What are QA and QC, and how it different?
How it relate to SPC?
In the decision-making process of purchasing a product, quality is a key factor customers consider when selecting what they need. Therefore, business operators should manage product quality and manufacturing processes with standardized practices through industrial inspection systems known as Quality Assurance (QA) and Quality Control (QC), which are distinctly different.
How do these two functions operate, and how are they related to Statistical Process Control (SPC)? Answers to these questions can be found in this article.
What are QA and QC?
Quality Assurance (QA) and Quality Control (QC) are different stages in the manufacturing process, each with its specific methods and performance measurements aligned with their distinct purposes. Details are as follows:
| Topic | Quality Assurance (QA) | Quality Control (QC) |
| Objective | Focus on production planning to prevent errors in the process. | Emphasize product quality inspection to correct product standards. |
| Working description | Establish production system standards through documentation design, control of operational procedures, and employee training. | Test and inspect manufactured products through measurements and product testing. |
| Period | Take preventive actions before the production process. | Conduct quality inspections during and after the production process. |
What is Quality Assurance (QA)?
Quality Assurance (QA) is an essential step in the manufacturing industry that aims to guarantee that products meet specifications and standards. It builds customer confidence in products and the organization through preventive, evaluative, improvement, and development processes carried out by QA specialists.
The Importance of QA Efficiently produced products backed by QA processes bring multiple benefits to the organization, including fostering customer trust and loyalty, protecting against product defects, and enhancing long-term business sales.
What is Quality Control (QC)?
Quality Control (QC) is a stage within both quality assurance and manufacturing processes aimed at inspecting, maintaining, and enhancing the quality and quantity of products, thereby best fulfilling consumer demands.
The Importance of QC Planning and controlling production through QC processes are designed to ensure that products released to the market meet established standards. QC specialists inspect products at each stage, including raw materials, packaging, and final products for customer distribution.
Statistical Process Control (SPC) with QA and QC
Statistical Process Control (SPC) is a quality-maintenance tool used to monitor stability in manufacturing processes, achievable through both quality assurance and control processes.
Understanding Statistical Process Control Statistical Process Control (SPC) is a method aimed at controlling product quality and setting manufacturing standards within factories. It uses control charts to monitor various manufacturing stages, such as:
- Ensuring consistency and accuracy in production processes.
- Minimizing the impact of inefficiencies in production.
- Identifying and preventing potential issues that may affect product quality.
Principles of SPC
The working principles of SPC can be divided into three parts:
- Data Collection: Collecting key production data, such as size, weight, resistance, or other quality-impacting parameters, for statistical analysis.
- Control Chart Creation: Creating control charts from collected data to show production process variations over time.
- Chart Analysis: Examining indicators that reveal potential issues, allowing staff to identify causes and improve processes to prevent future problems.
Types of SPC Control Charts In SPC systems
various types of control charts are used to monitor and analyze production processes, depending on the nature of the data:
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X-bar and R Chart: Used for numerical data to control diverse measurements within each sample, assisting in identifying issues in the manufacturing process.
- X-bar Chart: Tracks changes in average values.
- R Chart: Tracks data variability.
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P Chart: Sample data representing the proportion of occurrences, used to monitor products that fail inspection in each batch.
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C Chart: Sample data showing the count of defects, used to control the number of issues occurring during production.
QA, QC, and Statistical Process Control (SPC) Quality Assurance (QA) and Quality Control (QC) are inspection processes designed to prevent errors that may occur during production, impacting product quality and potentially causing long-term damage.
In summary, QA, QC, and SPC are distinct quality control processes with different objectives and approaches:
- Quality Assurance (QA): Focuses on production planning and preventing errors through material standards, such as setting hardness standards for metal parts.
- Quality Control (QC): Emphasizes inspecting and correcting non-standard products by setting product standards, such as checking the hardness of each metal component.
- Statistical Process Control (SPC): Utilizes statistics to analyze and improve production processes for higher efficiency through control charts, such as monitoring hardness variation and identifying its causes.
As a business operator, developing these processes through efficient methods like Statistical Process Control software can help analyze production plans, streamline processes, address complexity issues, and prevent errors in production. SPC is thus an essential tool to help businesses conduct production smoothly.
Conclusion: Applying Technology for Quality Control Modern technology
In today's industry is not only a tool to shorten production time or increase product quantities but also plays a significant role in enhancing product quality through processes such as Quality Assurance (QA), Quality Control (QC), and Statistical Process Control (SPC). Using the MES System for data collection and quality analysis in production is highly effective.
ARES is a software systems company with expertise in installing modern operating systems, including ERP systems, MES systems, and more. Our goal is to help elevate factories and businesses into the Industry 4.0 production model. With over 40 years of experience in various sectors, our team of experts is ready to support you.
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