Industrial automation and control technology has become the lifeblood of the modern manufacturing
industry, acting like an invisible neural network that precisely coordinates every aspect of the production
process. However, in the pursuit of higher efficiency, better quality and greater resilience, enterprises are
still facing a series of key challenges that need to be resolved. These pain points, if not effectively overcome,
will become a “stumbling block” to intelligent transformation. In this paper, we will analyze the core issues in
the field of industrial automation and control, and provide practical ideas for breakthroughs.
First, the data silos: information fragmentation impede
decision-making optimization
Imagine: workshop equipment roaring operation, PLC (Programmable Logic Controller) faithfully record the
operating parameters, SCADA (Supervisory Control and Data Acquisition System) to monitor the status of the
production line, MES (Manufacturing Execution System) to manage the production of work orders, ERP (Enterprise
Resource Planning) co-ordination of the overall resources - however, these However, these key systems often work
in isolation. Equipment data cannot be uploaded to the management platform in a timely manner, production
progress is disconnected from inventory information, and quality data is difficult to trace back to specific processes.
This “data silo” phenomenon leads to managers being like blind men feeling an elephant, unable to obtain a
global, real-time, transparent view of production.
The solution: embrace openness and interconnectivity
Adopt unified standards and protocols: vigorously promote OPC UA (Open Platform Communication Unified
Architecture) and other industrial communication standards with cross-platform, high-security, information
modeling capabilities, to open up the data channel from the equipment layer to the information layer.
Deploy an industrial IoT platform: build a powerful IIoT platform as a “data hub” to realize unified access, cleaning,
storage and analysis of heterogeneous data from multiple sources.
Strengthen system integration: break the traditional vertical well architecture, through APIs, middleware and other
technologies to realize the depth of integration between MES, ERP, PLM and other systems, to ensure seamless
data flow through.
Second, real-time and deterministic bottlenecks: control problems
in complex scenarios
In high-speed precision machining, precision synchronized motion control (such as robot collaboration, printing
machinery), or the need for extremely fast response to the safety interlock scenarios, the traditional Ethernet or
fieldbus is often not enough. Data delays and transmission jitter can lead to loss of control accuracy, equipment
synchronization, and even safety accidents. This places stringent demands on the network's real-time performance
(fast response) and determinism (predictable and stable latency).
Technology Breakout:
Time-Sensitive Networking: TSN (Time-Sensitive Networking) technology enhances standard Ethernet to provide
guaranteed bandwidth and deterministic low latency for critical control traffic on the same physical network,
while being compatible with regular data communications, making it an ideal choice for building unified,
high-performance industrial networks.
Deterministic Industrial Ethernet Protocols: For specific ultra-high demand scenarios, protocols such as PROFINET
IRT, EtherCAT, and others continue to be advantageous.
Sinking edge computing: Deploying control logic with extremely high real-time requirements (such as rapid
start/stop of equipment and motion control loops) to be processed on edge computing nodes close to the
equipment significantly reduces the latency of uploading data to the cloud or remote control centers.
Third, the network security risk is intensifying: industrial control
systems have become a new target of attack
With the deep integration of OT (operation technology) and IT (information technology), the exposure of
traditional closed industrial control systems has expanded dramatically. Older equipment is generally vulnerable
and difficult to update patches, lack of effective protection measures, and weak security awareness.
Ransomware attacks have paralyzed production lines, and news of security incidents caused by the intrusion
of critical infrastructure are common. Industrial control network security has changed from “optional” to “survival”.
Construct defense in depth:
Network partitioning and isolation: Follow IEC 62443 standards to strictly divide security zones (e.g., shop floor
operation network, factory management network, enterprise IT network, and cloud), and deploy industrial
firewalls/net gates for access control and protocol filtering between zones.
Endpoint Security Hardening: Deploy industrial control host whitelisting software to strictly control executable
programs; update patches in a timely manner (to be verified in the test environment); disable unnecessary
ports and services.
Continuous monitoring and response: Deploy industrial intrusion detection system/intrusion protection system to
deeply analyze network traffic and industrial control protocols and detect abnormal behaviors; establish emergency
response plans for security incidents and conduct regular drills.
Personnel Awareness Enhancement: Strengthen network security training for all staff, especially the preventive
awareness of engineers and operators.
Fourth, system integration and interoperability complexity: upgrade and transformation of the roadblock
Enterprise automation systems are often built over the years, new and old equipment co-exist, brand and model.
Different vendors of PLC, HMI (human-machine interface), drive equipment, software systems using different
communication protocols and data models, resulting in system integration difficulties, high costs, long cycle, maintenance
and upgrading exceptionally difficult. This complexity seriously hinders the rapid introduction and application of new
technologies (e.g. AI, advanced analytics).
Decoupling and Standardization:
Modularized design: Adopt service-oriented architectural thinking, split system functions into independent and
reusable modules, and reduce the degree of coupling.
Embrace open standards: In new construction or renovation projects, prioritize equipment and software that support
open standards such as OPC UA and MQTT.
Utilize adapters/converters: For legacy devices that cannot be replaced, use protocol gateways or OPC UA wrappers
to connect them to the unified platform.
Define clear interface specifications: In the system planning and integration stage, clearly define the data interface
and interaction specifications between subsystems.
Fifth, the level of intelligence is not enough: the value of data needs to be
mined in depth.
Many automation systems still remain at the basic level of control and monitoring, the collection of massive operational data,
process parameters, quality data, energy consumption data has not been fully utilized. Equipment lacks predictive maintenance
capability, process optimization mainly relies on engineers' experience, quality analysis lags behind production, and there is
room for optimization of energy consumption. The huge potential value of data has not been effectively released.
Toward Intelligent Decision Making:
Deploying advanced analytics platform: Utilizing big data analytics and machine learning technologies to deeply mine
historical and real-time data.
Implement predictive maintenance: Establish a prediction model based on equipment operating status data (vibration,
temperature, current, etc.) to accurately predict failures and turn reactive maintenance into proactive maintenance.
Process parameter optimization: Apply artificial intelligence algorithms to find the optimal combination of process
parameters to improve product quality, yield and efficiency.
Energy management: Monitor energy consumption in real time, identify waste points, optimize equipment operation
strategies and scheduling, and reduce energy costs.
Explore digital twin application: build digital twin models of key equipment or production lines for virtual simulation,
predictive analysis and optimization verification.
Conclusion: Toward a Smart Future with Technology as Spear and Safety
as Shield
The challenges in industrial automation and control are complex, but not insurmountable. Solving “data silos” requires
open architecture and a unified platform; overcoming real-time bottlenecks relies on innovative network technologies
such as TSN; fending off security threats requires the establishment of a defense-in-depth system; reducing the complexity
of integration calls for standardization and modularity; and unlocking the value of data requires embracing AI and advanced
analytics. Only by embracing new technologies with an open mind, building solutions with a systematic mindset, and placing
network security at the core, can enterprises effectively break through these key bottlenecks, truly unleash the full potential
of automation and control, win the first opportunity in the fiercely competitive marketplace, and drive steadily toward the
broad future of intelligent manufacturing.