《DHL:物流中的数字孪生(英文版)(39页).pdf》由会员分享,可在线阅读,更多相关《DHL:物流中的数字孪生(英文版)(39页).pdf(39页珍藏版)》请在三个皮匠报告上搜索。
1、A DHL perspective on the impact of digital twins on the logistics industry DHL Trend Research Powered by Page 1/39 Digital Twins in Logistics Contents Contact us 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 Underl
2、ying 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 Di
3、gital 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 compute
4、r-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 evo
5、lution 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 5/39Contents Contact us Attributes of a digital twin A digital twin is a virtual representation of a physical asset Continuously collects d
6、ata (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
7、, 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 p
8、oint 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 organ
9、izations 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 ind
10、ustrial machinery and services built on digital twins. Also companies offering product lifecycle management (PLM) such as PTC and Dassault Systmes are embracing digital twins as a fundamental core technology to manage product development from initial concept to end of life. Digital twin opportunitie
11、s 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 per
12、fect 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 pr
13、e-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 co
14、mputer 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
15、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
16、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
17、 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 6/39Con
18、tents Contact us Renders the spatial model and visualization of the digital twin, providing the medium for colla- boration and interaction with it. Virtual Reality Augmented, Mixed they also propose the corresponding solution. Digital twins will play a significant role in the development of future s
19、mart factories capable of making autonomous decisions about what to make, when and how, in order to maximize customer satisfaction and profitability. Early adopters of digital twins commonly report benefits in three areas: Data-driven decision making and collaboration Streamlined business processes
20、New business models Figure 5: GEs digital wind farm project leverages digital twin technology to make predictions on power output. Source: Harvard Business Review Figure 5 Page 8/39Contents Contact us “DRIVING IMPROVED BUSINESS OUTCOMES WITH DIGITAL TWINS” by Sam George, Director, Azure Internet of
21、Things, Microsoft For years now, Microsofts partners and customers have been using the cloud and digital twins to create breakthrough applications for a wide variety of industries. Weve learned that most digital transformation efforts benefit from context about the physical world by creating a digit
22、al twin. For example, customers have been creating digital replicas of buildings, connecting building systems like heating and cooling, as well as electrical systems to space utilization and people within the building. Energy sector customers are modeling their distribution grid to even out usage sp
23、ikes and ensure substations arent over utilized, enabling them to avoid unnecessary, costly infrastructure updates. Digital twins support the entire lifecycle, from design time, through construction and commissioning, all the way through to operations. With these digital twins customers can then pre
24、dict the future state of their models, as well as simulate potential changes. A digital twin helps bring value to the various IoT data into the model which fits their domain, and without it the IoT data has less value. It is critical to connect to the devices and sensors in the physical world to pro
25、vide real-time and operationalized data not just the idealized state of the system. This enables customers to take advantage of replicas alive with data. By adding artificial intelligence (AI) to the mix, customers can identify trends, forecast the future, optimize and simulate changes. This enables
26、 customers to save money and improve planning, products, and customer relationships. Because each digital twin presents a single visualization to key decision makers, it provides a single source of truth for an asset that drives stakeholder collaboration to resolve problems expediently. Digital twin
27、s can be used to automate tedious error-prone activities such as inspections, testing, analysis, and reporting. This frees teams to focus on higher-value activities. Digital twins are a major driver of product-as-a-service business models or servitization this is when companies abandon the one-time
28、sale of a product to instead sell outcomes by managing the full operation of the asset throughout its lifecycle. Digital twins allow manufacturers to monitor, diagnose, and optimize their assets remotely, helping to improve availability and reduce service costs. 1.5 THE DIGITAL TWIN THROUGH THE PROD
29、UCT LIFECYCLE Since their inception, digital twins have been closely associated with product lifecycle management (PLM). Digital twins are now used throughout the full product lifecycle, with a products twin emerging during the development process and evolving to support different business needs as
30、a product progresses through design, manufacturing, launch, distribution, operation, servicing, and decommissioning. Product Development. Data from the digital twins of previous products can be used to refine the requirements and specifications of future ones. Virtual prototyping using 3D modeling a
31、nd simulation allows faster design iterations and reduces the need for physical tests as depicted in figure 6. During the design phase, tests with digital twins can detect clashes between components, assess ergonomics, and simulate product behavior in a wide variety of environments. Together these m
32、easures help to reduce development costs, accelerate time to market, and improve the reliability of the final product. Production. Digital twins facilitate collaboration between cross-functional teams in the manufacturing process. They can be used to clarify specifications with suppliers and allow d
33、esigns to be optimized for manufacturing and shipping. If the organization manufactures a new digital twin with every product it makes, each model will incorporate data on the specific components and materials used in the product, configuration options selected by end customers, and process conditio
34、ns experienced during production. Digital twins of production lines as illustrated in figure 7 allow layouts, processes, and material flows to be tested and optimized before a new manufacturing facility is commissioned. Figure 6: Digital twins enable faster design iterations and rapid prototyping be
35、fore going into production. Source: Forbes Figure 6 Page 9/39Contents Contact us Operate & Service. Once the product passes into the hands of the end-user, its digital twin continues to accumulate data on its performance and operating conditions. This data helps to support maintenance planning, trou
36、bleshooting, and optimizing product performance. As products are updated and adapted or parts replaced, the digital twin is amended accordingly. Aggregate information from multiple digital twins can be analyzed to identify usage trends and optimize future designs. End-of-life. When a product is no l
37、onger required by the user, digital twin data guides appropriate end-of-life actions. Data on the operating conditions of specific components informs decisions on whether to re-use, recondition, recycle, or scrap these items. Material data can help to determine appropriate recycling and waste stream
38、s. And the data accumulated by the digital twin during this process can be retained for future analysis. 1.6 CHALLENGES IN APPLYING DIGITAL TWINS There are significant challenges to the widespread adoption of digital twins. Matching complex assets and their behavior digitally with precision and in r
39、eal time can quickly exceed financial and computing resources, data governance capabilities, and even organizational culture. This section identifies the stumbling blocks that may be encountered when leveraging digital twins. Cost. Digital twins require considerable investment in technology platform
40、s, model development, and high-touch maintenance. While most of these costs continue to fall, the decision to implement a digital twin must always be compared to alternative approaches that might deliver similar value at lower cost. If a company is interested in a small number of critical parameters
41、, these insights may be gathered more cost effectively via an IoT system based on sensors and a conventional database. Precise Representation. For the foreseeable future, no digital twin will be a perfect representation of its physical counterpart. Matching the physical, chemical, electrical, and th
42、ermal state of a complex asset is an extremely challenging and costly endeavor. This tends to force engineers to make assumptions and simplifications in their models that balance the desired attributes of the twin with technical and economic constraints. Data Quality. Good models depend on good data
43、. That may be a difficult thing to guarantee in digital twin applications which depend on data supplied by hundreds or thousands of remote sensors, operating in demanding field conditions and communicating over unreliable networks. As a minimum, companies will need to develop methods to identify and
44、 isolate bad data, and to manage gaps and inconsistencies in product data streams. Figure 7 Figure 7: Siemens is applying digital twin technology to optimize processes within its production lines. Source: Siemens Page 10/39Contents Contact us Interoperability. Despite significant progress in opennes
45、s and standardization, technical and commercial barriers to the exchange of data remain. And where a digital twin relies on simulation or AI technologies supplied by a specific vendor, it may be difficult or impossible to replicate that functionality using alternative providers, effectively locking
46、companies into long-term single-supplier relationships. Education. The use of digital twins will require staff, customers, and suppliers to adopt new ways of working. That presents challenges in terms of change management and capability building. Companies must ensure users have the skills and tools
47、 they need to interact with digital twins and must be sufficiently motivated to make the necessary transition. Leveraging the new technologies required for digital twins typically requires a profound cultural shift to fully realize the value afforded by this change. For more on this topic, see the e
48、xpert viewpoint on this page by Janina Kugel, Board Member and Chief HR Officer of Siemens on leadership and digital skills in times of technological transition. IP Protection. A digital twin is a reservoir of intellectual property and know-how. The models and data incorporated into a twin include details of a products design and performance. It may also contain sensitive data on customer processes and usage. That creates challenges around data ownership, identity protection, data control, and governance of data access by different