1、Place chart/diagram/image hereREPORT2023The Industrial MetaverseMaking the invisible visible to drive sustainable growth Greg Egan,science fiction author,Permutation City“What am I?The data?The process that generates it?The relationships between the numbers?”Blue Shift /REPORT 0033The Industrial Met

2、averseMaking the invisible visible to drive sustainable growth AuthorsDr.Albert Meige,Director of Blue Shift,Arthur D.LittleRick Eagar,Partner Emeritus,Arthur D.LittleContributorsEngin Beken,Partner,Arthur D.LittleMartin Glaumann,Partner,Arthur D.Little Bernd Schreiber,Partner,Arthur D.LittleArnaud

3、Siraudin,Associate Director,Arthur D.LittleJaime Capdevila,Consultant,Arthur D.LittleOlivia Dehlin,Business Analyst,Arthur D.LittleArtist-in-residenceLeo Blondel,scientistCONTENT-CONTENT-CONTENT-CONTENT-5Executive summary 6Preamble 81.What is the context for the Industrial Metaverse?122.What does In

4、dustrial Metaverse really mean?22Interlude:Make the invisible visible 323.Where is Industrial Metaverse technology today?364.What is the potential value of the Industrial Metaverse to business?505.What should companies do?60Appendix#1:Technology readiness levels 68Appendix#2:Selected company profile

5、s 72Appendix#3:Industrial Metaverse use cases 806Blue Shift /REPORT 003Executive summary In business and popular media,the Metaverse hype wave is already entering its disillusionment phase,superseded by artificial intel-ligence(AI).Yet the Industrial Metaverse,perhaps less exciting in the popular im

6、agination,has never really been part of the hype.Is this where the real value of the Metaverse will be realized?There are differing views about what the Industrial Metaverse is versus the Metaverse as a whole,and how it differs from existing digital twin technologies normally considered under Indust

7、ry 4.0.In this Report,we provide an evidence-based perspective,assessing current technology status,summarizing use cases and market potential,and offering recommendations for companies going forward.We conclude that the Industrial Metaverse is best defined as a“con-nected whole-system digital twin w

8、ith functionalities to interact with the real system in its environment,allowing decision makers to better understand the past and forecast the future.”As such,the Industrial Metaverse is a further evolution of discrete digital twin technologies that already exist today(e.g.,for factories or plants)

9、but progressively extended to ultimately represent an end-to-end,real-world industrial system,including external elements outside the company and the environment within which it operates.The Industrial Metaverse thus provides a transformative tool to elevate the use of digital simulation technology

10、to the level of strategic decision-making.This is important for dealing with the increasing complexity and accelerated pace of development company leaders face and is especially valuable for developing effective sustainable growth strategies.7Blue Shift /REPORT 003While achieving a full-scale,connec

11、ted,end-to-end,whole-system digital twin may be five or more years away especially due to devel-opment gaps in connectivity,computing capacity,and scaled-up AI intermediate steps are possible in the short term,and many Industrial Metaverse use cases already exist.These can be grouped into four cate-

12、gories:(1)optimization(e.g.,digital twins and augmented reality AR for operations/maintenance efficiency and productivity improvements);(2)training(e.g.,virtual/remote training tools);(3)technical tools(e.g.,design/construction/maintenance digital tools);and(4)management tools(e.g.,virtual meeting/c

13、ollaboration/interaction tools).The next development steps will include extending digital simulations beyond discrete physical assets toward multiple connected assets,internal processes,and functions,and finally extended upstream and down-stream activities involving the entire industrial system.We e

14、stimate the current Industrial Metaverse market to be around US$100-$150 billion,with a conservative 2030 forecast of around$400 billion but with a potential upside of more than$1 trillion.The benefits to business in terms of productivity could be multiple double-digit percentages.The growth of the

15、Industrial Metaverse will not necessarily depend on widespread adoption of the consumer Metaverse because its utility and value for business depend more on the quality of complex system simulation and less on features such as immersivity and human-machine interface technology.Our con-clusion is that

16、 the Industrial Metaverse has elements of both evolu-tion and revolution:evolution in terms of the potential for further stepwise penetration of Industry 4.0 technologies,and revolution in terms of how the convergence of these technologies especially connectivity,AI,complex systems simulation,and vi

17、sualization pow-ered by increasing computing capacity has the potential to trans-form business productivity.Companies need to consider their strategy for the Industrial Metaverse in the context of their broader digitalization strategy,while also considering implementation barriers.We recommend that

18、companies consider four steps to reap the benefits:1.Review strategy.Develop a clear picture of the digitalization strategy,journey,and current position.2.Identify opportunities.Discover value-adding Industrial Metaverse opportunities and develop a roadmap.3.Implement pilot projects.Adopt a test-and

19、-learn approach and manage change proactively.4.Build and align the ecosystem.Create a win-win situation with ecosystem partners.The Industrial Metaverse provides a transformative tool to elevate the use of digital simulation technology to the level of strategic decision-making.Preamble When I was a

20、 child,10 or 11 years old,I remember thinking that if it were possible to“scan”the positions and speeds of all the atoms and molecules that make up my body at a given moment and put all this information in a computer capable of simulating all the physico-chemical reactions that govern the universe,t

21、hen this digital copy would not be distinguishable from the original.There would then be two of“me”the original,based on carbon chains,and the digital copy,whose substrate would be silicon.The copy would be as conscious as the original,and it would be just as convinced of being me.Blue Shift /REPORT

22、 0038I didnt know it yet,but I had a materialistic approach to conscious-ness.I didnt know about Heisenbergs uncertainty principle,which prohibits knowing with infinite precision the position and the speed of the same particle.Thus,the perfect scan was therefore not pos-sible.Not to mention the comp

23、uting power required to run such a simulation is still quite far from being available.However,without knowing it,I had conceptualized what the industry would one day call“digital twins.”Many years later,my friend David Louapre,Scientific Director at Ubisoft and creator of the popular“Science tonnant

24、e”YouTube channel,recommended that I read the science fiction book Permutation City by Australian author Greg Egan,released in 1994.Immersing myself in Permutation City,the digital twin story of my childhood suddenly came back to me like a Proust digital madeleine.Because indeed,one of the key eleme

25、nts of the plot is based on the fact that in the near future,around 2050,it became possible to upload ones consciousness to a computer.The problem is that in order for a persons digital twin to be able to interact with a person in the real world,their simulation must run fast enough that is,enough c

26、omputing power must be available.If the computing power is insufficient,the time of the simulated person,although remaining subjectively unchanged,passes more slowly than the real time.And if,on the contrary,the computing power is excessive,the simulated world unfolds faster than the real world.It t

27、hen becomes possible to foresee the future.9Blue Shift /REPORT 003There would then be two of“me”the original,based on carbon chains,and the digital copy,whose substrate would be silicon.These are exactly the objectives that we seek to achieve with the Industrial Metaverse.The Industrial Metaverse is

28、 the extension of what has been called“Industry 4.0”for at least a decade.It is the digital twin of a complex system that allows you to project yourself through time and immerse yourself in space.It makes it possible to numerically anticipate the future consequences of a decision or an event on a co

29、mplex system whatever this system:a machine,a factory,a company,a value chain.As we will see in this Report,the Industrial Metaverse has three major advantages over Industry 4.0:1.Modeling and simulation of complex systems approaches that were still part of the academic world 10 years ago and that a

30、re now changing the game in the industrial world make it possible to create virtual what-if scenarios.The accessible data is no longer just data from the past and the present,but is now also data about the future.It becomes possible to project in time.2.Thanks to AI and virtual reality(VR),it finall

31、y becomes possible to bring out meaning and visualize the industrial system that must be managed and thus overcome the limits of the human brain,which is not well adapted to apprehend a complex system and its emergences the famous butterfly effect,resulting from a decision or an event.3.Interoperabi

32、lity and interconnection between the physical industrial system,its digital twin,and the various stakeholders now,more and more,make it possible to manage it optimally.10Blue Shift /REPORT 003Thanks to the Industrial Metaverse,it has become pos-sible to make the invisible vis-ible to drive sustainab

33、le growth.While ensuring economic growth,we believe that the Industrial Metaverse will be part of the solution to the climate challenge.And,actually,it is interesting to note that an anagram of Mtavers Industriel(Industrial Metaverse)is:11Blue Shift /REPORT 003verdures militantes/militant greenery A

34、lbert Meige,PhDThis is quite intriguing and,as always,anagrams move in mysterious ways.CHAPTER12113WHAT IS THE CONTEXT FOR THE INDUSTRIAL METAVERSE?1What is the context for the Industrial Metaverse?Industrial Metaverse is a term com-monly applied to the set of Metaverse applications designed for bus

35、iness users.In our previous Report,“The Metaverse,Beyond Fantasy,”we looked at the Metaverse as a whole,its appli-cations,underlying technologies,and impact.In this Report,we focus spe-cifically on Metaverse applications for businesses and enterprises,therefore excluding applications and experi-ence

36、s for individual consumers(e.g.,gaming,entertainment,and social interaction),although there is an overlap where consumers interact with businesses at the customer interface.Blue Shift /REPORT 00314Today,the Industrial Metaverse as a concept is both commonly understood and,at the same time,variously

37、interpreted.Business managers are already well-versed in the potential of digitalization,and many are already well along the digital transformation journey.Digital models of physical products and assets,increased connec-tivity,and new visualizations are very much part of this journey.So what does th

38、e Industrial Metaverse really bring in addition?How sig-nificant is the creation of an immersive virtual environment to run-ning a typical business?Is Industrial Metaverse really revolutionary,or is it in fact more evolutionary?In this Report,we examine the background and context of the Indus-trial

39、Metaverse,define what it means,set out a conceptual architec-ture,explore its key technological building blocks,assess its value to business both now and in the future,and propose how businesses should go about exploiting its potential.The Report is based on in-house research,client experience,and c

40、ontributions from interviews with experts across industry and academia.Industry 4.0&the Industrial Metaverse todayThe Industrial Metaverse is frequently cited as the next phase of evolution after Industry 4.0,moving from cyber-physical systems to a fully virtualized world(see Figure 1).Fig 1 The Ind

41、ustrial Metaverse is often seen as the next phase of evolution after Industry 4.0Source:Arthur D.LittleSource:Arthur D.LittleFig 1 The Industrial Metaverse is often seen as the next phase of evolution after Industry 4.0Industry 1.0Mechanization 18th/19th centuryIndustry 2.0Mass production 19th/20th

42、centuryIndustry 4.0Cyber-physical systems2010s onwardIndustry 3.0Automation1960s onwardIndustrial MetaverseVirtualizationToday onward15Blue Shift /REPORT 003The term“Industry 4.0”(or the Fourth Industrial Revolution)was popularized around a decade ago and refers to the deployment of a wide range of

43、technologies with the potential to transform industry through new cognitive tools,connectivity,virtual modeling(including digital twins),collaboration tools,and new techniques for manufacturing and supply chain,including advanced robotics and blockchain(see Figure 2).Of these various technologies,so

44、me are especially relevant for the Industrial Metaverse.These include AI,connectivity technologies,virtualization and simulation technologies,and collaboration/interaction tools(see Chapter 3 for further exploration of key technological building blocks).Industry 4.0 technologies already provide sign

45、ificant benefits to those companies that have successfully deployed them to help transform their businesses.For example,according to data from case examples in Arthur D.Littles(ADLs)Operational Excellence Database,these benefits are often double-digit in scale:-15%-30%reductions in operational capit

46、al deployed-10%-30%reductions in supply chain costs-30%increased utilization of production capacity-10%-40%reductions in maintenance costsFig 2 Industry 4.0 building blocksSource:Arthur D.Little;Operational Excellence Database,2020 Source:Arthur D.Little;Operational Excellence Database,2020 Fig 2 In

47、dustry 4.0 building blocksBlockchainVirtual workplace/workplace 4.0ConnectedthingsVirtual modeling/simulationAugmentedreality(AR)Cyber-physical systems/virtualized networksCognitive,self-learning systems/botsCONNECTEDSmart energysystemsCollaborative,smart machines&robotsHUMAN-CENTEREDE-learning/mass

48、ive open online course(MooC)Collective intelligence/crowdsourcingVIRTUALCOGNITIVEAutonomoustransport systemsBig data/advanced analyticsVALUE-ADDIntegrated ecosystems/decentral(mobile)value addAdditive manufacturing/3D printingTechnologies relevant to Industrial Metaverse16Blue Shift /REPORT 003Howev

49、er,overall progress in achieving Industry 4.0 maturity still has a long way to go.For example,a 2020 survey of 70 German compa-nies by Acatech that measured progress against a six-stage Industry 4.0 maturity scale showed that the vast majority of firms(80%)were still in the second stage(connectivity

50、),with only a minority(4%)having progressed toward the next stage of creating digital twins(visibility).1 No companies had progressed toward the last three maturity stages,which involved modeling complex interactions,sim-ulating future-oriented what-if scenarios,or creating self-governing systems.As

51、 we will show later in this Report,these functions are key parts of what the Industrial Metaverse promises to deliver.It is well-known that making progress on implementation of digital and Industry 4.0 technologies is challenging for any large company.This is because it typically involves fundamenta

52、l transformation of the way the business operates;it is not possible to simply“bolt on”these new technologies to existing assets,business processes,and ways of working.Typical challenges include:-High initial investment,especially in data gathering and management-Limitations imposed by legacy IT sys

53、tems-A reluctance to embrace the extent of the required business transformation-Difficulties in realizing the targeted business returns from digital investments within short-enough timescalesThe Acatech study also highlighted common barriers toward Industry 4.0 progress,including:-A lack of common s

54、tandards-Fragile information system integration-A reluctance to engage in interdepartmental cooperation-Inadequate employee involvement in change processesIf we accept that the Industrial Metaverse is a further stage of evolution beyond Industry 4.0,then it follows that its successful implementation

55、 at scale will also require overcoming these common barriers toward Industry 4.0 implementation.1 Schuh,Gnther,et al.“Using the Industrie 4.0 Maturity Index in Industry:Current Challenges,Case Studies and Trends.”Acatech,German National Academy of Science and Engineering,2020.Making progress on impl

56、ementation of digital and Industry 4.0 technologies is challenging for any large company.17Blue Shift /REPORT 003Why the changing business landscape is leading to unmet needsIt is useful to consider how the business landscape has transformed since the early days of Industry 4.0.Today,as well as the

57、constant need to further improve productivity,one of the biggest challenges facing business leaders is how to achieve sustainable net-zero impact growth.Contributing to this challenge are three key factors,as illustrated in Figure 3:complexity,acceleration,and cognition.ComplexityIndustrial systems

58、are increasingly becoming complex systems that are subject to emergent properties making them much harder to manage.A complex system is a system having a large number of:-Elements(or parts)-Relations(connections between the parts)-Nested systems(systems within the system)Examples of complex systems

59、include cities,the climate,and living organisms.Complex systems differ from complicated systems.Complicated systems run essentially like clockwork,in a predictable manner.They may have many elements,sub-elements,and inter-actions,but the structure remains stable over time and they lend themselves to

60、 problem solving using structured analysis through decomposition of the elements.Up to now,most business manage-ment approaches have been based on the idea that a companys assets,processes,and organization can be approximated to behave,at least in large part,like a complicated system.Fig 3 The chall

61、enges to industrial organizationsSource:Arthur D.LittleSource:Arthur D.LittleFig 3 The challenges to industrial organizationsComplexityThe complexity of industrial systems has mushroomed Acceleration and the pace of change for business is accelerating Cognition which challenges the capacity of the u

62、naided human brain SUSTAINABLE GROWTH to tackle critical complex systemic problems,such as maintaining growth with net-zero cradle-to-grave sustainability impact.18Blue Shift /REPORT 003However,increasingly this approximation is becoming unrealistic.For example,consider the recent changes in:-Elemen

63、ts.In the last two years alone,the volume of enterprise data has risen by over 40%to more than 2 petabytes.2-Relations.As a proxy for relations,the number of Internet of Things(IoT)connections grew by nearly 20%in 2022 versus 2021,reaching 14.4 billion.3 Partner ecosystem networks have greatly incre

64、ased in size and complexity in the last decade.Demonstrating this,the proportion of a typical car manufactured by third-party suppliers increased from 56%in 1985 to about 82%in 2015,4 a proportion that is still largely the case today.-Nested systems.The number of nested“layers”in industrial system a

65、rchitectures has increased.In aerospace,for example,the number of specification elements in the latest passenger jet designs is more than 10 x that of its predecessors.What this means is that the industrial system of any large company plants,processes,people,finance,customers,supply chain,partners,s

66、hareholders,and their environment increasingly has to be treated as a complex system for management purposes.Complex systems are inherently difficult to manage due to three specific properties:1.Emergence.New,unexpected properties emerge from the interactions between the parts.2.Non-linearity.Feedba

67、ck loops between the parts may lead to exponential behaviors.3.Resilience.A small issue within part of the system does not necessarily lead to its failure.These properties mean that the behaviors of a com-plex system are very hard to predict,introducing a high degree of uncertainty into the impact o

68、f management decisions.Managers relying on simplified models of their systems to help make decisions find that those models are often inadequate.Indeed,failure to ade-quately recognize inherent uncertainties is one of the main reasons why new IT systems often fail to deliver the expected benefits.2

69、Taylor,Petroc.“Volume of Enterprise Data Worldwide 20202022,by Location.”Statista,23 May 2022.3 IoT Analytics.“IoT 2022:Connected Devices Growing 18%to 14.4 Billion Globally.”IoT for All,1 September 2022.4 Kallstrom,Henry.“Suppliers Power Is Increasing in the Automobile Industry.”Yahoo!News,6 Februa

70、ry 2015.AccelerationThe pace of change for business is continuing to accel-erate,causing these unpredictable emergent effects to occur faster and faster.Three factors are driving this acceleration:1.Knowledge and enabling technologies are being developed and adopted at an increasingly rapid rate,wit

71、h a greater number of exponential technologies driving transformational change.2.The lifecycle of companies and products is shortening.For example,the average lifespan of S&P 500 companies has fallen from around 35 years in the 1970s to around 20 years today.Product lifespans in many sectors are red

72、ucing,with increasing rates of disruption by new entrants and faster market penetration times.3.Supply chains are increasingly subject to change and disruption.Ever more complex supply chains and partner ecosystems are being impacted by global and rapid disruptions such as climate change,pandemics,w

73、ar in Europe,and other geopolitical instabilities.Additionally,sustainability trends such as bio-sourcing are leading to more supplier variability.This acceleration means that companies need to be able to respond to changing circumstances more rapidly and make strategic decisions faster.19Blue Shift

74、 /REPORT 003CognitionThe limitations of human cognition mean that making good deci-sions within these faster-moving,unpredictable systems is difficult.The human brain is not designed to deal well with complex systems humans tend to think in a Cartesian way,breaking problems into smaller parts,which

75、oversimplifies complexity.In these situations,humans tend to be especially susceptible to cognitive biases relying on information that matches previous ideas and belief systems which often leads to incorrect decisions being made.More and more business phenomena feature nonlinear or exponen-tial beha

76、viors,which the human brain does not evaluate well.For example,a small error in a predicted technology development“curve fit”can cause a big discrepancy down the line,as shown in Figure 4.Examples of this include maturity in autonomous vehicles and nuclear fusion,both of which have been repeatedly o

77、verestimated,while recently AI has become exponential after many decades in the flat part of the curve.Fig 4 The impact of small errors grows over timeSource:Arthur D.Little;Miller,George A.“The Magical Number Seven,Plus or Minus Two:Some Limits on Our Capacity for Processing Information.”Psychologi

78、cal Review,Vol.63,1956.Source:Arthur D.Little;Miller,George A.“The Magical Number Seven,Plus or Minus Two:Some Limits on Our Capacity for Processing Information.”Psychological Review,Vol.63,1956.Fig 4 The impact of small errors grows over timeTimeMaturityTodayYesterdayTomorrowCommercializationDevelo

79、pmentA small error in maturity estimation today means a big difference in time to market tomorrow.20Blue Shift /REPORT 003SustainabilityMeanwhile,sustainability imperatives mean that end-to-end complex industrial system control is growing in importance.Most developed countries have set goals to be n

80、et zero between 20402060.This means that companies face the challenge of continuing to achieve economic growth while reducing their environmental impact to net zero.Achieving progress on net zero impact while main-taining growth requires companies to exert control across the end-to-end complex indus

81、trial system(e.g.,managing Scope 3 as well as Scope 1 and 2 emissions).This means:-Sharing current relevant data(e.g.,operational and environmental performance data)across the entire industrial system of which they are a part,including all third parties involved.-Being able to predict the sustainabi

82、lity impacts of changes to any part of this system,including supply chain,manufacturing,distribution,sales,in-service,and end-of-life disposal/recycling.Today this control typically is attempted by conducting discrete impact analyses and collaborating with third parties to share information and take

83、 collective action.However,achieving true end-to-end control is difficult due to a mix of technical challenges and institutional,organizational,and cultural barriers around the neces-sary degree of data sharing and collaboration.On the technical side,the technologies required for gathering,monitorin

84、g,analyzing,and simulating the large amounts of data necessary to predict end-to-end sustainability impacts in a complex,large-scale industrial system are not fully mature.While none of these challenges are completely new to business,approaches used to tackle them today will increasingly be inadequa

85、te to meet the needs of the coming years,given current trends.How the Industrial Metaverse addresses these unmet needsIndustry 4.0 technologies,including digital twins,are already providing significant benefits,but as we have discussed,without further evolution they cannot meet all of tomorrows need

86、s for a variety of reasons:-They are mainly limited to discrete physical systems,not the“whole system.”-Decision-making is too static and siloed.-Net zero obligations on industry mean that more effective,whole-system management will be increasingly essential in the coming years.The Industrial Metave

87、rse provides a way to move beyond Industry 4.0 and overcome challenges around com-plexity,acceleration,and cognition to deliver sustain-able growth by:-Allowing companies to make informed C-level decisions based on a dynamic,forward-looking,whole-system approach.-Identifying performance improvements

88、 and the impacts of change more rapidly and effectively.-Helping deliver on obligations to manage overall impacts and achieve sustainable growth.In the next chapter,we will consider in more depth what Industrial Metaverse means and look at its key components.21Blue Shift /REPORT 003CHAPTER22223WHAT

89、DOES INDUSTRIAL METAVERSE REALLY MEAN?2What does Industrial Metaverse really mean?In this chapter,our contention is that the concept of the Indus-trial Metaverse extends far beyond being merely a digital replica of a piece of machinery or a manufacturing plant.It fundamentally serves as a digital re

90、flection of an entire corporation in its opera-tional environment,furnishing decision-makers with insights into historical events and facilitating future predictions.The quintes-sential role of the Industrial Metaverse is to illuminate the unseen make the invisible visible.It plays a crucial role in

91、 promoting a holistic perspective across compartmentalized units,enabling detailed what-if simulations that provide foresight into poten-tial future scenarios.It unveils the intricate interrelationships within the system.Additionally,it enhances our understanding of the overall behavior and impacts

92、of the entire system,an asset of immense importance for fostering sustainability.Blue Shift /REPORT 00324Definition&frameworkThere are many definitions of what the term“Industrial Metaverse”means,as can be seen in the descriptions shown in Figure 5 from some of the key players contributing to its de

93、velopment.While there are differing emphases,one element of broad consensus is that Industrial Metaverse is centered on the creation of digitalized models,simulations,or twins of the real world.Another way to build a useful definition is to consider the techno-logical building blocks that are releva

94、nt for the Industrial Metaverse,which,as we have seen,all exist already under the Industry 4.0 umbrella(see Figure 6).Fig 5 Industrial Metaverse definitionsSource:Arthur D.LittleSource:Arthur D.LittleFig 5 Industrial Metaverse definitionsA virtual world in which we can interact in real time with pho

95、torealistic,physics-based digital twins of our real world.We believe digital twins are the building blocks for the Metaverse.Industrial Metaverse enables humans and AI to work together to design,build,operate,and optimize physical systems using digital technologies.Industrial Metaverse enables indus

96、trial companies of all sizes to create closed-loop digital twins with real-time performance data,ideal for running simulations and AI-accelerated processes for advanced applications such as autonomous factories that rely on intelligent sensors and connected devices.A systematic discipline that combi

97、nes hardware data conversions through analytics/machine learning,time histories through cyber-infrastructure,cognitionthrough human-machine interface,and configuration through the Metaverse.A real-time,persistent simulation space that is the sum of all virtual worlds,digital twins,and augmented real

98、ity that connects digital economic assets and infrastructure on a global scale in the industrial and commercial setting.The Industrial Metaverse enables the creation of digital twins of places,processes,real-world objects,and the humans who interact with them.IndustrialMetaverse.orgConsider the tech

99、-nological building blocks that are relevant for the Industrial Metaverse.25Blue Shift /REPORT 003As shown in Figure 6,what makes the Industrial Metaverse distinct from Industry 4.0 is the ongoing development and convergence of many of these technologies.Digital twins,with some form of real data exc

100、hange with their physical counterparts and application of AI to assist interpretation and analysis,already exist today(e.g.,BMWs iFACTORY;see also Chapter 4 and Appendix 3).It is the further development and convergence of these technologies alongside complex system modeling and simulation,data visua

101、lization,and collaboration technologies,all enabled by greater computing power that are leading to the Industrial Metaverse as a new concept able to meet the challenges of complexity,acceleration,and cognition as well as sustainable growth,as set out in Chapter 1.Based on all of this,we propose the

102、following definition for what,ultimately,the Industrial Metaverse should become:Fig 6 The differences between Industry 4.0 and the Industrial MetaverseSource:Arthur D.LittleSource:Arthur D.LittleFig 6 The differences between Industry 4.0 and the Industrial MetaverseDigital twins Real data exchange(I

103、oT)AIComplex system modeling&simulationData visualization Collaboration technologies(incl.Web3/blockchain)Computing capabilityIndustry 4.0Industrial MetaverseToday&tomorrow2010 to todayConvergenceConvergenceImportantLess important26Blue Shift /REPORT 003“The Industrial Metaverse is defined as a conn

104、ected whole-system digital twin with functionalities to interact with the real system in its environment,allowing decision makers to better understand the past and forecast the future.”Blue Shift /REPORT 00327As defined,the Industrial Metaverse has the potential to transform how business decision ma

105、kers analyze past activities and predict the future at strategic as well as operational levels.We have defined a conceptual framework for the Industrial Metaverse illustrating its key components(see Figure 7).As shown in the figure,the core of the Industrial Metaverse is the creation and operation o

106、f a whole-system digital twin for a real-world industrial system,including all its elements,relations,and layers.Lets consider each of the key components in turn.Whole-system digital twinA whole-system digital twin differs significantly from what we would call a digital twin today,which tends to foc

107、us mainly on one or more physical assets,typically plants and products.Its functionalities are representative of an end-to-end real-world industrial system,including external elements outside the company and the broader environment within which it operates.It does not simply cover a factory,plant,or

108、 product line and is persistently and bidirectionally connected to the real-world system.It therefore needs to be constructed as a complex system to enable realistic and dynamic system behaviors to be modeled across all functions,departments,processes,assets,and players.Extensive what-if simulation

109、functionality is a key part of the twin.Simulations are not based on past data alone,but present and future data as well.While it may include immersive and realistic rendering of a real system,asset,and/or product,this is not the twins key defining fea-ture.Visualizations may be in a variety of diff

110、erent forms,depending on the needs for management decision-making.Fig 7 The components of the Industrial MetaverseSource:Arthur D.LittleSource:Arthur D.LittleFig 7 The components of the Industrial MetaverseINDUSTRIAL METAVERSEWhole-system digital twin Real-world industrial systemConnectComputeConcei

111、veCollaborateDataActions28Blue Shift /REPORT 003Real-world industrial system“Real-world industrial system”refers to the set of elements,rela-tions,and nested systems that represents a business within its environment.It comprises all the strategy,processes,organization,and resources of a business.It

112、stretches across all business func-tions and assets,including external players such as supply chain and other ecosystem partners,as well as the broader external environ-ment in which the business operates.By its nature,a real-world industrial system is therefore a complex system,including multiple e

113、lements,relations,nested systems,and subsystems,as shown in Figure 8.It is subject to emergent and nonlinear effects.Connect,compute,conceive&collaborateThe Industrial Metaverse needs to include four key functions to enable a whole-system digital twin that adequately represents the real-world indust

114、rial system.These are:1.Connect real-time bidirectional connections between the whole-system digital twin and the real-world industrial system to enable data collection and actuation back onto the system.2.Compute processing of very large data volumes from the real system,including analytics,complex

115、 system modeling,pattern recognition,and simulation,to enable future-scenario formulation.3.Conceive visualizations of both physical and nonphysical data,which may or may not be fully immersive.These visualizations interpret and present complex data in different ways,not only to simulate reality but

116、 also to facilitate understanding and to illustrate what-if scenarios.4.Collaborate functionality to enable a range of interactions,including between internal staff,ecosystem partners,value chain players,customers,and others.Interactions may include commercial transactions as well as everyday cowork

117、ing.Fig 8 The components of a real-world industrial systemSource:Arthur D.LittleSource:Arthur D.LittleFig 8 The components of a real-world industrial systemSupply chainOperationsStrategyProcessesOrganizationResourcesFinanceR&DCustomer mgmt.Etc.Plant&assetsSales&marketingLogistics29Blue Shift /REPORT

118、 003How the Industrial Metaverse helps meet executive challenges The essence of the Industrial Metaverse based on the above defini-tion and model is to make the invisible visible.By accomplishing this,Industrial Metaverse offers some key capabilities that are very valuable to C-level decision makers

119、(see Figure 9).Among these,it helps take a systemic view across the silos.It also helps see into the future through enabling realistic what-if simulations.And it helps to make visible the complex interactions across the system.Finally,it offers better visibility on overall whole-system behaviors and

120、 impacts,which is invaluable for achieving sustainability.Fig 9 How the Industrial Metaverse solves todays executive challengesSource:Arthur D.LittleSource:Arthur D.LittleFig 9 How the Industrial Metaverse solves todays executive challengesIndustrial Metaverse offers Executive challenges C-level dec

121、ision support tool Systemic a whole-system enhanced digital twin,including not just plant but also organization,processes,supply chain,customers&the wider environment.“See across the silos”Dynamic real-time,realistic future-scenario simulation to anticipate all consequences of a decision.“See the fu

122、ture”Visual new visualizations to enable complex system parameters to be better understood by human brain.“Understand complexity”Holistic capability to model overall system impacts on the environment of strategic decisions.“Manage overall impact”Complexity Acceleration Cognition Sustainability The e

123、ssence of the Industrial Metaverse is to make the invisible visible.30Blue Shift /REPORT 003Industrial Metaverse vs.Industry 5.0A legitimate question would be:Why speak about the Industrial Metaverse if it is just the next phase of Industry 4.0?Why not call it Industry 5.0?The answer can be found in

124、 the fact that this next phase of Industry 4.0 shares properties with the Metaverse,as we defined it in“The Metaverse,Beyond Fantasy”:“The future version of the Internet blending the frontiers between reality and virtuality,at the convergence of immersive spaces,collab-oration platforms,social exper

125、iences,and the creator economy.”The Industrial Metaverse shares the three key features of the Metaverse that distinguish it from the Internet namely,immer-sion,interaction,and persistence although there are also some differences of emphasis(see Figure 10).As shown in the figure,immersion and interac

126、tion are key for the Consumer Metaverse.Immersion,in the sense of realistic rendering,is still important but not key for the Industrial and Enterprise Metaverse.For example,it is perfectly possible to interpret,analyze,and visualize complex system data without being immersed in a vir-tual environmen

127、t.Interaction and persistence are,however,both key for the Industrial and Enterprise Metaverse business management relies heavily on interaction,and digital twins need to be“perma-nent,”just like their physical counterparts.One obvious key difference is that the Industrial Metaverse will gen-erally

128、comprise discrete areas only accessible to authorized persons,rather than the vision of the Metaverse,which is to create an open environment accessible to all.This lowers one of barriers toward greater Metaverse adoption,namely full interoperability.Fig 10 Differences between Industrial Metaverse an

129、d other segmentsSource:Arthur D.LittleSource:Arthur D.LittleFig 10 Differences between Industrial Metaverse and other segmentsIMMERSIONINTERACTIONPERSISTENCEConsumerInteraction is key;persistence is important for e-commerce;immersion not essential for now but will be key for futureEnterpriseInteract

130、ion is key;immersion not essential but growing;persistence is not so relevant for enterprise Metaverse toolsIndustrialInteraction and persistence are key for industrial systems;immersion is not essential but will increase in importance over time as the broader Metaverse matures FEATURESSEGMENTVery i

131、mportantImportantLess important31Blue Shift /REPORT 003INTERLUDE Make the invisible visible 32Blue Shift /REPORT 003With a PhD in computational biology from Harvard University and a masters degree from the Ecole Normale Suprieure(ENS),my research centered on computational methods to understand devel

132、-opmental biology.In parallel,I cofounded the nonprofit organization Just One Giant Lab to promote open science across the globe.Utilizing my expertise in data science and machine learning(ML),I developed musical and visual artistic practices that center around art-science and the question:what is d

133、ata?A child of the cyber-space,I brought my fascination for immersivity into my research and my art by working with virtual reality as a medium to reveal the invis-ible threads,from cellular trajectories to the link between projected consciousness and avatar embodiment.-Leo Blondel,Cyber Wizard My a

134、rt reflects my life,a set of connected yet separate facets,moving together in the digital space.33Blue Shift /REPORT 00334echo cos(pi)/dev/null5Objects are not always what they appear to be.When everything around us is data,what happens when we manipulate it to reveal hidden meanings?The secrets beh

135、ind complexity can sometime be revealed by changing our perspective.Starting from a pyramid,applying transformation can reveal other shapes in their complexity.In the virtual realm,data is meaning,but only through the eyes of one who seeks to look beyond the visible layer.5 The title is a reference

136、to a Linux command line that sends a repeating value to/dev/null,which is the void the abyss.Blue Shift /REPORT 00335Light is matter,matter is lightModern science has opened the doors to digitizing information in unprec-edented ways.By recording the photons emitted by molecules,matter becomes light,

137、which then becomes bytes.Entering the cyber realm,we can shine a new light on reality.In the virtual world,the limits are bounded only by the imagination.Here,an embryo simulated from light data becomes matter again,with simulated photons coloring its invisible matter.From a transparent embryo,where

138、 light goes through only slightly scattered,the digital process allows the generation of an opaque object,upon which light will reflect:from transparency to opacity,revealing invisible shapes and processes.Blue Shift /REPORT 003CHAPTER36337WHERE IS INDUSTRIAL METAVERSE TECHNOLOGY TODAY?3Where is Ind

139、ustrial Metaverse technology today?In this chapter,we explore the techno-logical building blocks of the Industrial Metaverse:their maturity,challenges to overcome,and what this means for the timescales at which the full poten-tial of the Industrial Metaverse could be realized by business.Blue Shift

140、/REPORT 00338Fig 11 Evolution of the Industrial MetaverseTRL=technology readiness level Source:Arthur D.LittleConsider the tech-nological building blocks that are relevant for the Industrial Metaverse.Source:Arthur D.LittleFig 11 Evolution of the Industrial MetaverseTypicalapplicationsCriticaltechno

141、logiesWhole-system digital twins Whole-life product management/circularity Whole-system industrial design Strategic what-if decision-making Supply chain&ecosystem optimization Financial/investment strategy Management training Collaboration tools Virtual transactions Data visualization,HMI devices Re

142、al data exchange(IoT&ERP)AI Complex system simulation Blockchain/Web3/transaction toolsTRL 3 to 8Present onwardCurrentmaturitiesIndustry 4.0Industrial Metaverse1990 to present Computerization&connectivity Apply IT to operations&business management Connect&integrate processes Building information mgm

143、t.Design tools(e.g.,CAD)Asset inspection/maintenance(AR tools)Digital process control Data visualization,HMI devices Real data exchange(ERP)TRL 7 to 9Discrete digital twins Operational staff training Plant/line operational efficiency optimization Predictive maintenance optimization Product-quality o

144、ptimization Remote asset troubleshooting/problem-solving Design/construction integration Data visualization,HMI devices Real data exchange(IoT&ERP)AITRL 6 to 92010 to present Evolution of technologies relevant for Industrial MetaverseIt is helpful to begin by considering relevant technologies that a

145、re already in place in industry as a result of the wave of computeriza-tion and connectivity that has been ongoing for at least three dec-ades(see Figure 11).Technologies that involve digital simulation and virtualization,such as building information management(BIM),computer-assisted design(CAD),and

146、 digital process control,are already mature although still not fully deployed.The use of AR(e.g.,for improving asset construc-tion,maintenance,and inspection)has also become well-established,although there is still further to go in terms of adoption by industry.In more recent years,digital twins to

147、represent plants,factories,and products have started to be deployed,although often still more as pilot schemes and trials(see also Chapter 4 and Appendix 3 for selected use cases).These are already being used effectively to improve operational efficiency,maintenance,and quality.And in many industrie

148、s,virtual simulations are already being used for oper-ational training.In this sense,therefore,aspects of the Industrial Metaverse already exist,overlapping with Industry 4.0,as shown in Figure 11.However,the availability of“connected whole-system digital twins with func-tionalities to interact with

149、 the real system in its environment,”as per our definition in Chapter 2,is still five or more years away(we provide more detailed analysis later in this chapter).39Blue Shift /REPORT 003Global tire manufacturer Michelin has 70 production plants with operations in more than 170 countries and a growin

150、g number of product models.In 2021,the logis-tics costs alone for these operations was$2.1 billion.As Michelins strategy is to manufacture products near the point of sale,global sourcing is a major challenge.The company was looking for a way to test different sourcing strategies and scenarios agains

151、t each other to identify the best options and to ask“how-to”questions to help determine the best actions to optimize cost,quality of service,carbon footprint,and stock levels.Although Michelin had used digital twins in product development for around 30 years,it had not applied the technology to sour

152、cing,given the complexity of the global sourcing system with its multiple elements,rela-tions,and nested subsystems.Working with technology partner Cosmo Tech,Michelin built a complex systems digital model for global sourcing,including key indicators such as service levels,CO2 emissions,inventory,di

153、stribution,and plant capacity.This covered 1,700 product models,all within a complex manufacturing and distribution matrix.This simulation digital twin allowed Michelin to run more than 80,000 simulations,each with more than 3,000 dif-ferent and dynamic decision variables,as well as built-in optimiz

154、ation algorithms to determine the best strategy to adopt.As a result,Michelin was able to identify an actionable strategic sourcing plan for the next five years that would reduce its logistic costs by approximately$11 million annually.This also optimizes the global profit margin by several percentag

155、e points and reduces trans-port and customs costs by more than 60%.Moving toward connected whole-system digital twinsThere is a fairly clear path of evolution toward whole-system digital twins that involves progressively extending and integrating digital simulation beyond discrete plant operations.S

156、tarting with technology that is already deployed today,this could mean,for example,the following steps:-Step 1:Digital twins for discrete,selected plant operations including the use of AI-enabled data analytics,increased automation,AR tools for maintenance,and virtual training-Step 2:Digital twins c

157、reated for multiple plant operations including supply chain linkages and data integration;increasing the extent of simulation,such as by including sourcing and/or logistics optimization-Step 3:Digital twins created for the whole operational system including upstream and downstream players.In this st

158、ep,various management processes are increasingly integrated into the simulation in addition to purely operational processes-Step 4:Digital twins created for the whole end-to-end industrial system including all management and operational processes,assets,people,and the environmentSome early examples

159、of Step 2 already exist today.Michelin,for instance,has already piloted an approach that involves digital twin simulation for global sourcing strategy optimization,enabling the company to test and compare different strategies for optimization across a range of parameters and achieving impressive res

160、ults(see sidebar“Michelin:Evolving digital twins beyond plant level to optimize global sourcing strategy”).Michelin:Evolving digital twins beyond plant level to optimize global sourcing strategy40Blue Shift /REPORT 003Technology building blocks Maturity&further development needsRealizing the next ph

161、ase of the Industrial Metaverse at a whole-system scale will require further development and integration across a range of technology building blocks,as outlined in Figure 12,and must consider the four functions we discussed earlier:1.Connect requires bidirectional data exchange between the digital

162、twin and the real-world system,comprising both“hot”current data via IoT technology and“cold”stored data via enterprise resource planning(ERP)systems.2.Compute requires technology to simulate complex systems and enable future scenarios to be run.It also leverages AI for data analytics and interpretat

163、ion,especially for prediction.3.Conceive requires VR,AR,and mixed reality(MR)visualization technologies,as well as other types of visualization for complex data interpretation,sometimes integrated with AI.Advanced human-machine interface(HMI)technologies are also a key building block.4.Collaborate r

164、equires a range of technologies,including enterprise collaboration tools(such as Mesh and Horizon in use today),and transaction-based tools such as those often linked to Web3,including blockchain and non-fungible tokens(NFTs).API architecture that ensures easy software interoperability and data exch

165、ange is also important.Underpinning all this functionality is the need for increased com-puting capability,which involves a wide range of developing tech-nologies at different levels in the stack,from new chip design,quantum computing,and high-performance computing through to cloud and edge infrastr

166、uctures and availability of low-code/no-code software solutions.Fig 12 Developing technology building blocks for the Industrial MetaverseSource:Arthur D.LittleSource:Arthur D.LittleFig 12 Developing technology building blocks for the Industrial MetaverseWhole-system digital twin INDUSTRIAL METAVERSE

167、ConnectComputeConceiveCollaborateReal data exchange IoT:Exchange“hot”current data from/to real-world physical system ERP:Exchange“cold”stored data from/to corporate information systemComputing capabilityHPC,cloud,edge,quantum computing,low-code/no-code softwareCollaboration technologies Enterprise c

168、ollaboration tools(Horizon,Mesh,etc.)Web3 transaction tools API architecturesAILearn from both real&synthetic data leveraging“statistical”AIComplex system modeling&simulationSimulate real system&run future what-if scenarios to generate synthetic dataData visualization&dashboards VR/AR/MR visualizati

169、ons AI-driven visualizations Advanced HMI technologiesReal-world industrial systemDataActionsTechnology building blocks41Blue Shift /REPORT 003Table 1 summarizes our assessment of the maturity levels of each of these technology building blocks,including key development needs and timescales to full m

170、aturity.In Table 1,“scale of development gap”refers to the extent of devel-opment still needed to be able to deliver the functionality envisaged for adoption of large-scale whole-system connected digital twins.“Degree of uncertainty”refers to the likelihood that current chal-lenges may not be overco

171、me during development.It may be seen that for the most part,the various technology building blocks have only moderate development gaps.All have only moderate degrees of uncertainty in terms of if/when current challenges will be overcome.Overall,the picture suggests a timescale of five or more years

172、before whole-system digital twins as described in our Industrial Metaverse definition could be realized,although in the meantime there will be continued progress with intermediate steps.AI is assessed as having a larger development gap than others mainly due to the challenges of algorithm scalabilit

173、y,data volumes,and computing-capacity limi-tations.Computing capability itself is a critical enabler for achieving very large,connected digital twin simulations.For example,many optimization problems involving multiple elements and interactions qualify as“intractable problems,”where the solution com

174、plexity rises exponentially beyond the reach of conventional computing power.Such problems would be suitable for quantum computing approaches,but this technology is probably at least 10 years away from maturity.Table 1 Snapshot of maturity/development of Industrial Metaverse technology building bloc

175、ksSource:Arthur D.LittleSource:Arthur D.LittleTable 1 Snapshot of maturity/development of Industrial Metaverse technology building blocksTECHNOLOGYSIGNIFICANCE FOR INDUSTRIAL METAVERSESCALE OF DEV.GAPDEGREE OF UNCERTAINTYKEY DEVELOPMENT NEEDS&CHALLENGES LIKELY TIMESCALES TO FULL MATURITY Real data e

176、xchange(IoT)Provides the means by which digital twins can exchange“hot”(current)data to/from real-world industrial systemNew sensor technologies and applications will be required to extend data capturing to include a wider range of parameters across industrial system;further AI integration is also k

177、ey5-10 years Real data exchange(ERP)Provides the means by which digital twins can exchange“cold”data to/from corporate industrial systemIncreasingly sophisticated digital twins will pose heavy demands on data storage,sharing,and processing;development areas are around scalability and flexibility2-4

178、years AIKey tool for VR/avatar creation at scale;also for advanced HMIs and data analytics/interpretation,especially predictiveAI at scale poses multiple challenges,including algorithm scalability,data volume,and computing-capacity limitations;data quality,privacy,ethics,and security are also key ar

179、eas for development3-5 years Complex system modeling&simulationA critical technology building block for simulation of large-scale industrial systems to enable what-if scenarios and strategic decision-makingCore challenges include system decomposition and algorithm methods that would be capable of co

180、ping with complexity of a whole end-to-end industrial system;computational capacity and data acquisition are also key challenges1-3 years(limited scale)3-5+years(whole industrial systems)Data visualization&dashboardsVR/AR/MR visualizations enable staff and managers to understand and interpret comple

181、x data;they also enable realistic digital twin renderingGraphics,presence,logic,and physics engines still require significant further development for real-world rendering;immersive complex system data visualizations are still relatively immature3-5 yearsCollaboration technologiesFacilitates person-t

182、o-person collaborations in virtual environment,including commercial transactionsImproving quality of experience,network speeds,and security are current challenges1-3 yearsComputing capabilityHigh HPC,cloud,and edge computing capacity will be needed to operate complex whole-system digital twin;quantu

183、m computing could be a solution for complex simulations;low-code/no-code is a key enabler for required new softwareIncreases in capacity of up to an order of magnitude could be needed for large-scale whole-system digital twins;development areas include edge/cloud hybridization,new HPC technology,and

184、,at much lower maturity,quantum computing3-5 years10 years+for quantumHigh Moderate42Blue Shift /REPORT 003Real data exchange(IoT)In the section that follows,we provide more details of the status,maturity,and implications for the Industrial Metaverse of each of these technology building blocks.6 Art

185、hur D.Little,IoT Analytics.7 Arthur D.Little,Oracle,Fortune Business Insights.IoT technology provides the means by which digital twins can exchange“hot”(current)data to and from the real-world industrial system.IoT has applications across nearly all industry sectors as well as for cities and domesti

186、c use.The basic technology is already fairly mature at technology-readiness level(TRL)7-8(see Appendix 1 for full description of TRLs),but further developments are needed to enable more sophisticated digital twins with real-time end-to-end connectivity.Description Development needsCurrent developmen

187、t priorities include improving cybersecurity platforms and extending edge computing solutions(edge IoT devices),smart sensors and AI conver-gence,sensor fusion/miniaturization,and new sensor applications.Convergence with AI(smart sensors),improving performance,and extending applications across a wid

188、er range of parameters are all enablers for developing more sophisticated digital twins.Key data6Global market size(2021):$150 billionMaturity:TRL 7-8 while basic IoT technology is mature,further tech development potential is still significantForecast market growth:20%CAGR next five yearsDevelopment

189、 gap:moderateNo.of machine-to-machine connections:10-30 billion(20222025)Degree of uncertainty:moderate Real data exchange(ERP systems)Increasingly sophisticated digital twins,which include business processes as well as data from IoT,AI,and other new tech applications,will pose heavy demands on ERP

190、data storage,sharing,analytics,and visualization.Description Development needsCurrent development priorities include end-to-end security,data storage systems(such as the cloud),greater scalability and flexibility,adoption of blockchain(such as for crypto transactions),and additional cloud-based serv

191、ices(such as remote assistance and ML-driven data analysis).Key data7Global market size(2021):$45 billionMaturity:TRL 8-9 ERP technology is mature,but the Industrial Metaverse will require significant new capacity developmentForecast market growth:9%CAGR next eight yearsDevelopment gap:moderateDegre

192、e of uncertainty:moderate43Blue Shift /REPORT 0038 Arthur D.Little,Precedence Research.9 Arthur D.Little.“Review of Complex Systems and Their Concrete Applications in the Transportation Industry.”Presans,2016.Complex system modelingA broad spectrum of approaches exists for modeling complex systems.T

193、he analysis of complex systems cannot be carried out with only classical methods of system decomposition and logic analysis;a framework is needed to integrate several methods capable of viewing the problem from different perspectives,including:-Structural/topological methods based on system analysis

194、,graph theory,statistical physics,etc.-Statistical and logical methods based on system analysis,hierarchical and logic trees,Bayesian networks,etc.-Phenomenological methods based on transfer functions,state dynamic modeling,input-output modeling&control theory,agent-based modeling,etc.-Flow methods

195、based on detailed,mechanistic models(and computer codes)of the processes occurring in the systemDescription Key data9Global market size(2022):$10-$15 billionMaturity:TRL 6-8 while the basic approaches are well-developed,there is still limited experience in this type of very large-scale industrial sy

196、stem applica-tion,and many challenges remainForecast market growth:CAGR 10%-15%next eight yearsDevelopment gap:moderateDegree of uncertainty:moderateAI categories include general intelligence,reasoning,knowledge representation,planning,learning,natural language processing(NLP),perception,and the abi

197、lity to move and manipulate objects.Its applicability for industry and society is vast.Some NLP apps are now growing exponentially after decades of slower-than-expected AI development and adoption.AI is a key technology for the Industrial Metaverse across multiple areas.It is a tool for VR/avatar cr

198、eation at scale,advanced HMIs,and data analytics/interpretation,especially predictive analytics.Convergence with other relevant technologies,such as IoT and complex system modeling,are also key for realizing sophisticated digital twins.Description Development needsWhile it is maturing rapidly,AI at

199、scale faces multiple challenges including algorithm scalability,data volumes,and computing-capacity limitations.Current development priorities include improved processor chips(e.g.,Tensor Processing Units),higher-speed networks,larger network-bandwidth guarantees,AI R&D cloud platforms,overcoming st

200、orage limitations(e.g.,through high-quality labeled data sets),data quality,data security,and ethics.Key data8Global market size(2022):$100 billionMaturity:TRL 6-7 different subgroups of AI are at different maturities;we may now be entering a much steeper phase of developmentForecast market growth:3

201、8%CAGR next eight yearsDevelopment gap:highDegree of uncertainty:moderateArtificial intelligence44Blue Shift /REPORT 00310 Arthur D.Little,GlobalNewsWire,Statista.Development needsCore challenges to realizing the Industrial Metaverse include developing system decomposition and algorithm methods that

202、 would be capable of coping with the complexity of a real end-to-end industrial system.Computational capacity and data acquisition are also key challenges.Complex system modeling will have a key role in the development of the Industrial Metaverse.It will be essential in order to simulate real-world

203、industrial systems,moving beyond physical asset operations,and running future scenarios for management decision-making.Data visualization technologies are fundamental to the Metaverse in three areas:1.VR/AR/MR visualizations.The“world engine”for the Metaverse comprises graphics,presence,logic,and ph

204、ysics dimensions.Complex data visualization is a further key category,in which novel visualizations and dashboards are used to enhance data interpretation(immersive or otherwise).2.AI-driven visualizations.AI is a key tool for developing Metaverse simulations and representations.AI-based data analyt

205、ics benefits from visualization technologies.3.HMI technologies.“Output”HMI technology includes VR/AR headsets,holography,haptics,and brain-computer interfaces(BCIs).BCI and on-body tech are also key for“input”HMIs to pass data from humans to the machine.Description Development needsGraphics,presenc

206、e,logic,and physics engines still require significant further develop-ment for real-world rendering.Computing power is also a barrier.However,for many Industrial Metaverse applications,a“perfect”reality is likely to be less important than in the consumer Metaverse,given that many non-immersive data

207、visualization tools and dashboards already exist.However,poor HMI performance is still a barrier to greater use of immersive representations.There is scope for further development of immersive tools that may be suitable for understanding highly complex and dynamic data sets,while HMI technologies(pa

208、rticularly BCI)also require significant further development.Key data10Data visualizationGlobal market size(2021):$9 billionMaturity:TRL 6-7 there is scope for further development of immersive tools that may be suitable for understanding highly complex and dynamic data sets;HMI technologies also requ

209、ire significant further development Forecast market growth:10%CAGR next six yearsHMIGlobal market size(2021):$4 billionForecast market growth:10%CAGR next four yearsDevelopment gap:moderateDegree of uncertainty:moderateData visualization&dashboards45Blue Shift /REPORT 00311 Arthur D.Little,The Busin

210、ess Research Company.12 Arthur D.Little,Future Market Insights,Grand View Research.Computing capability MicrocontrollersA scalable processing unit capability is required to operate an Industrial Metaverse system or integrate other technologies into its architecture.This computing capability is drive

211、n by two distinct types of microchips general-purpose central processing units(CPUs)that determine data-processing speeds and specialized graphical processing units(GPUs),used for rendering images or videos and underpinning high-performance computing(HPC),AR/VR,AI,or ML applications.While CPU perfor

212、mance will only see limited efficiency improvements,due to Moores Law,the optimal utilization level for GPUs has not yet been reached,and there are still software challenges.Description Key data12Global market size(2021):$80 billionMaturity:TRL 7-8 hardware technology is mature,although further tech

213、 development is still required to adapt to Metaverse applicationsForecast market growth:4%CAGR next eight yearsDevelopment gap:moderateDegree of uncertainty:moderateIn the Metaverse,collaboration technologies take three distinct forms:1.Enterprise collaboration tools.Virtual work environment tools s

214、uch as Microsoft Mesh and Meta Horizon Workrooms already exist,although the user experience requires further development.2.Web3 transaction tools.These relate to token-based economics and decentralization to facilitate commercial transactions in the Metaverse.Blockchain,cryptocurrencies,and NFTs are

215、 all relevant Web3 tools.3.API architectures.APIs enable applications to exchange data and functionality easily and securely.They are an enabler for software innovation and interoperability.Description Development needsWhile collaboration tools are already deployed,they require significant improve-m

216、ents.This is particularly true of enterprise collaboration tools.As this is the first experience most employees will have of the Industrial Metaverse,the current poor user experience acts as a significant barrier to adoption.While Web3 tools do not depend on the Metaverse,their availability and effe

217、ctiveness would help to drive further Metaverse adoption by businesses.Current areas for development in collaboration technologies include cybersecurity,higher-speed networks,larger network-bandwidth guarantees,and cloud-based storage systems.Key data11Global market size(2022):$10 billionMaturity:TR

218、L 8-9 collaboration tools are already deployed,although there are still significant improvement needsForecast market growth:3%CAGR next five yearsDevelopment gap:moderateDegree of uncertainty:moderateCollaboration technologiesDevelopment needsCurrent developments are therefore focused on creating so

219、lutions,often cloud-based,that parallelize CPU/GPU processing power while optimizing the capacity and utilization of GPUs.46Blue Shift /REPORT 00313 Arthur D.Little,The Business Research Company.The conventional computing-capability category covers three areas:1.Edge computing.A distributed IT archi

220、tecture in which client data is processed at the periphery of the network,as close to the originating source as possible.2.Cloud computing.On-demand delivery of IT resources over the Internet with pay-as-you-go pricing,instead of needing to buy,own,and maintain physical servers.3.High-performance co

221、mputing.Using supercomputers and computer clusters to solve advanced computation problems.Description Development needsThe infrastructure around conventional computing capacity is evolving at a fast pace.A hybridization of edge and cloud computing will be extremely valuable for the Industrial Metave

222、rse.These hybrid clouds will help providers increase opera-tional efficiencies while hosting the Industrial Metaverse and help eliminate outage issues by bringing hosting resources closer to end users while safeguarding personal information.As well as hybrid clouds,current development priorities inc

223、lude cyber-security,improving enabling technologies(such as cooling),increasing bandwidth,and creating a more diverse range of solvers/compilers to allow flexible and parallel utilization of computing capacities,combined with the semiautomated selection of computing power mixture.Limitations on proc

224、essing speed at a microchip level can be compensated for by these current developments in conventional computing,with the actual demand defined by the Industrial Metaverse application.Key data13Global market size HPC(2021):$65 billionMaturity:TRL 6-8 infrastructure technology is mature,although furt

225、her tech development is still required to adapt to Metaverse applicationsForecast market growth HPC:7%CAGRDevelopment gap:moderateDegree of uncertainty:moderateComputing capability Conventional47Blue Shift /REPORT 00314 For an in-depth review of quantum computing technology and impact,see:Meige,Albe

226、rt,et al.“Unleashing the Business Potential of Quantum Computing.”Arthur D.Little,2022.Quantum computing(QC)harnesses the properties of arrays of entangled quantum bits(qubits)to provide computing power suitable for tackling problems with exponentially scaling complexity.It can be applied in areas i

227、ncluding complex system simulation,opti-mization,AI/ML processing,and cryptography.Description Development needsQC is still at least 10 years away from commercial viability and is still at proof-of-con-cept lab scale(TRL 3-4).There are at least five competing hardware paradigms and major technical c

228、hallenges still to be overcome.Consequently,there is a high degree of uncertainty that QC will bridge the wide development gap to achieve maturity.If successful,the first Industrial Metaverse QC applications are likely to be around communications,security,and encryption.QC power could also ultimatel

229、y provide a solution to key Industrial Metaverse complex system simulation tasks,which would be beyond conventional computing.To be viable,QC processing would need to be available in real time via cloud access,probably in combination with conventional computing power.Key data14Estimated global inves

230、tment(2022):$25 billionMaturity:TRL 3-4 quantum computing technology is still at the proof-of-concept lab scale,with commercial penetration likely 10-30 years awayGrowth projection 2030:$60 billion(depending on the source)Development gap:highDegree of uncertainty:highComputing capability Quantum48Bl

231、ue Shift /REPORT 003“Overall,the picture suggests a timescale of five or more years before whole-system digital twins could be realized.”Blue Shift /REPORT 00349CHAPTER50WHAT IS THE POTENTIAL VALUE OF THE INDUSTRIAL METAVERSE TO BUSINESS?451WHAT IS THE POTENTIAL VALUE OF THE INDUSTRIAL METAVERSE TO

232、BUSINESS?4What is the potential value of the Industrial Metaverse to business?In this chapter,we assess the size and future growth of the Industrial Metaverse market,provide an overview of current use cases across different functional categories and industries,and look at potential future use cases

233、that could be possible when the Industrial Metaverse evolves further.Blue Shift /REPORT 00352Market size of the Industrial MetaverseIn order to assess the size of the Industrial Metaverse market,it is important to be clear on what is included and excluded.We have defined the Industrial Metaverse mar

234、ket to include the following:-All of the digital twin market-The industrial segment of the VR/AR/MR market,excluding the consumer segment-Most of the IoT,AI,and blockchain markets(50%,depending on definitions)Other Industry 4.0 technologies such as robotics and 3D printing are excluded.Computing and

235、 telecoms infrastructure markets are also excluded,as they are relevant for the entire digital market.Based on this scope,we have looked at a range of market forecasts from different sources to indicate market potential(see Figure 13).Note that we have not conducted any bottom-up primary research.53

236、Blue Shift /REPORT 003As shown in Figure 13,the Industrial Metaverse market size in 2023 is in the$100-$150 billion range.This is smaller than many existing Industry 4.0 market estimations,which is logical given that it excludes technologies such as robotics and 3D printing.There is much variability

237、 in forecast market growth from Industry 4.0 from different sources,and,indeed,any such forecasts should be treated with a great deal of caution.Our conservative 2030 forecast for the Industrial Metaverse market is$400 billion,although the upside could be$1 trillion,with CAGR between 20%and 30%.This

238、 range is wide but in line with previous ADL estimates of the broader Metaverse,which included a conservative$500 billion low end and a multi-trillion-dollar high end(see“The Metaverse,Beyond Fantasy”).Irrespective of the actual number in eight to 10 years time,it is clear that the market potential

239、is very significant.Current Industrial Metaverse use casesTodays Industrial Metaverse use cases can be broadly divided into four categories:optimization,training,technical tools,and manage-ment tools.Each of these has a number of subcategories,as follows:Optimization-Plant/line operational-efficienc

240、y improvements-Predictive maintenance optimization-Product-quality optimization-Supply chain operational improvements-Sales operational improvement-Customer service improvementsFig 13 Sizing the Industrial Metaverse market,20212030(in US$billion)Source:Arthur D.LittleSource:Arthur D.Little;Transpare

241、ncy Market Research;Acumen;Research and Markets,MarketsandMarkets,Fortune Business Insights,IMARC GroupFig 14 Sizing the Industrial Metaverse market,20212030(in US$billion)CAGR(2130)18%21%16%17%16%27%20212022202320242025202620272028202920301,000100600200700300800400500900Transparency Market Research

242、Research and MarketsFortune Business Insights&GlobalNewsWireAcumenMarketsandMarketsIMARC Group54Blue Shift /REPORT 003Training-Operational staff training-Safety and emergency training-Remote training-Product trainingTechnical tools-Design/construction integration and design tools-Asset inspection/ma

243、intenance tools-Remote asset troubleshooting/problem-solving-Advanced data analytics-BIMManagement tools-Virtual meeting tools-Virtual collaboration/workshop tools-Customer-interaction toolThese use case categories stretch across most business functions of a typical company,as shown in Figure 14.Fig

244、 14 Current and potential use cases for Industrial MetaverseSource:Arthur D.LittleSource:Arthur D.LittleFig 15 Current and potential use cases for Industrial MetaverseOptimizationTrainingTechnical toolMgmt.toolPotential use caseUse case aimsWhole-system digital twins Computerization&connectivityDisc

245、rete digital twins BUSINESS FUNCTIONSIndustry 4.0Industrial MetaverseOperationsMaintenanceDesign&developmentSupply chainBusiness managementHuman resourcesSales,marketing&customer mgmt.FinancePlant/line operational efficiency optimization Predictive maintenance optimization Operational staff training

246、Asset inspection/maintenance(AR tools)Supply chain&ecosystem optimizationWhole-life product management/circularityStrategic what-if decision-makingProduct-quality optimizationMaintenance strategyBuilding information managementDesign/construction integrationDesign toolsManagement trainingCollaboratio

247、n toolsRemote asset troubleshooting/problem-solvingWhole-system industrial designFinance process automation&efficiency improvementDigital sales&marketing channels Advanced data analyticsSales&marketing strategyFinancial/investment what-if decision-makingCustomer service improvementSupply chain data

248、integrationMATURITY STAGESThere is much var-iability in forecast market growth from Industry 4.0 from different sources.55Blue Shift /REPORT 003Current use cases cover the first stage of maturity of the Industrial Metaverse,overlapping with Industry 4.0.Please see Appendix 3 for a non-exhaustive lis

249、t of 30 use cases based on real-world examples and organized by industry,category,and aim.These are illustrated in Figure 15.Future Industrial Metaverse use casesTaking an in-depth look at future use cases demonstrates that they provide major breakthrough benefits to large companies by enhancing str

250、ategic management effectiveness,as shown in Figure 16.Although it is impossible at this stage to predict absolute numbers,it is safe to assume that they could deliver double-digit percentage performance improvements.Fig 15 Illustrative use cases organized by industry and categorySource:Arthur D.Litt

251、leSource:Arthur D.LittleFig 16 Illustrative use cases organized by industry and categoryOperations&maintenanceHuman resourcesDesign&developmentSales,marketing&customer mgmt.Business mgmt.Automotive&manufacturingAero&defenseChemicals&materialsEnergy&utilitiesHealthcare&life sciencesEngineering/constr

252、uctionTelecoms&technologyLogistics Services(incl.financial,education)BMW iFactoryHyundai Meta-FactorySiemens Dig.Native Fact.GSK vaccine digital twinFord/Bosch VR toolLlamasoft demand predictionORBCOMM fleet mgt.solutionGXO warehouse solutionFesto VR engineering platformGroupe E digital twinCisco pr

253、edictive maint.solutionDHL AR systemFinance Boeing&Microsoft HoloLensRetail&consumerSmart gridsHyundai Meta-FactoryJP Morgan virtual loungeKroger digital twinNurea digital twinPhilipDNA DTToyota&Microsoft HoloLens Enhatch AR surgery planning&supportNon-exhaustiveRenault Smart FactoryTesla smart fact

254、oryAR in construction(various)Rolls-Royce digital twinJAL VR trainingDassault SystemesLiving HeartFundamental VR trainingChevron&BP digital twinsChevron&BP digital twinsDow/Siemens digital test bedEricsson network digital twinsSupplychainMichelin sourcing strategyOptimizationTrainingTechnical toolMg

255、mt.toolFig 16 Potential benefits of future Industrial Metaverse use casesSource:Arthur D.LittleSource:Arthur D.LittleFig 17 Potential benefits of future Industrial Metaverse use casesWhat would be enabled Potential benefits Identify sustainability impacts of key strategic decisions&investment option

256、s Identify optimal net-zero growth strategies Increased control of sustainability impacts,better transparency Better design of optimal maintenance strategy,processes&organization Reduced maintenance costs,improved effectiveness&efficiency Provide tailored training in top-level business strategy&mana

257、gement,using whole-system digital simulations Virtualization of all functional&professional training Assess business value-add of new/modified products Optimize production systems,integrating products&processes Better optimization of sales networks&operations Assessment of likely impacts of marketin

258、g strategies Optimize sourcing&procurement strategies Understand better supply chain vulnerabilities&risks Better understand overall impacts of acquisitions&divestments Faster strategic response to changing situations Better understanding of unexpected or hard-to-predict outcomes Find optimized solu

259、tions prior to committing funds Faster strategic response to changing situations Simulate sustainability impacts end to end from raw material to end of life Simulate entire maintenance effort including people as well as physical assets Extension of VR/AR training beyond operational to include busine

260、ss management Holistic,integrated approach to designing products&processes Simulate sales networks,customer relationships,customer behaviors Simulate entire supply chains&partner ecosystem networks Simulate business impacts of financial options Simulate business impacts of strategic optionsSupply ch

261、ain&ecosystem optimizationWhole-life product management/circularityStrategic what-if decision-makingMaintenance strategyManagement trainingWhole-system industrial designSales&marketing strategyFinancial/invest-ment what-if decision-making56Blue Shift /REPORT 003Current players in the Industrial Meta

262、verseThere are hundreds of companies currently active in providing the technologies that underpin the Industrial Metaverse.Figure 17 shows a limited selection of these companies,grouped according to the technology building blocks outlined in Chapter 3.We share more detailed profiles of selected Indu

263、strial Metaverse/digital twin players in Appendix 2.It should be noted that many players are involved in multiple tech-nology building blocks.Obviously,many of these companies are also involved in developing the architecture of the wider Metaverse,as set out in“The Metaverse,Beyond Fantasy.”Fig 17 C

264、urrent players in the Industrial Metaverse ecosystem Source:Arthur D.LittleSource:Arthur D.LittleFig 18 Current players in the Industrial Metaverse ecosystem REAL DATA EXCHANGECOMPUTING CAPABILITYAICOMPLEX SYSTEM MODELING&SIMULATIONCOLLABORATION TECHNOLOGIESDATAVISUALIZATIONNon-exhaustiveMany player

265、s are involved in multiple technology building blocks.57Blue Shift /REPORT 003Recent market movements in the Industrial MetaverseThe Industrial Metaverse market is still relatively immature,with predicted major growth likely to come in the medium term.Given the current pressures on the technology in

266、dustry,and the Industrial Metaverses current lack of significant revenues,this has led to the scaling back of some initiatives as part of wider technology industry downsizing.For example:-In November 2022,Meta announced that it would lay off 11,000 employees,13%of its workforce.Plans to cut a furthe

267、r 10,000 employees were announced in April 2023.-In February 2023,Microsoft shut down its 100-person Industrial Metaverse team,founded just four months earlier,as well as closing its AltspaceVR and Mixed Reality Toolkit teams.This was part of a wider 10,000-person downsizing initiative.However,a Mic

268、rosoft spokesperson stated the company“remains committed to the Industrial Metaverse.”15At the same time,multiple companies have recently announced partnerships:-Microsoft/Meta(2022)product integration between Teams,Office,and Windows software and Metas VR headsets-Microsoft/NVIDIA(2022)collaboratio

269、n on AI,including cloud AI computing,AI applications,and services-Siemens/NVIDIA(2022)connecting the NVIDIA Omniverse and Siemens Xcelerator platforms to enable full-fidelity digital twins and software-defined AI systems.The partnership is focused on the manufacturing industry-Unity/Hyundai(2022)dev

270、eloping Meta-Factory,a Metaverse-based digital twin factory to optimize plant operation and allow virtual problem solvingFrom these recent events,it seems reasonable to conclude that,despite short-term setbacks and retrenchments,which reflect the prevailing global economic climate,the longer-term dr

271、ivers for Industrial Metaverse growth remain strong.As we have shown,the business benefits are significant,and the barriers toward adoption are less dependent on key obstacles for the broader Metaverse,such as consumer willingness to engage in immersivity and level of realism.15 Miller,Rosemarie.“Mi

272、crosofts Industrial Metaverse Aspirations Can Wait.”Forbes Digital Assets,14 February 2023.58Blue Shift /REPORT 003“Despite short-term setbacks and retrenchments,which reflect the prevailing global economic climate,the longer-term drivers for Industrial Metaverse growth remain strong.”Blue Shift /RE

273、PORT 00359CHAPTER60561WHAT SHOULD COMPANIES DO?5What should companies do?In this chapter,we draw some overall conclusions on where the Industrial Metaverse is today and look at what companies should do to ensure that they respond appropriately to ongoing developments and reap the benefits.Blue Shift

274、 /REPORT 00362Overall conclusions The extended and enhanced use of digital twins is at the core of the Industrial Metaverse.Digital twin technology has already existed for many years and is part of many digitalization and Industry 4.0 transforma-tion programs.In this sense,therefore,the first stage

275、of the Industrial Metaverse is already well underway.This is a source of confusion in many discussions where people question whether the Industrial Metaverse is really any-thing new,or if it is instead just hype.It is the extension of digital twin concepts to cover the entire organization at operati

276、onal,management,and strategic levels that is the essence of what the Industrial Metaverse will bring in the coming years.The Industrial Metaverse does not depend fully on general Metaverse adoption in order to be suc-cessful.Many Industrial Metaverse applications,such as the use of digital twins for

277、 operational improvement,are not heavily reliant on a“perfect”rendering of reality or providing a truly immersive user experience.They also do not depend on full interoperability between com-peting Metaverse worlds.In this sense,further progress is possible even if adoption of the consumer Metaverse

278、 fails to take off,as some observers have predicted.The convergence of several key technologies is providing a strong driver for Industrial Metaverse development in the coming years.The convergence of further technology developments in real data connec-tivity,AI,data visualization,collaboration,and

279、complex system simulation,powered by new computing capacity,acts as a powerful driver for progress in the Indus-trial Metaverse.This is despite shorter-term barriers resulting from global economic conditions.The key technologies for achieving extended whole-system digital twins are not yet mature,an

280、d there are other barriers including data sharing between players.While progress is accelerating in areas such as AI,realizing the next stage of development of the Indus-trial Metaverse will require further development across all key technologies,as well as overcoming barriers that prevent data shar

281、ing between players in an industrial system.Moving ahead with the Industrial Metaverse is inex-tricably linked to the digitalization journey.Com-panies therefore need to consider their strategy for the Industrial Metaverse in the context of their broader digitalization strategy.It will usually not b

282、e possible to“leapfrog”to Industrial Metaverse implementation unless digitalization is already fairly mature.63Blue Shift /REPORT 003How companies should respond Essentially,the approach companies should take for moving forward on the Industrial Metaverse should be based around normal IT or ERP syst

283、em implementation good practice.Generically,we suggest con-sidering four main steps(see Figure 18).Review strategy:Develop a clear picture of the digitalization strategy,journey¤t position As a starting point,companies must have a clear vision and pathway for their overall digitalization journ

284、ey.For most companies,this is already far-reaching,starting with the introduction of digitally strengthened processes and value offerings and culminating in fully digitalized operating and business models(see Figure 19).Fig 18 What companies should doSource:Arthur D.LittleFig 19 Digitalization journ

285、ey toward Industry 4.0 and beyondSource:Arthur D.LittleSource:Arthur D.LittleFig 19 What companies should doReview strategyIdentify opportunities Implement pilot projects Build&align ecosystem 1234Develop a clear picture of the digitalization strategy,journey¤t positionDiscover value-adding In

286、dustrial Metaverse opportunities&develop roadmapAdopt test-and-learn approach&manage change proactivelyCreate win-win situation with ecosystem partnersSource:Arthur D.LittleFig 20 Digitalization journey toward Industry 4.0 and beyondFully digitalizedLimitedExtended value offeringDigitallystrengthene

287、d processesNew digitaloperating modelRedefined value offeringDigitalization of value offeringFully digitalizedLimitedDigitalization of operating modelValue offering includes:products&services,customer channels,marketing,pricing,etc.Operating model includes processes,systems,structures,people skills,

288、working environment,etc.Current positionCurrent positionReengineereddigitalprocessesDisruptivedigital businessmodel64Blue Shift /REPORT 003Digitalization usually requires fundamental reengineering and transformation of the business and typically takes place over sev-eral years.Understanding the curr

289、ent position on the digitalization journey helps to determine:-What still needs to be done in terms of digitalization basics before embarking on Industrial Metaverse development(such as data availability and infrastructure)?-What is a realistic pace of development going forward?-What would be a feas

290、ible target destination to align with the rest of the digitalization journey?The initial applications for Industrial Metaverse are likely to be along the“operating model”dimension such as improving the effi-ciency and effectiveness of engineering,operations,maintenance,and training.Indeed,these are

291、the measures that overlap most with Industry 4.0.As we have shown,however,an even bigger prize that the Industrial Metaverse can deliver is to enable better and faster strategic what-if decision-making.As an example,if a companys broader digitali-zation strategy encompasses,say,moving toward high-va

292、lue digi-talized services,changing its role in the value chain,or shifting the boundaries of its business,the use of complex system simulations will be a key enabler to help assess the impact of different business model options prior to committing investment.Initial applica-tions for Industrial Meta

293、verse are likely to be along the“operating model”dimension.65Blue Shift /REPORT 003Identify opportunities:Discover value-adding Industrial Metaverse opportunities&develop a roadmapThe next step involves identifying Industrial Metaverse opportunities that add value and developing a roadmap.There are

294、many existing applications and uses cases.In developing the roadmap,companies should consider:-What potential applications and use cases would add greatest value?For example,in manufacturing-and process-based sectors,operations,maintenance,and supply chain business cases are often initially the most

295、 attractive.-What is already technically feasible today versus potentially feasible in the coming years?As we have seen,although there are many currently feasible opportunities,large-scale complex industrial system simulations are still some way away.-What is the vision and ambition for Industrial M

296、etaverse applications in a 5+years time frame?The scale of ambition may depend on the scale of digital transformation that companies may already be committed to.-What level of detailed digital modeling is appropriate to aim for initially?Companies should usually avoid taking on very detailed modelin

297、g at the start,since this is not necessary to implement initial use cases.Detailed modeling requires large amounts of data for setup and maintenance,slowing down early progress and diminishing economic attractiveness.-What new processes and approaches are needed to manage data?As well as the use cas

298、es themselves,the roadmap should consider what new processes are needed to access,update,analyze,interpret,and act on the required data.Implement pilot projects:Adopt a test-and-learn approach&manage change proactivelyA long-term project pipeline involving learnings capture helps to ensure that ther

299、e is enough value generation to begin to self-finance the transformation process.An agile and responsive test-and-learn approach is important in implementing the roadmap,including the following features:-Short time to deploy.Select initial proof-of-concept projects with a relatively short payback ti

300、me.Ensure that initial projects dont require difficult and time-consuming IT platform upgrades.-Start small and prove value.The first use cases should clearly create value that can be shown internally and externally to the company.-Internal projects first.Start with projects that affect only limited

301、 numbers of staff and do not need involvement from external stakeholders.-High replication potential.Try to focus on applications with high replication potential based on currently available technology.Multiple vendors should be allowed to trial their products and services.-Manage people impacts pro

302、actively.The internal social impact of Industrial Metaverse implementation should not be underestimated.This may require employees to perform new roles and develop new skills that will require training and support.-Be prepared to support adoption over an extended period.After deployment,employees wi

303、ll need support.The deployment team should not necessarily be the IT department.Using transversal teams helps to broaden involvement and embed the change.Especially for use cases involving field workers,the adoption curve is usually longer than expected.For example,AR/VR devices sometimes have a neg

304、ative productivity impact initially,so strong support is key,both technically for procedures but also in terms of change management.66Blue Shift /REPORT 003Build&align the ecosystem:Create a win-win situation with ecosystem partnersAs we have described,the major benefits of the Industrial Metaverse

305、are realized through involving not only internal operations but also the whole partner ecosystem and supply chain.This requires sharing much more data than is traditionally shared between commercial partners,requiring a new mindset and culture.Moreover,developing the necessary capabilities to implem

306、ent the Industrial Metaverse will require an ecosystem-based approach.This means that a key step is to actively engage with and align eco-system partners on the project.For partners to be comfortable with sharing of internal data for this purpose,companies will have to ensure that,for example:-The b

307、enefits for partners are assessed and demonstrated.The benefits are likely to be in areas such as faster customer response,reduced working capital,smoother customer/supplier interfaces,easier demonstration of sustainability impacts,greater overall efficiencies,and closer customer/supplier relationsh

308、ips.-Data safety and security is ensured.Ecosystem partners will have to satisfy each other with regard to the safety and security of shared data.-Standards are defined and agreed.Ecosystem partners will have to ensure that there are agreed standards and procedures for data management to manage the

309、risks involved.An ecosystem approach is essential for the success of the Industrial Metaverse(see Figure 20).Without it,the essential value of a digi-tally simulated business is hard to realize.Fig 20 Building an Industrial Metaverse ecosystemSource:Arthur D.LittleSource:Arthur D.LittleFig 23 Buildi

310、ng an Industrial Metaverse ecosystemCOMPANY Suppliers Technology vendorsService providersCustomersResearch&technology providers Logistics&distribution partnersPeers&competitorsAcademiaFunders67Blue Shift /REPORT 003APPENDIX16869TECHNOLOGY READINESS LEVELS1Technology readiness levelsTRLs provide a co

311、mmonly accepted means of describing technology maturity.Fig 21 Technology readiness levelsSource:Arthur D.LittleSource:Arthur D.LittleAppendix 1.Technology readiness levelsPre-concept refinementTRL 1Basic principles are described or observed at theoretical or experimental stageTRL 2Technological con

312、cepts are formulated and not yet necessarily testedTRL 3Proof of concept is carried out in a laboratory at the level of technical processConcept refinementTRL 4Technology is validated in the laboratory as a wholeTechnology developmentTRL 5Technology model in a production-grade environment is created

313、TRL 6Technology prototype is demonstrated in an environment representative of intended use caseSystem dev.&demoTRL 7Prototype is evaluated in an operational environmentProduction&deploymentTRL 8Complete system has been evaluated and qualifiedTRL 9Complete system is operational and qualified in produ

314、ctionBlue Shift /REPORT 00370“The Industrial Metaverse is the extension of what has been called Industry 4.0.It is the digital twin of a complex system that allows you to project yourself through time and immerse yourself in space.”Blue Shift /REPORT 00371APPENDIX27273SELECTED COMPANY PROFILES2Selec

315、ted company profilesBlue Shift /REPORT 00374BoschFounded:1886HQ:Gerlingen,GermanyRevenues:$79 billion(2021)Employees:403,000(2021)Highlights-Digital twin running on AWS-VR training toolSelected partnerships-Amazon Web ServicesRelevant products&services(within Industrial Software Solutions division)-

316、Digital twin IAPM(Integrated Asset Performance Management)solution -Digital twin maintenance-IoT-AI-AnalyticsCosmo TechCosmo TechFounded:2010HQ:Lyon,FranceRevenues:$4.8 million(2022)Employees:100(2022)Highlights-Digital twin platform deployed with Microsoft Azure-Digital twin platform to train AI-Si

317、mulation for Michelin global sourcing strategySelected partnerships-Microsoft-SAS-IBMRelevant products&services(within Industrial Software Solutions division)-Simulation Digital Twins software solutions simulation software platform for industrial businesses in manufacturing,energy and utilities,and

318、mobility-Cosmo Tech Supply Chain planning capabilities for all stages of the manufacturing process-Cosmo Tech Asset cloud-based solution to make strategic investment decisions for short-,medium-,and long-term operations-Simulation modeling ability to map real-world systems and their interconnections

319、 and relationships to create Simulation Digital Twins-Predictive and prescriptive analytics simulation and optimization to deliver optimal action plans-Platform as a service delivering Simulation Digital Twins in a scalable,powerful,and collaborative environmentBlue Shift /REPORT 00375Dassault Systm

320、esDassault SystmesFounded:1981HQ:Vlizy-Villacoublay,FranceRevenues:$5.8 billion(2021)Employees:22,523(2022)Highlights-Digital twin of human heart-Digital twin of the Eiffel Tower-Plan to create NumSpot a joint effort to build a European sovereign cloud service for the financial,health,and public sec

321、torsSelected partnerships-MultipleRelevant products&services(within Industrial Software Solutions division)-3DEXPERIENCE portfolio 3D modeling applications,simulation applications creating virtual twins of products or production systems,social and collaborative applications,and information intellige

322、nce applications-Product lifecycle management-Supply chain planning and optimization-Analytics,big data,and AI-Design and engineering-Design and engineering simulationGeneral ElectricGeneral ElectricFounded:1892HQ:Massachusetts,USARevenues:$74 billion(2021)Employees:168,000(2021)Highlights-New gener

323、ation of iFIX and CIMPLICITY software used in HMI/SCADA applicationsSelected partnerships-Amazon Web ServicesRelevant products&services(within Industrial Software Solutions division)-SmartSignal and Asset Performance Management(APM)solutions create digital twins based on operational/fleet data of co

324、mponents(pumps or compressors),critical assets(turbines),or systems of assets(an entire power station)-AI-Cybersecurity-HMI76Blue Shift /REPORT 003MicrosoftMicrosoftFounded:1975HQ:Washington,USARevenues:$198 billion(2022)Employees:221,000(2022)Highlights-Microsoft Mesh collaboration and communicatio

325、ns platform-HoloLens 2(for training)-$10 billion investment in OpenAISelected partnerships-Meta-NVIDIARelevant products&services(within Industrial Software Solutions division)-HP Reverb G2 collaboration with Valve.MR headset that allows users to have a 144-degree field of view-HoloLens MR headset wi

326、th sensors,cameras,microphones,and Holographic Processing Unit,enabling:-Virtual collaboration-Design reviews with spatial recognition-Remote user assistance-Hosting of virtual meetingsSiemensSiemensFounded:1847HQ:Munich,GermanyRevenues:$78 billion(2022)Employees:311,000(2022)Highlights-Partnership

327、with NVIDIA for AI-driven Industrial MetaversesSelected partnerships-NVIDIARelevant products&services(within Industrial Software Solutions division)-Product and process digital twin solutions-Siemens Xcelerator open digital business platform for helping to achieve digital transformation-Open applica

328、tion suites leveraging IoT and AI for different sectors(e.g.,rail and buildings)77Blue Shift /REPORT 003NVIDIANVIDIAFounded:1993HQ:California,USARevenues:$27 billion(2022)Employees:22,400(2022)Highlights-AI-driven Industrial Metaverse(with Siemens)-Mercedes-Benz to implement OmniverseSelected partne

329、rships-Siemens-Microsoft-BMWRelevant products&services(within Industrial Software Solutions division)-NVIDIA Omniverse platform for creating and operating Metaverse applications:-3D design collaboration-Modular development platform for building and operating Metaverse applications-Large-scale world

330、simulations(including digital twins and training AIs)UnityUnityFounded:2004HQ:San Francisco,California,USARevenues:$1.1 billion(2022)Employees:5,200(2021)Highlights-Acquired WetaFX(digital visual effects)Selected partnerships-HyundaiRelevant products&services(within Industrial Software Solutions div

331、ision)-Unity engine 3D,AR,VR game platform-Unity Industrial Collection software bundle for industrial use-3D models import and optimize 3D data,create and operate immersive experiences,use CAD models to create and operate 3D applications-Develop and deploy applications connect HMI development proces

332、ses and create interactive,real-time 3D product configurators-Training and simulation78Blue Shift /REPORT 003Blue Shift /REPORT 00379“We will create a future in these metaverses before actually downloading the blueprints to be fabed in the physical world.”Jensen Huang,founder&CEO,NVIDIAAPPENDIX38081

333、INDUSTRIAL METAVERSE USE CASES 3Industrial Metaverse use casesThis appendix contains more than 30 illustrative use cases for the Industrial Metaverse in its current stage of evolution(i.e.,discrete digital twins).It is organized by industry and is non-exhaustive.Blue Shift /REPORT 00382-Lean more flexible,efficient production-Green more consistent circularity and better sustainability across the s