MIIT’s Industrial Digital Transformation Blueprint: How China Plans to Upgrade Manufacturing by 2026

China’s top industry regulator is moving to turn industrial digitalization from a policy aspiration into an operational roadmap. In September 2025, the Ministry of Industry and Information Technology (MIIT) released the Scenario-based and Graph-based Reference Guide for Promoting Digital Transformation in Key Industries (2025 Edition; hereinafter, the “guide”), a document that lays out how the Chinese government intends to upgrade the country’s manufacturing base over the coming year and beyond.

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MIIT maps specific digital tools to concrete production scenarios in the document, indicating where and how technologies such as industrial internet platforms, intelligent manufacturing systems, and data-driven management tools should be deployed along the industrial value chain.

In this article, we examine the key features of MIIT’s new guide, assess how it could reshape manufacturing operations in the near term, and outline what it means for businesses and investors navigating China’s evolving industrial and regulatory landscape.

Policy context and background

MIIT’s new reference guide lands in a policy environment where the government has become explicit about what it wants China’s next growth phase to look like. Over the past two years, the leadership has increasingly framed industrial policy around the development of New Quality Productive Forces (NQPFs), a concept that, in official explanations, refers to advanced productive capacity driven by innovation and characterized by “high-tech, high efficiency, and high quality,” rather than by factor-heavy, investment-led expansion.

Analytical treatments of the concept generally highlight three core components:

• Technology and innovation as the primary driver;
• The development of future-oriented industrial capabilities; and
• The strengthening of industrial and supply chains, supported by enabling reforms and talent development.

Against that backdrop, industrial digital transformation is being positioned less as a standalone modernization initiative and more as an implementation pathway for NQPFs: particularly in the large, established manufacturing sectors that still account for a substantial share of output, exports, and employment.

Key drivers and objectives of China’s industrial digital transformation blueprint

Productivity enhancement

At the factory and enterprise level, digital transformation is expected to improve productivity by reducing downtime, improving yield rates, strengthening quality control, and optimizing energy and materials use through better data capture and process management.

These gains are incremental rather than spectacular, but they compound, especially when deployed across industrial clusters and supply chains. The significance of MIIT’s scenario-based and “graph-based” framing is that it implies a push for repeatable, scalable deployment models: not just what firms should do, but where digital tools should be applied across production, logistics, maintenance, and management processes.

This move is consistent with China’s broader goal of turning industrial upgrading into a standardized, measurable program rather than a patchwork of local pilots.

Industrial upgrading

Digitalization also supports industrial upgrading in the narrower sense used in Chinese policy: moving toward higher value-added production, higher-end product mixes, and more sophisticated manufacturing capabilities. NQPFs messaging repeatedly links “high quality” outcomes to advanced production factors and improved allocation of those factors (data and software being central to that shift).

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For many industries covered by MIIT’s guide (such as robotics, new energy vehicles, medical equipment, lithium batteries, and smart devices), digital transformation is closely intertwined with R&D, product iteration, and compliance with emerging technical standards.

For legacy sectors like steel and petrochemicals, the policy logic is different but complementary: raising efficiency, cutting waste and emissions, and improving consistency and traceability, capabilities that increasingly shape market access and competitiveness.

Supply-side competitiveness amid weak domestic demand

The timing of MIIT’s push also reflects a macroeconomic constraint that China has openly acknowledged: a persistent imbalance between strong supply capacity and weak domestic demand. The 2025 Central Economic Work Conference (CEWC) readout highlighted this “prominent contradiction,” underlining policymakers’ concern that insufficient demand and deflationary pressures could weigh on growth even as industrial output remains robust.

In that setting, productivity-led upgrading has a dual function. Domestically, it aims to support growth by improving efficiency and sustaining investment in industrial modernization. Externally, it strengthens export competitiveness by improving cost structure and product quality, an outcome that may be economically useful for China but politically sensitive abroad, given heightened scrutiny around industrial policy, overcapacity, and trade imbalances.

Hence, MIIT’s digital transformation blueprint should be read as part of the same policy mix signaled at the CEWC: stabilize weak demand without reverting to broad-based stimulus, while keeping the strategic focus on innovation and industrial upgrading. Recent reporting on the CEWC has emphasized that China’s leaders intend to maintain proactive fiscal support while continuing to push structural rebalancing, particularly by addressing the supply-demand mismatch and strengthening longer-term growth drivers.

Overview of the 2025 guide

The 2025 guide provides a structured framework for advancing industrial digitalization across China’s manufacturing sector. The guide is intended to support the implementation of the Manufacturing Digital Transformation Action Plan and to accelerate the comprehensive application of next-generation information technologies across industrial value chains.

Broad coverage, but not a one-size-fits-all framework

The guide covers 14 manufacturing industries, spanning both capital-intensive heavy industry and consumer-facing manufacturing. These include steel, petrochemicals, construction machinery, new energy vehicles, robotics, medical equipment, home appliances, beauty and personal care products, lithium batteries, printed circuit boards, smart mobile devices, and others.

Importantly, each industry is treated as a distinct system, with its own production logic, constraints, and digital maturity profile.

For each sector, MIIT provides a dedicated industry scenario map, which decomposes the full industrial value chain into specific, widely recognized business scenarios. In the steel sector, for example, the guide breaks production down across ironmaking, steelmaking, rolling, equipment management, energy management, environmental compliance, quality control, safety, and supply chain coordination—each further divided into dozens of sub-scenarios, such as “blast furnace intelligent control,” “AI-based scrap steel grading,” “predictive maintenance for key equipment,” and “carbon asset management.”

This level of detail makes clear that the guide is not merely descriptive. It is designed to allow enterprises, industrial parks, and local governments to identify precisely where digital intervention is expected, and to benchmark their current capabilities against an implicitly defined national standard.

Scenario-based logic: breaking transformation into operational problems

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The guide’s core methodological innovation is its scenario-based approach, which treats digital transformation not as an enterprise-wide abstraction, but as a series of discrete, solvable operational problems. MIIT explicitly frames scenarios as the “basic business units” of manufacturing, arguing that while digital transformation is narrow in scope (“one meter wide”), it is extremely deep in technical complexity (“one hundred meters deep”).

In practice, this means each scenario is defined with:

• A current maturity rating
• A set of core pain points
• The expected transformation value (cost reduction, quality improvement, safety, energy efficiency, or new business models)

For example, in the petrochemical sector, the “crude oil refining plan optimization” scenario highlights challenges such as modeling complex crude blends, coordinating multiple production units, and responding to volatile downstream demand. The guide then links these pain points to specific digital solutions, including process simulation software, optimization algorithms, and integrated production–market data systems.

This framing shifts the conversation from whether firms should “go digital” to which problems they are expected to solve first, and what digital capabilities regulators believe are necessary to do so.

Graph-based architecture: standardizing the building blocks of digitalization

The second key innovation is the graph-based structure, which connects each scenario to a standardized set of digital elements. MIIT refers to this as the “one map, four lists” (一图四清单) framework, consisting of:

• Data elements: such as production parameters, equipment state data, quality inspection data, and energy consumption metrics).
• Knowledge models: including physical and chemical process models, optimization algorithms, rules-based control models, and AI models).
• Tool software: ranging from industrial internet platforms and simulation software to machine vision systems and cloud-based industrial applications).
• Talent and skill requirements: spanning automation engineering, data science, industrial software development, and domain-specific engineering expertise).

Crucially, these elements are decoupled from individual enterprises and presented as reusable, modular components. This design is intended to make digital solutions portable across firms and regions, lowering implementation costs and accelerating replication.

What this means for business and investment decisions: Insights from the steel and medical devices industries

Taken together, the steel and medical equipment maps show that MIIT’s digital transformation agenda is highly selective. Capital is being steered toward:

• Integrated, closed-loop industrial intelligence rather than isolated automation;
• Data integration and governance as foundational infrastructure;
• Digital systems that embed regulatory compliance directly into operations; and
• Domestic substitution at critical control, software, and algorithm layers.

For businesses and investors, the guide does not eliminate risk, but it significantly narrows the field of options. It clarifies where regulatory alignment, policy support, and sustained demand are most likely to converge as China pushes its manufacturing base toward productivity-led growth.

How China’s manufacturing model could change by 2026

China’s industrial strategy is unmistakably shifting away from the old paradigm of capacity-led expansion to a model increasingly defined by efficiency, data, and innovation. This transformation has its roots in national policy frameworks, such as Made in China 2025, and the latest push for digital transformation, and it is gaining traction in corporate practice as firms deploy advanced automation, analytics, and intelligent systems across their operations.

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At its heart, this shift is about extracting more economic value from fewer resources. Digital technologies (particularly industrial internet platforms, AI, robotics, and advanced analytics) are helping manufacturers improve precision, reduce waste, and accelerate decision-making. For example, industry reports show that by 2025, more than 70 percent of large manufacturing firms in China will have substantially completed digital networking and built demonstrator “smart factories,” laying the groundwork for widespread adoption of data-driven operations.

Differentiated impact across industry segments

The transition is not uniform. Its pace and nature vary by industry:

• Capital-intensive heavy industry: In sectors like steel and petrochemicals, digital transformation is optimising asset utilization, cutting energy use, and strengthening environmental compliance. These gains matter most where margins are tight, and customers are sensitive to quality and traceability.
• Consumer-oriented manufacturing: For industries such as electronics, appliances, and personal goods, digital tools are enabling more responsive production systems that can handle shorter product cycles and more frequent design iterations. This supports China’s broader push up the value chain in consumer markets.
• Strategic emerging sectors: In areas like robotics, AI hardware, and advanced medical equipment, the integration of digital systems is not just about efficiency but about establishing new competitive advantages. These sectors are often more R&D-intensive and are expected to lead the next wave of export-oriented growth.

China’s pivot toward efficiency and data is already reshaping industrial investment patterns and competitive dynamics. While challenges remain (such as uneven digital capability across firms and the need for significant upskilling), this new model sets the stage for sustained productivity growth even as traditional drivers like low-cost labor and capacity expansion become less reliable.

For investors and businesses, understanding this transformation is critical: the winners in China’s manufacturing landscape over the next decade will be those that can align with, contribute to, and capitalize on the data-driven, innovation-led regime that Beijing is actively cultivating.

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