Recommended Reading | Development of Quality Management Systems under the Trend of Digital Transformation

　　Promoting high-quality development in manufacturing is an inevitable choice to adapt to the changes in China's economic development stage. Digital transformation has become an irreversible trend, injecting new momentum into the transformation, upgrading, and high-quality development of manufacturing. As quality management in manufacturing accelerates towards a new stage of digitalization and intelligence, how should enterprises develop and innovate their quality management systems under the trend of digital transformation? This is a pressing issue faced by many enterprises today.

　　Currently, the status quo of quality operations in many enterprises is still far from meeting the demands of digital quality management development trends. This is mainly reflected in: low informatization level in quality control processes, with many paper-based or manual statistical methods; limited quality management tools, with delayed quality data that cannot be dynamically displayed and monitored on a single platform; quality data statistics that fail to comprehensively reflect the company's status, making timely and efficient decision-making difficult; product change processes are unidirectional and not interconnected with involved systems, causing deviations in implementing change requirements that are hard to detect promptly; lack of database and informatization support for key quality technical data and product failure information, currently relying mainly on manual operations with low efficiency; absence of failure and experience databases, difficulty in knowledge sharing, and quality data information silos. In response, this article, through research and practice on enterprise digital and quality management transformation, and understanding and reflection on the development of quality management systems under digital transformation trends, proposes some ideas and solutions for digital quality management transformation and quality management system development and innovation. It also offers practical implementation measures and improvement suggestions, hoping to benefit a wide range of enterprises.

　　1. The Core Concept of Management Systems and Its Relationship with Digital Quality

　　The new version of the ISO 9000 family standards clearly reflects three core concepts: process, risk-based thinking, and the PDCA cycle. Its main purpose is to use the PDCA cycle and risk-based thinking to keep the organization's current focus continuously on effective process management to produce the desired results.

　　The process approach of the management system demonstrates its big data orientation, emphasizing standardized process application, centralized control, and business integration, focusing on process performance, and emphasizing the "transparency" of quality data to achieve a single source of truth, rapid problem localization and root cause tracing, efficient and accurate management decisions, and knowledge accumulation. Additionally, the new standard emphasizes attention to the process approach and the interrelationship of processes, using the process approach to address the internal and external environment of the organization, processes, and even activities, as well as internal and external stakeholders. By sorting out process relationships, it achieves quality collaboration across enterprises, business domains, and departments. The core concept of this management system aligns highly with the vision of digital quality development, where quality is no longer about defect reduction but value enhancement.

　　2. Trends and Changes in Digital Quality Management Development

　　(1) Focus Areas

　　Traditional quality management mainly targets the relatively stable development environment of the industrial era, focusing more on quality issues in mass production. In contrast, digital quality management mainly addresses the uncertainties of the digital era, paying more attention to quickly meeting and efficiently responding to users' personalized and differentiated needs while still focusing on mass production quality issues.

　　(2) Management Scope

　　Traditional quality management mainly focuses on the enterprise and supply chain scope. With the deepening of digitalization, enterprise boundaries are increasingly blurred, and the scope of quality management is accelerating from enterprise quality to ecosystem quality. The emphasis shifts from quality management job division and upstream and downstream quality responsibility division to customer-centric quality collaboration, paying more attention to comprehensive management of product production cycles, industrial chain supply chains, and even ecosystem quality.

　　(3) Key Management Links

　　Digitalization drives the focus of quality management from mainly manufacturing processes to equal emphasis on multiple links such as research and development, design, manufacturing, and service. It deepens the cross-departmental, cross-link, and cross-enterprise collection, integration, and shared use of quality data, promoting quality collaboration and innovation in quality management.

　　(4) Application of Management Tools

　　Digital quality management, based on the application of traditional quality management methods and tools, further applies digital and intelligent equipment, system platforms, and other technical conditions. It focuses on customer-centric process optimization, reconstruction, and management method transformation, fully tapping the driving role of data in quality management innovation, and systematically enhancing enterprises' digital quality management capabilities.

　　3. Strategies and Measures for Digital Quality Management Development

　　(1) Enhancing Digital Quality Management Capabilities by Introducing Systematic Method Standards

　　Based on the existing quality management system construction foundation and management practices of enterprises, integrate standards or guidelines for digital management system construction such as GB/T 23001 "Requirements for Informatization and Industrialization Integration Management System," GB/T 23006 "New Capability Grading Requirements for Informatization and Industrialization Integration Management System," and T/AIITRE 20001 "Digital Transformation New Capability System Construction Guide." Build an operational management mechanism to systematically promote digital transformation, create a capability system to enhance digital capabilities, and accelerate quality management innovation. Starting from business processes, organizational structure, data development and application, and technical implementation, promote integration of the Quality Management System (QMS) with platforms such as Enterprise Resource Planning (ERP), Warehouse Management System (WMS), Office Automation System (OA), Laboratory Information Management System (LIMS), Supplier Relationship Management System (SRM), Manufacturing Execution System (MES), Computer-Aided Process Planning (CAPP), and Product Lifecycle Management System (PLM) to achieve refined quality control throughout the product lifecycle. An example of the quality control platform architecture is shown in Figure 1.

　　(2) Identification and Building of All-Element, Whole-Process Digital Quality Management Capabilities

　　Apply systematic thinking to identify and build all-element, whole-process digital quality management capabilities, plan technical implementation schemes for digital quality management capability construction, and clarify capability construction guarantee mechanisms. Use the PDCA cycle mechanism to ensure that all digital quality management capability construction is lawful, traceable, and verifiable, implementing step-by-step and continuously improving to ultimately achieve the overall goal of digital quality management transformation. The identification of all-element, whole-process quality management capabilities covers the entire product realization process, such as design quality, production quality, service quality, procurement and supplier collaboration quality, and industrial chain/supply chain quality management linkage. The element dimension mainly focuses on technical implementation schemes for digital quality management capability construction, such as data, technology, processes, and organization. The management dimension mainly focuses on guarantee mechanisms for digital quality management capability construction, such as digital governance, organizational mechanisms, management methods, and quality culture. The process dimension mainly focuses on the PDCA cycle mechanism for digital capability construction. The identification and building of all-element, whole-process digital quality management capabilities are shown in Table 1.

　　(3) Building Production Quality Control and Whole-Process Product Traceability Capabilities

　　Through automation, intelligence, and networking upgrades of production or testing equipment, optimize cross-departmental and cross-link quality management processes, and build a unique identification product full-process traceability system. Achieve online and real-time quality management across departments and the entire process, including production, testing, warehouse management, equipment management, and quality traceability, to maintain the product's industry-leading position.

　　4. Implementation Path of Digital Quality Management

　　The implementation path of digital quality management mainly includes four aspects: quality requirement standardization, quality control automation, quality performance visualization, and quality decision intelligence. The four aspects and their main contents are shown in Table 2.

　　The connotations, requirements, and understandings of each aspect of the digital quality management implementation path are as follows:

　　(1) Standardization of Quality Requirements

　　Informatization is the precursor or initial development stage of digitalization. The core content of informatization includes business standardization, standardized processes, and process informatization. To implement informatization, one must first sort out business standards and management norms, link or systematize management standards in the form of processes, and then realize them through informatization. It can also be said that before informatization, standardization and process optimization or reengineering must be done well. Among them, the standardization of quality requirements mainly includes four aspects: standardization of quality processes, standardization of product quality criteria, standardization of quality gates/valves, and standardization of product quality data.

　　(2) Automation of Quality Control

　　Product quality is made, not inspected. To improve quality performance, one must start with quality control activities, namely the application of quality gates or quality valves. Quality gates (valves) implement the "three no's" concept of quality control: no acceptance of defects, no creation of defects, and no transmission of defects. Automation of quality control mainly includes applications such as automation of quality inspection/monitoring, informatization of quality gates/valves, informatization of product quality data queries, and informatization of product quality information traceability.

　　No acceptance of defects means that in procurement and inbound logistics, suppliers whose quality performance does not meet requirements will no longer be purchased from, and nonconforming materials are not allowed to be warehoused, processed, or assembled. No creation of defects means that new products whose processes and quality assurance do not meet requirements will not be mass-produced, or external customer orders for products with quality abnormalities will not be accepted until product quality meets requirements. No transmission of quality means that defective work-in-progress products are not allowed to flow into the next process, defective products are not allowed to be released offline, printed with certificates of conformity, or shipped.

　　From the perspective of digital construction, automation of quality control is a further deepening of the visualization of quality performance. To achieve automation of quality control, the enterprise's quality IT system needs to integrate with IT systems such as ERP and MES to transform quality control measures into value chain control activities. For example, in the case of no acceptance of defects, if a supplier's delivery quality does not meet company requirements, the quality IT system will send a command to ERP to freeze the supplier master data in ERP, thus preventing purchase orders from being placed for this supplier in the ERP system. Similarly, to achieve no creation of defects, when manufacturing process quality does not meet requirements or abnormalities occur, the quality IT system will send a command to the MES system to control the production line operation, pausing the production line until the quality issue or abnormality is resolved before resuming operation.

　　(3) Visualization of Quality Performance

　　The purpose of visualizing quality performance is to represent the product's quality performance (or quality level, quality status) with numbers, which are usually quantitative and relatively objective. Quality performance expressed in numbers is easier to be recognized by all parties and thus has credibility. Common business scenarios for visualizing product quality performance include visualization of key quality characteristics, key quality indicators, key quality control points and requirements, and quality control results. To achieve visualization of quality performance, enterprises need to identify key quality indicators based on their actual situation and industry characteristics, such as PPM, defect rate, MTBF, etc., then collect data on related indicators through quality inspection and other means, and subsequently perform statistics, analysis, and evaluation on these indicators.

　　From the perspective of digital construction, to realize visualization of quality performance, enterprises need to implement and apply relevant IT systems to record the results of quality inspections such as IQC, PQC, FQC, OQC into the IT system, and through data statistics and analysis models, perform statistics and analysis on related quality indicators, then display and transmit them to relevant links or personnel.

　　(4) Intelligent Quality Decision-Making

　　The most important function in enterprise management activities is decision-making, and the same applies to quality management. How to make decisions both accurate and fast is the problem that intelligent decision-making needs to solve. Intelligent decision-making in the quality field covers a wide range and takes many forms. Common practices in enterprises include online detection and judgment, dynamic adjustment of inspection plans, online fault prediction, online nonconformance warning, product design optimization, product quality cost optimization, and dynamic adjustment of quality control strategies.

　　For example, traditional quality inspection work is often judged manually, resulting in delayed quality decisions. It is well known that the timelier the decisions made by an enterprise, the greater their effectiveness. Online detection and result judgment aim to make detection result judgments in the shortest possible time (close to "real-time"). Among various forms of quality inspection such as IQC, PQC, FQC, OQC, PQC has the highest real-time requirement. If an enterprise can perform real-time or online quality inspection and result judgment during the manufacturing process, it can not only minimize the production of nonconforming work-in-progress products but also ensure smooth manufacturing processes and efficient output. From the perspective of digital construction, through the application of technologies such as visual inspection, edge computing, and artificial intelligence, enterprises can achieve a certain degree of online detection and result judgment.

　　5. Understanding Data Flow Management

　　Data Flow is used to describe how data is used by IT systems. For IT systems, the main focus is on which system created the data and which system read, updated, or deleted the data. Enterprises can achieve "same source multiple uses" of quality data by unifying data sources and transforming IT integration channels. An example of a data flow diagram is shown in Figure 2.

　　6. End-to-End Quality Management

　　To ensure product quality assurance from the source, enterprises should implement end-to-end quality management, incorporating the entire value chain activities such as market research, new product planning, product design and development, component procurement, finished product manufacturing, sales, and service into the scope of quality management. Hence, there are so-called R&D quality management, manufacturing quality management, supplier quality management, market quality management, and so on. Another meaning of end-to-end quality management is the PDCA closed loop of quality management. It requires enterprises to clarify quality strategy, set quality objectives, formulate quality plans, implement quality control and inspection, perform quality statistics, evaluation, and analysis, manage problems, and continuously improve, forming a complete PDCA closed loop. In fact, although there are many ideas and methods for quality management, the core concept remains the PDCA closed loop. The better the PDCA closed loop, the more significant the effectiveness of quality management work.

　　From the perspective of digital construction, to achieve end-to-end quality management, enterprises are required not only to implement and apply quality IT systems in procurement and manufacturing fields but especially to design and deploy quality IT systems from an end-to-end and PDCA closed loop perspective. At this time, the quality IT system is no longer just a departmental or field-level IT system but effectively integrates quality control requirements into various business management activities of the enterprise through the quality IT system, thereby truly realizing the return of the organizational quality management system to the business management system, the return of quality requirements to business processes, and the return of quality responsibilities to process owners.

　　Conclusion

　　The essence of digital construction is the transformation of problems, that is, converting the issues existing in enterprise operation and management into digital problems. Answers can be sought from elements such as networks, data, models, processes, and scenarios. Regarding the digitalization of quality management, all five elements or levers mentioned above are effective, with data and processes being especially critical. For a company's quality, there is process quality and product quality; process quality focuses on the process, while product quality focuses on the result. The result is determined by the process; only good process quality can lead to good result quality. Therefore, automation of quality control and end-to-end quality management must be implemented. The process serves the result, and process quality must closely revolve around product quality. Hence, visualization of quality performance and intelligent quality decision-making are necessary. If the essence and logic of digitalization is data thinking, then the advanced technical form of digital quality management is to effectively manage process quality data and product quality data to form a quality data platform centered on "Critical to Quality (CTQ)" characteristics, thereby achieving interconnected, intelligent, and autonomous digital quality management based on the quality data platform.

　　Therefore, under the development trend of digital transformation, the development of organizational quality management systems should also conform to the needs of development, making more management and technical explorations, improvements, and innovations in aspects such as building an end-to-end, process-oriented quality management system involving all employees, the entire process, and all elements.