Activity 2: Digital Supply Chain Conceptualization - Part 2 (Conceptualization of digital twins)
Activity 2: Digital Supply Chain Conceptualization - Part 2 (Conceptualization of digital twins)
Note:This activity is a continuation ofActivity 2 from the previous workshop.This is an important activity and is closely linked to yourAssessment 3.
As part of the move to digitize their supply chain network, Coolies is considering implementing a digital twin. While the benefits of a digital twin are clear, it requires a significant investment. To gain greater insights on how to go about developing digital twins and the nature of the capital and technology required, the IT and Supply Chain teams collaboratively plan on implementing a digital twin for a small portion of the supply chain network as a pilot project. This pilot project will consist of an ice cream production plant from New Zealand (manufacturer), and a DC andselected retailers based in Victoria. Based on the insights gained from the pilot project, Coolies can determine how they want to go about the implementation of a digital twin for their entire supply chain network and the technologies they may require.
The first and second step in developing a digital twin project was done in Activity 2 of the previous workshop. In this activity, the focus will be onSteps 3-6 of our frameworkto conceptualize digital twins. By Step 6, the identification of the appropriate smart devices and the manner in which digital measurements will be taken for each process level KPI (KPI-Ps) will defined.
A simple visualization of the processes are providedin the input data.The visualization represents how data flows from one process to the other.
The factory that is to be included in the digital twin is based in NZ and manufactures ice cream.It consists of 4 processes: Making the Ice Cream, Filling and Quality Control/Quality Assurance, Hardening, and Packaging and Distribution. All retailers are based in VIC, too.
Using theinput dataset provided here, and by referring to the tutorial video and other content of this workshop, students must focus onfactory processesand perform the following:
Process Mapping - Develop a Turtle Diagram for each process that occurs in the thefactory(Step 3).
Based on the Turtle Diagram, identify suitable KPIs for thefactoryprocesses. Highlight the acceptance criteria and calculation method for each KPI (Step 4).
The KPIs identified in this activity are at the process level (KPI-Ps), while those identified in in Activity 2 of the previous workshop were at the supply chain level (KPI-SCs).Map the KPI-Ps against the KPI-SCs to understand which processes contribute to the supply chain level goals (Step 5).
Create a comprehensive table that lists what is measured for each process, how it is measured, the KPI(s) for that process and their acceptance criteria, the associated supply chain KPIs (KPI-SCs) from Activity 2 of the previous workshop, the digital data source, and how the measurement is digitally taken (Step 6).
Are there any KPI-SCs you would change to ensure that the measurements taken at the process level add insight and value to the measurements taken at the supply chain level? Discuss your answer in-class .
1. Introduction to Digital Twins
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Note:
This workbook is partially built on the concepts discussed in the resources listed under the Required Readings for this workshop. Some contents from those materials have been used throughout this workbook.
Students are required to study and complete the important weekly activities located on the summary page of this workbook before attending the workshops.
Grieves & Vickers (2017)defined Digital Twin (DT) 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 simulation and 3D modelling technologies that are already being implemented in various industries today (DHL, 2021). What sets digital twins apart from these technologies, however, are certain characteristics: digital twins are the virtual replications of physical assets or environments which simulate their behavior and state. They are usually very specific to the organization employing them, and the digital twin constantly updates itself based on changes that occur to the physical environment or object. This information is then used to deliver the required visualizations, predictions, analyses, or process optimization. These characteristics can be achieved in a variety of ways, and therefore allow for a range of possibilities and industries in which digital twins can operate.
As the awareness and implementation of digital twins is increasing in Industry 4.0, companies such as IBM, Microsoft, Capgemini, SAP, and SAS are offering solutions that enable the development and implementation of their clients digital twins.
The following video by SAP provides an overview of the capabilities of digital twins (2 minutes).
Play Video
Value of Digital Twins & Their Role in Analytics
In an interview with the Azure IoT Partner General Manager at Microsoft, The CEO of The Marsden Group mentioned that 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 (Shakib, 2021).
As digital twins can be utilized in a wide range of scenarios and applications, the value they deliver will be dependent on the particular use-case. In general, the value delivered by digital twins can be categorized as follows (DHL, 2021):
Descriptive Value:Data that is collected from assets in isolated or hazardous locations are more easily accessed and interpreted through digital twins. This data can be visualized and used for effective monitoring and decision-making without the need for a person to physically be near the asset.
Analytical Value:If the digital twin has simulation capabilities, it can generate information that would not have been provided by a physical object directly. This is useful for providing insights into a product that can be used for troubleshooting purposes and for improvements in future iterations.
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. This information can be used to understand the relationship between events or set guidelines for organizations to follow based on their existing knowledge.
Predictive Value:Digital twins can be used to run what-if scenarios and predict future conditions of the physical asset or environment. Highly advanced twins with the required capabilities can go one step further by suggesting a solution that addresses any potential issues identified. This will be huge driver of smart factories in the coming years.
Digital twins allow for better integration as they provide a single visualized source of information for all stakeholders in a supply chain network. Overall, digital twins facilitate fewer equipment breakdowns, quicker maintenance processes, improved customer value through enhanced product quality and service, and generally a more efficient workforce (Microsoft, 2017).
The following video provides an overview of how data generated by digital twins can be analyzed and presented, and the benefits gained from this data (4 minutes).
Introduction to Digital Twins
Technologies Behind Digital Twins
Of the technologies available today, there are five in particular that allow for the development and operation of digital twins. These are application programming interfaces (APIs) and open standards, cloud computing, IoT, AI, and AR/VR.Please read the full DHL whitepaperhere(required reading) for comprehensive understanding of digital twins, the value they generate, their role in analytics, and the applications of digital twins in various industries.
We will briefly look into the role of these 5 technologies in digital twins (DHL, 2021).
APIs and Open Standards:In the past, companies used to safeguard the data models they developed. The development of these models and the infrastructure they require would be done for each product, and these were often taxing processes. 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. This helps organizations keep the costs down without limiting the capabilities at the disposal of the digital twin.
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. IoT allows for data to be collected in high volumes from multiple sources in an environment, in a relatively cost-effective manner. The complex data is collected, which the digital twin then structures and analyzes.
AIand 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. The popularity of 3D rendering has grown in recent times. With mixed reality, the digital twin can be interacted with in the physical environment it is rendered.
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Figure 1. Underlying technologies of digital twins.Reprinted from "Digital twins in logistics," by DHL. Copyright 2021 by DHL.
References:
Grieves, M., & Vickers, J. (2017).Digital twin: Mitigating unpredictable, undesirable emergent behavior in complex systems. In Transdisciplinary perspectives on complex systems (pp. 85-113). Springer, Cham.
DHL. (2021).Digital twins in logistics. Retrieved fromhttps://www.dhl.com/content/dam/dhl/global/core/documents/pdf/glo-core-digital-twins-in-logistics.pdfSAP Digital Supply Chain. (2020, November 18).Digital twins and the future of Industry 4.0[Video file]. Retrieved fromhttps://www.youtube.com/watch?v=Szjz_4QY628Shakib, T. (2021).Revolutionizing the retail supply chain with digital twins. Retrieved fromhttps://cloudblogs.microsoft.com/industry-blog/retail/2021/01/26/revolutionizing-the-retail-supply-chain-with-digital-twins/Microsoft. (2017).The promise of a digital twin strategy. Retrieved fromhttps://info.microsoft.com/rs/157-GQE-382/images/Microsoft%27s%20Digital%20Twin%20%27How-To%27%20Whitepaper.pdfReichental, J. (2020).Digital twin analytics[Streaming video]. Retrieved from LinkedIn Learning database.
1.1. Application of Digital Twins in Industry
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One of the primary applications of digital twins has been in the manufacturing industry(DHL, 2021). 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 for these processes. These digital twins provide insights on how to optimize ongoing manufacturing operations. Digital twins can also be implemented in the material sciences industry. By simulating the properties of various materials, analyses that are often too challenging to perform in the physical world 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 behavior through detailed modeling, or to practice intricate procedures digitally before they actually need to be performed.
Some of the largest digital twins developed are for infrastructure purposes, such as urban planning and public transportation. Such digital 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 a manner which does not significantly interrupt operations. Large assets in isolated locations are often used for energy production. In the energy sector, digital twins can monitor these assets remotely to ensure they perform safely and reliably. This also helps maintain operational costs in this industry at an acceptable level (DHL, 2021).
In the video below Siemens explains how they use digital twins, such as in the development of an electric aircraft motor, and discuss why digital twins are going to be integral to industry in the future (3 minutes).
Play Video
The IBM video below discusses ways in which a virtual testing platform (i.e., a digital twin) can be used for a better and safer driving experience through real-time tracking and simulation of vehicles (3 minutes).
Play Video
Please read the full DHL whitepaperhere(required reading).
References:
DHL. (2021).Digital twins in logistics. Retrieved fromhttps://www.dhl.com/content/dam/dhl/global/core/documents/pdf/glo-core-digital-twins-in-logistics.pdfSiemens. (2019, July 4).Why digital twins will be the backbone of industry in the future[Video file]. Retrieved fromhttps://www.youtube.com/watch?v=ObGhB9CCHP8IBM Research. (2018, February 7).Virtual testing platform for IoT systems[Video file]. Retrieved fromhttps://www.youtube.com/watch?v=8Jw2Bc2XNR02. Digital Twins in Supply Chains
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IoT, AI, and Blockchain are crucial in the development of autonomous supply chains and digital twins. The following video explains how these technologies, when applied to a strong digital backbone, help enable supply chain digital twins (2 minutes).
Play Video
When utilized in supply chains, digital twins simulate a physical supply chain using real-time data to predict dynamics in the supply chain (HYPERLINK "https://lms.latrobe.edu.au/pluginfile.php/9407725/mod_book/chapter/930931/supply-chain-digital-twins.pdf" t "_blank"anyLogistix, 2021).The information gained from the digital twincan then be used by analysts to comprehend supply chain behavior, anticipate unusual situations, and develop an appropriate plan to respond to them. In other words, analysts can leverage supply chain digital twins for understanding, learning, and reasoning purposes.
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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 anyLogistixFor a system 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 up-to-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.
Application of Supply Chain Digital Twins
Supply chain digital twins enable organizations to participate in improved short and mid term decision-making (HYPERLINK "https://lms.latrobe.edu.au/pluginfile.php/9407725/mod_book/chapter/930931/supply-chain-digital-twins.pdf" t "_blank"anyLogistix, 2021). Short term decisions pertain to the identification of possible issues the supply chain can face and analyzing the available information to develop appropriate solutions. Generally, for these decisions to be made the simulation of just a few days or weeks of activity will be needed. Mid-term decisions are concerned with higher-level aspects of the supply chain, such as its design, planning, and optimization. Digital twins can provide insights on the reasoning, resources, and activities carried out in the supply chain, making it essential to such decision-making. These decisions will need the simulation of a few months of activity.
In the retail industry for example, digital twins allow retailers to visualize all their individual supply chains as one that is linked in one large environment (Shakib, 2021). The digital twin can simulate all the supply chain processes that occur as well as the assets and resources employed in the supply chain using real-time data. This can help them comprehensively determine the performance of the supply chain and identify areas that need to be optimized. Digital twins allow retailers to explore other fulfillment strategies, such as curb side pickup, as they gain continuous real-time visibility of their supply chain. Moreover, digital twins also help with supply chain integration, and allow for more cost effective and sustainable supply chains.
In the video below, DHL presents how they implement digital twins in their supply chains and benefits gained from it (2 minutes).
Play Video
Supply Chain Control Tower
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, which serves as a key resource in providing better visibility when taking all manners of business decisions. A control tower consists of the following modules (HYPERLINK "https://lms.latrobe.edu.au/pluginfile.php/9407725/mod_book/chapter/930931/supply-chain-digital-twins.pdf" t "_blank"anyLogistix, 2021):
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.
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 anyLogistixDigital twins are gaining more importance in supply chains today and will be an integral part of supply chains in the future. They can help the development of cost effective and sustainable supply chain networks through their real-time simulation, analysis, and visualization capabilities.
Please read the full anyLogistix whitepaperhere(required reading) for comprehensive understanding of the role and applications of digital twins play in supply chains.
Control Towers in Industry 4.0:
The Agency for Science, Technology and Research (A*STAR) is a leading research and development organization located in Singapore. They have participated in several projects spanning various industries, focusing specifically on the benefits of these projects to society as well as the economy. Students are encouraged to follow the link below to see the work done by A*STAR in developing Manufacturing Control Towers in Industry 4.0, along with the other projects undertaken.
Model Factory@SIMTech (a-star.edu.sg)
References:
OpenText. (2019, May 6).Enabling the autonomous supply chain with IoT, AI and blockchain[Video file]. Retrieved fromhttps://www.youtube.com/watch?v=f7Nu3HWnhnganyLogistix. (2021).Supply chain digital twins definition, the problem they solve, an how to develop them.
Shakib, T. (2021).Revolutionizing the retail supply chain with digital twins. Retrieved fromhttps://cloudblogs.microsoft.com/industry-blog/retail/2021/01/26/revolutionizing-the-retail-supply-chain-with-digital-twins/DHL. (2019, July 2).DHL supply chain asia digital twin warehouse[Video file]. Retrieved fromhttps://www.youtube.com/watch?v=S4jE-h37B4IA*STAR. (2021). Model factory @ SIMTech. Retrieved fromhttps://www.a-star.edu.sg/simtech/model-factory@simtech/overview
3. Developing and Implementing Digital Twins
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To implement digital twins, it is first critical that a digital twin concept centered on supply chain digital transformation (discussed inprevious workshop) to be developed. A digital twin concept for a supply chain, if developed successfully, will provide visibility of all levels of the supply chain, including all the facility activities and supply chain processes that occur (Microsoft, 2017). This allows for the generation and processing of information that is reliable for use in decision-making. 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.
At present, these concepts are developed with a focus on the technologies that will be implemented rather than the value that will be generated by the digital twin. Instead, the value gained should be the focus of the digital twin concept. Once that is established, the relevant technologies can be selected based on the use case and data output required.
Conceptualization of a Digital Twin Solution with Systems thinking: A Step by Step Guide
Often processes are seen individually. In order to develop a digital twin concept that generates value for the organization and its consumers, however, it is important to implement process mapping with a systems thinking approach. Systems thinking encourages that processes should not be seen as isolated from each other, but as components in a larger system that depend on and respond to changes in the other processes in order to achieve a determined objective (England and Miller, 2016). Moreover, systems thinking maintains an organization's focus on the purpose of the entire system rather than its individual components alone (i.e., what is the system trying to achieve?). When conceptualizing a supply chain 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. It is essential to approach process mapping with systems thinking to ensure that the aim of the digital twin concept is not limited to the optimization of certain processes alone, but to alter processes in such a manner that the entire system generates value for the organization and its key stakeholders.
As discussed inprevious workshop(see Figure 13), 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 (KPI-SCs). This is discussed inprevious workshop.STEP 2.Visualizing the supply chain network including the identified KPI-SCs for each node using supply chain mapping tools (discussed inprevious 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 sourceSTEP 5.Developing a dependency matrix to map KPI-Ps againstKPI-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
The above steps should be taken in a collaborative environment by digital business analysts (you!) in cooperation with IT specialists and the process owners across the supply chain. Building on the work you did (steps 1-2) in the previous workshop, we next walk you through Steps 3-6 in more detail. Step 7 will be discussed very briefly as it is more closely related to and expected from the IT team, rather than the digital business analysts.
Step 3. Process Mapping
To understand where and how value can be generated, it is important for the organization to have a comprehensive understanding of their current objectives and activities. Process mapping is one of the ways to understand what each process in the supply chain provides, and what it requires for successful execution.Process mapping is a great approach for educating all levels of an organization about key business processes, as well as key external stakeholders (Logistics Bureau Group, 2021). 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 (UPS, 2012).
Process mapping, when applied to 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, and how does the finished product reach consumers. It should be noted however, that process mapping can be applied to more specific organizational operations as well.
The following video provides a brief overview of the steps that should be taken to develop a process map (5 minutes).
Play Video
There are several benefits in using process mapping. It is especially effective to use, however, in three situations: (Logistics Bureau Group, 2021):
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. The KPIs and process maps for each business unit are compared, providing insights to what drives the successful processes.
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. This helps identify the specific activity or activities that require improvement, leading to clear solutions and more effective troubleshooting.
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.
It is clear, therefore, that process mapping must occur as part of the digital twin conceptualization to ensure that the technologies that are implemented aid and improve the business processes currently in place. Focus must be maintained, however, that the concept developed delivers value to all key stakeholders rather than only optimizing certain processes.
There are many tools that can be used to conduct process mapping. TheSCOR modelis a popular form of process mapping used for supply chain management purposes. The Turtle Diagram is another simple visual tool that can be easily used by analysts for process mapping. We next look at this tool in more detail.
Process Mapping through Turtle Diagram
The Turtle Diagram is a tool that provides a detailed breakdown of all the components that go into a process (NQA Global Certification Body, 2017). It provides visibility of the entire process, including activities that are interconnected with those of different business functions and organizational levels. The Turtle diagram also simplifies the process framework, making it easier for stakeholders to understand the process and its requirements. The tool gets its name because of its general layout. 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 (The 9000 Store, 2021). Before starting the process mapping, it is suggested to create a simple visualization of the processes that show the order in which they occur.Listed below are the key elements of the Turtle Diagram. We take a detailed look at these factors, including the inputs and outputs of the process, what is required to facilitate the process, how the process will be executed, who will execute it, and what the expected results of the are.
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.
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 previousworkshopfor 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.
The above list includes critical information which can be collected by business analysts through focused observation, a series of interviews and discussions with the process owners, by reviewing existing procedures andpolicies, by extracting data from the existing performance evaluation systems, etc.Depicted below is a Turtle Diagram template which provides a foundation for how this tool can be implemented in practice (Figure 4).
right512445Figure 4. Turtle diagram template.Copyright 2021 by The 9000 Store
Depending on the process being mapped, the 6 (or 7) sections might be named differently or contain different characteristics. Figure 5 below is an example of the process map for a traditional procurement process in a company. It lists all the standard requirements as well as industry and organization standards considered in the process.
Figure 5. Procurement process turtle diagram.Copyright 2017 by NQA Global Certification Body
References:
Microsoft. (2017).The promise of a digital twin strategy. Retrieved fromhttps://info.microsoft.com/rs/157-GQE-382/images/Microsoft%27s%20Digital%20Twin%20%27How-To%27%20Whitepaper.pdfEngland, L. A. & Miller, S. D. (2016).Systems thinking, process mapping, and implications for ERM.Maximizing Electronic Resources Management in Libraries : Applying Business Process Management(pp. 97-120). Waltham, USA: Elsevier Science & Technology.
Logistics Bureau Group. (2021).Supply chain and logistics process mapping: Why and when to do it. Retrieved fromhttps://www.logisticsbureaugroup.com/supply-chain-and-logistics-process-mapping-why-and-when-to-do-it/UPS. (2012).Supply chain mapping. Retrieved fromhttps://www.ups.com/assets/resources/media/en_GB/Supply_Chain_Mapping.pdfLeanOhio. (2016, November 24).Process Mapping[Video file]. Retrieved fromhttps://www.youtube.com/watch?v=Y7g8vWv11VkNQA Global Certification Body. (2017).How important are turtle diagrams for an organization?Retrieved fromhttps://www.nqa.com/en-US/resources/blog/june-2017/turtle-diagramsThe 9000 Store. (2021).Using turtle diagram in ISO 9001. Retrieved fromhttps://the9000store.com/articles/using-turtle-diagram-in-iso-9001/ASCM. (2021). SCOR model. Retrieved fromhttps://scor.ascm.org/processes/introduction3. Developing and Implementing Digital Twins
3.1. Next Steps
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Step 4: Identifying the Process Level KPIs
After successfully characterizing all value-adding processes using a process mapping tool in Step 3, in this step we should determine and document the process level KPIs (KPI-Ps) for all characterized processes and tabulate them in a single source which will be used in next steps. This is a critical step and is usually done through a series of interviews and discussion with the process owners, reviewing existing procedures andpolicies, extracting data from the existing performance evaluation systems, etc.
Step 5: Developing a KPI Dependency Matrix
Once we identify all the process level KPIs (KPI-Ps) in Step 4 for the characterized processes that occur across all the nodes in the supply chain, we then map them against the supply chain level KPIs (KPI-SCs) that were determined in Step 1 for these node. The purpose of this is 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. Based on the KPI-SC impacted by the issue encountered and the KPI-Ps mapped to it, the issue can be traced back to the process where it might be occurring. The mapping of KPI-Ps against KPI-SCscreates better transparency by connecting the operational level activities to the supply chain level activities.
Step 6: Framing a Comprehensive (digitally transformed)KPI Database
After mapping the process level KPIs (KPI-Ps) to the supply chain level KPIs (KPI-SCs) in the previous step, we then develop a comprehensive KPI database in this step. Here, we aim to omit any duplication in the information provided as we consolidate the information of various processes and their KPIs from multiple sources across the supply chain. Care should be taken that processes and their KPIs are not repeated in the database. 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.
Recall from the previous workshop that there is a correlation between theDSCI'sdigital supply chain transformation guide (seeFigure 6) and our proposed conceptualization framework. The Fine Tuning stage of the transformation guide will be conducted in Step 4 of our framework, with the remaining stages (Assess, Create, and Develop) covered in Step 6.
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Figure 6.Digital supply chain transformation guide Reprinted from "Digital supply chain transformation guide: Essential metrics," by DSCI. Copyright 2017 by DSCI.
Step 7: Implementing Digital Twins
After the digital twin concept is developed, there are three stages to implementing the digital twin (Microsoft, 2017):
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. At this stage of the digital twin, the data generated does not need any visual content. If the data is interactive, it can be visualized on a dashboard, but it would not require any form of rendering (2D or 3D) to provide insights. To extend the use of this data for predictive analytics purposes, a cloud platform can be employed in the digital twin.
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. The platform developed not only provides a physical view of objects and processes, but the data generated at this stage provides deeper insights that promote innovation and assist in generating greater value 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. This stage allows the possibility to integrate advanced capabilities into the digital twin such as AI. Such technologies expand the potential of digital twins and bring in a new level of insights that data from sensors alone could not provide. AI capabilities at this stage can be used to run what-if scenarios, identify abnormalities, and suggest appropriate actions accordingly. These systems tend to learn and improve as more quality interactions occur between people and machines in the supply chain.
An example of these stages is represented by Microsoft in the figure below (Figure 7), indicating which of their products can be used in which stage. Step 7.a is represented by the activities under Device Connectivity. Here we see that IoT technology is utilized across the physical supply chain to generate and transfer data to a designated cloud platform. Devices that cannot directly communicate with the cloud can send the generated data to a field gateway, which then transfers it to the cloud.
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Figure 7. Microsoft digital twin implementation framework.Reprinted from "The promise of a digital twin strategy," by Microsoft. Copyright 2017 by Microsoft
Step 7.b is shown by the activities under Data Processing, Compute, Analytics and Management. Here, the data received by the cloud is utilized to generate insights as required. This includes analyzing the data and processes to take business decisions, as well as detailed monitoring and management of remote devices. At this stage, simulation and visualization is also conducted to generate better insights on the activities or assets in the supply chain.
The final stage (Step 7.c) is represented in the figure as the activities under Business, Connectivity, Presentation & Interaction. This stage looks to leverage the insights generated in Step 7.b to create a digital ecosystem that delivers value to the organization or its consumers. The first step in doing this is to create an interface that allows for the generated insights to be presented. The presentation methods can range from a tabular display, to interactions through mixed reality, to physically holding a prototype, product or component with 3D printing. If the insights are immediately actionable for business use, they can directly be implemented for customer service purposes, or for further testing and analysis of products/services.
Note:The above steps (7.1-7.3) are more related to or expected from the IT team, rather than the digital business analysts.Different functions within a digital twin can exist at different stages depending on the activities undertaken by each function. It is important to notice here that all the data flows between the stages are two way, meaning that all the devices across the digital twin are constantly communicating to ensure that the right decisions are taken at the right stages in real-time based on the data, insights, knowledge, and feedback gained across the supply chain.
Please read the full Microsoft whitepaperhere(required reading) for comprehensive understanding of the considerations to take into account when conceptualizing and implementing digital twins.References:Microsoft. (2017).The promise of a digital twin strategy. Retrieved fromhttps://info.microsoft.com/rs/157-GQE-382/images/Microsoft%27s%20Digital%20Twin%20%27How-To%27%20Whitepaper.pdf5. Summary
Digital twins are virtual replications of physical assets or environments which simulate their behavior and state. They are very particular and unique to the organization employing them. Digital twins use the real-time information generated by the physical environment or object to provide visualizations, predictions, analyses, or process optimization. Given the variety of ways digital twins can be achieved, they can be applied to many various industries. Digital twins are generally built on 5 key technologies: APIs and open standards, cloud computing, IoT, AI, and AR/VR. The value provided by them can be categorized as descriptive, analytical, diagnostic, and predictive. Overall, digital twins allow for fewer equipment breakdowns, quicker maintenance processes, improved customer value through enhanced product quality and service, and generally a more efficient workforce.
When utilized in supply chains, digital twins simulate a physical supply chain using real-time data to predict dynamics in the supply chain, and analysts can leverage them for understanding, learning, and reasoning purposes. Supply chain digital twins enable organizations to participate in improved short and mid-term decision-making, ranging form risk identification and mitigation to supply chain network design and optimization. As supply chain digital twins have the ability to integrate with the digital ecosystem of an organization, they play a key role in supply chain control towers.
When implementing a supply chain digital twin, it is essential to develop a digital concept first. This conceptualization must be data centric and focused on the value that the digital twin can generate. Once that is established, the relevant technology can be selected based on the use case and data output required. A 9-step framework has been provided for the conceptualization of a digital twin, with the first two steps covered in the previousworkshop. Process mapping is is an effective method to deconstruct processes to gain a better understanding of them, and for stakeholders to understand what each process in the supply chain provides and what it requires for successful execution. Process mapping is especially useful when implementing IT solutions, such as digital twins, as it allows the organization to identify which IT solutions best fit their business processes. Once the digital twin is conceptualized, its implementation goes through three stages (Steps 7 of our framework): monitoring smart devices remotely, creating a platform for generating insight, and utilizing smarter machines.
Assessment 3-part 1 instructions
Assessment 3-part 1:Conceptualization, modelling and analysis of supply chain digital twins
Contribution: Individual
Percentage: 40%
Required Length:1400 words.
Format:Individual written | MS Word
Intended Learning Outcome(s):SILO1, SILO2, SILO3, SILO4
Students are required to submit their answers in a single MS word file under two separate headings as follows:
Introduction to Digital Twins
Part 2:In the second part of this assessment, you are asked to use the learning obtained from theDigital Twins in Smart Supply Chainworkshop and the Conceptualization of digital twins tutorial (sample activity) provided to write an analytical report and address the following problem.Maximum word count is1100 words.
Problem Statement:As part of their move to digitize their supply chain network, Coolies is considering implementing a digital twin. To gain greater insights on how to go about developing digital twins and the nature of the capital and technology required, the IT and Supply Chain teams collaboratively plan on implementing a digital twin for a small portion of the supply chain network as a pilot project. This pilot project will consist of an ice cream production plant from New Zealand (manufacturer), and a DC andselected retailers based in Victoria. Based on the insights gained from the pilot project, Coolies can determine how they want to go about the implementation of a digital twin for their entire supply chain network and the technologies they may require.
In this assessment, the focus will be on theDC processesandSteps 3-6 of the digital twin conceptualization framework. By Step 6, the identification of the appropriate smart devices and the manner in which digital measurements will be taken for each process level KPI (KPI-Ps) should be defined.A simple visualization of the DC processes isprovidedin the input data.The visualization represents how data flows from one DC process to the other.
The DC which receives the ice cream from factory is based in VIC and consists of 4 processes: Receiving and Storing, Picking, Packaging, and Distribution and Delivery.
In sample activity, you learned how to carry out Steps 3-6 of digital twins conceptualization framework for thefactory. A complete solutions for Factory and template forms for DC are provided in thesample activity done.Using the Factory solution tabs as benchmark (you do not need to do anything with Factory tabs just focus on DC tabs), students mustfocus onDC processes,perform the following tasks and complete the provided template forms:
Process Mapping - Develop a Turtle Diagram for each process that occurs in the DC (Step 3). Based on the Turtle Diagram, identify suitable process level (KPI-Ps) for theDCprocesses.Highlight the acceptance criteria and calculation method for each KPI-Ps (Step 4).
Describe any cyber risks associated with all identified digital KPI-SCs identified inAssessment 2.Then map the KPI-Ps against the supply chain level (KPI-SCs) identified in yourAssessment 2to understand which processes contribute to the supply chain level goals (Step 5).
Using answer provided to above questions, create a comprehensive table that lists: what is measured for each DC process, how it is measured, the KPI-Ps for that process and their acceptance criteria, the associated supply chain KPIs (KPI-SCs) from yourAssessment 2, the digital data source for KPI-Ps, and how the measurement is digitally taken (Step 6) and the cyber risk associated with each KPI-Ps? Are there any KPI-SCs you would change to ensure that the measurements taken at the process level add insight and value to the measurements taken at the supply chain level?
You must create and submit your report as a single MS word file (including screenshots from inputs of all performed analysis and visualization). You need to explain and justify your answers with relevant information, output table, and visualizations.Further information can be found inthis workshop.
Important Hints:1- When answering the above questions, students should support their recommendations and insights by usingrelevantand appropriate sources ofacademic literature. The referencing style for this subject isAPA7.2- Students are required to submit their reports using only a single MS-Word file, which should contain their analysis, results, and discussion. No other format is acceptable.3- Make sure that the final results of your MS Excel analysis (relevant graphs and tables) are integrated into your MS-Word report using screenshots.
Marking Rubic
