Digital
Twins in Supply Chains ~ how do we use them and unlock their potential
A brief guide for companies to optimize their supply
chain functions by unlocking the potential of digital twins
Prof Archie D’Souza
(Source: Google)
Since the start of this millennium myriad industries
have started adopting digital twin technology. It’s a revolutionary
technology that has become more accessible and affordable. Still, it remains
underutilized in supply chains, way below the potential it has. Why does this
happen? The main reason has been attributed to the complex nature of supply
chains. However, according to me, the reason is the reluctance of logistics
players to adopt it. Misunderstandings about the technology’s applications,
capabilities, and potential value has been a common excuse. This may be
partially true. What prevents the players from getting experts and starting its
adoption on a pilot basis? I feel that immense benefits across a wide range of
supply chains can be delivered with a proper adoption and implementation
strategy.
Let’s, to begin with, look at what digital twins are. A digital twin is a digital representation of a
physical object, process, service, or environment that behaves and looks like
its counterpart in the real-world. [source: https://www.twi-global.com/technical-knowledge/faqs/what-is-digital-twin], e.g., a
digital replica of an object in the physical world, such as a missile, jet
engine, or computer chip. Items could be tiny or exceptionally large, and could
include buildings, manufacturing plants, townships, or even entire cities. Digital
twin technology can also be used to replicate processes in order to collect
data to predict how they will perform. So, in essence, a digital twin is a
computer programme that uses real world data to create simulations that can
predict how a product or process will perform. The beauty about these programmes
is their ability to integrate IoT, artificial intelligence and software
analytics to enhance the output.
These virtual models have turned out to be a staple
in modern engineering to drive innovation and improve performance. For this we
need to thank the advancement of machine learning and factors such as big data.
Creating such a model can allow the enhancement of strategic technology trends,
besides preventing costly failures in physical objects; additionally, by using
advanced analytical, monitoring, and predictive capabilities, test processes
and services. The prospects are numerous.
Digital Twins – Distinguishing Characteristics
Digital twins, as we’ve seen, are virtual replicas of
physical entities and their interactions. They consist of a combination of
enabling technologies and analytics capabilities. However, very often the
technology is misunderstood. It is assumed by many that digital twins are
themselves sensors, 3D models, simulators, or applications of AI technology.
This assumption is erroneous. Also, some mistakenly consider digital twins to
be largely theoretical and hence, not relevant for supply chain management. It
is also often assumed that a digital twin can be built only after the physical
twin has been created. None of these assumptions is true.
As stated, digital twins are a combination of multiple
enabling technologies, e.g., sensors, cloud computing, AI & advanced
analytics, simulation, visualization, and augmented & virtual reality, to
name some of them. A customised mix of technologies is used depending on the
individual user’s needs and expectations. One of the most distinguishing features
of digital twins is their ability to emulate human capabilities, support critical
decision-making, and even make decisions on behalf of humans, a feature that makes
them so powerful. Digital twins observe their physical environment through a
network of sensors that dynamically gather real-time data. By learning from
this information, they evolve, and it isn’t just the information but also the
contexts. They keep interacting with humans, devices, and other networked
digital twins. These capabilities make them active social tools, it enables
them to continuously communicate and collaborate with their associated physical
and digital objects and also with humans. The technology supports end-to-end
visibility and traceability, so essential in supply chains. Thus, supply chain
practitioners can spot patterns of overly complex and dynamic behaviour.
Another
feature of digital twins is their ability to build nonlinear supply chain
models by overseeing many internal and external moving parts in end-to-end
supply chains. They can thus compute thousands of what-if scenarios. The technology
learns from these decisions and gains in maturity over time. Managers thus can
make faster, more accurate, and better-informed decisions. This obviously has a
long-term impact with the added bonus of considerably lower costs.
Digital
Twins – their Working
Digital twin are virtual models designed to accurately
reflect physical objects. To cite an example, a jet engine is various sensors related
to vital areas of functionality are fitted to it. The fitted sensors produce
data about different aspects of performance of the physical object, such as
energy output, temperature, weather conditions and more. This data is then
relayed to a processing system and applied to the digital copy. Once the data
is obtained, simulations can be run using the virtual model. Not just that,
it’s even possible to study performance issues, and come up with improvements.
Once these valuable insights are obtained, they can be applied back to the
original physical object.
Differences between digital twins & simulations: Are simulations and digital twins the
same? Or are they different? Let’s examine.
Both utilize digital models to replicate a system’s processes. A digital twin being
a virtual environment, is far richer for study. So, what’s the difference? It is
largely a matter of scale. Simulations typically study a particular process. A digital
twin, on the other hand, can itself run several useful simulations to study
multiple processes. That’s not all. Does one
see simulations benefiting from having
real-time data? No! The beauty of digital twins is that they are designed
around a two-way flow of information. A flow that first occurs when object
sensors provide relevant data to the system processor. It happens again when
insights created by the processor are shared back with the original source
object. Digital twins can study more issues from far more vantage points than
standard simulations can perform many tasks. They do this by having better and constantly updated
data related to a wide range of areas. This, combined with the added computing
power that accompanies a virtual environment, gives is greater ultimate
potential to improve products and processes.
Types of digital twins: Digital twins are of different
types depending on the level of product magnification. The area of application is
the biggest difference between these twins. It isn’t uncommon to see different
types of digital twins co-existing within a system or process. Let’s look at some
of these types. This will enable us to learn the differences and how they are
applied.
1. Component twins may be looked at as the basic unit
of digital twin. They are the smallest example of a functioning component.
2. Parts twins pertain to components of slightly
less importance. But for that they are the same as component twins.
3. Asset twins: Two or more components work
together form what is termed as an asset. Asset twins let a person study the
interaction of those components. In the process, they create a wealth of
performance data that one can process and then turn into actionable insights.
4. System or Unit twins are the next level of magnification.
They enable one to see how different assets come together to form an entire
functioning system. These twins provide visibility regarding the interaction of
assets. Therefore, they may suggest performance enhancements.
5. Process twins, the macro level of magnification,
reveal how systems work together to create an entire production facility. They
enable a person to judge whether those systems all synchronized to operate at
peak efficiency, or whether delays in one system will affect others. With process
twins one can help arrive at the precise timing schemes that ultimately
influence overall effectiveness.
Digital Twin Technology – a brief history
American computer scientist David Gelernter was the first
person to come up with the idea of digital twin technology. He did so in 1991, in
a book he authored Mirror Worlds. However, the person who first
applied it in manufacturing was Dr Michael Grieves, a faculty member at the
University of Michigan. He did so in
2002, formally announcing the digital twin software concept. Eventually, NASA’s
John Vickers introduced a new term digital twin in 2010.
The term may have been conceived as late as in 2010, however,
the core idea of using a digital twin as a means of studying a physical object dates
back several decades before that. One can rightfully credit NASA with pioneering
the use of digital twin technology during its space exploration missions of the
1960s. At that time, each voyaging spacecraft was exactly replicated in an
earthbound version that was used for study and simulation purposes by NASA
personnel serving on flight crews.
Digital
Twins - advantages & benefits
Nancy White, a
content marketing strategist for the Corporate Brand team at PTC
has written a very interesting article entitled Top 8 Digital Twin
Benefits. [see: https://www.ptc.com/en/blogs/corporate/digital-twin-benefits
]. Some of the points she makes are listed out below.
Better
R&D: It’s use enables more
effective research, especially if it involves designing new products. This is because
an abundant amount of data is created, about likely performance outcomes. The information
created leads to insights that help companies make needed product refinements even
before starting production.
Greater
efficiency: Digital twins can
help mirror and monitor production systems even after a new product has gone
into production. It’s done with an eye to achieving and maintaining peak
efficiency throughout the entire manufacturing process.
Product
end-of-life: Manufacturers can decide what
to do with products that reach the end of their product lifecycle and need to
receive final processing, with the help of digital twins. This is done through
recycling and/or other measures. Digital twins help in determining which
product materials can be harvested.
Digital Twins in Supply Chains
Before we
look at the working of digital twins in supply chains, let’s look at how they
work in manufacturing. Digital twins do offer a great deal. Some of their features are
prized. However, their use may not be warranted for every manufacturer or
every product created. We’ll see why. Not all objects are so complex as to require
the intense and regular flow of sensor data that are a must for digital twins. Also,
it may not always be financially feasible. A great deal of resources is
required to create them. As a digital twin is an exact replica of a physical object, its creation is expensive.
According to anyLogistiz.com a Supply
Chain Digital Twin is a detailed simulation model of an actual supply chain
which uses real-time data and snapshots to forecast supply chain dynamics. From
this, analysts can understand a supply chain’s behaviour, predict abnormal
situations, and work out an action plan. [see: https://www.anylogistix.com/features/supply-chain-digital-twins/
]
A supply chain digital twin can be used for:
·
Understanding supply chain
dynamics and behaviour
· Bottleneck
discovery
· Testing
supply chain design changes and development
· Monitoring
risk and testing contingencies
· Transportation
planning
· Inventory
optimization
· Cash
to serve and cost to serve analysis
· Forecasting
and testing operations over the coming days and weeks
By
clicking on the link above you will get access to a white paper which investigates
how digital twins and control towers are resolving challenges in the supply chain.
Read the white paper and estimate the value digital supply chain twins can
offer your business!
https://www.mckinsey.com/capabilities/quantumblack/our-insights/digital-twins-the-key-to-unlocking-end-to-end-supply-chain-growth
[To be
concluded]
[The subject is wide and cannot be covered in such a short space. Please read Part Two (to be published soon) for more]
