Page 41 - FoodFocusThailand No.219 June 2024

SPECIAL FOCUS

these processes, AI can improve overall errors and reduced reliance on manual labor. This not only minimizes costs but
production efficiency, product quality, also significantly boosts production efficiency. With automated systems operating
and safety standards within the food tirelessly, manufacturers can achieve continuous production, maximizing output
processing industry. For instance, AI and ultimately driving long-term profitability. However, the future of manufacturing
systems can be trained to identify subtle lies not just in automation, but in the integration of AI and machine learning
defects in food products, such as technologies. Food manufacturing businesses can leverage AI and ML to unlock
blemishes on fruits, which might escape a new level of operational excellence. These technologies can enhance resource
human inspectors. This not only allocation, streamline workflows, and increase overall efficiency. But this is just
improves the overall quality of processed the beginning. As AI and ML continue to evolve, we can expect even greater
beverages but also reduces waste by advancements in areas.
preventing the packaging of flawed
items.
2. IoT monitoring systems utilize
affordable sensors and widespread More Information Service Info C003
connectivity to enable real-time
monitoring throughout the supply chain.
This is particularly crucial for dairy
products, where constant monitoring of
temperature and humidity is essential
to ensure quality and safety. For
instance, sensors installed on trucks or
smart crates can track temperature,
humidity, and location during delivery,
providing detailed information about
product conditions to prevent spoilage.
Additionally, AI-powered condition
monitoring offers a significant advantage
by providing real-time insights into
equipment performance through
continuous analysis of sensor data. This
enables early fault detection, allowing
for proactive interventions before
breakdowns occur and cause costly
downtime.
3. In the study mentioned earlier,
edge computing also plays a crucial
role in cyber-physical systems (CPS)
and the Internet of Things (IoT)
applications within milk production.
Edge computing involves placing
computing resources such as
processing power, storage, and
analytics closer to where data is
generated, rather than relying solely on
centralized data centers. By doing so,
edge computing reduces latency, or the
delay in data processing, and enables
real-time management of large volumes
of IoT data. For beverage manufacturers,
sensors attached to milk tanks on farms
can monitor milk temperature, fill level,
and other vital data. Edge computing
on the farm then processes this data in
real time, triggering alerts for abnormal
conditions, such as temperature
fluctuations that could indicate spoilage.
Additionally, it can optimize milk
collection schedules based on tank fill
levels.
In conclusion, automation in
manufacturing offers a compelling value
proposition. By automating repetitive
tasks, manufacturers achieve consistent
and precise production, leading to fewer

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