Effective 4 Areas of Quality Control and Quality Assurance

To achieve maximum score of customer satisfaction, essential to match product and process quality control and quality assurance with the customer requirement. In the organization product and process quality decisions to be manage by quality department. Now a day’s every business professional focused on to developed quality products for maintaining stable position in a competitive market and also try to optimize internal processes in operation management. In this article we dip dive into quality control and quality assurance department detail activities in operation management as well as try to understand quality control and quality assurance differences and types of quality data.

Area of product or process quality

Product or process quality is basically divided into two categories as per mention below.

1. Quality Control.
2. Quality Assurance.

Quality Control: –

Quality control departments majority looking to check or verify the product quality based on various measurement according to product or process category. The main activity of quality control department to set the system for measuring product quality at different operational stages and identify non conformance products at various production stages to provide good quality products to various customers. Quality control and quality assurance both are focusing to maintain perfect quality management system.

Quality control working areas: –

1. Data collection & measurement: –

Established structural system for measuring various product & process parameters quality dimensions. Data collection and measurement activity are mainly divided into three phases.

Manually: –

In manually data collection techniques, quality person manually checks the various product or process quality parameters with the help of various measuring instruments as per the criticality of product or process. In this manual data collection task majority of work done by the checking operator and data accuracy is also dependent on operator.

Example: – Operator check the product dimension with the help of vernier calliper.

Semi–Automatic: –

In semi-automatic data collection and measurement system majority of data collection work to be done by measuring instruments, quality person only focused for programming the instruments & feeding the material to be tested. In this semi-automatic system majority of work done by the instrument rather than data operator.

Example: – Co-ordinate Measuring Machine (Automobile)

Automatic: –

In Automatic system, measuring instruments or testing machines are directly put in assembly or continuous production line and instrument itself the check products dimensions as per the parameters input by the quality person.

Example: – Checking product quality with digital cameras.

2. Analyze & investigate the collected data: –

After the data collection, the next step to investigate data trends and identify ways to minimize process rejections for produce consistent output. Data itself doesn’t speak, we required to collect relevant information from data with the help of data analysis. There are many ways to analyze data as per below.

• 7 QC Tools.
• Statistical Process Control.
• Why-Why Analysis.
• Various Surveys.

As per above mention tools, majority organization are using Microsoft office and Minitab software to analyze these data and survey conduct with the offline and online mode for analysis.

3. Decision making for product acceptance & product failure.

4. Identify correct root cause for product failure & developed report.

Quality Assurance: –

Quality assurance department mainly focused to developing quality procedure to produce defect free products. There are various quality documents required to maintain on regular basis as per the customer requirement that task to be done by quality assurance department.

Quality assurance working areas: –

1. Products and services approval with the customer. (quality point of view)

• Advance Product Quality Plan
• Control Plan.
• Product Part Approval Process.
• Failure Mode Effect Analysis.

2. Conducting quality audit as per requirement.

3. Calibration and testing measuring instruments & other testing machines.

4. Developed manufacturing quality plan & various quality procedure as per customer requirements.

Now, I hope everyone clear about the major roles and responsibilities of quality control and quality assurance department individually, so let try to understand difference between quality control and quality assurance as per below.

Difference between quality control and quality assurance: –

Quality Control	Quality Assurance
Quality control mainly focused on to reactive approach.	Quality assurance mainly focused on to proactive approach.
In QC department defect to be detect after product manufacturing.	In QA department defect to be detect before product manufacturing.
The role of QC department to identify defect from various process stages.	The role of QA department is to prevent defect from various process stages.
Quality control is focused to measure product or process quality.	Quality assurance is process oriented approach to developed various procedures for maintaining quality standards.
Quality control is a time consuming and its frequency is daily basis.	Quality assurance is not a much time-consuming activity & its frequency is periodically basis.
Cost of internal failure and external failure are the part of quality control.	Prevention and appraisal cost of quality are part of quality assurance.
Quality control is a platform for verify product acceptance or rejection.	Quality assurance is a platform for set the structural procedures for producing good quality products.

Types of data in quality: –

In quality measurement system there are two types of data. attribute and variable we will try to understand in detail as per below. For quality control and quality assurance authentic data generation is very essential as well as correct analysis is also play a vital role for concluding root cause of any problems.

Attribute Data: –

Attribute data is a qualitative data type that can not be count. Attribute data is not a number type data but it described into a “yes” or “no”, In another way “true” or “false” of requirement to the product. Attribute data type is also used in comparing one item to another item.

Variable Data: –

Variable data is a quantitative data type and it varies over timeline. Control chart is really helpful to monitor variable data. Variable data is number type data and its population is majority high.

Let’s we try to understand difference between variable and attribute data to more easily clarify quality control and quality assurance topics.

Difference between variable data type and attribute data type

Variable Data Type	Attribute Data Type
Variable data is a measurable data type for examples dimensions, length, distance etc.	Attribute data is not able to measure for example reliability, smartness, intelligence etc.
Variable data easily plotted on the control chart and easily able to statistically able to analyze.	Attribute data set are not easily statistically analyzing it. Data specifically identify the value of object.
Variable data is collected based on measurement with the help of measuring instrument.	Attribute data is collected based on asking specific question with survey or other methods.

Frequently asked questions about quality control and quality assurance.