How descriptive statistics helps in obtaining information to quality control process?
The word quality is often used to signify fitness or superiority or excellence of a product or service. Quality can be defined in various aspects viz. product-base, user based, manufacturing based, value-based and motivation based feature (Gravin, 1984). Descriptive statics help to obtain the information to quality control process in the following ways:
- Used to measure variability.
- Maintain the overall quality of the product.
- Helps in manufacturing, ensure that the procedures will produce more specification conforming products.
- Helps to create less waste.
- Brief descriptive coefficients that that summarizes a given data sets which can be either a representative statistics are broken down into measure of central tendency and measure of variability.
- Different descriptive statistics measure such as mean, standard deviation, range and distribution of the data are used to describe quality characteristics and relationship.
- With the help of control charts, we can inspect the process whether it is within control or out of control.
Descriptive statistics are brief which can be either a representation of the entire or a sample of a population. They provide the principal means by which a product is sampled, tested, and evaluated, and the information in those data is used to control and improve the process and the product. The descriptive statistics shows how simple graphical and numerical techniques can be used to summarize the information in sample data.
SPC is defined as the use of the use of statistical techniques to control a process or production method. SPC tools and procedures can help us to monitor process behavior, discover issues in internal systems and find solutions for production issues.
A descriptive statistics is:
- Concerned with the describing, interpreting and analyzing the data.
- An essential element in any improvement process
Importance of inferential statistics in decision making regarding SPC:
Inferential statistics are powerful tools for making inference that rely on frequencies and probabilities. Inferential statistics helps to deciding when to make corrective action. Estimating process capability.
Making inference from the control chart is analogous to testing hypothesis. If the process is out of control when it is really in control then it is probability of type I error (α) which is also called producer risk. On the other hand, if we conclude the process is in control when it is really out of control then it is the probability of the type II error (β) which is also called the consumer risk. Value of α is determined on the condition when there is the null hypothesis true and value of β is determined on the condition when alternative hypothesis is true.
People also ask:
Explain how quality affects productivity.
The term quality is used to indicate the conference of manufactured product for the certain specification sets before manufacturing. And the term productivity is defined as the rate of output per unit input when the quality increases productivity also improves since waste materials are removed and it is not necessary to rework and also materials are optimally utilized.
If defect, rework and unwanted things should be decrease productivity increases that means as quality increases productivity also increases. So as a result quality and productivity are directly proportional to each other.
Quality and productivity are related with each other. Both these are concerned with the performance aspect of the system/product/service.
Mathematically, the relation between quality and productivity is
Q is quality, P is performance of the product and E is the expectation of the customers.
When P>E, the quality of the product is very good.
When P=E, the quality of the product is satisfactory.
When P<E, the quality of the product is poor.
Implications on the basis of cost:
Better quality leads the less defects due to which the cost of poor quality will be minimum and if the poor quality is minimum, then the production unexpected will be minimum which further the minimum gives total cost of production. Higher quality goods take more time and labor and higher quality raw material to produce. So a drive to greater productivity almost always means a diminution of quality.
Higher productivity enables the organization to reduce the cost and gain the advantages both in terms of quality. The cost of the quality product will have an immediate effects on the enhanced productivity of an organization. If productivity increases with high quality product, sales increases and it reduces the cost of an organization.
Every nations wants to increase productivity and quality of product at lower price. For this, they should reduce wastage of resources and must find the other substitutes that will be environment friendly. Proper education and training of labor should be given for better quality.
The fundamental conditions that must exist in manufacturing industry for statistical process control to be effective.
Statistical process control (SPC) is a method of quality control which employ the statistical method to control the method to control the process. SPC is applied to any process where the conforming product output can be measured. One example of process where SPC is applied is manufacturing lines and widely used in industry for the process monitoring quality improvement.
Fundamental conditions that must exist in manufacturing industry for statistical process control to be effective are discussed below:
- The quality characteristics of manufacturing product must lies inside the control limits.
- Control charts must be constructed because it gives the idea about( when to take corrective action, when to leave the process alone, whether the process out of control or not?, how to estimate capability of the process and hence its ability to meets the customer requirements)
- Variability of the product should be measured since if more the variability, there should be less quality. That’s why special cause should be minimized as much as possible to get the less variability.
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