A DHL perspective on the impact of digital twins on the logistics industry DHL Trend Research Powered by Page 1/39 DigitalTwins inLogistics Contents Page 2/39 Preface 1 Understanding Digital Twins 4 1.1 The Digital Twin Comes of Age 4 1.2 What Makes a Digital Twin? 6 1.3 Underlying Technologies Enabling Digital Twins 7 1.4 How Digital Twins Create Value 8 1.5 The Digital Twin Through the Product Lifecycle 9 1.6 Challenges in Applying Digital Twins 10 2 Digital Twins Across Industries 12 2.1 Digital Twins in Manufacturing 13 2.2 Digital Twins in Materials Science 14 2.3 Digital Twins in Industrial Products 15 2.4 Digital Twins in Life Sciences and Healthcare 16 2.5 Digital Twins in Infrastructure and Urban Planning 17 2.6 Digital Twins in the Energy Sector 19 2.7 Digital Twins in Consumer, Retail and E-commerce 20 3 Digital Twins in Logistics 21 3.1 Packaging computer-aided design (CAD) and simulation tools are commonly used in product development, for example. Many products, including consumer electronics, automobiles, and even household appliances now include sensors and data communication capabilities as standard features. Figure 1 Figure 2 Figure 1: The evolution of digital twins. Source: DHL Figure 2: GE has created a digital twin of the Boeing 777 engine specifically for engine blade maintenance. Source: GE Page 5/39Attributes of a digital twin A digital twin is a virtual representation of a physical asset Continuously collects data (through sensors) Associated with a single, specific instance of a physical asset Continuously connected to the physical asset, updating itself with any change to the assets state, condition, or context Represents a unique physical asset Provides value through visualization, analysis, prediction, or optimization Figure 3 As corporate interest in digital twins grows, so too does the number of technology providers to supply this demand. Industry researchers expect the digital twins market to grow at an annual rate of more than 38 percent over the next few years, passing the USD $26 billion point by 2025. Plenty of technology players have an eye on this potentially lucrative space. The broad range of underlying technologies required by digital twins encourages many companies to enter the market, including large enterprise technology companies such as SAP , Microsoft, and IBM. These organizations are well positioned to apply their cloud computing, artificial intelligence, and enterprise security capabilities to the creation of digital twin solutions. In addition, makers of automation systems and industrial equipment such as GE, Siemens, and Honeywell are ushering in a new era of industrial machinery and services built on digital twins. Also companies offering product lifecycle management (PLM) such as PTC and Dassault Systèmes are embracing digital twins as a fundamental core technology to manage product development from initial concept to end of life. Digital twin opportunities are also attracting the attention of start-ups, with players such as Cityzenith, NavVis, and SWIM.AI developing their own offerings tailored to particular niches and use cases. 1.2 WHAT MAKES A DIGITAL TWIN? In practice with so many different applications and stakeholders involved, there is no perfect consensus on what constitutes a digital twin. As our examples show very clearly later in this report, digital twins come in many forms with many different attributes. It can be tempting for companies to ride the wave of interest in the approach by attaching a digital twin label to a range of pre-existing 3D modeling, simulation, and asset-tracking technologies. But this short sells the complexity of a true digital twin. Most commentators agree on key characteristics shared by the majority of digital twins. The attributes that help to differentiate true digital twins from other types of computer model or simulation are: A digital twin is virtual model of a real thing. A digital twin simulates both the physical state and behaviour of the thing. A digital twin is unique, associated with a single, specific instance of the thing. A digital twin is connected to the thing, updating itself in response to known changes to the things state, condition, or context. A digital twin provides value through visualization, analysis, prediction, or optimization. The range of potential digital twin applications means that even these defining attributes can blur in some situations. A digital twin may exist before its physical counterpart is made, for example, and persist long after the thing has reached the end of its life. A single thing can have more than one twin, with different models built for different users and use cases, such as what-if scenario planning or predicting the behavior of the thing under future operating conditions. For example, the owners of factories, hospitals, and offices may create multiple models of an existing facility as they evaluate the impact of changes in layout or operating processes. Figure 3: Characteristics of a digital twin. Source: DHL Page 6/39Renders the spatial model and visualization of the digital twin, providing the medium for colla- boration and interaction with it. Virtual Reality Augmented, Mixed & Provide the necessary tools to extract, share, and harmonize data from multiple systems that contribute to a single digital twin. Standards APIs and Open High-precision sensors enable continuous collection of machine data, state, and con- dition from the physical asset to its digital twin in real time via wireless networks. Internet of Things Leverages historical and real-time data paired with machine learning frameworks to make predictions about future scenarios or events that will occur within the context of the asset. Artificial Intelligence Allows storage and pro- cessing of large volumes of machine data from the asset and its digital twin in real time. Cloud Computing Underlying technologies of digital twins Today, researchers and technology companies have built digital twins at every scale from atoms to planets. The smallest digital twin can represent the behavior of specific materials, chemical reactions, or drug interactions. At the other extreme, a large digital twin can model entire metropolitan cities. The majority of digital twins sit somewhere in the middle, with most current applications aimed at more human- scale problems, especially the modeling of products and their manufacturing processes. One notable trend is the development of larger, more complex digital twins as organizations evolve from modeling single products or machines to modeling complete production lines, factories, and facilities. Similarly, efforts are underway to create digital twins of entire cities or even of national-scale energy infrastructure and transport networks. The UK is even working on plans to develop a digital twin of the whole country to serve as a repository for multiple sources of data related to buildings, infrastructure, and utilities. 1.3 UNDERLYING TECHNOLOGIES ENABLING DIGITAL TWINS Five technology trends are developing in a complementary way to enable digital twins, namely the internet of things, cloud computing, APIs and open standards, artificial intelligence, and digital reality technologies. The Internet of Things (IoT). The rapid growth of IoT is one important factor driving the adoption of digital twins. IoT technologies make digital twins possible because it is now technically and economically feasible to collect large volumes of data from a wider range of objects than before. Companies often underestimate the complexity and volume of data generated by IoT products and platforms, requiring tools to help them manage and make sense of all the data they are now collecting. A digital twin is often an ideal way to structure, access, and analyze complex product-related data. Digital twins rely on a host of underlying technologies that are only now reaching the point where they can be applied reliably, cost effectively, and at scale. Cloud Computing. Developing, maintaining, and using digital twins is a compute- and storage-intensive endeavor. Thanks to the continually falling cost of processing power and storage, large data center networks with access provided via software-as-a-service (SaaS) solutions now enable companies to acquire exactly the computing resources they need, when they need them, while keeping costs under control. APIs & Open Standards. Closed, proprietary-by-design simulation tools and factory automation platforms are increasingly becoming a thing of the past. Technology companies created and protected their own data models, requiring intensive, ground-up software development to build infrastructure from scratch for each new product. Figure 4: Technologies behind digital twins. Source: DHL Figure 4 Page 7/39