The “15th Five-Year Plan” Intelligent Leap: The Redcoast Path for Digital and Intelligent Transformation of China’s Process Industry

I. The “15th Five-Year Plan” Ushers in the Intelligent Economy Phase: Process Industry Enters a Cycle of Capability Restructuring

During the “15th Five-Year Plan” period, China is transitioning from “Digital China construction” to an “intelligent economic paradigm.” Unlike the “14th Five-Year Plan,” which focused on digital infrastructure and data resource accumulation, the core logic of the “15th Five-Year Plan” is undergoing a fundamental shift—moving from “data-driven” to “intelligence-driven.”

From the policy perspective, “digital and intelligent transformation” (intelligent digitalization) has become a keyword. Its essence is:

Achieving an intelligent reconstruction of production systems based on data elements, supported by computing power, and centered on algorithms.

This shift is particularly critical for the process industry. Industries such as petrochemicals, metallurgy, power generation, and building materials are inherently highly complex continuous systems. Their production stability, safety, and efficiency are heavily dependent on equipment and process conditions. Without intelligent capabilities and relying solely on automation systems, enterprises can only “maintain operation” but cannot “continuously optimize.”

Therefore, the significance of the “15th Five-Year Plan” for the process industry is not a simple upgrade, but a capability-level leap:

From an automatic control system to an intelligent operation system equipped with predictive and decision-making capabilities.

II. Deepening of the Main Policy Direction: Five Key Signals from “Digital China” to “Intelligent Economy” (In-depth Version)

Regarding the upgrade of “Digital China,” the “15th Five-Year Plan” is not a continuation of previous efforts but rather exhibits significant structural reinforcement across multiple key dimensions. From the perspective of policy language, data elements, artificial intelligence, computing power systems, and pathways for industrial reconstruction are being integrated into a unified capability framework oriented toward an “intelligent economy.” Its core characteristic is no longer point-specific breakthroughs, but rather a systematic construction centered around “intelligent capability.”

The policy signals at this stage can be understood through five key directions, each of which directly points to the capability upgrade pathway for the process industry.

(1) Deepening of Data Elements: From “Resource Utilization” to “Factor of Production”

During the “15th Five-Year Plan” period, the framing of data elements has been significantly strengthened, shifting from “development and utilization of data resources” to “market-based allocation of data elements,” with an emphasis on establishing a data property rights system, circulation and trading mechanisms, and revenue distribution mechanisms. This signifies an essential change in the role of data — from passive recording to active participation in production.

The policy concept of “data elements × real economy” essentially requires that data be directly embedded into the production process, participating in decision-making and optimization, rather than remaining at the analytical level.

For the process industry, this shift imposes higher requirements:

Enterprises must not only “be able to collect data,” but also “use data to drive equipment and processes.”

At the capability level, this means that the following must be constructed:

• A unified data foundation (breaking down silos between DCS, MES, and other systems)
• Data semantics and model frameworks (making data understandable and computable)
• A closed-loop capability from data to decision-making (truly participating in production optimization)

This directly aligns with Redcoast’s core value in data platform and intelligent digital twin.

(2) Deepening of “AI+”: From Technology Application to Integration into Production Systems

“AI+” is no longer merely an encouraging direction during the “15th Five-Year Plan,” but has become a clear pathway for industrial upgrading. The policy emphasizes promoting the large-scale application of AI in key sectors such as manufacturing and energy, and calls for the development of industry-specific foundation models and specialized models.

Compared with the “14th Five-Year Plan,” a notable shift is that:

Policy no longer focuses on “whether AI exists,” but rather on “whether AI has entered the production system.”

The underlying logic is that only when AI can participate in production decisions can its true productivity value be unleashed.

For the process industry, this direction implies that AI applications must complete a three-level transition:

• From analytical tool → into business workflows
• From point-specific models → to building systemic capability
• From decision support → to participation in real-time control

This also determines that the core of AI deployment lies not in the algorithms themselves, but in the ability to achieve deep coupling with production systems.

(3) Restructuring of the Computing Power System: From Centralized Provision to “Cloud-Edge-Device Collaboration”

The “15th Five-Year Plan” explicitly calls for improving the computing power infrastructure system. Building on the “Eastern Data, Western Computing” initiative, it further emphasizes edge computing and distributed computing capabilities. This shift means that computing power is transforming from a “remote resource” into an “on-site capability.”

For the process industry, the traditional “cloud-centric centralized computing” model struggles to meet real-time and safety requirements. The strengthening of edge computing, however, enables intelligent capabilities to be deployed directly on production lines and at the equipment side.

The future industrial computing power system will exhibit a clear layered structure:

• Cloud: Model training and global optimization
• Edge: Real-time inference and local decision-making
• Device side: Instant response and control execution

The core change brought by this structure is: AI is no longer post-hoc analysis, but rather becomes part of real-time operations.

For Redcoast, this means it needs to possess comprehensive technical capabilities spanning from the cloud platform to the edge, rather than solely the ability to build cloud-based systems.

(4) Upgrading the Algorithm System: From General-Purpose Capabilities to Industry-Specific Assets

At the algorithmic level, the policy explicitly calls for the development of “industry-specific models” and “specialized models,” marking the entry of AI into an “industrialization phase.” While general-purpose foundation models possess generalization capabilities, under the complex operating conditions of the process industry, they must be deeply adapted to equipment mechanisms and process characteristics.

This direction is essentially driving the transformation of algorithms from “tools” into “assets.” In the process industry, algorithmic capabilities need to possess three core characteristics:

• Adaptability to complex operating conditions
• Interpretability of equipment behavior
• Embeddability into production systems

Therefore, a purely data-driven model alone cannot meet the requirements, and the model integrating mechanism and data will become the mainstream approach.

From a higher-level perspective, whoever can accumulate industry-specific models will be able to build long-term competitive barriers. This also provides a clear path for Redcoast in the direction of algorithm assetization.

(5) Clear Pathway for Industrial Reconstruction: “Intelligent Transformation, Digitalization, and Networking Connectivity” Moving Toward Systemic Implementation

“Intelligent transformation, digitalization, and networking connectivity” is no longer a conceptual slogan during the “15th Five-Year Plan,” but has been given a more clearly defined implementation connotation. Its essence is the systemic reconstruction of the industrial system.

This reconstruction is not about localized optimization, but rather focuses on the entire production process:

• At the “digital transformation” level, achieve data integration and the construction of a unified foundation.
• At the “intelligent upgrade” level, introduce AI to realize predictive and optimization capabilities.
• At the “networking connectivity” level, achieve comprehensive connection of equipment and systems.

It is worth noting that the policy repeatedly emphasizes predictive maintenance as a key capability, with its significance being that:

Equipment management has risen from a supporting function to a core variable affecting production stability. Drawing from industry practice, the value brought by predictive operation and maintenance is not only reflected in cost and efficiency, but also in its ability to ensure the safety of continuous production.

The essence of these policy signals is a “restructuring of capability architecture.”

Taken together, the main policy direction of the “15th Five-Year Plan” is not simply to promote digitalization or AI applications, but rather to restructure the capability system of industrial enterprises:

• Data → from a resource to a production factor
• Computing power → from support infrastructure to production capability
• Algorithms → from tools to core assets
• AI → from assistance to a decision-making entity

For the process industry, the core of this shift lies in:

Whoever can first complete the transition from “system construction” to “capability building” will occupy a dominant position in the intelligent economy phase.

And Redcoast is positioned precisely at the key juncture in this capability restructuring process, transforming policy-driven capabilities into industrial deployment realities.

III. The Reality Gap in the Process Industry: Policy Direction Is Clear, but Capabilities Have Yet to Catch Up

While policies continue to advance, there remains a significant capability gap in the current structure of the process industry. This contradiction—“policy in place, capabilities not yet keeping up”—is precisely the core opportunity during the “15th Five-Year Plan.”

Based on actual project experience, the issue is not whether enterprises possess a digital foundation, but rather that these foundations have not yet been transformed into intelligent capabilities.

The most typical manifestation is “plenty of data, but no ability to make decisions.” A large amount of data is continuously generated by DCS, PLC, and MES systems. However, due to the lack of unified modeling and semantic frameworks, the data cannot form a closed loop, let alone support AI applications.

At the same time, equipment management remains heavily dependent on experience. Scheduled maintenance and reactive repairs are still the mainstream approaches, which reveals a significant gap compared to the “predictive maintenance” emphasized by policy.

Looking again from the perspective of AI applications, most enterprises remain at the pilot stage, lacking a replicable and scalable model system. This also makes it difficult for AI to truly enter production systems.

In summary, the core problems currently facing the process industry center on three points:

• Data has not yet become a usable asset
• AI has not yet developed into a systemic capability
• Equipment management has not yet been predicted

And these problems correspond precisely to the key focus areas of the “15th Five-Year Plan” policy.

IV. AI and Predictive Operation & Maintenance: The Core Technical Pathway for Policy Implementation

Driven by policy, AI and predictive operation & maintenance are becoming key enablers for the digital and intelligent transformation of the process industry. From a technological evolution perspective, the pathway has gradually become clear.

The current mainstream model is no longer a single algorithm, but rather a multi-model fusion framework. Data-driven models have advantages in anomaly detection and trend prediction, while mechanism models offer stability in high-safety scenarios. The fusion models of mechanism and data have thus become the mainstream choice, balancing accuracy and interpretability.

An even more important shift is that the application boundary of AI is expanding. From early-stage equipment fault prediction, it is gradually extending to production line optimization and plant-wide scheduling, ultimately pointing to a higher-level goal—predictive manufacturing.

This evolutionary pathway can be summarized as:

• From “equipment health assessment” to “production system optimization”
• From “outcome analysis” to “process decision-making”

In terms of technical direction, the following areas will see focused evolution during the “15th Five-Year Plan” period:

• Edge AI: Enabling real-time prediction and control at the field level
• Industry-specific models: Enhancing adaptability to complex operating conditions
• Intelligent digital twins: Upgrading from simulation tools to decision-making systems
• Predictive manufacturing: Achieving collaborative optimization of equipment and production

In essence, AI is transforming from a tool into an integral part of industrial systems.

V. Redcoast’s Strategic Window: Building a “Capability Bridge” Between Policy and Industry

Under the policy framework of the “15th Five-Year Plan,” Redcoast’s opportunity is not merely to participate in project construction, but to serve as a key node in translating policy-driven capabilities into practical industrial implementation.

From a policy mapping perspective, Redcoast’s business directions are highly aligned with national priorities:

• Data as a production factor→ Corresponds to unified data foundation capabilities
• AI+ → Corresponds to AI algorithms and industry-specific models
• Intelligent transformation, digitalization, and networking connectivity → Corresponds to digital twins and system integration
• Predictive maintenance → Corresponds to equipment health management platforms

In other words, the value of Redcoast lies in:

Translating the capability requirements outlined in policy into system-level capabilities that enterprises can practically deploy.

In terms of a specific implementation pathway, a three-layer capability system can be established:

First layer: Data and platform capabilities

Build a unified data foundation to achieve multi-system data integration and standardization, providing the basis for AI applications.

Second layer: AI and model capabilities

Establish an industry-specific algorithm and model library, with a focus on developing mechanism + data fusion models to enhance model reliability and interpretability.

Third layer: Scenario and application capabilities

Take predictive operation and maintenance as the core entry point, gradually expand to production line optimization and plant-wide scheduling, and achieve a transition from point-specific to system-level applications.

In terms of implementation pace, a progressive pathway is recommended:

• Single-equipment prediction (key equipment such as pumps, compressors, etc.)
• Production line optimization (process and equipment coordination)
• Plant-wide intelligent scheduling (production and equipment integration)

Ultimately achieving the upgrade from “predictive operation and maintenance” to “predictive manufacturing.”

The essence of the 15th Five-Year Plan is the redefinition of industrial capability

The “15th Five-Year Plan” is not merely a simple technology upgrade cycle, but rather a cycle of industrial capability restructuring.

Digital China addressed the problem of “informatization,” whereas the intelligent economy must address the problem of “decision-making.”

For the process industry, this shift can be summarized as a three-stage transition:

• From “being able to see” (digitalization)
• To “being able to judge” (intelligentization)
• And then to “being able to decide” (autonomous optimization)

AI and predictive operation & maintenance lie precisely at the core of this transition.

For Redcoast, this is not only a policy opportunity but also a capability window. Whoever can first close the loop of “data–algorithm–decision-making” and build a sustainably evolving digital and intelligent system will take the initiative in the new wave of competition within the process industry.

Looking toward the “15th Five-Year Plan,” the industry will not lack systems.

What will truly be scarce is — a capability system that can continuously create decision-making value.