In today's fierce competition in the global market, the quality level of the manufacturing industry
is not only the basic requirement to meet customer needs, but also the core lifeblood that determines
the survival and development of enterprises. Quality improvement is no longer the icing on the cake,
but the key to survival.
Analysis of pain points: the reality of the dilemma of quality
improvement in the manufacturing industry
Quality challenges under supply chain fluctuations: Batch differences in raw materials and poor supplier
management make it difficult to guarantee product consistency. A senior purchasing manager once said frankly:
“The quality of incoming materials is like opening a blind box, and the production process is like walking on thin
ice; if you are not careful, the whole batch of products will deviate from the standard.”
The double pressure of technology and personnel fault: On the one hand, the rapid iteration of advanced
manufacturing technology puts forward higher requirements for the skills of operation and maintenance personnel;
on the other hand, experienced technical workers are facing a wave of retirement, and the skills training and
experience of the new generation of workers has become a problem. The urgent need for technological upgrading
and the contradiction of talent gap have become the bottleneck of quality improvement.
The “last kilometer” problem in the implementation of standards: Even with perfect quality system documents,
there is still a huge gap in the actual implementation. The arbitrariness of the operation specification, the ambiguity
of the inspection standard, and the incompleteness of the data records make the quality system fail to really take root.
Aging equipment and data silos: the old equipment precision decline, the failure rate climbed, directly affecting the
product qualification rate. At the same time, the production site data are scattered in various systems or paper records,
forming an information island, which cannot provide effective support for quality analysis and continuous improvement.
Core strategy: build a solid quality foundation
Strengthen supply chain coordination and source control:
Deepen supplier cooperation: Establish a strategic alliance with key suppliers that goes beyond a simple buyer-seller
relationship, share quality goals and standards, and work together on process improvement. Establish a clear supplier
performance evaluation system (e.g., on-time delivery rate, incoming material qualification rate, problem response speed)
and implement hierarchical management.
Strict control of incoming material inspection: Develop scientific sampling program and strict acceptance criteria (AQL),
introduce automated testing equipment to improve inspection efficiency and accuracy. Implement stricter inspection or
factory inspection for high-risk materials. Promote early supplier involvement to ensure that they understand and meet
design requirements.
Establish traceability system: Implement batch management to ensure a complete traceability chain from raw materials to
finished products. In the event of a quality problem, we can quickly locate the source of the problem, accurately recall the
product, and reduce losses.
Focus on personnel capacity improvement and quality culture construction:
Systematic skills training: Design a hierarchical and customized training system for different positions (operators, quality
inspectors, team leaders, engineers). The content covers standard operating procedures (SOP), quality control tools (such
as SPC, FMEA), equipment operation and maintenance, problem solving skills (such as 8D). We emphasize the combination
of theory and practice to ensure that learning is put into practice.
Create a quality culture for all employees: Integrate the concept of “quality first” into the DNA of the company, and the
management will set an example by continuously promoting the importance of quality through various channels such as
meetings, bulletin boards, and internal publications. Encourage employees to find problems, report problems and participate
in improvement. Set up a quality improvement proposal system to recognize and reward effective proposals in a timely manner.
Establish an effective experience transfer mechanism: Organize senior technical experts to prepare casebooks and
operation know-how manuals, implement the “mentor-apprentice” system, and use video, AR and other technical
means to assist the transfer of skills to ensure that valuable experience is not lost.
Promote the ultimate implementation of standardization and processes:
Optimize operating instructions (SOPs): Ensure that SOPs are clear, accurate, illustrated and easy to understand and
implement. Place them within easy reach of the workstation. Review and update SOPs regularly to reflect best practices
and change requests.
Apply error-proofing technology: Incorporate Poka-Yoke concepts into product design, fixtures, and production processes,
such as using sensors to prevent missed and incorrect installations, setting fixtures to ensure a unique and correct mounting
position, and distinguishing between similar parts by color, to reduce the possibility of human error at the source.
Enhanced process inspection and data-driven: Set up inspection points in key processes to clarify inspection items,
methods, frequency and standards. Utilize SPC tools to monitor process stability in real time and identify abnormal
fluctuations. Ensure that the inspection data is true, accurate and entered into the system in a timely manner to provide
a basis for analysis and decision-making.
Embrace digital empowerment and intelligent analysis:
Promote intelligent equipment upgrading: Assess the feasibility of renovating or replacing old equipment, and introduce
intelligent equipment with data collection interface, higher precision and stability. Utilize sensors to collect equipment
operating parameters (e.g., vibration, temperature, current), process parameters (e.g., pressure, temperature, speed) and
quality inspection data in real time.
Break down data silos and build a unified platform: Integrate data from multiple sources such as MES (manufacturing
execution system), QMS (quality management system), equipment, sensors, etc. to build a unified data platform or data lake.
Use AI and big data for prediction and optimization: Apply machine learning algorithms to analyze historical and
real-time data, build prediction models, and achieve prediction of key quality characteristics, predictive maintenance
of equipment failures, and intelligent optimization of process parameters. Use big data analysis tools to dig deeper
into the root causes of quality problems and identify potential risks and improvement opportunities.
Practical insights from a precision parts manufacturer:
A precision parts factory, once plagued by unstable incoming materials and equipment downtime, has realized quality
transformation through supply chain and digitalization:
Supply chain depth of coordination: and core suppliers to establish a joint laboratory, jointly develop raw material
specifications and testing methods, the implementation of strict factory inspection standards and suppliers of quality
incentives and penalties mechanism.
Real-time monitoring of equipment status: Installing sensors on key machine tools, real-time collection of vibration,
temperature, current and other data, combined with production information from MES and inspection results from QMS
to build a unified data analysis platform.
AI-driven predictive maintenance: Using machine learning models to analyze equipment operation data, we successfully
predicted several critical bearing failures and avoided unplanned downtime. Meanwhile, intelligent fine-tuning of
processing parameters significantly reduced the fluctuation range of critical dimensions and improved product consistency.
Conclusion: Quality is the lifeline of manufacturing
Improving manufacturing quality is a continuous journey, not a one-time project. It requires companies to return to the
fundamentals, make continuous investment and deep plowing in supply chain management, personnel empowerment,
standards implementation and digitalization application. Only by building a quality management system with full participation,
data-driven and continuous improvement can we build an unshakeable core competitiveness in the complex and changing
market environment and realize the leap from “manufacturing” to “quality manufacturing”.
When the torque of every screw is precisely controlled, the data of every process is effectively utilized, and every small
improvement is valued, the quality is sublimated from a cold index to the blood of the enterprise, driving the manufacturers
to never stop on the road of refinement.