What is Statistical Quality Control?

Basic Terminologies

 

1. Statistics: Statistics means data, a good amount of data. Or simply, the collaborative study of accumulation, analysis, interpretation and presentation of massive volumes of data.

 

“Statistics are valuable representations of data that assist in the analysis and decision making process”

 

2. Statistical tools: Applications of statistical methods in order to visualize, interpret and anticipate outcomes over collected data.

3. Quality “ a characteristic of fitness for purpose at lowest cost” , or “degree of perfection that suffices the customer requirements”. Quality can be defined as “the entirety of features and characteristics for products and services satisfying implicit and explicit demands of customers. 

4. Control: An approach of measuring and inspecting a certain phenomenon for a product or a service, control suggests when to inspect, and how much to inspect. The system includes feedback to understand the causes for poor quality and necessary corrective steps. The control system basically determines the quality characteristics of an item, correlates the same with predefined quality standards and distinguishes between defective items from non-defectives ones.

5. Quality control: Quality control is one of the most important tools deployed to check the definite level of quality of products, or services. In todays’ highly competitive business environment, quality control has evolved as a prominent tool and a critical factor via any successful industry to ensure standard quality. In 1982, Peters and Waterman recognized quality as a crucial element in the virtue of excellence. 

 

(Related blog: Types of statistical analysis)

 

Therefore, quality control is the employment of appropriate techniques and activities in order to accomplish, sustain and upgrade the quality of products and services and to satisfy customer’s needs in terms of price, safety, availability, reliability, usability, etc.

 

The method employs statistical techniques based on probability theory to establish standards of quality and uphold it in the most economical manner. (From)

 

Let’s understand the technique of applying statistical methods for quality control systems.

 

Statistical Quality Control (SQC)

 

Employing a number of statistical methods, SQC validates the quality of premium goods and services. In 1924, Walter A. Shewhart produced the basic ideas for statistical quality control, since after the area of SQC has been scattered its foundation with extensive work of researchers, quality controlled philosophers and statisticians. 

 

Making use of statistical tools and techniques in order to monitor and manage product quality across various industries including food, pharmaceutical and manufacturing units, the process is named as Statistical Quality Control. The method can be conducted as 

 

• A part of production process, 

• A part of last-minute quality control check

• A part of eventual check by quality control department

 

“Statistical quality control can be simply defined as an economic & effective system of maintaining & improving the quality of outputs throughout the whole operating process of specification, production & inspection based on continuous testing with random samples.” -YA LUN CHOU

 

Statistical quality control techniques are extremely important for operating the estimable variations embedded in almost all manufacturing processes. Such variations arise due to raw material, consistency of product elements, processing machines, techniques deployed and packaging applications. Moreover, any of these factors or combination of two can impact the eventual quality of finished product. 

 

The method incorporates legislation allowing manufacturing units to make sure that the finished product must contain the net quantity mentioned in packaging. Any overfilled quantity can lead to financial loss for the manufacturer and therefore must be avoided. Fill control, validating weight and weight variation are hugely deployed statistical quality control techniques that make use of weights of individual products in the statistical data analysis.

 

In case of pharmaceutical goods, such as tablets, pills, capsules, syrups etc, the standard weight must not be exceeded the upper limit that saves consumers from taking high doses of active ingredients that might result in severe consequences. At the same time, the weight shouldn’t be too less, if not the drug might not be effective. In this case, the weight variation based statistical quality control test is used to ensure the consistency of the dosage unit, and also to support product identity, reliability and quality. 

 

Another example would be, in the production of food and beverages, it is required to inspect the weight of packages rendering quick confirmation such that filled quantities fulfil the legal necessities. Any deviation from standard value signifies errors in the production process, imprecise ingredient-quantities leading to impactful consequences.

 

In addition to this, while confirming consumer satisfaction, safety and compliance with regulations, SQC with weight determination is highly important. Though, it is recommended to employ actual balances or measuring scales and software suitable for particular applications.

 

(Must read: Statistical data distribution models)

 

Example

 

For example, SQC serves as a medium allowing manufacturers to attain maximum benefits by following controlled testing of manufactured products. Using this procedure, a manufacturing team can investigate the range of products with certain values that can be expected to reside under some existing conditions. The information is precisely validated for a number of similar products and be informed to the producer and the purchaser. 

 

In addition to this, the information determines compliance with specifications and looks at whether the manufacturing process/unit is capable of producing products within its unit. Also, if existing specifications are unable to meet the end outcome or economically unacceptable, then quality control data is helpful in providing minimum criteria for developing the improved standards.

 

(Suggested blog: Sampling distribution)

 

Advantages of Statistical Quality Control

 

One of the excellent scientific tools, SQC has the following advantages;

 

1. Cost reduction: In this method, only a fragmentary output is inspected to ensure the quality of product, therefore probe cost would be reduced greatly. 

2. Huge efficiency: Inspection of a fractional portion requires lesser time and tedium in comparison to holistic investigation leading to huge escalation in efficiency and production. 

3. Easier to use: Pitching SQC not only reduces process variability but also makes the process of production-in-control. Even, it is much to apply by an individual without having such extensive specialized guidance.

4. Authentic anticipation: SQC is the most preeminent approach that can accurately predict future production. To ensure the degree of perfection and product performance, SQC provides a great predictability. 

5. Prior fault detection: Any deviation from standard control limits depicts signs of danger in the underlying production process that invites necessary corrective measurement to be taken earlier. SQC is helpful in early detection of faults. 

 

While in holistic inspection, unnecessary fluctuations under quality control process would be detected in the final stage, but for the time being numerous defective items have already been produced. 

 

In such conditions, SQC (using chart controls) enables a pictorial view of how the production process is performing and where curative steps must be accounted for for smooth functioning of the process. (Source)

 

 

SQC vs SPC 

 

Both SQC and SPC support smooth operations in order to escalate efficiencies, desired output and optimized performance while playing a key role in overall success in operations, but in different ways. Lets’ understand the difference;

 

1. SPC: is the procedure of collecting and computing parameters of a process such as speed, pressure, vernier caliper etc with respect to standard values using various statistical methods validating values must reside within limits while aiming to minimize variation and execute to achieve desired/optimum targets.

 

SQC: is the process of compiling and determining data on the subject of particular specifications regarding a product and to meet requirements, for example, size, weight, texture etc. while aiming at validating process outcomes to meet the user requirements or the next stage of the manufacturing process.

 

2. SPC is responsible for reduction of variation in processes and run efficiently, in contrast to this, SQC facilitates manufacturers to accomplish user requirements. 

 

For example, in food and beverage manufacturing there are various numbers of different products being produced, SPC monitors that operations are executing effectively at their entirety, SQC controls measurable quality characteristics used during production so that finished products must live up with customer requirements/expectations.

 

(Read also: Data types in statistics)

 

 

Conclusion

 

“Statistical quality control should be viewed as a kit of tools which may influence decisions to the functions of specification, production or inspection. -EUGENE L. GRANT

 

SQC has turned out to be a vital platform as a business operation that is deployed to enhance productivity and sustain competitive advantages. The method reflects a systematic approach of efficient statistically-oriented experimentation particularly in terms of characterization, optimization, sample acceptance along with ensuring active determination/inspection of deployment process with respect to real-world applications.