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A FRAMEWORK TO INVESTIGATE KEY CHARACTERISTICS OF DIGITAL TWINS AND THEIR IMPACT ON PERFORMANCE

The modern world of manufacturing is in the middle of an industrial revolution with the digital and physical worlds integrating through cyber-physical systems.  Through a virtual model that is able to communicate with its physical system known as the Digital Twin, catered decisions can be made based on the current state of the system.  The digital twin presents immense opportunities and challenges as there is a greater need to understand how these new technologies work together. 

This thesis is an experimental investigation of the characteristics of the essential components of the Digital Twin.  A Digital Twin Framework is developed to explore the impacts of model accuracy and update frequency on the system’s performance measure. A simple inventory management system and a more complex manufacturing plant is modeled through the framework providing a method to study the interactions of the physical and digital systems with empirical data.

As the decision policies are affected by the state changes in the system, designing the Digital Twin must account for the direct and indirect impact of its components. 

Furthermore, we show the importance of communication and information exchange between the Digital Twin and its physical system.  A key characteristic for developing and applying a digital twin is to monitor the update frequency and its impact on performance.  Through the study there are implications of optimal combinations of the digital twin components and how the physical system responds.  There are also limits to how effective the Digital Twin can be in certain instances and is an area of research that needs further investigation.  

The goal of this work is to help practitioners and researchers implement and use the Digital Twin more effectively.  Better understanding the interactions of the model components will help guide designing Digital Twins to be more effective as they become an integral part of the future of manufacturing.

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• Doctor of Philosophy
• Industrial Engineering

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• West Lafayette

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Additional committee member 2, additional committee member 3, additional committee member 4, usage metrics.

• Modelling and simulation
• Manufacturing processes and technologies (excl. textiles)
• Manufacturing engineering not elsewhere classified
• Manufacturing management

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Methodology and application of digital twin-driven diesel engine fault diagnosis and virtual fault model acquisition

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Bibliometrics & citations, view options, recommendations, on undetectable faults and fault diagnosis.

The presence of an undetectable fault u i may modify the response of a detectable fault d j to a test set used for fault diagnosis. This may impact the accuracy of fault diagnosis based on the responses of single faults. Many state-of-the-art diagnosis ...

Selection of a fault model for fault diagnosis based on unique responses

In this paper, we describe a preprocessing step to fault diagnosis of an observed response obtained from a faulty chip. In this step, a fault model for diagnosing the observed response is selected. This step allows fault diagnosis to be performed based ...

Multiple Fault Diagnosis Based on Multiple Fault Simulation Using Particle Swarm Optimization

Fault diagnosis is extremely important to ramp up the manufacturing yield and in some cases to reduce the product debug time as well. In this paper, we have proposed a novel technique to analyze multiple fault diagnosis based on multiple fault ...

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• Diesel engine
• Fault diagnosis
• Classification algorithm
• Optimization algorithm
• Digital twin
• Research-article

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Thesis: Investigating the use of digital twins in networked commercial UAVs

Submitted by Kautilya Vemulapalli.

Abstract: The Commercial UAS industry is relatively new and has significant growth potential as new technology are incorporated into it, new applications are found, and new regulations are coming in place. Digital Framework, also a relatively new concept, has found acceptance in various industry but has not yet been applied to Commercial UAS while having great potential. This thesis uses the ARIES framework to investigates how this concept can be applied to Commercial UAS, the possible applications and architecture. Towards this end, a study of the enterprise landscape and a stakeholder analysis are conducted. Next the current architecture of the Commercial UAS is identified. From this understanding, a possible future is identified and possible applications from integrating Digital Framework into Commercial UAS are identified. Finally, an architecture for the future UAS was proposed and four possible architectures that incorporated a Digital Framework into Commercial UAS were identified.

To read the complete thesis, visit DSpace at the MIT Libraries .

Digital twins: optimizing warehouse performance to reduce costs

Ecommerce is the business success story of the last few years. Faster processing and improved technology provide consumers with access to a wider range of products than ever. With behavior change accelerated by the Covid-19 pandemic, millions of consumers have switched their shopping habits to embrace the online marketplace. Global ecommerce sales are expected to reach $6.3 trillion in 2023, an increase of 10.4% from the previous year. China makes up the bulk of online sales, with its revenue exceeding that of the rest of the countries in the top 10 combined, including the U.S., the U.K. and Japan. In fact, half of the 10 biggest ecommerce markets are in Asia, the most populous continent.

There’s room for more growth for ecommerce retailers but with the current slowdown in trade casting a shadow over economies around the world, there’s been a collective belt-tightening. More than ever, success hinges on the ability to manage the supply chain efficiently. It is essential to integrate technology into every aspect of logistics to reduce costs and optimize performance. Businesses need to ensure they continue to operate flexibly—able to manage both high and low volumes—in response to customer demand.

One tool to support reducing overheads for ecommerce retailers that’s emerging in this environment is the logistics digital twin (LDT), like the one that CEVA is currently trialing at a site in Größbeeren, Germany. Using a logistics digital twin will help fulfilment centers (warehouses) reduce hours and better allocate resources on the plant line to provide the necessary flexibility, while optimizing overheads and reducing cost.

What is a digital twin?

Grieves , the inventor of the technology, defines a digital twin as:

“ … based on the idea that a digital informational construct about a physical system could be created as an entity on its own. This digital information would be a ‘twin’ of the information that was embedded within the physical system itself and be linked with that physical system through the entire lifecycle of the system. ”

In other words, a digital twin is a form of simulation modeling: a parallel digital entity to a physical system based in the real world. The digital and physical systems are linked via the flow of data between them in real time, so the digital simulation updates and is informed by the information available from a real, physical system.

And it’s not just about warehouses. The possible applications of this technology are extremely wide. For example, local governments could create a digital twin of traffic movement around a city to help improve its flow. The digital twin would receive all the information from traffic lights, traffic cameras and so on, to provide an accurate model, which would update in real time. This type of digital twin could provide forecasts to support planning, for example of how traffic would flow if traffic light phasing changed.

Benefits of a digital twin

In the digital age, data is invaluable. Access to data-driven insights allows companies to make informed decisions, forecast demand and adapt to changing customer preferences swiftly.

The logistics digital twin has several distinct benefits when it comes to warehouse operations. The first is in the planning and design of the facility. A logistics digital twin could use data from equivalent facilities to ensure the optimum layout, staffing and tech for product movement.

In an operational logistics warehouse, a digital twin could use information from the warehouse management system coupled with data on the physical layout of the warehouse, as well as updates on employee numbers and availability, to plot the most effective movement of product through the warehouse, the numbers of staff required at which stations and so on. The difference between a digital twin and a straightforward warehouse management system is that the digital twin is a more sophisticated tool that provides an additional level of forecasting and processing based on constantly updated real-world data.

A logistics digital twin would also receive input on any anomalies in its predicted performance. For example, if a product wasn’t available where it was expected to be or there was additional pressure on a particular picking area, the digital twin could analyze actual performance against its predictions and identify areas for improvement, which could then be applied in the facility itself.

CEVA's logistics digital twin project

CEVA Logistics prioritizes streamlined logistics operations, ensuring that products move seamlessly from suppliers to warehouses and, ultimately, to customers. CEVA’s commitment to optimizing transportation, warehousing and inventory management helps ecommerce businesses to reduce costs and improve delivery speed.

CEVA’s logistics digital twin project started at Größbeeren, near Berlin just over a year ago. The LDT operates with real-time data from a range of sources within and external to the warehouse. The core of the LDT is a discrete events Monte Carlo simulator engine. Essentially, it’s a black box that uses algorithms employing a mathematical model of repeated random sampling of data to obtain numerical results.

This central engine is located on site at Größbeeren, tracking, mapping and forecasting physical activity from data collected in real time from cloud and on-premise databases. It aims to help management make the right decisions about resource allocation inside the different processing areas of the warehouse.

The LDT processes and verifies the data it receives and then uses it to run scenarios, based on forecasts of the likely number of customers at given times. It combines the resulting predictions with the different KPIs relating to production and tracks the projected volumes against the actual volumes that get processed in the warehouse. This gives a complete picture of the optimum possible performance and how the different areas and factors within the warehouse are performing in relation to this optimum result.

Once the tool is operating at full strength, with trained operators in place, it will reduce unproductive time and help the control room track what’s happening on the warehouse floor, in

almost real time, to keep production to the forecasted plan. The insights will allow the warehouse team to improve its use of resources and increase the overall operating capacity of the warehouse.

Movement of data through the digital twin

As the schema indicates, the central LDT engine collects activity tracking data in real time from the warehouse management system database and other databases that the simulation then processes and verifies. Once the core engine of the digital twin has processed the data, it sends the information to a reporting tool (TableauTM) to be displayed in the form of charts in the control room. Here, a trained operator can make decisions relating to the resources needed for upcoming warehouse shifts.

The LDT displays its results via Tableau on an hourly basis so managers can compare the processed volumes and see the optimal allocation of people and products. Decisions made by the operator close the digital twin’s information loop, as these real-world decisions affect the data collected by the database.

Closing the loop between the real and the digital world is a key point in the operation of a digital twin. There should be a constant exchange of information between the two worlds.

Development of the digital twin industry

The ultimate aim of the digital twin is to allow people to make the best use of the data, to empower management to make informed decisions when running a facility, to optimize operations, to enhance visibility, and to improve overall efficiency. The result of this optimization will mean more efficient use of resources; in terms of energy, people and raw materials, all of which increases sustainability and reduces cost.

There’s huge potential for digital twin use to improve logistics operations worldwide. But the digital twin industry is still in its infancy. Many of the building blocks of the technology have been available for some time but it’s only the recent increase in process monitoring and real time data collection that has enabled the development of digital twin operations.

Standardization is crucial for the industry to take its next steps. CEVA’s logistics digital twin in Größbeeren is just one project. Other projects in different countries and companies will not necessarily use the same system design or methodology. Future developments have to focus on the integration of systems, with interoperability the buzzword of the moment. It’s essential that the various systems can all ‘talk’ to each other effectively.

The International Standards Organization (ISO) responsible for industrial product data, ISO TC184, is currently collecting business cases of digital twins to understand how best to transform this new concept into a mature tool. It’s only with standardization of the digital twin domain that single projects can grow into an easily developed, routine tool that is straightforward and cost effective to use. That level of development would then give rise to mass adoption by logistics operators, so the ecommerce industry could realize all of the cost and sustainability benefits.

Defining Smart City Digital Twins

Two of those cities,  Columbus and Warner Robins , Georgia, received the awards for projects that involve digital twins. But what, exactly, is a digital twin? And how can the technology be used to solve community problems? 

We talked with  John Taylor , the Frederick Law Olmsted Professor and associate chair for graduate programs and research innovation in the School of Civil and Environmental Engineering, and  Neda Mohammadi , city infrastructure analytics director in Georgia Tech’s Network Dynamics Lab to get some answers. These are edited highlights from an interview.  

Q: What is a digital twin? 

Taylor: A digital twin is an intelligent, adaptive system that pairs virtual and physical worlds. In community development work, a Smart City Digital Twin (SCDT), like those used in Warner Robins and Columbus, pairs a real city to its digital counterpart to generate data-driven feedback loops of interactions between cities’ three main components: (1) human systems, which includes government, industry, and residents; (2) infrastructure systems, which are physical systems and the services they provide; and (3) technology systems, such as devices, sensors, and data analytics infrastructure. 

Q: They’ve been used in manufacturing for some time. How is that different from a SCDT?

Taylor: They're somewhat easier to implement in a manufacturing context, because everything's under control, under a roof. They model all the different manufacturing machinery and they use that to see when a part might need to be changed, and when they need to do maintenance. And they can play with the system, using real-time running data to see what happens if this piece does wear out. How bad would it be? They could either adjust that piece or adjust that machine or maintain it, whatever it might be, based on the scenario analysis. 

Q: How does that translate to the less controlled environment of a city? 

Taylor: It involves replicating multiple systems. For example, if a tall building is on fire, there will be multiple systems brought into play. First, you can see what's happening in the city at a basic level. You can see that there's traffic building up, for example. The next level is, why is it happening? And that's where it gets a little bit more interesting. Most of the digital twin work that we've seen — that anyone's doing out in the world — is to understand why things are happening the way they're happening. But really, the value starts to unlock the third and fourth levels. 

The third level is the “what if” scenario. In the context of a city, for example, in Midtown they've just installed new traffic signals. Hopefully, someone tested that out in advance. But one “what if” analysis could be: We've got bad traffic in Midtown. What if we put these traffic signals in the Tech Square area? What effect will that have on the flows in the city? With a digital twin, you can know that before you install the lights. That is one of the big opportunities. 

The fourth level is the idea that the infrastructure could start to intervene on behalf of the citizens. And so in the example of the tall building fire, the traffic signals might preemptively allow the fire trucks through. But they could also do other things like make all of the signals around the building red, so no traffic is moving and there's more space for people to evacuate the building. That would be something we might allow the systems to do for us. 

Q: How is that different from, for example, a project in Valdosta that allows first responder vehicles to change the traffic lights so they can get to an emergency more quickly? 

Mohammadi: A digital twin will update itself based on data that keeps coming in. If you think about the interaction with the traffic signal, it doesn't care about what happened five minutes ago, 10 minutes ago. At that moment, they know that the driver probably has a better situational awareness than the automated system. So they let the driver interfere and put useful inputs into the systems to make a better decision. 

The digital twin is accumulating data as it comes because it is based on prediction. The definition of prediction is looking at past data and, based on past experience, predicting what's likely to happen in the future. We know that time is a moving target. As we move on, things that happened in the past accumulate. There are more things that we know. A digital twin is really at the edge of this moving target. 

Q: Tell us about the river safety project in Columbus, which uses a digital twin to create an alert system to prevent drownings in the Chattahoochee River. The city was recently named a  Smart 20 award  winner by Smart Cities Connect for the  Citizen Safety Digital Twin project.  

Taylor: A good project from our perspective involves a complicated enough scenario where multiple sensors are involved. With the river safety project, we had to understand and predict water levels with a water level sensor. We use visual sensing to understand, if people were in the environment when hazardous conditions might begin to occur, whether we could get them out of harm's way before they get swept away into the water. 

We had to build a digital twin of the entire river basin, so we would know just what the danger is if the water level rises this much. Are the islands that people are standing on before the water level rises going to vanish? 

That one was particularly interesting to us. If you look at the smart city digital twin work we did first, it was related to energy consumption. We're increasingly excited about having a more direct effect on people's lives. This one is stopping people from drowning. 

Q: Tell us about the digital twin you developed for the  Warner Robins’  Citizen Safety Digital Twin for Community Resilience project, which deploys dynamic license plate reader cameras to help deter crime. It received the Intelligent Community Forum’s  Smart21 Community Award  at the  2024 Taipei Smart City Summit and Expo . 

Taylor: This project is pretty complicated from our perspective, because we had to build a geographic information systems (GIS) map of the city. We also have to know where crimes have been occurring. We've got more than 10 years of crime data, including very recent crime data. We're deploying sensors in part to deter crimes, but also to detect and collect more information about crime patterns. It comes down to taking the information about where crimes are occurring and coupling that with predictions about routes people would take if they did commit a crime, so that the car would come into view of one of the cameras. We don't hide the camera; we put it on a very visible structure, where we predict most likely the crimes are going to occur this week. We put this very visible thing to discourage people from doing anything once they realize they're being watched. And we found that it did in fact, reduce crimes in those high-crime spots by 20%.

Q: What are some other ways communities can use digital twins? 

Taylor: We published something this spring, and we're working on a funding proposal now, about how ambulances move around during a period of inundation — coastal flooding, coastal inundation, or heavy rains. We’ve met with Charleston, South Carolina, and Savannah about this. We looked at data in Virginia Beach to see if, in real time as the flooding is changing, we could deploy ambulances in different parts of the city ahead of where they're needed. It’s ambulance routing during a natural disaster event.

Q: Are there limitations to smart city digital twin technology?

Taylor: When we travel around and we present this, some clever student or faculty member will say, “Wouldn't a great research project be to figure out how to build a central platform for the collection of this data or a standard format for the way this data should be sent so that all the systems can talk to each other?” And they’re right. It's difficult to get the value across a whole city if you're only looking at one system at a time. A future research topic is figuring out those data flows and the centralization of that data. 

Karen Kirkpatrick

[email protected]

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Planning, maintenance, ROI: examples of the digital twin concept at Exotec

From industry to construction, healthcare and more, the digital twin is already used in various sectors, and is rising in logistics. In a recent article, we explored the features and benefits of this technology. Today, with insights from Paul Margot , Head of Product Management at Exotec, we delve deeper into how Exotec understands and applies digital twin logistics throughout our value chain, enhancing value for both current and future clients.

A digital twin solution for modelling and refining intralogistics automation systems, before installation

Before installing an automated system, during the pre-sales or deployment phase, the primary benefit of a digital twin is its ability to replicate the complete process of future intralogistics operations considering the expected performance, but also the spatial constraints of the warehouse. By simulating the behaviour of the planned system – such as good flows, robotic processes and operations of other automated equipment – based on collected data, a digital twin enables the representation and optimisation of a warehouse automation project , even before actual installation.

In intralogistics environments, with limited space, the upstream use of a digital twin supply chain is very beneficial: “ It allows us to ensure that the system integrates perfectly into the warehouse without requiring additional square metres and therefore, additional investment “, explains Paul Margot.

After installation: reduced downtime and improved maintenance, thanks to the digital twin

According to Paul Margot, another major advantage of the digital twin as designed by Exotec involves maintenance, and the rapid resolution of any issues that may arise over the long term in an automated warehouse . “ Being able to see what’s happening in real time and where it’s happening in the warehouse gives a clear picture of the incident “, describes Paul Margot, “ It’s a huge plus! “.

Our Warehouse Execution System (WES), Deepsky, provides comprehensive visualisation of warehouse operations and equipment interactions within the Skypod system , whether they are Exotec’s own or integrated from other manufacturers, regardless of their installation timeline. “ DeepSky is capable of aggregating all warehouse data, including data produced by our robots and data from third-party suppliers , ” he explains. “ It does not only display real-time data, it can also go back in time “. For example, if an incident occurs at 10.00am, you can know what happened in the preceding minutes. This highly useful feature facilitates the diagnosis and resolution of any incidents . Most of these can be solved remotely, from Exotec’s three global control centres, operating 24 hours a day, 365 days a year.

In other cases, if a problematic system component – such as a robot – requires attention, it will be isolated and taken to the maintenance area for repair by a technician, avoiding the need to halt all intralogistics operations. A complete shutdown could lead to significant economic consequences for customers. “ The digital twin not only allows customers to anticipate the correct operation of the automation system purchased and its integration into the warehouse, but also improves processes over time and optimise maintenance, so they can see that their purchase is secure “, he summarises. This means it takes less time to see a return on investment, a key benefit for them! “

Ensuring the ROI of a warehouse automation system, with or without a digital twin

While optimising system performance can certainly be achieved without a digital twin, using one helps visualise and test operational improvements , before they are implemented. This is particularly valuable given the increasingly central – albeit complex – role of return on investment in intralogistics decision-making.

But what exactly is the ROI we’re talking about here? Paul Margot explains: “ Approaches vary depending on the project. Some estimate the time required for their initial investment to be repaid. Others assess the “Total Cost Benefit” (TCB) over ten years, encompassing system costs meaning usage costs (such as energy expenditure) and maintenance costs. ”

Additionally, potential savings, particularly when it comes to labour, along with overall improvements in storage and order-fulfilment performance , contribute to ROI. “ Let’s take the example of a supermarket chain aiming to boost the performance of its Click & Collect service “, explains Paul Margot. “Manual preparation significantly limits the ability to supply customers more quickly and increase delivery slots. Automating this preparation process instantly enhances delivery performance and customer service capacity, and therefore, increases revenue. This revenue aspect should be taken into account when measuring the benefits of an order fulfilment system. ”

To minimise the return on investment time, and maximise the benefits from an automated system, continuous performance optimisation and leveraging operational insights are vital. With or without a digital twin, this ongoing monitoring and optimisation of warehouse operations are essential to make the most of your automated system. Long-term.

• July 9, 2024 Five warehouses of the future equipped by Exotec
• June 17, 2024 SME Corner: Checklist for Successful Automated Storage & Retrieval System Implementation
• June 17, 2024 5 Essential Change Management Strategies for Implementing Warehouse Automation
• July 4, 2024 Vandewiele automates with warehouse robots from Exotec
• June 11, 2024 Renault Group positions itself at the forefront of innovation in automotive logistics with Exotec
• May 22, 2024  A1 Sorter awarded Exotec Global Integrator of the Year 2024
• July 18, 2024 Exotec World Tour
• September 29, 2024 | Nashville CSCMP Edge
• October 2, 2024 | Manchester Tomorrow's Warehouse - Manchester

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