A presentation on the Industry 5.0 evolution which builds upon Industry 4.0 and Society 5.0 to reintroduce the lost social, environment and human dimensions.
2. Basic training rules
• Breaks
• Cell phones – please mute or turn off
• Questions – feel free to ask anytime
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3. The 4th Industrial Revolution
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What does Powered by Industry 4.0 mean?
• The concept of standing on the shoulders of Industry 4.0
o An inter-connected world driven by real-time data
o Data is collected from everything and everywhere
o Data drives decision making, automation and controls
processes
o Automated processes and integrated value chains reduce
costs and increase profit
o Smart Factories using interconnected cyberphysical
systems to produce smart products
• For Business - Industry 4.0 delivers the potential for growth,
revenue and capturing greater market share
• For Governments
o Industry 4.0 adds to a country’s industrial contribution to
GDP
o Industry has the potential to re-industrialize through the
on-shoring of manufacturing
4. The Connected Factory
Technologies such as Wireless Sensor Networks (WSN) enable
machine-to-machine communication (M2M), which interact and
“talk” to each other in a digital way and Cyber-Physical Systems
(CPS), which merge the physical world with the digital world.
Big Data, which is information that surfaces patterns hidden
within the data, which in return can be translated into new
business opportunities for organizations’.
The Big Data, that these sensors generate, enables IoT to bring
value through real-time data analysis and improving decision-
making for organizations.
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5. Business Drivers of Industry 4.0
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Industry 4.0 benefits manufacturing and industrial processes that have
resulted in huge improvements in operational efficiency, lower costs and
higher profits
It achieves this from a business perspective by:
• Changing the emphasis from products to a service - buy light not light
bulbs
• Addressing the service paradigm – extract value through data and
business intelligence
• Delivering value from data as a product – value added service
• Creates new services for profit and business agility – X as a Service
• Designs new data collection and simulation models to reduce
operational costs
• Encourages fast prototyping and modeling to lower time to market
• Collects data from everywhere for better informed decision making
• Data analysis drive logistics and supply chain efficiency (lower
inventory, less waste, improved time to profit)
6. Core Industry 4.0 Technologies
Machine-2-Machine operation (Machine learning)
Cyber Physical Machines (Interoperability of data, robots, and
computers)
Intelligent systems – self learning machines, self calibrating processes
with fix-before-break operation)
Intelligent products – smart products that know what they are, how
they are to be made, and their history
Deterministic networks – essential for real-time manufacturing
Deterministic feedback and control
Big Data collection (Cloud & Edge deployments)
Digital Twins (clones of machines or processes used for analysis,
modeling and testing)
Real time and batch analysis – streaming and in-memory analytics to
enhance process control and gain insights into operational efficiencies
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7. Technology Enablers of Industry 4.0
Some of the key technologies that enable Industrial 4.0 are very new but
others have been around for a long time, it’s their current state of maturity
and our ability to interconnect them that enables Industry 4.0 today.
Some key technologies are:
• Industrial Internet of Things
• Cyber Physical Systems
• Cloud – provides global reach and near infinite resources (newish)
• Advanced Robotics –high speed, accurate and tireless
• Additive Manufacturing (3D Printing) – P.o.C, remote assembly
• Big Data – provides for storage and data handling methods at vast scale
• Machine Learning - provides the advanced algorithms we need to make
sense of machine-tool data
• Artificial Intelligence – delivers the means for predictive and advanced
analysis of machine-tool data
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8. IoT, Big Data & Cyberphysical systems
What are the big 3 tech enablers?
IoT – is the interconnectivity of smart devices in an industrial context this means a
networking of sensors, actuators and controllers with human operated devices.
Big Data – the 3Vs but larger, more complex data sets, originating from new data
sources. These data sets are so voluminous that traditional data processing software
just can't manage them.
Cyberphysical systems - these are systems of collaborating computer
controlled machines which are connected with their surrounding physical world and
its processes, providing and using, at the same time, data-accessing and data-
processing services from the IoT.
Where are they used?
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9. The Connected World
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The Internet of Things, Big Data and Cyberphysical systems play a
vital role in our modern connected world.
Where they combine and collaborate so effectively is at the heart of the 4th
Industrial Revolution or as its become known, Industry 4.0.
IoT is ubiquitous it provides the
connectivity
Big Data is key in all of
these domains
Cyberphysical systems
live here!
10. Principles of Industry 4.0
The 4 Design Principles of Industry 4.0
1. Interoperability
The ability of machines, devices, sensors, and people to connect and communicate with each other via the Internet of Things (IoT)
and then make use of that information to function and execute improvements.
The next step within interoperability is to integrate this data with your LMS, MES, ERP, or other smart factory solution and analyze
the data in real-time. This principle dwells on the technology's ability to provide enhanced information for future decision-making.
2. Information Transparency
Information Transparency is an essential design principle of Industry 4.0 because the information is easy to access, providing a fast
and powerful method to extrapolate knowledge, which helps you monitor processes on the shop floor and allows management to
instantly adjust and optimize for higher efficiency.
3. Technical Assistance
Technical assistance is the ability of cyber-physical systems to support humans by aggregating and visualizing information
comprehensibly so that making informed decisions and solving urgent problems on short notice is simple and effective.
4. Decentralization of Decisions
The decentralization of decisions stems from the ability of cyber-physical systems to make choices independent of people.
Naturally, this leads to machines and systems that can take action and perform their tasks with little to no human intervention,
making factors like problem-solving, calibration, adjustments, and notifications a fast and autonomous system. Only in the case of
exceptions, interferences, or conflicting goals are tasks delegated to a higher level. A decentralized system is also highly adaptable
and scalable which determines how efficiently you can respond to industry changes.
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11. Goals of Industry 4.0
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The key objective of Industry 4.0 is to be faster, more efficient, and customer-
centric and to discover new business opportunities and models.
12. The Economical and Social Impacts
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• 4IR helps governments foster an open,
flexible, knowledge- and skills-based
economy,
• 4IR promotes trade outside traditional
trading blocs, improves efficiency and
effectiveness of health and social care
systems
• 4IR offers a “first mover” advantage in
defense and security sectors for those that
make best use of emerging technologies.
Forecasted increased in GDP 2015 to 2030: The United States
will probably reach the most significant benefits (7.1 billion
USD), then China (1.8 billion USD), Germany (700 billion USD)
and Great Britain (531 billion USD) (Petrillo et al., 2018)
Industry 4.0 will provide other tangible benefits and
impact policy-makers thinking as:
13. The Real Economic and Social Factors
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Governments need to be aware that change will be inevitable
and action require to address:
• (GDP, level of investment, consumption, employment,
trade, inflation, and other macro factors)
• Computer models suggest that 47 percent of
current U.K. jobs will be at risk, which presents
a massive social challenge.
• What role should government play in managing the
inevitable turbulence 4IR could bring?
• How do policy-makers protect employment,
o regulation and taxation of technology?
o A guaranteed basic income for all?
Yet, still be Open for Business
14. Adoption of Industry 4.0
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The key reason organizations adopt
Industry 4.0 are not always aligned to
all of the objectives. In this survey by
Jitterbug the key reason are biased
towards Processes Improvement with
little interest it seems in the Strategic
or Organizational goals.
Industry 4.0 has focused less on the
original principles of social fairness and
sustainability, and more on efficiency
and flexibility of production.
The concept of Industry 5.0 provides a
different approach and aims to
Reimagine Industry 4.0 with a focus on
Humans, Society and the Environment.
15. Introducing the Industry 5.0 Evolution
Industry 5.0 complements and extends Industry 4.0. It reaffirms the
environmental and social factors and not just economic or
technological, by framing how industry and emerging societal trends
and needs can co-exist.
By developing innovative technologies in a human-centric way,
Industry 5.0 can support and empower, rather than replace, workers;
we increase industries’ resilience and make it more sustainable.
Greening the economy will require that industry takes a strong
leadership role. Industry 5.0’s environmental goals can be achieved by
incorporating new technologies and rethinking the production
processes in respect to the environmental impacts.
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16. An Industry 4.0 reprise - Industry 5.0
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Industries have a responsibility in providing solutions to challenges for society including the
preservation of resources, climate change and social stability.
Hence, Industry 5.0 has goals beyond just process efficiency and productivity and reinforces the
role and the contribution of industry to society.
Therefore, I5.0 takes a predominantly humancentric stance by placing the worker at the centre of
the production process and uses emerging technologies to provide prosperity while respecting
the production limits of the planet.
As such, Industry 5.0 brings benefits for industry, for workers and for society.
• It empowers workers, as well as addresses the evolving skills and training needs of employees.
It increases the competitiveness of industry and helps attract the best talents.
• It is good for our planet as it favours circular production models and support technologies that
make the use of natural resources more efficient.
• Revising existing value chains and energy consumption practices can also make industries
more resilient against external shocks, such as Covid-19 crisis.
17. Industry 5.0
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Adopting Industry 4.0 as a purely profit-driven initiative is increasingly untenable.
In a globalized world, a narrow focus on profit fails to account correctly for
environmental and societal costs and benefits.
For industry 5.0 to address any of I4.0’s shortcomings it must include social, environmental
and societal factors. This includes responsible innovation, that increases prosperity for all
involved: investors, workers, consumers, society, and the environment.
18. Industry 5.0 technologies
Industry 5.0 identifies the following six enabling technologies;
1 - Individualized human-machine interaction technologies that interconnect and
combine the strengths of humans and machines.
2 - Bio-inspired technologies and smart materials that allow materials with
embedded sensors and enhanced features while being recyclable.
3 - Digital Twins and simulation to model entire systems.
4 - Data transmission, storage, and analysis technologies that are able to handle
data and system interoperability.
5 - Artificial Intelligence to detect, for example, causalities in complex, dynamic
systems, leading to actionable intelligence.
6 - Technologies for energy efficiency, renewables, storage and autonomy
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19. I5.0 Enabling Technologies
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Bio-inspired technologies and
Smart materials
• Recyclable
• Lightweight
• Self-healing/Self-repairing
Human Machine Interaction
• Multi-lingual speech recognition
• Tracking of employees’ physical or mental stress
• Cobots
• Augmented reality
• Enhanced human physical capabilities – exoskeletons, etc.
• Enhanced cognitive human capabilities – decision support systems,
Digital Twins
• Virtual simulation of products and processes
• Multi-scale simulation for modelling products and processes
• Simulation of impact on environment and society
20. Industry 5.0 – Enabling Technologies
Advanced Cyber Physical Systems
Smart Additive Manufacturing (SAM)
Machine Learning
AI Advanced Algorithms
5G Communications
Advanced Blockchains
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21. Wireless Connectivity – 5G
The introduction of 5G into the manufacturing workspace will be potentially huge
for Industry 4.0.
5G itself is a vast game-changer as it bring huge capacity, bandwidth, low-power
consumption and very low latency.
• Up to 10Gbps data rate - > 10 to 100x speed improvement over 4G and
4.5G networks
• 1-millisecond latency
• 1000x bandwidth per unit area
• Up to 100x number of connected devices per unit area (compared with
4G LTE)
• 99.999% availability
• 100% coverage
• 90% reduction in network energy usage
• Up to 10-year battery life for low power IoT device
Private 5G Networks – Manufacturers can run their own private 5G networks to
provide guaranteed spectrum, coverage and security.
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22. Private 5G Networks
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A private network is an enterprise-dedicated
network that provides communication
connections to people or things belonging to a
specific enterprise and provides specific services
necessary for the business of the enterprise.
The enterprise operates its own network and uses it exclusively. Unlike public networ
only allowed people and devices can access this network, and data generated within
the enterprise is processed locally only within the enterprise's dedicated network,
ensuring high security and data privacy.
23. Private 5G Deployment Models
Two basic types of 5G Private Networks:
• Dedicated, on-premises networks. An enterprise
deploys a dedicated, on-premises network
(radio access network and core) that is purpose-built
for the sole use of a single enterprise. The enterprise
deploys its own edge computing assets.
• Hybrid networks. The network is based on a
combination of public mobile network components and
dedicated on-premises elements. For example, a slice of the public radio network may be combined
with a dedicated on-premises core network.
Spectrum. There are four main types of spectrum,
• Industrial spectrum.
• Shared spectrum.
• Public spectrum. This approach uses a mobile network operator’s public network spectrum to
support enterprises. Operators lease their spectrum to enterprises for a fee.
• Unlicensed spectrum. Unlicensed spectrum bands are designated by regulators, are non-exclusive
and free-to-use, but are are making 6GHz licence-exempt spectrum available for 5G and Wi-Fi use in
some countries (such as the USA).
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24. Private 5G Networks: Organizations
Why are organizations using private LTE/5G networks?
The demand-side factors for private LTE/5G networks include the following:
• Operational efficiency. The demand for private LTE/5G networks is growing because large organizations' digital
transformation programs are underway. Enterprises are in the process of digitizing their data and using it to drive
processes and create new digital products and services.
• IT and OT convergence. The convergence of IT and OT is also a key consideration. Ultimately, the need for high-
bandwidth, low-latency networks to support increased automation will grow as enterprise data processing
requirements increase.
• Data privacy. Enterprises deploy private networks because data privacy is a key concern. They require more control
and visibility of their data.
• Cable substitution. Enterprises deploy private LTE/5G networks to support new applications as a more cost-effective
alternative to extending their fixed networks.
• Replacing legacy networks. Existing networks such as TETRA are reaching the end of their life and cellular
technologies offer viable alternatives.
• Wi-Fi limitations. Enterprises have used Wi-Fi successfully but have found that it has limitations in terms of
supporting mobility and/or other factors such as reliability.
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25. Advanced Robotics
Robots were once considered fit only for dangerous, dirty and dull
work. Today the typical applications of industrial robots include
welding, painting, ironing, assembly, pick and place, palletizing,
product inspection, and testing, all accomplished with high endurance,
speed, and precision.
Three types of robots:
• Industrial arm robots:
o 6-axis arms used for repetitive or potentially dangerous tasks
o SCARA robots
o Delta or Spider robots
• Collaborative robots: designed to work alongside humans to
perform the dull repetitive tasks
• Software robots: used for the automation of reading forms and
data entry
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26. Industrial Robots
Industrial robots are typically large, fixed equipment designed for high-volume,
extremely high-accuracy, and high-speed production.
A serial robot arm can be described as a chain of links that are moved by joints which
are actuated by motors. An end-effector, also called a robot hand, can be attached to
the end of the chain. As other robotic mechanisms, robot arms are typically classified
in terms of the number of degrees of freedom.
Typical industrial use cases:
• Assembly and Handling
• Welding and Cutting
• Packaging and Palletizing
• Painting and Dispensing
Industrial robots can present safety risks to human workers, so they usually require
safety measures such as a cage to keep humans out of the robot’s work envelope.
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27. 6-axis Robotic Arms
The number of axes corresponds to the number of ‘joints’ – or points – along
the arm where it can bend or twist. Load capacities for industrial 6-Axis arms
have the largest range, anywhere from small 3 kg units to monster 1000 kg
systems.
• Strengths: Very flexible and can mimic the motion
of a human arm. Very good at reaching in and around objects.
• Weaknesses: Can be more compliant and a little slower
than other configurations due to the nature of the design.
Number of Axes: Six, usually, but there is a new “snake”
variant robot that adds a 7th axis that gives the system
an even better ability to reach in and around obstacles.
Typical Load Capacities: 3-600 kg
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28. SCARA Robots
The SCARA or Selective Compliant Assembly Robot Arm is your high speed work horse,
but they aren’t limited to assembly or pick-and-place applications such as building
mobile phones. They can be very useful in applications such as dispensing, where you
need to precisely follow a path at constant speeds
while dispensing things like adhesives.
• Strengths: High Speed and very rigid with very good
repeatability.
• Weakness: Available work area can be limited and
not suited for manipulating objects in a vertical plane.
• Number of Axes: Three or Four – the fourth is
determined if you need a wrist or twist axis about the Z-Axis (vertical).
• Typical Load Capacities: 1-20 kg
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29. Delta Robots
Also referred to as “Spider Robots”, Delta Robots are one of the latest entrants into
the main stream industrial robot world. They are best suited for super high speed pick-
and-place applications with relatively light loads. Added vision technologies allow
Deltas to distinguish and select different size, colour, or shape options and pick and
place based on a programmed pattern.
• Strengths: Very High Speed.
• Weakness: Available work area can be limited in the vertical plane at the extents of
its reach and it’s not suited for manipulating objects in a vertical plane. Due to the
duty cycle requirements, there can be significant mechanical maintenance
required.
• Number of Axes: Three or Four – the fourth is determined if you need a wrist or
twist axis.
• Typical Load Capacities: 1-3 kg
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30. Collaborative Robots
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Cobots are ideal for manufacturers with low-volume, high-mix production or who
need to safely automate processes alongside human workers. That might include
automating a repetitive task and handing a part off to a human for inspection or to
complete a complex decision-based assembly process
Cobots typically have lower upfront costs and are easy to program with no previous
experience, so they offer fast ROI. They are small and lightweight enough that they
can be easily moved and redeployed to automate different processes throughout a
manufacturing facility.
Collaborative Robots are affordable, safe, highly
adaptable, easily programed or hand trained and
have interchangeable end of arm tooling (EoAT).
This makes them best suited for working along side
humans in a shared workspace or for providing assistance to an industrial robot in a
cage.
31. Autonomous Mobile Robots
Collaborative robots have been an important development in the robotics industry -
the first automation technology that allows safe operation directly alongside human
workers.
Autonomous Mobile Robots
Mobile robots are currently seen as the work-horses of ‘Industry 4.0' and their
adoption rate into production processes is expected to increase in the next few years.
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32. Robotic Exoskeletons
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Exoskeletons are increasingly being
used by manufacturing workers.
The primary benefit of the robotic exoskeleton
is to prevent muscle fatigue.
Research has shown that when using this
technology muscle activity in the back,
shoulder and knees drops by 50%. If muscle
activities drop that means the risk of muscle
injury is less.
• Workers are more productive,
• Insurance costs are lower,
• Less workdays lost to injury.
• There's less cost and more productivity.
SuitX's are now being tested by car
manufacturers General Motors and
Fiat.
Labour-intensive industries like manufacturing and
agriculture have always depended on a workforce that must
endure a certain level of physical exhaustion and risk.
33. Teaching Robots in the Virtual World
In the IoT robots are cyberphysical systems as they straddle the real and the virtual worlds.
They learn through interacting with the real world and through ML and AI enhanced big
data in the virtual world. As industrial robotics become more and more advanced and
traditional factories are upgraded to smart factories, the amount of work and expertise that
goes into training these robotics arms will increase commensurately.
• Teach Pendant: an operator using a teaching pendant can slow down the equipment so
that they can plot the movements of the robot to accommodate the change in
procedure.
• Programming by Demonstration: as with the teach pendant, the operator has the ability
to “show” the robot, with a high degree of precision, a series of new movements and
store that information into the robot’s computer.
• Offline Simulation: A danger to teaching the robot is the downtime so offline simulation
is an attractive alternative. The movements an travel can be analyzed and plotted using
a computer and the resulting code downloaded to the robot.
• Machine Learning: have an operator show the robot how to perform a particular task
and then allow the robot to analyze that information to determine the most efficient
sequence of motions that need to be completed in order to replicate the task. As the
robot learns the task, it has the opportunity to discover new ways to improve the way in
which the task is performed.
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34. Robot Process Automation
RPA: “RPA tools are the ‘Babel fish’ of the technology world as they can interact
with any type of system.”
The ideal task for RPA is predictable and repetitive. RPA is not new – it shares its
origins with rules engines and other basic business and workflow automation
tools. However, RPA developed to solve a specific problem: systems that were
not, and could not easily be, connected.
RPA sets out to free human operators from repetitive tasks, and to boost
efficiency and accuracy. It is especially useful where data needs to move between
disconnected applications. When teamed with ML, AI and Sentiment Analysis
Algorithms it can learn how systems are interconnected and the required flow of
data which can lead to developing smart products.
Its used in industries such as financial services, telecoms, government and
healthcare – businesses with rules-based processes that involve rekeying data.
More recently, the technology has gained ground in manufacturing and logistics.
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35. SAM
One of the prominent features of Industry 5:0 is additive manufacturing referred as
3D printing which is applied to make manufacturing products more
sustainable.
Additive manufacturing in Industry 4.0 focused on customer satisfaction by including
benefits in products and other services. It also facilitates transparency,
interoperability, automation and practicable insights.
On Industry 5.0 SAM defines the various processes in which the component to be
manufactured is developed by adding materials and the development is executed in
various layers.
SAM has capability to save energy resources, helps to reduce material and
resource
consumption which leads to pollution free environmental production.
To obtain the complete benefits of Industry 5:0, SAM is merged with integrated
automation
capability to streamline the processes involved in supply chain management and
reduces the delivery time of the products.
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36. 4D Printing
4D printing is the process through which a 3D printed object
transforms itself into another structure over the influence of
external energy input as temperature, light or other
environmental stimuli.
3D Printing is about repeating a 2D structure, layer by layer in a
print path, from the bottom to the top, layer by layer until a 3D
volume is created. 4D Printing is referred to as 3D printing
transforming over time. Thus, a fourth dimension is added: time.
So, the big breakthrough about 4D Printing over 3D Printing
technology is its ability to change shape over time.
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37. 5D Printing
In 5D the print head & the printable object have five degrees of
freedom. Instead of the flat layer, it produces curved layers. The
main advantage of this technology is to create a part with a
curved layer with improved strength.
Instead of 3 axes used in 3D printing, 5D printing technologies
use five-axis printing technique which produces objects in
multiple dimensions. In this five-axis printing, the print bed can
move back and forth on two axes besides of X, Y and Z axis of the
3D printing technologies. Thus this technology is highly capable
of producing stronger products in comparison to parts made
through 3D printing.
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38. Banking 4.0
Banking 4.0
The future of banking is not about, payment and credit utility—
it's embedded in voice-based smart assistants like Alexa and Siri,
available 24/7 to pay, book, transact or enquire.
Bank 4.0 means that either your bank is embedded in your
world, or it isn’t.
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With the advent of Industry 5.0 Fintech Hubs will increasingly
interconnect with each other to become ‘Smart Digital Fintech
Hubs’. This new age digital infrastructure will have the power to
assist in building a new digital financial system.
39. Cryptocurrency
Cryptocurrency has made tremendous inroads into everyday life since
Industry 4.0 was first introduced in 2011. As a form of currency it is still
in its infancy due to its volatility. But any decentralized payment
system can only be a good thing.
• For the full potential of Industry 4.0 to be realized and create
significant global value the development of an open and global
payment protocol is required
• The potential of cryptocurrency performs frictionless and
transparent financial transactions, without the intervention of
intermediaries
• Cryptocurrency and blockchain technology are ideal for
implementing the shift to the global, trustless, and open new
economy.
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40. Blockchain
Whereas cryptocurrency may be counter to the objectives of Industry 5.0. The
underpinning technology is still attractive as Data collected by IoT-based sensors can
be transmitted through a blockchain-based tamper-proof ledger. The use of
encryption algorithms ensure that information remains secure, while the
decentralized nature of the network eliminates single points of failure.
Blockchain is already being used for asset tracking applications in various industries.
By employing blockchain together with IoT, manufacturers will be able to streamline
how they aggregate, store and share data with partners across the supply chain.
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41. Artificial Intelligence in Industry 5.0
Manufacturing is responsible for a significant part of the worldwide energy consumption and
artificial intelligence has an enormous potential to benefit environmental sustainability and pave
the way to a more eco-friendly and energy-efficient manufacturing.
Artificial intelligence can solve a number of issues that are critical for sustainable manufacturing.
This includes:
• excessive use of materials,
• redundant production of scrap waste,
• inefficient supply chain management, logistics and
• unequal distribution of energy resources.
AI can eradicate all of these difficulties.
Artificial intelligence is an advanced technology that has the potential to fundamentally transform
the manufacturing industry and create unparalleled working opportunities within the “missing
middle” and forge the path towards smart, efficient and sustainable manufacturing.
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42. AI in Industry 5.0: Product Development
Generative Design for Manufacturing
It is a design branch focused on the union of human creativity with
artificial intelligence. By selecting parameters such as weight, size,
materials, manufacturing and operating conditions with generative
design software, engineers can generate different design solutions in a
short time and AI facilitates all possible design options for a given
product.
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43. Machine Learning in Industry 5.0
Manufacturing companies are committed to understanding market
trends, changes, and finding applications to remain competitive. ML
facilitates compliance with industry regulations and standards, improve
safety and address environmental concerns.
The intelligence derived from the analysis and monitoring of real-time
data is essential to generate profitable and sustainable solutions.
Avoiding bottlenecks within production chains is not a chimaera since it
is possible to visualize the processes at all times.
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44. ML in Industry 5.0
Machine Learning facilitates predictive maintenance, anticipating equipment failures,
scheduling maintenance at the right time, and reducing unnecessary downtime.
The ML models that depend on the objective or approach of the prediction that is sought
are;
1. RUL (Remaining Useful Life) models: Regression models to predict the remaining useful
life.
1. Historical and statistical data are used to predict how many days until a failure
occurs.
2. Classification models to predict a failure in a defined period.
1. Used to define a model that predicts failures within a defined number of days.
3. Anomaly detection model to identify items with potential problems.
1. The approach predicts failures by comparing and identifying differences between
normal system behaviour and failure events.
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45. ML – I5.0: Digital Twins
Digital Twins provide real-time diagnostics and evaluations of the production,
the performance and monitoring prediction, and the visualization process of all
kinds of key parameters.
To generate the models that understand the physical systems, machine
learning unsupervised algorithms are used. As the data is processed, these
algorithms look for patterns of behaviour and detect anomalies. They also can
process external data, such as research, industry data, social media, and
media.
Digital Twins are a tool not only applicable for product design, but also for
simulating the performance of existing physical products.
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46. ML-I5.0: Quality Control
Machine Learning can be applied for product inspection and quality
controls. ML-based algorithms learn from historical data that distinguish
good products from those with defects, thus automating the inspection
and supervision process.
Deep learning architectures, such as convolutional neural networks
detect visual clues indicative of quality problems in products and parts in
highly complex assembly processes. The advantage of this branch of
machine learning is that it is much more scalable through image learning
and object detection.
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47. ML in I5.0: Quality Improvement
Customers expects flawless products, as defective products cause non-
conformities that damage the reputation of the company and its profit
margins. ML can foresee quality problems right down the production
line.
Artificial vision is an example of a ML solution, in which high-resolution
cameras are used to monitor defects. This can be combined with a
cloud-based data processing framework to generate an automated
response.
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48. ML in Logistics and Inventory Management can address 9 key
pain points:
1. Predicative Analysis
2. Automated Quality Inspection
3. Real-time Visibility
4. Streaming Production Planning
5. Reduce Cost and Response Time
6. Warehouse Management
7. Reduce Forecasting Errors
8. Advanced Last-mile Tracking
9. Fraud Detection and Prevention
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ML in I5.0: Logistics & Inventory Mgmt
49. AI & ML in I5.0: Sustainability Issues
Sustainable manufacturing covers the three basic elements involved in
manufacturing i.e., processes, products and systems which enables economic
growth and sustainable value creation in industries.
To ensure sustainability in manufacturing these three elements must
individually demonstrate the benefits at the social, economic and
environmental levels.
Sustainable manufacturing can be described as the integration of systems and
various processes to produce a high quality of products with minimum
resource utilization, sustainable resources, being safer for customers,
employees and communities
In Industry 5.0 it is in the interest of small and medium-sized businesses to
pay attention to sustainability and environmental issues. For example,
information that arises during the production process could be used to
improve the energy consumption and capacity of machines. The positive
effect for the environment then stems from the reduction in greenhouse gas
emissions.
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50. Jumping the Gap from I3.0 to I5.0
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• Digitally transform the business and its processes and culture
o Digital transformation of the organization is critical to Industry
4.0
o Harvest data from everything and everywhere
• Optimize the Value Chain
o Reimagine the vertical value chain and understand how it
functions
o Reimagine the horizontal value chain and understand its
importance to operational efficiency
• Optimize the value of Supply Chain 4.0
• Optimize Quality Improvement and Lean thinking to reduce waste
• Optimize processes through continual improvement methods
• Automate processes where feasible
• Optimize operational efficiency and reduce maintenance costs and
downtime
Today, despite deploying advanced robots and the latest technologies most
manufacturers remain at 3.0 level. This is because to travel to Industry 4.0
and beyond we need to understand the importance of the value chain and
how we derive profit from efficiency. Consider the following prerequisites:
51. Getting from Industry 3.0 to Industry 5.0
Starting out on a transformation to industry 5.0 will require
mastering 4.0 first and to do that you will need to fulfill some
prerequisites.
• Determining existing shop floor technologies
• Integrating legacy and remerging technologies
• Operations Technology and Information Technology
convergence
• Vertical and horizontal Integration
• Systems Integration
• Identifying targets and goals
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52. …legacy technologies
It’s not just about robots and big data you still have all the legacy
technologies;
• Data communication/network technologies,
• Human Machine Interfaces (HMI) and SCADA,
• Manufacturing Execution Systems (MES),
• Enterprise Resource Planning (ERP, becoming i-ERP),
• Programmable Logic Controllers (PLC), sensors and actuators,
• Micro Electrical Mechanical Systems (MEMS) and transducers
• and all those innovative data exchange models
These all play a key role in connecting the workplace.
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53. Bridging the OT/IT divide
• Operations Technology - monitors and manages industrial process assets and
manufacturing/industrial equipment.
• Information Technology - the study or use of systems (especially computers and
telecommunications) for storing, retrieving, and sending information.
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Identify IT/OT Convergence Points: Every factory floor has its crossover points from OT to IT and
vice versa. Thus, it is essential to classify processes and map them appropriately.
54. Blurring the lines between OT/IT
For the most part, IT and OT have constituted completely different and disjoint aspects of
an organization’s infrastructure. But combining operational and enterprise information is
the secret ingredient for manufacturing excellence. It makes success repeatable and
scalable.
Since the advent of industry 4.0, there is an increasingly converging IT-OT pattern that is
changing how an organization’s infrastructure functions. Assets like assembly-line
machinery that have previously been offline are being brought online by the power of IoT.
• Minimizing unplanned downtime using predictive maintenance
• Better decision making with decision support systems
• Use of wireless technology in an operative environment
• Improvement in critical data management
• Enhanced efficiency and better first-pass yield rates
• Better safety standards in the work environment
Th
There is no playbook that guides successful interoperability and interaction between IT
and OT. This is because no organization has managed to perfect this. Blurring
Lines Between I
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55. Digitization: Harvest the Data
Digitization is all about collecting our manufacturing plant data and transforming it
into useful information which we can use to shape and optimize our processes.
Gathering external information helps us learn about our supply chain and how
customers use our products which leads to us making better and smarter products.
But gathering internal data can be hard. Historically, manufacturing businesses have
collected data manually, through shop floor paper-based systems and processed them
via spreadsheets. There is a vast amount of process data that could be collected to aid
in optimization but accessing it is not trivial.
Collecting manufacturing data allows for the analysis and calculation of essential
manufacturing performance metrics, including downtime, Overall Equipment
Efficiency (OEE), and throughput, while also calling attention to problems such as
equipment malfunctions, stoppages, supply chain quality and customer returns.
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The goal of every manufacturer today should be to optimize (OEE). OEE is the overarching
production efficiency measure that compiles a number of vital manufacturing KPIs into one
objective measure of manufacturing success. With accurate and timely manufacturing data
in hand, manufacturers can make better decisions that will drive OEE up and, as a result,
increase business profitability.
56. Gathering Manufacturing Data
“according to McKinsey & Company’s recent report, Industry 4.0: Capturing Value at
Scale in Discrete Manufacturing. The report shows that, although 68 percent of
companies see Industry 4.0 as a top strategic priority, “only about 30 percent of
companies are capturing value from Industry 4.0 solutions at scale today.”
There are many sources of process data:
• IoT (Internet of Things) sensor integration.
• Line HMI (Human Machine Interface) system integration.
• PLC (Programmable Logic Controller) integration.
• RTU (Remote Terminal Units) integration.
• SCADA (Supervisory Control and Data Acquisition) systems.
• Cyberphysical systems
Extracting data from all of these diverse sources and integrating it into a data lake of
big data is not going to be easy.
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57. Gathering Data from Production
The various types of industrial communication protocols are:
OT Domain
Ethernet/IP
BACnet
Modbus RTU
Modbus TCP
DeviceNet
OPC UA
ProfiNet
ProfiBus
IO-Link
EtherCAT
CC-Link
CAN Open
IEC 61131-3
ASI-Interface
LonWorks
The challenge is to get data from all those sources into a uniformed Namespace for harvesting the Big Data
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IT Domain &
the Internet
Ethernet
59. Digital Transformation
Digital transformation is the transformation of business, industrial products, operations, value chains and services
that are enabled through the augmentation of people, knowledge, and workplaces through the expanded use of
digital technologies.
Digital Transformation is about more than technology it is about:
• People - Digital transformation relies heavily on the knowledge and experience gained from
subject matter experts (SMEs) – the operators, engineers and other workers expertise regarding the
process, as well as business managers and data scientists regards data modeling and business
objectives.
• Processes – Transformation needs a rethinking of existing processes to find end-to-end ways
to meet customer needs, the seamless connection of work activities, and the ability to travel across
silos.
• Technology - It’s about taking newer disruptive technologies and integrating them or even
replacing aging systems, business models and aging processes with connected systems, connected
data, connected operations, and connected supply chains in a connected enterprise.
• Services - transformation is about taking non-digital formats of information and turning
them into digital formats.
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60. DX: Execution in Industry 5.0
To move beyond planning to digital transformation
execution, it is crucial to establish why your organization
needs to change.
• Align Business Goals with the Digital Vision
• Choose the right Data Analytics Capabilities
• Determine the Operational Impacts of DX
• Understand Employee readiness for technology change
• Craft Training to leverage the impact of technology change
Successful Execution Requires you Drive the Change!
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61. Integrating the Value Chain
The Value Chain in Industry 4.0 consists of the Horizontal and the
Vertical but the two concepts centre on technologies, processes,
and systems that enable the collection, collation, communication,
and use of data.
The Vertical Value Chain: is internal to the organization and connects all business units and
processes. With vertical integration, data flows between and is made available to all business
units. This includes the factory floor, marketing, sales, customer service, purchasing, accounting,
HR, quality control, R&D, and more.
The Horizontal Value chain: Horizontal integration applies within your production facility,
and externally across multi-site operations, and even extends to third-party partners in your
supply chain, both upstream and downstream.
Within your production facility, horizontal integration is about achieving the Smart Factory, where
all systems, processes, and machines are connected, enabling constant communication.
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62. Supply Chain 4.0
“Supply Chain 4.0 - Is the application of the Internet of Things,
the use of advanced robotics, and the application of advanced
analytics of big data in supply chain management: place sensors
in everything, create networks everywhere, automate anything,
and analyze everything to significantly improve performance and
customer satisfaction"
• Horizontal Value Chain integration with Industry 4.0 involves connecting all
parts of your supply chain to the manufacturing plant. This deeper alignment
improves visibility, flexibility, and productivity while also enhancing levels of
automation.
• The supply chain cloud forms the next level of collaboration in the supply
chain between customers, the company, and suppliers, providing either a
shared logistics infrastructure or even joint planning solutions.
• Especially in noncompetitive relationships, partners can decide to tackle
supply chain tasks together to save admin costs, and also to leverage best
practices and learn from each other.
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63. Quality and Process Improvement
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"Lean" is considered a philosophy of continuous improvement. A lean organization focuses
on increasing customer value, the elimination of waste and optimizing operations.
Lean thinking can provide improved value for the customer by:
• Improving the quality of work processes
• Reducing errors or defects in work processes
• Reducing costs
• Improving flow of the process
• Simplifying complex processes
• Reducing lead time
• Improving employee morale
64. Lean Continuous Improvement
1. Value. Value is always defined by the customer’s needs for a specific product. For example, what is the timeline for
manufacturing and delivery? What is the price point? What are other important requirements or expectations that must be
met? This information is vital for defining value.
2. Value stream. Once the value (end goal) has been determined, the next step is mapping the “value stream,” or all the
steps and processes involved in taking a specific product from raw materials and delivering the final product to the
customer.
3. Flow. After the waste has been removed from the value stream, the next step is to be sure the remaining steps flow
smoothly with no interruptions, delays, or bottlenecks.
“Make the value-creating steps occur in tight sequence so that the product or service will flow smoothly toward the
customer,”
4. Pull. With improved flow, time to market (or time to customer)
can be dramatically improved. This makes it much easier to deliver
products as needed, as in “just in time” manufacturing or delivery.
This means the customer can “pull” the product from you as
needed (often in weeks, instead of months).
5. Perfection. Accomplishing Steps 1-4 is a great start, but the
fifth step is perhaps the most important: making lean thinking and
process improvement part of your corporate culture.
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65. Predictive Maintenance
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Industry 4.0 makes predictive maintenance feasible and significantly reduces
operational expense (OpEx).
Predictive maintenance means businesses can schedule maintenance activities based
on accurate predictions about an asset’s lifetime.
Predictive maintenance offers many business benefits when compared to the
conventional reactive and preventive models:
• Improved Asset Utilization and OEE: With predictive maintenance, enterprises
make the best possible use of their assets and improve their OEE.
• Avoidance of Unscheduled Downtimes: Predictive maintenance provides visibility
on the actual condition of the assets, which minimizes possible unscheduled
downtimes.
• Optimal Planning of Maintenance Activities: Information about the asset’s
conditions and their anticipated EoL can be combined with insights on business
operations (e.g., production schedules, demand forecasts) towards maximizing
revenues and minimizing MRO costs.
67. Industry 4.0 Reference Architecture
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This is Industry and manufacturing today
And this is where we want to be to match
demand from our connected world.
68. Smart Factory Architecture
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IoT provides the connectivity
Big data is produced
and consumed here
Cyberphysical systems
connect to both worlds
69. Smart Factory Use-cases
• Remote Monitoring: With IoT-connected assets, you can monitor equipment usage and health in order to
assess performance and deploy service should there be any problems.
• Supply Chain Management and Optimization: Real-time tracking of assets and products, forecasting
greatly improves
• Digital Twins: A virtual, or simulated real-world object, concept, or area within a digital space, digital twins are an
interesting and powerful use case of IoT.
• Real-Time Machine Monitoring: provides a stream of data straight from the machine control to provide
accurate data analytics that can be used for in-the-moment decision making or in-depth analysis.
• Predictive Maintenance: enables manufacturers to get the absolute most out of their maintenance spend
while reducing downtime as much as possible.
• Production Visibility: operators have insight into all their machines with visual dashboards tracking the
performance against production goals.
• Integrating Systems: is that each system can use the valuable data provided by other systems in order to
perform its function in a better way.
• Compiling KPIs: IoT platforms are helping to compile and contextualize data into simplified reports and
dashboards that can quickly explain how well a business is performing.
• Automation: Industrial automation is one of the largest promises of Industry 4.0. as it helps to reduce
downtimes, provide predictable maintenance, and improve decision-making.
• Asset Utilization: one of the most important metrics in manufacturing, OEE gauges how well manufacturers are
using their equipment and allows them to optimize their usage through data analytics
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70. Case Studies
Digitization in action:
Big Data decision-making at Bosch Automotive factory in China
BJC HealthCare adopts IoT for inventory and supply chain
management
Volkswagen creates Automotive Cloud
Fetch Robotics help DHL improve warehouse operations
Developing an automated flight service cart system for New
Doha International Airport / Simulation
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