Walking into a modern production space, it is common to see robotic arms waving with precision, sensors quietly
collecting data, and assembly lines running smoothly - all thanks to the power of automation technology. Factory
automation and industrial automation, two terms that are often used interchangeably, represent key complementary
but distinct forces in modern manufacturing. Understanding the differences and connections between them is essential
for companies to grasp the direction of upgrading.
Factory automation (FA) is like the sophisticated nervous system inside a manufacturing plant. It focuses on the production
cell level, with the core objective of optimizing the execution efficiency, precision and consistency of a single manufacturing
process. Imagine an automotive assembly line: industrial robots accurately complete welding and painting; automated
guided vehicles (AGVs) intelligently move materials on a set track; conveyor belts seamlessly transfer semi-finished products
to the next station; and programmable logic controllers (PLCs) act as “field commanders,” receiving sensor signals and
directing equipment actions in real time. Factory automation is a visible and tangible “hard power”, which directly affects the
equipment and production line, and is committed to solving the problem of “how to make products faster, better and more stable”.
Industrial automation (IA), on the other hand, builds a more grandiose and interconnected “intelligent body”. It covers the
entire industrial value chain, from raw material procurement, manufacturing, quality control to energy management, equipment
maintenance and even enterprise resource planning (ERP) integration. Industrial automation is system-level, information-driven
optimization. Its core lies in integration: through Distributed Control System (DCS), Supervisory Control and Data Acquisition
(SCADA), and Industrial Internet of Things (IIoT) platforms, it realizes cross-departmental, cross-level, and cross-physical-space
data coherence and collaborative decision-making. A typical industrial automation scenario is: in a large chemical plant, DCS
not only controls the temperature and pressure of the reactor, but also uploads the data to SCADA system in real time for
plant-wide monitoring; this information is also interfaced with the ERP system to guide the raw material procurement scheduling
and energy optimization strategies; the predictive maintenance sensors on the equipment analyze the operation status through
the IIoT platform and issue early warnings before failures occur. Industrial automation solves the problem of “how to make
the whole enterprise/factory system work together, intelligently and sustainably”.
The two are closely intertwined, shaping the future of manufacturing together:
FA is the cornerstone and nerve endings of IA: Without efficient and reliable bottom automation (FA), there is no way to
talk about real-time data acquisition and precise control command execution required by the upper industrial automation
(IA), FA equipment is the “hands” and “eyes” of the IA system. FA equipment is the “hand” and “eye” of the IA system.
IA injects soul and global vision into FA: The potential of an isolated automation device or production line (FA) is limited, and
IA gives the FA system a “brain” through data integration and analysis, enabling higher-level intelligent decision-making such
as cross-line scheduling, optimal allocation of resources, and production scheduling based on global demand.
Technology integration is the core trend: the boundaries between the two are blurring, and IIoT, big data analytics, artificial
intelligence (AI), and cloud computing are deeply integrating FA and IA. e.g., defect data captured by machine vision (FA) on
the production line is uploaded to the cloud-based AI platform (IA) via IIoT for in-depth analysis, and the model is optimized,
and then the improved inspection parameters are sent to the equipment side in real time, forming a closed-loop optimization.
Closed-loop optimization is formed. Edge computing brings data processing closer to the source (FA equipment), while
uploading key results to the central system (IA) to achieve efficient collaboration.
Toward the Convergence of Intelligent Manufacturing: Truly intelligent
manufacturing is not a stack of isolated technologies,
but the deep integration of FA and IA in multiple dimensions:
Data-driven: FA equipment generates massive real-time data, which is aggregated, cleaned and analyzed by the IA
platform, and transformed into business insights that can guide actions.
Flexible Interconnection: Based on open standards and IIoT, FA equipment (e.g., new robots, intelligent machine tools)
can be plug-and-play, seamlessly exchanging information and collaborating with IA systems (e.g., MES, APS), and
responding quickly to changes in orders.
Intelligent decision-making: The results of advanced analysis at the IA level (e.g., predictive maintenance, energy
consumption optimization, quality root cause analysis) can be used to generate optimization strategies automatically or with assistance, which can be directly applied to the adjustment of equipment parameters or production scheduling at the FA level.
When factory automation and industrial automation are truly integrated, modern manufacturing will usher in a
fundamental transformation: every precise movement of the machine arm carries decision-making information for
global optimization; every product on the assembly line condenses data-driven value judgments; every breath of
shop floor operation connects the wisdom of the entire industry - this is what factory automation and industrial
automation are all about. -This is the future of factory automation and industrial automation woven together.
Factory automation and industrial automation are not an either/or choice, but rather a double helix that forms a
powerful manufacturing competitiveness. Understanding the difference between FA, which focuses on equipment
efficiency, and IA, which focuses on system synergy, and actively embracing the deep integration of the two in terms
of data, connectivity, and intelligence, is a key step for enterprises to grasp the rules of survival in the digitalization
wave, and move towards a more efficient, flexible, and intelligent future.