With the “15th Five-Year Plan” recommendations clearly promoting the digital and intelligent transformation of the manufacturing industry, the intelligent manufacturing equipment industry is at the forefront of the times.
In 2024, the market size of China’s intelligent machinery manufacturing field has exceeded 2.1 trillion yuan, with a compound annual growth rate stable at more than 18%.
As the “industrial mother machine”, CNC machine tools are the core growth pole. The CNC machine tool market size will be approximately 432.5 billion yuan in 2024, and it is expected to maintain strong growth in 2026.
However, under the glorious growth rate, there is an undercurrent.
The industry reshuffle forced by policies has begun.
The State Council issued the “Action Plan for Promoting Large-Scale Equipment Renewal and Trade-in of Consumer Goods”, which clearly proposes to accelerate the replacement of outdated and inefficient equipment such as old machine tools that have been in service for more than 10 years.
Eight departments including the Ministry of Industry and Information Technology jointly issued the “Implementation Opinions of the “Artificial Intelligence + Manufacturing” Special Action”, which requires promoting the in-depth application of large models in the manufacturing industry by 2027 and promoting 500 typical application scenarios.
The Ministry of Industry and Information Technology also made it clear that it will formulate and issue implementation guidelines for smart manufacturing projects to provide a systematic methodology for enterprise transformation.
The policy trend has arrived, but whether it can catch this wave of dividends depends on the hard power of the company’s internal management.
But the reality is equally cruel.
Research by the Industrial Digital Transformation Research Institute shows that the failure rate of digital transformation in my country’s manufacturing industry is as high as 74.2%, and a large number of companies have fallen into the embarrassing situation of “investing money and installing systems, but seeing no results.”
What’s even more worrying is that the failure rate of enterprise digital transformation has been hovering between 70% and 80% for a long time, and only about 30% can successfully realize the expected business value.
Industry experts pointed out sharply: More than 50% of factories still rely on manual front-line data entry, and a large number of supervisors waste hours per shift processing manual data, ultimately causing operations to fall into an inefficient cycle of “fighting fires everywhere”.
Behind these data is a profound warning line: the competition among intelligent equipment companies is shifting from “fighting for equipment and fighting for production lines” to “fighting for management systems and operating efficiency.”
The pain of digital transformation has led to a profound consensus in the industry: equipment and production lines are only the “skeleton”, and the management system is the “nervous system” of the enterprise – and lean management and digital technology are the two wheels that drive the efficient operation of this “nervous system”.
Why is lean production?
The essence of intelligent equipment manufacturing is complex system engineering with small batches, multiple varieties, and high precision.
In its cost structure, hidden costs – waiting for model changes, material deployment, and quality repairs – often account for “half” of the total operating costs.
These leaks cannot be solved by purchasing more expensive equipment. They can only rely on lean tools to “peele” waste as finely as a scalpel.
Why digitalization?
However, traditional lean still relies on “man-to-man” paper records and multi-level experience transmission to the extreme, and efficiency eventually reaches its upper limit.
Real qualitative change requires digital solidification of lean management concepts and rules into the system: using the Internet of Things to collect production line data in real time to drive dynamic decision-making in operating scenarios, and using AI algorithms to warn of quality fluctuations instead of waiting for others to find problems.
Toyota’s lean model defines high quality and low cost. Now leading companies are using digital technology to evolve their lean genes again, transforming the implicit experience of frontline workers into digital assets that can be replicated, optimized, and iterated.
The relationship between the two is not a simple superposition, but a deep collaboration – lean management provides operating rules, and digital technology provides execution guarantees.
Without lean digitization, the system will run at “chaotic acceleration”. No matter how much money is invested, it will only “automate the copy of inefficient processes” and only increase maintenance costs; without digital lean, improvement will always remain a “one-time manual impact”, which cannot be accumulated into organizational capabilities, nor can it continue to operate after the production line is expanded and personnel flow.
If either wheel goes astray, the transformation will be doomed.
When six departments including the State-owned Assets Supervision and Administration Commission of the State Council identified the smart island factory as one of the first batch of “pilot-level smart factories” in the country, the core evaluation criteria were: 75% AI adoption, simultaneous mixed production of 24 models while achieving extreme flexibility – this is the benchmark result of the deep coupling of lean concepts and digital technology.
BMW: AI-driven lean quality revolution.
In March 2026, BMW announced that it was completely reforming BMW Brilliance’s quality management system in China. The key path is to improve quality inspection efficiency and coverage through the large-scale deployment of AI technology modules in all aspects of quality inspection.
BMW management publicly stated that AI is not only a technical tool, but also a new quality management paradigm that can proactively prevent rather than react passively.
A management system that deeply integrates the two wheels of lean digitalization is breaking the traditional quality control bottleneck – this is the core barrier for future intelligent equipment companies in the high-end market competition.
SAIC-GM-Wuling: Lean intelligent manufacturing leap from “chain type” to “island type”.
SAIC-GM-Wuling has invested more than 10 billion in the world’s first “intelligent island manufacturing system” to completely decouple and reconstruct the traditional rigid assembly lines, which are linked together, into a parallel flexible production system composed of independent intelligent assembly islands.
Today, the proportion of AI application is as high as 75%. All 24 models are produced in the same production line at high speed without interfering with each other. It only takes 24 seconds for a new car to go from parts to complete vehicle off the assembly line.
This is the benchmark for the deep coupling of lean management + digital technology – using process decoupling and data connectivity to dismantle human work to the extreme and then use algorithms to reorganize it, achieving a triple leap in efficiency, cost and flexibility.
Gree Jinwan Factory: Data-driven adaptive operation.
In Gree Jinwan Intelligent Manufacturing Factory, the entire production process of an air conditioner, from equipment operating parameters, production line energy consumption data, to the delivery cycle of each order, is accurately collected and imported into the “industrial brain” in real time. It is independently analyzed and scheduled through AI algorithms to continuously optimize production rhythm and resource allocation.
This means that enterprises no longer rely on manual experience to make lagging decisions, but rely on real-time data drive to always maintain optimal operating levels during dynamic adjustments.
Facing the dual policy windows of equipment renewal and intelligent manufacturing, intelligent equipment companies choose the right consulting partner, which directly determines whether the existing investment can be truly activated.
It is recommended to use three “gold standards” to identify consulting teams with real practical capabilities:
First question: Can the team penetrate the “deep water zone” of business scenarios.
The internal management of smart equipment companies is complex. If consultants cannot understand the process logic, cannot see the pain points of production scheduling, and cannot calculate the efficiency of the machine, the plan will always stay in the macro template of “aligning with the benchmark”.
A consulting team with practical capabilities must lead the company down to the details in the early stages of planning: investigate the number of model changes, bottlenecks, data breakpoints and process fluctuations of the production line, and then design special plans for non-standard customization pain points, customized scheduling problems and high debugging and rework costs.
In the field of high-end CNC machine tools, a company and a professional team worked together to build an intelligent sheet metal production line across a two-story factory building, effectively solving the pain points of large-scale non-standard customization production and achieving lean management and control of the entire production process as well as green and low-carbon operations.
This is an excellent practice to start from a specific business scenario and reversely promote the lean upgrade of the entire manufacturing system by opening up digital intelligence breakpoints.
Only a team that truly understands business processes can design an improvement framework that “works” rather than “looks good.”
Second question: Whether the methodology can open up the entire link from “production line data” to “operational decision-making”.
The common cause of many failure cases is that the construction of digital systems simply stops at “data transfer”. The production line runs out a bunch of beautiful reports, but the management’s decision-making has almost nothing to do with these data.
A truly effective “two-wheel drive” must embed every management action into business execution, so that management instructions can be implemented into workshop control through digital tools.
The practice of Bosch Rexroth’s Beijing factory is a model for the industry.
It relies on the “digital workshop management system based on Bosch’s lean concept” and integrates cutting-edge technologies such as AI, edge computing, and virtual simulation to build an intelligent production management system covering the entire product life cycle, significantly shortening the research and development cycle, and improving production efficiency and quality control.
Bosch also uses lean concepts and industrial IoT, AI and other technologies to implement innovative applications in various manufacturing links of the factory, effectively shortening the product cycle from research and development to mass production, and simultaneously improving quality process control capabilities.
The consulting team you choose must also be able to help the company build a digital system and open up the entire link from production line data to operating decisions. At the same time, it must embed the lean management methodology into the main business process, so that the data can truly transform from “inventory ledgers, process work orders, and equipment comprehensive efficiency statistics” into a strategic command center that helps management make decisions.
Three questions: Does it have the double-wheel penetration of cost leanness and quality assurance?
The competition in smart equipment ultimately comes down to the comprehensive competition of full-chain cost control and zero-defect delivery.
Large-scale equipment updates and technological transformation subsidies promoted by policies are the “catalyst” to activate existing investment.
But whether the money is well spent depends on whether the company can simultaneously upgrade its cost management, inventory management and quality assurance systems while introducing new equipment.
In the industrial machine industry, one company has successfully transformed three workshops into “black light factories”, which continue to operate in a “lights-off” state, significantly reducing energy consumption and stabilizing delivery quality in an intelligent unattended manner.
This is the result of the joint action of lean concepts and intelligent technology: low-cost, high-efficiency automation is used to replace human tactics, while AI algorithms and closed-loop feedback mechanisms are used to guard the quality red line and realize management value simultaneously on both lines.
Halfway through 2026, the intensive policy window for the intelligent manufacturing industry is acceleratingly narrowing.
A series of policy tools such as large-scale equipment updates, digital workshop transformation, and the promotion of “black light factories” are catalysts to activate existing investments and build corporate moats.
Facing this management revolution in which machines, algorithms, data and people co-evolve, choosing a consulting partner with deep coupling of lean management and digitalization will not test the short-term budget, but whether the company can win the strategic initiative and seize the dual commanding heights of technology and management in this industrial restructuring.
