Page 39 - FoodFocusThailand No.242 June 2026

SPECIAL
SPECIAL FOCUS FOCUS

CYBER-PHYSICAL SYSTEMS:

THE INTELLIGENT INFRASTRUCTURE BEHIND

FULLY AUTOMATED FOOD FACTORIES

The global food industry is entering an era in which “data” has become a critical driver of manufacturing.
Amid rising operational costs, labor shortages, and raw material volatility, food factories are increasingly
transitioning toward smart manufacturing systems. In this transformation, Cyber-Physical Systems (CPSs)
have emerged as a key technology driving Industry 4.0 and advancing toward Industry 5.0, which emphasizes
automation, sustainability, and real-time decision-making.

Cyber-Physical Systems (CPSs) refer to the integration Together, these layers shift food manufacturing from
of machinery, sensors, and production lines with digital a reactive manufacturing—solving problems after they
systems through technologies such as Artificial Intelligence occur—to a predictive and autonomous manufacturing, where
(AI), the Internet of Things (IoT), cloud computing, and big issues are anticipated and prevented before they arise.
data. This enables factories to analyze, decide, and control
production processes automatically and in real time. In the Performance Gains from CPSs in
food industry, CPSs function as the “intelligent nervous Food Manufacturing
system” of modern factories, enhancing production efficiency, Research findings indicate that, following the implementation
reducing losses, strengthening food safety control, and of CPSs, food factories can reduce human labor requirements
optimizing resource management with greater precision. by approximately 20–60%, decrease food waste by
15–40%, and reduce machine downtime by 30–50%. At the
Architecture of Cyber-Physical Food Systems same time, product defect detection accuracy can exceed
CPSs in food manufacturing typically consist of four 95%. In addition, CPSs can improve energy efficiency by
interconnected layers: approximately 10–30%.
1. The Physical Layer: This layer includes physical For example, Zhang et al. (2023) reported that AI-enabled
equipment, machinery, and sensors within the factory, such CPSs in beverage factories reduced production downtime
as industrial robots, conveyor systems, and vision systems by 32% through predictive maintenance systems capable of
used for real-time data collection and food quality inspection. detecting early anomalies in pumps and valves. Similarly, Yang
2. The Communication Layer: This layer enables et al. (2024) found that integrating Digital Twin technology with
continuous data transfer and connectivity through CPSs in ready-meal production facilities reduced raw material
technologies such as IoT, 5G, edge computing, and waste by over 25% while significantly improving production
industrial ethernet, ensuring seamless communication across throughput.
production systems.
3. The Cyber Layer: This layer utilizes AI, machine Case Studies: Cyber-Physical Systems Across
learning, digital twin technology, and cloud platforms to Food Industry Sectors
analyze production data, predict abnormalities, and improve Meat and Seafood Processing
operational efficiency. The meat industry represents one of the most advanced
4. The Decision Layer: This layer supports autonomous applications of CPSs due to the high variability of raw materials
decision-making through technologies such as predictive in shape, size, color, and texture. According to Frontiers in
maintenance, automatic sorting, and real-time process Robotics and AI (2025), companies such as Tyson Foods
control, allowing machines to instantly adapt operations utilize Computer Vision and AI-integrated robotic systems for
based on real production conditions. meat cutting and sorting. These systems analyze fat content,

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