Page 40 - FoodFocusThailand No.242 June 2026

SPECIAL FOCUS

color, texture, and potential contamination
while robotic arms adjust their grip
strength in real time (adaptive gripping),
minimizing product damage and reducing
dependence on manual labor.
In seafood processing, CPSs are
combined with X-ray scanning and
hyperspectral imaging to detect bones,
foreign objects, and freshness levels,
significantly improving food safety and
reducing consumer complaints.
Smart Beverage Manufacturing
Beverage production is a classic
example of continuous-flow manufacturing
where CPSs play a crucial role. Global
companies such as Coca-Cola and Nestlé
have developed smart beverage factory
systems integrating sensors for Brix level
measurement, pH monitoring, flow rate
control, and automated Cleaning-in-Place
(CIP) systems.
These systems are connected to
cloud platforms and AI-based predictive
models that enable real-time quality
control across the entire production These systems monitor crispiness, moisture migration, oil absorption, and
line. Ahmed et al. (2023) found that texture stability in real time. AI-driven baking systems also analyze crust color and
CPSs implementation reduced water product expansion during baking, automatically adjusting oven temperature to
consumption in CIP systems by 20% and reduce defects and energy consumption while ensuring consistent product quality.
significantly decreased energy usage in
pasteurization processes—contributing Digital Twin: The Virtual Core of Smart Food Factories
directly to ESG and carbon reduction Digital Twin Technology is a critical component of CPSs, serving as a real-time
goals. digital replica of physical production systems. It enables continuous monitoring,
Ready-to-Eat Food Manufacturing simulation, and optimization of factory operations. Digital Twins allow manufacturers
Ready-to-eat (RTE) food factories are to perform bottleneck analysis, predictive maintenance, shelf-life forecasting, and
rapidly transitioning toward the concept energy optimization with high accuracy.
of the “Autonomous Factory” due to According to Mescia et al. (2024), Digital Twin implementation can reduce
their need for speed, precision, and high factory commissioning time by more than 30% and significantly lower trial-and-
food safety standards. Key enabling error costs during production setup and optimization.
technologies include Autonomous Mobile
Robots (AMRs), robotic pick-and-place Key Challenges of CPSs in Food Manufacturing
systems, smart cooking platforms, Despite its advantages, CPSs implementation in the food industry faces several
AI-based inspection, and digital twin challenges:
kitchens. 1) Variability of Food Materials: Food products are inherently complex and
In Japan, CPSs are widely deployed inconsistent in shape, moisture content, texture, and dielectric properties, requiring
in bento and ready-meal production highly adaptive AI and robotic systems.
facilities to automate cooking time, 2) Cybersecurity: Modern food factories are increasingly connected through
temperature control, portioning, and IoT, cloud systems, and digital networks across the entire production chain. As a
shelf-life prediction. This allows large- result, the risk of cyberattacks is also rising, particularly attacks targeting production
scale production with high consistency control systems, product formulation data, and supply chain information. Such
and minimal human error. breaches can potentially impact both food safety and overall business continuity.
Sato et al. (2024) reported that CPSs 3) High Technology Costs: The cost of AI systems, robotics, and sensor
adoption in RTE factories reduced human networks remains high, particularly for SMEs that face limitations in both budget
labor by more than 45% and decreased and digital expertise. At the same time, machinery from different vendors often
food waste caused by portion variability lacks seamless data integration due to data interoperability issues, making
by approximately 18%. full-scale integration of factory-wide information systems still incomplete.
Bakery and Frozen Food Industry However, despite these ongoing challenges, CPSs are increasingly becoming
One of the major challenges in frozen a critical infrastructure for the “food factories of the future.” Food manufacturers
and baked foods is maintaining product that can adapt and invest in digital systems at an early stage will gain long-term
quality after thawing, particularly texture, competitive advantages in terms of efficiency, product quality, sustainability, and
crispiness, and moisture stability. CPSs global competitiveness.
technologies are increasingly used
alongside moisture sensors, thermal
imaging systems, AI baking control, and
freeze-thaw prediction models.
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