Under the grand blueprint of smart factory, industrial automation is reshaping the manufacturing
industry at an unprecedented speed. However, when we walk into those brightly lit workshops, we
will find that workers are still manually transcribing paper reports next to the most advanced robotic
arms; brand-new AGVs are shuttling between production lines, while engineers in the control room
are frowning because the data of the equipment is not interoperable. These scenes reveal a key issue:
industrial automation in the rapid development at the same time, is facing serious practical challenges.
Challenge One: The Integration Dilemma of the Old and the New
Data silos abound: Workshops are filled with equipment that spans three decades - from old relay-controlled
machine tools to robotic arms equipped with the latest IoT sensors. These devices speak different “languages”:
Modbus, CAN bus, OPC UA... like a team that needs to be fluent in a dozen dialects to communicate. After
the introduction of a new vision inspection system in a North China automotive parts factory, the quality
inspector had to manually move data between the two systems due to the inability to access the original PLC
network, resulting in a 20% drop in efficiency.
Protocol Jungle: Industry research shows that nearly 65% of manufacturing companies have more than five
industrial communication protocols. In order to solve the problem of interconnecting different brands of
equipment, a South China electronics factory was forced to deploy seven protocol conversion gateways,
which not only increased the number of failure points, but also required weeks of reconfiguration for each
production line adjustment, resulting in a loss of flexibility.
Challenge two: the alarm of the talent gap
Scarcity of composite talents: industrial automation field is most thirsty for both OT (operational technology)
and IT cross-border talent. A well-known recruitment platform data show that the salary of industrial
Internet-related positions has increased by more than 40% in three years, but the average recruitment cycle is
still up to 45 days. An automation integrator in the Yangtze River Delta confessed, “Engineers who can debug
PLCs and write Python data analysis scripts at the same time, we are willing to pay a salary 50% higher than
the industry.”
The speed of knowledge iteration far exceeds the training: the cycle of updating textbooks in colleges and
universities often lags behind the development of technology by 3-5 years. Feedback from an industrial control
technology training center, the mainstream PLC models of five years ago are still the focus of teaching, while
the much-needed edge computing and industrial AI applications are rarely covered. Enterprises have to invest
heavily in internal training, and the annual cost of upgrading employee skills at a medium-sized manufacturing
plant has accounted for 15% of automation investment.
Challenge 3: Cracks in security defenses
The fusion of IT and OT triggered a security crisis: when the production line equipment directly connected to the
enterprise ERP system, the original closed industrial control network is exposed to Internet threats. 2023, an
international organization reported that the manufacturing industry has become the second largest target of cyberattacks.
A packaging materials factory due to a network coding machine was implanted ransomware virus, resulting in the
paralysis of the entire production line for three days, direct losses of more than a million.
Equipment vulnerabilities are alarming: security research shows that a mainstream industrial switch there are 11
high-risk vulnerabilities, attackers can use this to jump to the core control network. What is more worrying is that a
large number of old devices can not upgrade the firmware, becoming a factory network “time bomb”.
Challenge 4: The fog of input and output
Small and medium-sized cost anxiety: a medium-sized flexible production line transformation program offer often
reaches millions, so many small and medium-sized enterprises are discouraged. A North China machinery processing
plant owner calculated an account: “automation equipment payback cycle if more than three years, we do not dare to
invest, cash flow can not hold.”
Hidden cost black hole: In addition to hardware investment, system integration, customized development, later
operation and maintenance of hidden costs are often underestimated. A ceramic enterprise implementation of the
MES system found that the annual data storage and arithmetic leasing costs are as high as 30% of the purchase
price of the system, and the need to hire two additional IT staff to maintain.
Challenge five: lost in the flood of data
Massive data slumber: A medium-sized factory's sensors generate 20GB of data per day, but only 5% is used for
decision-making. Engineers admitted, "We can see real-time temperature profiles, but how can we predict which
piece of equipment will fail next week? No one can say."
Lack of Analytical Capabilities: Industry surveys show that more than 70% of manufacturing companies lack effective
data analytics tools and talent. Although an auto parts factory deployed a SCADA system, quality fluctuation analysis
still relies on engineers' experience, and the accuracy of equipment downtime prediction is less than 50%.
Challenge Six: The Supply Chain's Vulnerable Heel
Chain reaction of chip shortage: In 2022, the global industrial control chip shortage led to a domestic PLC manufacturer's
delivery cycle from 8 weeks to 40 weeks. The project director of an equipment integrator revealed: “The customer production
line was shut down due to the lack of a $20 chip, and we could only sweep the goods from the spot market at a high price.”
Geopolitical disturbances: Restrictions on imports of key components have forced enterprises to restructure their supply
chains. An industrial robot manufacturer spent 18 months redesigning and validating its servo drive to replace a specific
imported servo drive, and its new product launch program was delayed.
The Way to the Breaking Point: The Evolutionary Path of Pragmatic
Collaboration
Decoupling and Reconfiguration: Adopting microservices architecture to decouple traditional giant systems, an electrical
machinery factory split MES functions into independent modules such as order management and quality traceability,
gradually replacing old systems to avoid the risk of “starting all over again”.
Talent ecological co-construction: Leading enterprises are building “dual-teacher” courses with vocational colleges and
universities, and students can operate virtual PLC training platforms synchronized with enterprises at school. The company
has implemented the “digital mentorship system”, where OT engineers and IT experts pair up to solve production line problems.
Security Defense in Depth: Implementing a “zero-trust” architecture, a precision instrument factory divides security domains
at the network layer, carries out “authentication + least privilege control” for each device, and deploys industrial traffic
probes to monitor abnormal behavior in real time.
Step-by-step investment strategy: SMEs can focus on pain point scenarios, for example, a hardware factory took the lead
in introducing AI vision in the quality inspection process, and recovered the cost in 6 months, and then gradually expanded
to warehouse automation.
Closed-loop data value: From “monitoring and visualization” to “decision-making intelligence”, a chemical plant combines
process parameters and energy consumption data, and saves more than a million dollars of steam costs annually by
optimizing the reaction temperature curve.
Supply chain toughness layout: the establishment of key components “dual-supplier” mechanism, an automation equipment
vendors on the core chip localization verification, reserve three qualified suppliers, procurement risk reduction of 70%.
The journey of industrial automation is by no means a simple change of equipment, but a systematic change involving
technology, talent and management. Those who successfully traverse the challenges of the enterprise, often both with a
vision of the future, but also with down-to-earth wisdom - to solve the reality of the pain points rather than chasing the
concept as the goal. When the workshop robotic arms and MES systems to truly realize the interminable collaboration,
when the equipment data into accurate decision-making, the evolution of manufacturing really touch the essence.
There is no end to this transformation, and only those who continue to evolve can win the ticket to the factory of
the future.