimum and maximum variables
(2) Statistical Process Control (SPC) : – Statistical process control (SPC) is a method of quality control which employs statistical methods to monitor and control a process. This helps ensure the process operates efficiently, producing more specification-conforming product with less waste (rework or scrap). SPC can be applied to any process where the “conforming product” (product meeting specifications) output can be measured. Key tools used in SPC include run charts, control charts, a focus on continuous improvement, and the design of experiments. An example of a process where SPC is applied is manufacturing lines.
SPC must be practiced in 2 phases: The first phase is the initial establishment of the process, and the second phase is the regular production use of the process. In the second phase, a decision of the period to be examined must be made, depending upon the change in 5M&E conditions (Man, Machine, Material, Method, Movement, Environment) and wear rate of parts used in the manufacturing process (machine parts, jigs, and fixtures).
(3) Acceptance Sampling achieve the desired quality: – Acceptance sampling uses statistical sampling to determine whether to accept or reject a production lot of material. It has been a common quality control technique used in industry. It is usually done as products leaves the factory, or in some cases even within the factory. Most often a producer supplies a consumer a number of items and a decision to accept or reject the items is made by determining the number of defective items in a sample from the lot. The lot is accepted if the number of defects falls below where the acceptance number or otherwise the lot is rejected.
VARIABLE & ATTRIBUTE
Both variable data and attribute data measure the state of an object or a process, but the kind of information that each describes differs. Variable data involve numbers measured on a continuous scale, while attribute data involve characteristics or other information that you can’t quantify. Each has its own benefits over the other.
In science and research, attribute is a characteristic of an object (person, thing, etc.). Attributes are closely related to variables. A variable is a logical set of attributes. Variables can “vary” – for example, be high or low. How high, or how low, is determined by the value of the attribute (and in fact, an attribute could be just the word “low” or “high”). (For example see: Binary option)
While an attribute is often intuitive, the variable is the operationalized way in which the attribute is represented for further data processing. In data processing data are often represented by a combination of items (objects organized in rows), and multiple variables (organized in columns).
Age is an attribute that can be operationalized in many ways. It can be dichotomized so that only two values – “old” and “young” – are allowed for further data processing. In this case the attribute “age” is operationalized as a binary variable. If more than two values are possible and they can be ordered, the attribute is represented by ordinal variable, such as “young”, “middle age”, and “old”. Next it can be made of rational values, such as 1, 2, 3…. 99.
Benefits of Variable Data
Variable data provide detailed and concrete information about a product. In contrast, attribute data may be obscure or unhelpful. For example, if nails need to be made to a one-inch specification, with a leeway of 0.1-inches either way, variable data about each nail would provide the exact length. Attribute data would only state whether each nail fit the specification or not. It wouldn’t state whether the nail was too long or too short.
Benefits of Attribute Data
Attribute data are often more helpful when qualitative information is needed. Examples include the state of an object, non-numerical characteristics and customer feedback. For example, the attribute data might count the number of people who shop at a specific store, or the size of a product, such as a small or large serving of food. Attribute data are useful for analysis as you can use attribute data to create ratios, percentages or charts, whereas variable data don’t lend itself as freely to this.