DHL-数字孪生技术赋能物流业（英文）-2019.8-39页.pdf

A DHL perspective on the impact of digital twins on the logistics industry DHL Trend ResearchPowered byPage 1/39Digital Twins in LogisticsContentsContact us ContentsPage 2/39Preface1 Understanding Digital Twins 41.1 The Digital Twin Comes of Age 41.2 What Makes a Digital Twin?61.3 Underlying Technologies Enabling Digital Twins 71.4 How Digital Twins Create Value 81.5 The Digital Twin Through the Product Lifecycle 91.6 Challenges in Applying Digital Twins 102 Digital Twins Across Industries 122.1 Digital Twins in Manufacturing 132.2 Digital Twins in Materials Science 142.3 Digital Twins in Industrial Products 152.4 Digital Twins in Life Sciences and Healthcare 162.5 Digital Twins in Infrastructure and Urban Planning 172.6 Digital Twins in the Energy Sector 192.7 Digital Twins in Consumer,Retail and E-commerce 203 Digital Twins in Logistics 213.1 Packaging&Container Digital Twins 223.2 Digital Twins of Shipments 233.3 Digital Twins of Warehouses and Distribution Centers 233.4 Digital Twins of Logistics Infrastructure 263.5 Digital Twins of Global Logistics Networks 274 Logistics Implications of Implementing Digital Twins 284.1 Inbound to Manufacturing 294.2 In-plant Logistics 304.3 Aftermarket Logistics 324.4 Orchestrating the Supply Chain 32Conclusion&Outlook 34Sources 36Pictorial Sources 37Further Information 38Recommended Reading 39Contact usFor centuries,people have used pictures and models to help them tackle complex problems.Great buildings first took shape on the architects drawing board.Classic cars were shaped in wood and clay.Over time,our modeling capabilities have become more sophisticated.Computers have replaced pencils.3D computer models have replaced 2D drawings.Advanced modeling systems can simulate the operation and behavior of a product as well as its geometry.Until recently,however,there remained an unbridged divide between model and reality.No two manufactured objects are ever truly identical,even if they have been built from the same set of drawings.Computer models of machines dont evolve as parts wear out and are replaced,as fatigue accumulates in structures,or as owners make modifications to suit their changing needs.That gap is now starting to close.Fueled by developments in the internet of things(IoT),big data,artificial intelligence,cloud computing,and digital reality technologies,the recent arrival of digital twins heralds a tipping point where the physical and digital worlds can be managed as one,and we can interact with the digital counterpart of physical things much like we would the things themselves,even in 3D space around us.Led by the engineering,manufacturing,automotive,and energy industries in particular,digital twins are already creating new value.They are helping companies to design,visualize,monitor,manage,and maintain their assets more effectively.And they are unlocking new business opportunities like the provision of advanced services and the generation of valuable insight from operational data.As logistics professionals,we have been thinking about how digital twins will change traditional supply chains,and how the logistics sector might embrace digital twins to improve its own processes.Our objective in writing this report is to share our findings and to help you answer the following key questions:What is a digital twin and what does it mean for my organization?What best-practice examples from other industries can be applied to logistics?How will my supply chain change because of digital twins?Looking ahead,we believe that the adoption of digital twins across industries will drive better decision making in the physical world.That,in turn,will drive significant changes in the operation of supply chains and logistics processes.In the logistics industry itself,digital twins will extend the benefits of IoT already being applied today.They will bring deeper insight into the planning,design,operation,and optimization of supply chains,from individual assets and shipments to entire global supply networks.We think there has never been a more exciting time for industries and logisticians to work together to leverage the full potential of digital twins.On behalf of us all at DHL,we look forward to collaborating with you in this exciting and potentially transformative field.PrefaceMatthias HeutgerSenior Vice PresidentGlobal Head of Innovation&Commercial Development,DHLMarkus Kueckelhaus Vice President Innovation&Trend Research,DHLPage 3/39ContentsContact us Chapter 1 Understanding Digital Twins1.1 THE DIGITAL TWIN COMES OF AGEFor many years,scientists and engineers have created mathematical models of real-world objects and over time these models have become increasingly sophisticated.Today the evolution of sensors and network technologies enables us to link previously offline physical assets to digital models.In this way,changes experienced by the physical object are reflected in the digital model,and insights derived from the model allow decisions to be made about the physical object,which can also be controlled with unprecedented precision.Page 4/39ContentsContact us1960198520002015The evolution of digital twins2017 Gartner lists digital twins as a top 10 tech trend2018Digital twins in product portfolios of all major software and industrial companies2002 Dr.Grieves concept of a digital twin emerges McLaren F1 digital twin technology for product develop-ment and performance prediction2011 NASA&USAF papers on digital twins2015 GE digital wind farm initiative1983-2001 AutoCAD becomes a de facto tool in nearly all engineering and design 1982 AutoCAD is born1977Flight simulators with computer simulation1970 NASA pairing technology on Apollo 13 missionSimulation tools drop in price,broadening availability and applicability to many engineering and design fields.Advanced simulation becomes central to complex,multi-disciplinary system design and engineering.An enhanced range of simulation applications enables model-based systems engineering.A virtual model(once only used in simulation)is seamlessly and continually updated across the entire lifecycle of a product,where the virtual model supports operation of the physical product through direct linkage and representation of its operational data.Simulation emerges in specific and highly specialized fields for expert use only.Computer-drivenSimulationSimulationApplicationsSimulation-driven System DesignDigital TwinsWhile the digital twin concept has existed since the start of the 21st century,the approach is now reaching a tipping point where widespread adoption is likely in the near future.Thats because a number of key enabling technologies have reached the level of maturity necessary to support the use of digital twins for enterprise applications.Those technologies include low-cost data storage and computing power,the availability of robust,high-speed wired and wireless networks,and cheap,reliable sensors.The use of a high-fidelity simulation or a direct physical replica to support the operation and maintenance of an asset has a long history.NASA pioneered a pairing approach during the early years of space exploration.When the Apollo 13 spacecraft suffered significant damage on a mission to the moon in 1970,NASA engineers were able to test and refine potential recovery strategies in a paired module on earth before issuing instructions to the stricken crew.To this day,pairing-now using digital models-remains a central part of the US space agencys strategy for managing space missions.At first the complexity and cost involved in building digital twins limited their use to the aerospace and defense sectors(see the timeline in figure 1)as the physical objects were high-value,mission-critical assets operating in challenging environments that could benefit from simulation.Relatively few other applications shared the same combination of high-value assets and inaccessible operating conditions to justify the investment.That situation is changing rapidly.Today,as part of their normal business processes,companies are using their own products to generate much of the data required to build a digital twin;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 1Figure 2Figure 1:The evolution of digital twins.Source:DHLFigure 2:GE has created a digital twin of the Boeing 777 engine specifically for engine blade maintenance.Source:GEPage 5/39ContentsContact usAttributes 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 assetContinuously connected to the physical asset,updating itself with any change to the assets state,condition,or contextRepresents a unique physical assetProvides value through visualization,analysis,prediction,or optimizationFigure 3As 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 Systmes 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:DHLPage 6/39ContentsContact usRenders the spatial model and visualization of the digital twin,providing the medium for colla-boration and interaction with it.Virtual RealityAugmented,Mixed&Provide the necessary tools to extract,share,and harmonize data from multiple systems that contribute to a single digital twin.StandardsAPIs and OpenHigh-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 ThingsLeverages 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 IntelligenceAllows storage and pro-cessing of large volumes of machine data from the asset and its digital twin in real time.Cloud ComputingUnderlying technologies of digital twinsToday,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 TWINSFive 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