The rapid advancement of information technologies such as artificial intelligence and traditional manufacturing technologies has fueled the transformation and upgrading of China’s traditional manufacturing sector. At present, smart manufacturing has become the primary development direction for China’s manufacturing industry, yet the rollout of Industry 4.0 and smart manufacturing still faces certain challenges.

Dating back to early craftsmen with simple tools, tangible scientific and technological progress has always been embodied in manufacturing evolution. Applying technologies to manufacturing best illustrates humanity’s rational utilization of advanced tools and techniques to maximize profits and operational efficiency. Logically, manufacturing is bound to evolve toward informatization and intelligence. Simply put, human intelligence has matured to this stage, and further breakthroughs are inevitable to demonstrate humanity’s superior capabilities.

As an outstanding pioneer, Siemens’ digital factory in Germany has achieved 75% automation of production operations over 25 years. The production line is equipped with more than 1,000 online monitoring nodes, collecting over 50 million data points daily. It produces 3 billion components annually, delivers goods to customers around the clock, and boasts an eightfold increase in production capacity compared with its pre-digital era.

Smart manufacturing is a systematic project. Even when launching improvements from a single link, systematic planning is required before phased implementation.

Challenge 1: Full Connectivity

Missing the connection of any single node may hinder the realization of full automation. How numerous are these connections in practice? Take motorcycles, a less ubiquitous product nowadays, as an example: its engine alone contains over 250 parts, while an automobile has roughly 30,000 components. No single screw can be overlooked in manufacturing, and the same logic applies to all connections in smart manufacturing. Beyond physical parts, comprehensive interconnection is required for all relevant links involving capital flow, management information flow, logistics information flow, service information flow and more.

In the informatization era, ERP systems were plagued by difficulties in implementing reverse processes. The intelligent era demands not only full connection nodes but also bidirectional monitoring points and production management links embedded within the entire interconnected network. To support real-time mass data transmission and multi-node control, independent dedicated channels for connection and data flow are indispensable to ensure uninterrupted transmission without data packet loss throughout the whole process. Additionally, smart products serve as a prerequisite for establishing direct connections with end users.

Challenge 2: Full Control

Smart manufacturing takes data flow as its core to link all manufacturing and auxiliary links. The entire intermediate production process operates like a "black box", making real-time visibility of production status critical to enable manual correction, early warning and intervention. The interactive design and computing capacity of every node lay the foundation for full control. Apart from process control, continuous monitoring and regulation of intelligent equipment (including industrial robots) are also required. Multiple intelligent devices replace manual labor to perform operational tasks on smart production lines. Human-machine collaboration, as well as human supervision and management of equipment, constitutes a high-risk area prone to unexpected incidents in smart manufacturing.

Challenge 3: Resource Integration

Social environments and end users are key influencing factors for smart manufacturing. Two primary factory models will prevail in this era: large integrated manufacturing platforms and small personalized workshops. Large platforms are capable of meeting small-batch customized demands, while small workshops feature more direct, streamlined and efficient interaction with customers. Meanwhile, integrated intelligent supply chain platforms will emerge to deliver fast, zero-inventory supplies tailored to diverse personalized requirements. As a systematic project, smart manufacturing needs to integrate supply chains, production lines, logistics, service platforms, marketing resources and other segments to maximize automation and production output.

Given the stringent requirements of smart manufacturing, two viable development paths can be summarized as follows. First, leading enterprises can carry out independent digital transformation, replicate their proven successful experience across the industry, drive overall industrial upgrading, and realize large-scale smart manufacturing deployment. Second, major industry players can integrate relevant resources, centralize all upstream and downstream links on a unified shared platform that operates as an independent OEM center. It is evident that industrial alliances and third-party providers offering dedicated solutions and data services will become indispensable stakeholders.

In conclusion, although full-scale smart manufacturing is yet to be fully realized, its development aligns with the laws of socio-economic progress and represents a long-term arduous undertaking. Furthermore, the implications of these challenges vary widely for enterprises at different digital maturity stages and cannot be generalized uniformly.

Challenge 4: Data Collection, Integration and Application

The collection, integration and utilization of internal and external corporate data form the cornerstone of smart manufacturing efficiency. Data generated by smart products also provides fundamental support for product iteration and upgrading. The capacity to collect and integrate data—especially external market data, industrial data and user data—incurs the highest costs yet best demonstrates an enterprise’s resource integration capabilities. Smart manufacturing imposes stringent data capability requirements on enterprises, including the number of accessible data entry points, diversified data collection modes (such as the emerging crowdsourcing model), planning and construction of data centers, computing resource deployment, and proficiency in intelligent algorithm development and deployment.

Challenge 5: Data Transmission Channels and Real-Time Interaction

This challenge covers network infrastructure construction and unified protocol standards for multi-node systems. Multi-node interaction, monitoring and control, alongside cross-industry, cross-domain and cross-product multi-scenario application demands, necessitate the formulation of new systematic and unified protocol standards. Beyond overall architecture and basic IoT frameworks, standardized specifications should first be refined and rolled out within individual industries or sectors. Moreover, current network bandwidth and transmission speeds fall short of the massive real-time data load required by smart manufacturing. The industry now pins its hopes on 5G technology and new IoT protocol standards, waiting expectantly for their widespread implementation.

Challenge 6: Development and Interoperability of Multi-Scenario Data Models

The era of relying on a single statistical method or universal data model to fit all scenarios has passed. While core big data and intelligent algorithms remain relatively consistent, the true test for smart manufacturing lies in building customized data architectures and models tailored to distinct scenarios and operating conditions, as well as enabling seamless interoperability of data and models across multiple modes and application contexts. Deviations are inevitable in all production activities; even without discrepancies, timely adjustments must be made in response to external changes. Pure reliance on machine interpretation and induction of data is impractical. Therefore, professional analysts with profound insight into industrial trends and business workflows are essential to adjust, optimize, upgrade and phase out operational rules.

In the future, data will become the lifeblood of smart manufacturing. Every link including data collection, storage, rapid scheduling, model development, rule formulation, integration, computation and application is closely tied to interconnection, process control and automation. Data service capabilities will evolve into a key growth sector for third-party service providers, with data specialists and engineers becoming highly sought-after talents.

Some experts point out that the Internet has long been reshaping the tertiary industry, and its next major breakthroughs will surely lie in agriculture and manufacturing with promising prospects.

Nevertheless, the complexity of these challenges means market forces alone cannot drive sufficient progress. The world’s two largest economies have both launched targeted initiatives in this field. The US government enacted legislation to revitalize domestic manufacturing by supporting smart manufacturing. The Smart Manufacturing Innovation Institute, the ninth manufacturing hub established under the Obama administration, plans to set up five regional manufacturing innovation centers nationwide, each focusing on local technology transfer and workforce training. In May last year, China’s State Council issued Made in China 2025, a medium-to-long-term national strategy to build a manufacturing powerhouse. The plan targets full intelligent transformation of key manufacturing sectors by 2025, with a 50% reduction in operational costs, product development cycles and defect rates for pilot demonstration projects.

Policy incentives, talent cultivation, corporate investment and scientific research backing are all essential prerequisites for progress. The development and application of new technologies inevitably involve countless setbacks and trials. Many pioneers missed the perfect window for technological and market integration, yet those who seize the opportunity become industry trailblazers, while others forge ahead fearlessly to overcome subsequent obstacles. The development of smart manufacturing demands pioneers brave enough to overcome repeated difficulties, draw lessons from failures and persist in iterative optimization. Without the resolve to embrace challenges, there can be no chance of ultimate success.

Source: Information and Software Services Network