Table of Contents:-
- Meaning of Statistical Quality Control
- What is Statistical Quality Control?
- Approaches of Statistical Quality Control
- Elements of Statistical Quality Control
- Techniques of Statistical Quality Control
- Advantages of Statistical Quality Control
- Limitations of Statistical Quality Control
- Key Advantages of Statistical Quality Control
Meaning of Statistical Quality Control
Statistical Quality Control (SQC) monitors, controls, and enhances a process through statistical analysis. Also known as statistical process control, statistical quality control employs statistical techniques to identify deviations in processes or product quality from specified standards. The primary objective of statistical quality control is to maintain and improve processes by implementing sampling and process improvement projects to minimise product variations. Statistical process control utilises control charts to observe changes in processes, machinery, labour, or the environment. Inspections and sampling are necessary for identifying when a process deviates from control parameters, potentially reducing product quality.
The process should be adjusted to ensure the number of defective items remains within acceptable limits. This adjustment is based on preventive principles. In such a situation, one can even forgo the inspection of individual components/products if there is assurance that the defectives will not exceed a predetermined value within a specified confidence level. The method employed for this purpose is Statistical Quality Control (SQC); control charts are also utilized for this objective.
What is Statistical Quality Control?
In today’s highly competitive market, the primary objective for manufacturers or producers is to attain quality assurance in manufacturing and service organizations. To achieve this goal, various statistical tools have been developed to control the quality of products in comparison to specifications or standards. The practice of employing statistical tools to maintain product quality concerning specifications is known as Statistical Quality Control (SQC).
Statistical quality control is defined as the technique of applying statistical methods based on the theory of probability and sampling to establish quality standards and maintain them most economically.
Approaches of Statistical Quality Control
There are following two approaches to statistical quality control:
1) Statistical Process Control (SPC): Statistical process control involves inspecting a random sample of the output from a process and determining whether the process produces products with characteristics that fall within a predetermined range. SPC answers the question of whether the process is functioning correctly or not.
2) Acceptance Sampling: Acceptance sampling randomly inspects a sample of goods and decides whether to accept the entire lot based on the results. It is a method to determine whether a batch of certain goods should be accepted or rejected.
Acceptance sampling aims to determine the disposition of goods or services, i.e., whether to accept, reject, or screen them. The focus is on the product after it has been produced. Statistical Process Control (SPC) refers to utilising statistical techniques to manage and regulate a process effectively. The focus is on the process and the product as it is being produced.
Elements of Statistical Quality Control
The main elements of Statistical Quality Control are as follows:
1) Use of Statistical Methods
Various statistical tools, including random sampling, mean, range, standard deviation, mean deviation, standard error, and concepts such as probability, binomial distribution, Poisson distribution, normal distribution, etc., are employed in SQC. Because the quality control method extensively involves statistics, it is termed Statistical Quality Control.
2) Decision Making
SQC aids in deciding whether the quality of the product or the manufacturing/production process is under control.
3) Fundamental Objective
The fundamental objective of SQC is to determine whether the produced unit meets its specifications. If the unit does not conform to its specifications and there is a variation in quality, it becomes necessary to identify and eliminate the causes of variation if possible.
4) Specifications, Production, and Inspection
The SQC method assists in making decisions regarding the specifications, production, and inspection of a product.
5) Sample Inspection
We understand that 100% inspection requires significant time, money, labour, and resources. Furthermore, for products that are destroyed during inspection, such as bulbs, candles, ammunition, food, etc., 100% inspection could be more practical. Therefore, SQC is based on sampling inspection. In the sampling inspection method, a random selection of items or units (a sample) is made from the process, and each unit in the sample is then inspected.
Techniques of Statistical Quality Control
The essential techniques used for statistical quality control can be broadly classified into two categories:
- Statistical Process Control (SPC) or simply Process Control and
- Product Control.
Statistical Process Control (SPC)
Statistical Process Control (SPC), or simply process control (PC), constitutes the initial component of SQC. To comprehend SPC, it is essential first to understand the concept of a process in quality control.
A process is a series of operations or actions that transforms input into output. It is deemed stable or repeatable when the resulting output product meets the specified standards or adheres to standard quality. However, disruptions can occur due to various factors, such as poor quality of raw materials, changes in machine settings, utilization of unskilled workforce, or improper machinery. In such scenarios, a tool or technique is needed to control the process, and this technique is known as statistical process control (SPC).
Statistical process control is a technique employed to comprehend and monitor the process by periodically collecting data on quality characteristics, analyzing them, and taking appropriate actions whenever there is a variance between actual quality and the specified standards.
This technique is widely applied in nearly all manufacturing processes to achieve stability and enhance product quality. Its primary tools include:
- Histogram
- Check sheet
- Pareto chart
- Cause and effect diagram
- Process flow diagram
- Scatter diagram
- Control chart
- Product Control
In numerous scenarios, the complexity of a product is such that a manufacturer cannot produce all components or parts internally. Consequently, one or more product components are acquired from external agents or suppliers, and the manufacturer needs more direct control over the quality of these components. In such instances, the manufacturer grapples with the challenge of  controlling the quality of externally sourced components. Additionally, the manufacturer must oversee the quality of the final product, ensuring that it adheres to specifications and that various lots of the product contain a manageable number of defective items. Such challenges fall under the category of product control.
Product control
Product control involves managing products to ensure they are free from defects and conform to their specifications.
Initially, product control relied on 100% inspection, where each unit produced or received from external suppliers underwent inspection. While this type of inspection guarantees complete assurance that all defective units have been eliminated from the inspected lot, it is time-consuming and costly. Moreover, if a product is destroyed during inspection (e.g., light bulb, crackers, ammunition, TV picture tube), 100% inspection becomes impractical.
As an alternative to 100% inspection, acceptance sampling was developed. Acceptance sampling is a technique in which a small fraction of items or units are randomly selected from a lot, and these chosen items or units are inspected to determine whether the lot should be accepted or rejected based on the information obtained from the sample inspection. This is how product control is achieved through acceptance sampling.
Advantages of Statistical Quality Control
When a lot of items/units are manufactured, the manufacturer has two methods to check the quality of the lot. Firstly, they could inspect every item and decide about the quality of the product, i.e., through 100% inspection. Secondly, they could utilize SQC methods, i.e., inspect a small number of items and decide about the quality of the entire lot of the produced product. SQC has many advantages over 100% inspections, which are listed below:
1. Reduction in Costs
The inspection cost is reduced in SQC, as only a part or fraction of a lot is taken and inspected.
2. Early Detection of Faulty Units
SQC involves continuous checking of the quality of the product. When a sample point falls outside the control limits, it signals that the process is not under statistical control. If assignable causes are present, necessary corrective action can be taken. Therefore, SQC ensures early detection of faults, resulting in minimum wastage of items.
3. Ensures Overall Coordination
SQC methods ensure coordination between managers managing specifications, production, and inspection. It provides a basis to resolve differences among various interests in an organization.
4. Equilibrium in Consumer’s and Producer’s Risk
Methods such as quality control and acceptance sampling help maintain an equilibrium between the consumer and producer risks.
5. Unique Method
Statistical quality control is helpful for items that get destroyed on being examined for a given quality characteristic, for example, the intensity of matchsticks, average life of compact fluorescent lamps (CFL), strength of glass, etc. In such cases, 100% inspection would spoil the entire lot and create a huge loss.
6. Wider Applications
SQC helps examine items produced in small numbers and when bulk production needs to be done.
7. Ease of Application
An excellent feature of statistical quality control is its easy application. While developing statistical methods for quality control requires skilled and intelligent individuals, even those without extensive specialized training can easily use statistical methods.
8. Greater Efficiency
Inspection of every item is bound to reduce the efficiency of quality control inspectors due to dullness. Inspectors are more alert while using SQC, as only a part is inspected.
9. Determination of the Effect of Change in the Process
With the help of control charts, we can easily detect whether a change in the production process results in a significant change in quality.
10. Helpful in Specification
Using SQC, we can determine whether the produced item is under control, i.e., whether it meets specifications within the tolerance limits. If the variation is beyond the tolerance limits, SQC gives a danger signal, and necessary corrective action can be taken. As long as statistical control continues, specifications can be accurately predicted for the future, which cannot be guaranteed by 100% inspection.
Limitations of Statistical Quality Control
SQC has some limitations, which are described below:
1. Limited Action for Improvement
SQC, when applied to a production process, provides information solely on whether the process is under or out of control. However, it cannot initiate specific actions for improvement.
2. Non-Representative Samples
When a sample of items drawn from the lot is not a true representative of the entire lot and does not possess the same characteristics as the lot from which it is removed, there is a risk of rejecting a good lot and accepting a bad one. This represents the primary limitation of SQC.
3. Dependence on Process Understanding
SQC can only be mechanically applied to any production process by studying it and acquiring adequate knowledge. It requires a nuanced understanding of the specific production process.
Key Advantages of Statistical Quality Control
Statistical Quality Control is a vital tool for scientific  management and offers the following main advantages over 100 per cent inspection:
1) Greater Efficiency
It requires less time and reduces boredom compared to 100 per cent inspection, leading to increased efficiency.
2) Accurate Prediction
Specifications can be accurately predicted for the future, a capability not achievable with 100 per cent inspection.
3) Early Detection of Faults
When a sample point falls outside the control limits, it serves as a danger signal, prompting necessary corrective measures. In contrast, 100 per cent inspection may detect unwanted variations in quality after several defective items have already been produced. Utilizing control charts provides graphic insights into the production process, identifying where corrective action is required and where not.
4) Reduction in Costs
Inspection is conducted only on a fractional production time, resulting in a significant reduction in the cost of inspection.
5) Easy to Apply
Once the statistical quality control plan is established, it is easily applicable, even to individuals without specialized training.
6) Applicability in Destructive Inspection Cases
In situations where the inspection requires the destruction of the product, more than 100 per cent inspection is needed.
Reference: https://egyankosh.ac.in/bitstream/123456789/20753/1/Unit-1.pdf