This white paper describes 15 high impacted AI use cases in manufacturing.

1. The Role of Artificial Intelligence in Manufacturing
High Impacted AI Use Cases
A Point of view by

2. According to the World Economic Forum, the global manufacturing sector could be one of the
sectors most influenced by the latest technological trends like AI, machine learning and IoT -
collectively termed Industry 4.0 - with great potential for disruption and transformation if these
technologies are employed intelligently
While AI has proven to be one of the most broadly disruptive technologies of the digital
revolution, it best maximizes its potential when deployed in conjunction with two augmentative
domains - robotics and Internet of Everything (IoE)
Robotics has become an integral part of the manufacturing sector over the past two decades
and the finesse, complexity and sophistication of robotic tasks have been significantly enhanced
via AI. Tasks which were previously relegated to the human domain due to complexity and labor
constraints are now routinely completed by robots.
As for IOE, the ease of deployment and advanced capabilities of sensors allow for the
universalization of AI in the manufacturing sector. Because sensors collect data continuously and
can be placed nearly anywhere, manufacturers can expect to increase productivity, connectivity
and scalability as IOE becomes more engrained in the sector.
The question for manufacturers remains, where in the operation is AI most applicable? We will
outline a list of 15use cases across 7segments.

3. Predictive and Preventive Maintenance
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Use Case
Real-time alerts for wear, tear, faults,
breakdowns and even fatigue
Lifespan prediction for machine assets
which helps with capex planning
More informed asset maintenance schedule triggering and
MRO schedule optimization which helps coordinate effort,
cost and quality across assets
AI has transformed the maintenance process from reactive to preventive through AI-enabled, data driven, predictive
capabilities. Globally, a whopping $647 billion is lost each year in industrial asset downtime per the International Society of
Automation The preventive maintenance process is driven by real-time information feeds connected to AI engines, which
are themselves fed by data gathered from sensors embedded in machines and IOT-enabled devices. The industrial internet
of things (IIOT), armed with sensors and in conjunction with AI is reducing downtime in below ways:
Use Case
Use Case

4. Enhanced Robot Effectiveness
Robots have gone mainstream in manufacturing facilities but
in conjunction with AI, robotic task handling is enhanced
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AI facilitates better human-robot
interaction allowing more effective
utilization of robots.“Cobots” are
emerging due to this increasingly AI-
enabled capability
AI with more powerful software allows
robots to perform a wider range of
tasks that require more complexity
Use Case
Use Case

5. Real-time tracking of supply vehicles improves fleet utilization, logistics
planning and scheduling, which all contribute to improved production
scheduling
Inventory analysis powered by AI can help lower holding costs and
improve procurement schedules
Shipping and delivery lead time can be predicted and optimized by
applying AI algorithms
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AI combined with IoT has tremendous potential to give manufacturers greater visibility into
their supply chains with potential to increase productivity and improve planning
Manufacturing Supply Chain

6. Design Disruption
AI increasingly figures into more creative tasks like art and music
creation. Related use cases in manufacturing are becoming more
tangible every day
AI-based generative design is already in use by large design houses
in auto manufacturing, for example. Aerospace manufacturers use
AI to enable more creative machining and part designs limited by
human designers and their limited ability to iterate
Use Case
9

7. Quality Management and Improvement
BOptions
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Quality management and improvement in manufacturing processes is
increasingly data driven. AI can be an asset in the following ways:
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AI can help understand limitations,
shortcomings or deficiencies of current
manufacturing quality processes and data
mined from these processes can be
harnessed for many improvement
opportunities
AI-powered scanning and
recognition technologies
can find defects in
products and increase end-
product quality

8. 12
Process simulation using
AI to identify “what if”
scenarios which helps
manufacturers realize
implications of
configuration, design or
process changes
Digital twins are computerized replicas of physical assets that in the manufacturing context
can be useful for examining how an IoT device operates and behaves throughout its lifespan.
Use cases of digital twins include:
AI-wired exception
management processes
remove the onus from
humans and allow these
actions to be routed to and
automated by a computer
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Design and
manufacturing
feasibility tests
can be conducted
and automated
using digital
twins
Digital Twin

9. AI can help manufacturers design, test and
manufacture products with a high level of
customization. With AI-driven BTO models,
companies can swiftly adapt to changing customer
needs
Use Case 15
Mass Customization and N=1
AI can help product managers monitor their customers more closely
and tailor offerings to their exact specifications

10. “Industry 4.0” isn’t a
fleeting fad. We can
safely say that AI, in
conjunction with
robotics and IoT
technology, is a major
disruptive trend in
manufacturing with
loads of opportunity
for those willing to
embrace these trends
intelligently
Closing Thoughts

11. ABOUT US
Established in 2009 by proven executives with strong leadership and deep expertise in the
global IT services industry.
Global Presence with offices in the USA, Canada, Europe, India
Having partnered with SAP & Informatica Canopy One offers its clients the flexibility to build a
business that works best for them.
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