Manufacturing high-quality products
at minimum cost is the goal for
most companies, and Industry 4.0
initiatives can get us closer than ever before.
Despite being in varying stages of digitizing
operations, many in the manufacturing
industry are seeing the huge opportunities
these initiatives offer. One of the most talked
about initiatives is artificial intelligence (AI).
Mckinsey’s “State of AI” survey in 2020
reported that 22% of respondents who
adopted AI saw revenue growth of more than
5%, particularly in areas such as finance and
supply chain management.
AI can also bring benefits to manufacturing,
which we’re going to look at in this article.
Improving product quality
Maintaining consistent product quality is
a significant challenge in food and beverage
production. Using machine learning can
maintain higher levels of product quality
overall, while enabling faster quality checks
through visual inspection.
Video and image recognition tools can
detect and analyze products in real-time,
determining whether a product passes the
quality check based on input specifications.
These tools can determine a pass/fail outcome
for a range of needs, such as packaging
fill levels and label placement.
Image recognition tools are more accessible
today, making implementation easier.
Usually, it doesn’t require an overhaul of
current processes, a large-scale installation
within your plant, or significant investment
to get started.
How AI helps with quality:
- Maintains a high accuracy of visual
inspections;
- Detects quality issues in real time;
- Identifies the root cause of quality issues,
thereby improving future production
processes.
More efficient maintenance
Predicting issues in machinery performance
before they arise makes a huge difference to
a manufacturer’s bottom line.
Using sensors and data on past performance
provides the ability to anticipate possible
failures, allowing action to be taken
before equipment fails. For example, using
sensors to monitor machine vibration and
trigger alerts when the vibration range
changes.
Condition-monitoring solutions have
become popular because they simply attach
to the machine and communicate operating
data to the cloud, where it can be analyzed
and used to monitor equipment health, triggering
an alert if abnormal performance is
detected. These types of tools use AI to take
the guesswork out of predicting maintenance
issue and deliver alerts as required, instead of
requiring someone to investigate data logs.
AI can also take sensor data and machine
history to predict when maintenance should
be performed—allowing it to be scheduled
appropriately to minimize breakdowns—delivering cost savings over time.
Integrating data analysis tools can then be
used to track what the ideal production process
looks like (often referred to as a ‘golden
batch’). For example, equipment becoming
too hot can have impacts on the outcome of
the product. Taking that information to build
an ideal ‘temperature range’ for the equipment
means it can be monitored and the
data analysis tool can trigger an alert if the
temperature increases above the ideal range.
How AI helps with maintenance:
- Reduces cost through predictive maintenance
which minimizes unplanned breakdowns
and downtime;
- Recognizes patterns of imperfection or
production anomalies and triggers an
alert when there is an issue;
- Reduces waste due to breakdowns.
Insights from sensor data
Most manufacturing equipment is already
collecting data; it adds value to operations
when you have a way of making sense of it all.
Using sensors to capture and correlate
information relevant to the task, such as temperature
or throughput data, enables process
improvements. The benefit of an AI tool
comes from taking real-time sensor data and
combining it to extract insights and improve
situational awareness.
An integrated AI or machine-learning tool
takes the raw data to begin identifying patterns
and recommending actions to improve
efficiency. For companies operating across
multiple production sites, or with different
shifts, this ability to compare operational conditions
and draw insights is hugely valuable.
With business intelligence solutions in
place, your plant can capture performance
data that AI technologies use to identify patterns.
These solutions allow the capture of a
wider business picture, not just into equipment
but into energy use and efficiency of
the production line. You can also derive more
comprehensive insight into product quality
metrics and begin combining other sources
of data such as customer feedback and supply
chain efficiency.
How AI helps analyze sensor data:
- Extracts patterns and identifies opportunities
from raw data;
- Monitors operational conditions and
allows for adjustments to be made for
optimal production;
- Analyzes the production cycle and identifies
which factors influence output.
All food and beverage organizations can
benefit from reducing operating costs and
reduced risks. Machine learning and AI tools
offer huge promise in this area—from performing
visual inspections to monitoring
essential manufacturing equipment. The ability
to detect quality issues, or even the wrong
packaging on a product, through image recognition
tools can dramatically reduce the risk
of a reputation-damaging (and costly) recall.
