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Digital Twins in Smart Supply Chains
BUS3SSP
Melbourne Campus
What are we doing today?
• Understand the technologies that enable digital twins.
• Recognize the benefits and applications of digital twins,
specifically in the supply chain and logistics domain.
• Follow a framework for the implementation of digital twins
• Systematically conceptualize a digital twin framework by mapping
supply chain processes.
• Learn how software can help implement digital twins, allowing
supply chain managers to anticipate potential events and take
major decisions in real-time.
Required Readings and Material
• This PowerPoint is based on excerpts from the following readings and resources:
– Reichental, J. (2020). A digital twin means better results [Streaming video]. Retrieved
from LinkedIn Learning database.
– DHL. (2021). Digital twins in logistics. Retrieved from
https://www.dhl.com/content/dam/dhl/global/core/documents/pdf/glo-core-digitaltwins-in-logistics.pdf
– anyLogistix. (2021). Supply chain digital twins definition, the problem they solve, an
how to develop them.
– Microsoft. (2017). The promise of a digital twin strategy. Retrieved from
https://info.microsoft.com/rs/157-GQE-
382/images/Microsoft%27s%20Digital%20Twin%20%27HowTo%27%20Whitepaper.pdf
Introduction to Digital Twins
• Grieves & Vickers (2017) defined Digital Twin as : “A set of virtual information
constructs that fully describes a potential or actual physical manufactured
product from the micro atomic level to the macro geometrical level. At its
optimum, any information that could be obtained from inspecting a physical
manufactured product can be obtained from its Digital Twin”
• Digital twins are essentially the extensions of existing simulation and 3D
modelling technologies already being implemented in various industries today
• Digital twins are the virtual replications of physical assets or environments
which simulate their behaviour and state
Value of Digital Twins and Their Role in Analytics
• Digital twins are best suitable for problems which encompass large volumes of data,
possess intricate decision making which can have varying results, provide great value for
the organization even with minute alterations, and are scalable and repeatable in
various locations
• Digital twins allow for better integration as they provide a single visualized source of
information for all stakeholders in a supply chain network
• The value delivered by digital twins can be categorized as follows:
• Descriptive Value: Data that is collected from assets in isolated or hazardous locations are more easily
accessed and interpreted through digital twins.
• Analytical Value: If the digital twin has simulation capabilities, it can generate information that would
not have been provided by a physical object directly.
• Diagnostic Value: When developed with diagnostic capabilities, a digital twin can utilize data to
recommend the most likely causes of certain states or behaviors of objects or the environment.
• Predictive Value: Digital twins can be used to run what-if scenarios and predict future conditions of
the physical asset or environment.
Technologies Behind Digital Twins
• APIs and Open Standards: APIs and open standards now make it possible for users to
consolidate data from different sources and set up reliable models much more easily.
• Cloud Computing: Digital twins require significant data storage and computation
capabilities. Cloud computing, when offered as a service by a provider, allows
organizations to utilize these abilities by employing the platform as and when required.
• IoT: One of the most important aspects of digital twins is the ability to access large
amounts of data, which is made possible with IoT. The complex data is collected, which
the digital twin then structures and analyzes.
• AI and Machine Learning: Organizations are now more comfortable working with large
amounts of complex data and drawing insights from it. With advances in machine
learning, frameworks have been developed that enable systems to forecast future
conditions and take decisions without the need of human interference.
• AR/VR: Once the data is processed and collected by the digital twin, it must be visualized
for insights to be drawn from it. This can be done in either a 2D format, where it is
displayed on a screen, or a 3D format where the render is done in the physical
environment.
Technologies Behind Digital Twins
Figure 1. Underlying technologies of digital twins. Reprinted from “Digital twins in logistics,” by DHL. Copyright 2021 by DHL.
• In manufacturing, IoT has been leveraged by many organizations to achieve
automation in various production processes. It has also led to the generation of
high volumes of data, which can be utilized in the development and analysis of
digital twins in order to optimize ongoing manufacturing operations
• By simulating the properties of various materials, analyses that are often too
challenging to perform in the physical world in the material science industry can
be conducted digitally, allowing companies to better understand the properties
and use-cases of these materials
• In the healthcare industry, digital twins can be implemented to allow doctors to
better understand the human body and its behaviour through detailed
modeling, or to practice intricate procedures digitally before they actually need
to be performed
Applications of Digital Twins in Industry
• Some of the largest digital twins developed are for infrastructure purposes, such
as urban planning and public transportation. Such twins enable the real-time
monitoring and tracking of assets and processes, allowing for their optimized
planning and execution, while ensuring the occurrence of timely maintenance
• In the energy sector, digital twins can monitor these assets remotely to ensure
they perform safely and reliably
Applications of Digital Twins in Industry
• IoT, AI, and blockchain are crucial in the development of autonomous
supply chains and digital twins
• When utilized in supply chains, digital twins simulate a physical supply
chain using real-time data to predict dynamics in the supply chain
• The information gained from the digital twin can then be used by
analysts to comprehend supply chain behaviour, anticipate unusual
situations, and develop an appropriate plan to respond to them
Digital Twins in Supply Chains
• For a model to be considered a supply chain digital twin it must meet certain
characteristics:
– It must possess a high level of detail in order to effectively analyze interactions within the
supply chain, even at the macro level, and allow for improved functions such as better financial
and demand forecasting.
– It must utilize real-time data to determine the current state of the supply chain and develop upto-date forecasts.
– The digital twin must be able to alert the user in the occurrence of an anomaly to the
determined plan.
– The user must be able to set triggers for certain activities which can then occur autonomously if
triggered.
– Leveraging the information provided by the digital twin, the user or organization must be able
to develop and test solutions to potential risks before they are implemented.
– The supply chain digital twin must be able to integrate with the digital ecosystem developed by
the organization, consisting of a variety of other systems and tools.
Digital Twins in Supply Chains
Digital Twins in Supply Chains
Figure 2. Supply chain digital twins. Reprinted from “Supply chain digital twins definition, the problem they solve, an how to develop
them,” by anyLogistix. Copyright 2021 by anyLogistix
• Supply chain digital twins enable organizations to participate in improved short
and mid term decision-making
• Short term decisions pertain to the identification of possible issues the supply
chain can face and analyzing the available information to develop appropriate
solutions
• Mid term decisions are concerned with higher-level aspects of the supply chain,
such as its design, planning, and optimization
• In the retail industry, digital twins allow retailers to visualize all their individual
supply chains as one that is linked in one large environment
• Digital twins allow retailer to explore other fulfillment strategies as they gain
continuous real-time visibility of their supply chain.
• Digital twins help with supply chain integration, allowing for more cost effective
and sustainable supply chains.
Applications of Supply Chain Digital Twins
• One of the characteristics of supply chain digital twins is their ability to integrate
with the digital ecosystem created by an organization. This means that digital
twins play a key role in the organization’s supply chain control tower
• A control tower consists of the following modules:
– Data Module: This module unites all the data silos in the supply chain. This ensures that the
decisions promoted by the digital twin are applicable and made with the latest information.
– Visualization Module: This module helps gain insights from the available data through
analysis and data presentation.
– Current State/History Module: This module allows the user to analyze historical information
to gain an understanding of the current state of the supply chain and ensure that the
required standards in terms of quality and defined in regulatory policies are maintained.
– Decision Support/Forecast Module: This module is primarily focused on what-if scenario
analysis. Potential risks are identified, and the appropriate response strategies are
developed.
– Task and Case Management Module: This module is concerned with the actual execution of
plans and allows for the activities to be tracked in real-time through their execution.
Supply Chain Control Tower
Supply Chain Control Tower
Figure 3. Supply chain control tower. Reprinted from “Supply chain digital twins definition, the problem they solve, an how to develop
them,” by anyLogistix. Copyright 2021 by anyLogistix
• To implement digital twins, it is first critical that a digital twin concept centered
on data be developed
• A digital twin concept will provide visibility of all levels of the supply chain,
including all the facility activities and supply chain processes
• The first step in conceptualizing a digital twin is identifying the needs of the
business, the outcomes that are desired, and how stakeholders and activities
are impacted
• The digital twin conceptualization should be done with a focus on the value that
can be gained through it
• Once that is established, the relevant technologies can be selected based on the
use case and data output required.
Developing and Implementing Digital Twins
• In order to develop a digital twin concept that generates value for the
organization and its consumers it is important to implement process mapping
with a systems thinking approach
• Systems thinking encourages that processes not be seen as isolated from each
other, but as components in a larger system that depend on and respond to
each other in order to achieve a determined objective
• When conceptualizing a digital twin, process mapping helps deconstruct the
activities that occur within a process while systems thinking ensures that all the
processes that occur in a certain function work together to achieve a specified
objective
Step by Step Digital Twin Conceptualization
• To develop a digital twin concept for supply chains, we suggest the following step by
step guide:
– STEP 1. Identification of traditional and digital supply chain KPIs (discussed in previous workshop)
– STEP 2. Visualizing the supply chain network including the identified KPI-SCs for each node using
supply chain mapping tools (discussed in previous workshop)
– STEP 3. Mapping out all value-adding processes that occur across/within the supply chain (using
tools such as turtle diagram)
– STEP 4. Identifying the process level KPIs (KPI-Ps) for all characterized value-adding processes and
tabulating them in a single source
– STEP 5. Developing a dependency matrix to map KPI-Ps against KPI-SCs (here we try to indirectly
monitor how the identified processes contribute to the supply chain level KPIs)
– STEP 6. Framing a comprehensive (transformed) KPI database (by removing duplicate information
and adding detailed information to clearly describe how each KPI functions and is going to be
measured digitally)
– STEP 7. Implementing Digital Twins: (a) Monitoring Smart Devices Remotely; (b) Insight Generating
Platform ; and (c) Smarter Machines
Step by Step Digital Twin Conceptualization
• Process mapping helps to understand what each process in the supply chain
provides, and what it requires for successful execution
• When applied to a supply chain, it is a great way to visualize all the activities
that go into providing a product, starting from the procurement of materials all
the way to delivery of the finished product
• Process mapping for the entire supply chain should cover three major areas:
– How are raw materials or components ordered from suppliers
– How are they received by the organization
– How does the finished product reach consumers
Step 3: Process Mapping
• Process mapping is effective in three situations specifically:
– Internal Benchmarking: Process mapping enables organizations to establish performance
benchmarks in their supply chain networks and determine why some business processes are
executed more effectively than others.
– Problem Solving: If there are issues in certain processes, it helps to create a map of all the
activities to truly understand how the process is executed.
– IT Solution Implementation: In Industry 4.0, this could be considered one of the most
important uses of process mapping. When implementing an IT solution, such as a digital
twin for an organization, mapping out all the existing business processes will help the
implementation team identify which IT solutions would be best suited for the business. This
information also helps them build or configure IT solutions so that they effectively enable
the processes.
Step 3: Process Mapping
• The Turtle Diagram is a tool that provides a detailed breakdown of all the
components that go into a process
• It provides a view of the entire process, including activities that are
interconnected with those of different business functions are organizational
levels
• At the center is the process itself, represented by the body of the turtle. The
head, tail, and legs represent the 6 factors concerning the process
• Before starting the process mapping, it is suggested to create a simple
visualization of the processes that show the order in which they occur.
Process Mapping Through Turtle Diagram
• The following are the factors of the Turtle Diagram:
– Process: This part of the diagram lists any steps that add value or come under the scope of
the process.
– Inputs: This section outlines the intricacies of the process, listing the information,
documentation, and other requirements.
– Outputs: This section outlines the outcomes of the process, such as new documentation or
guidelines, or a product.
– What: This section lists the resources required to run the process, such as the equipment or
materials.
Process Mapping Through Turtle Diagram
• The following are the factors of the Turtle Diagram (Cont.):
– How: This section references guidelines established within the organization that determine
how the process is to be performed successfully. This includes any methods or activities
deemed suitable by the organization.
– Who: This section lists all the personnel in the organization who are responsible for the
activities required to perform the process, or the skills required for those activities.
– Results: This section determines how the organization will measure the success of the
process, and whether it achieved the objectives set. This includes KPIs and other
performance indicators, such as the ones outlined in the previous workshop for digital
supply chains.
– Support Processes: Depending on the process, this seventh factor might be required. This
section lists any information or materials that aid the process and the value-add steps
identified.
Process Mapping Through Turtle Diagram
Process Mapping Through Turtle Diagram
Figure 4. Turtle diagram template. Copyright 2021 by The 9000 Store
• After the successfully characterization of all value-adding processes using a
process mapping tool in Step 3, we determine and document the process level
KPIs (KPI-Ps) for all characterized processes.
• These KPI-Ps are then tabulated in a single source which will be used in next
steps
• This step is done through a series of interviews and discussion with the process
owners, reviewing existing procedures and policies, extracting data from the
existing performance evaluation systems, etc.
Step 4: Identifying the Process Level KPIs
• Once we identify all the KPI-Ps in Step 4 for the characterized processes that
occur across all the nodes in the supply chain, we map them against the supply
chain level KPIs (KPI-SCs) that were determined in Step 1 for these node
• This is done to understand which processes contribute to the higher-level value
creation objectives of the supply chain, and to monitor and track any issues that
may be faced in the day-to-day supply chain operations
• The mapping of KPI-Ps against KPI-SCs creates better transparency by
connecting the operational level activities to the supply chain level activities
Step 5: Developing a KPI Dependency Matrix
• Here, we aim to omit any duplications in the data provided as we consolidate
the information of various processes and their KPIs from multiple sources across
the supply chain
• Once all duplicate data is removed, the database must be checked to eliminate
any information regarding processes or KPIs that might be outdated based on
the current practices implemented by the organization or supply chain
• We then enhance our database by adding detailed explanations that clearly
define how each of the identified KPIs function, and how they will be measured
digitally
Step 6: Framing a Comprehensive KPI
Database
• Step 7.a (Stage 1): Monitoring Smart Devices Remotely: This step is initially
how a digital twin concept can generate value for an organization. Smart devices
allow for continuous monitoring of products and process performance, while
still observing the supply chain in totality.
• Step 7.b (Stage 2): Insight Generating Platform: The next stage is the
development of a platform to enable the digital twin objectives. This stage is
focused on representing the physical object and environment virtually, providing
deeper insights that promote innovation and value generation across the supply
chain network.
• Step 7.c (Stage 3): Smarter Machines: The last stage in implementing a digital
twin is concerned with the development of a system that is completely
integrated. This is achieved through the interaction of people with a real-time
simulation platform.
Step 7: Implementing Digital Twins
Step 7: Implementing Digital Twins
Figure 5. Microsoft digital twin implementation framework. Reprinted from “The promise of a digital twin strategy,” by Microsoft.
Copyright 2017 by Microsoft
Microsoft Azure Demo
• Go to page 4 of your workbook for this week. Follow the
instructions provided to setup and run the Microsoft Azure Demo.
Raise any doubts you may have. This demo will be explained in
class
ACTIVITY 2: Conceptualization of Digital Twins
• Go to the Workshop Summary page of your workbook for this
week. Read the question asked in Activity 2. By using the
provided data and referring to the relevant material, solve the
question. Raise any doubts you may have
You
Thank

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