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1、INDUSTRY 4.0BarometerMarket Trends and Experience Feedbacks2024 Edition2SummaryPREAMBLEEditorial 03Methodology 04Key concepts 05Key messages 06CHAPTER 1Tracking Industry 4.0 trendsand companies digital maturity 08Extracts from the Round Table 21CHAPTER 2Data management and AI:accelerating growth 23

2、Extracts from the Round Table 28CHAPTER 3Sustainability and responsibility:At the heart of concerns 29Extracts from the Round Table 38CONCLUSIONGlossary 39Acknowledgements 40Contact our experts 413OverviewVincent MOULIN WRIGHTManaging DirectorFrance IndustrieOlivier FONTANILLEAssociate Partner Waves

3、toneFranois-Xavier DE THIEULLOYDirector,Expertise DivisionBpifranceCaroline CHOPINAUDManaging Director Hub France IAMethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContact

4、sAn identical approach to previous editions:An 18-question questionnaire launched during the summer of 2024A panel of respondents from the network of Wavestone,France Industrie,the French Fab,and Hub France IAQualitative interviews to complete the survey resultsRespondents with characteristics repre

5、sentative of the French industrial ecosystemProgram/Project Management12%Operational Management25%Strategic Management32%Expertise 5%Operational5%Other21%FunctionSmall and medium-sized companies45%Intermediate-sized companies25%LargeCompanies29%Other1%Company sizeAgri-food/Pharmaceuticals19%Process&

6、Heavy Industry50%Energy 8%Transport 4%Consumer goods 5%IT Services14%Business sectorMethodologyMethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContacts5Key conceptsA simpl

7、e categorization of Industry 4.0 technlogiesMethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContacts6Key messagesIndustry 4.0:Performance,Data and Sustainability for Busin

8、essesDigitalization as a Major Lever for Industrial PerformanceIn a complex economic environment,digitalization and Industry 4.0 remain key levers for securing industrial performance.expertise in Industry 4.0 solutions continues to advance.However,projects are subject to stricter selection,prioritiz

9、ing initiatives that generate a significant return on investment(ROI).Small and medium-sized enterprises(SMEs),however,struggle to keep pace with large companies due to their limited capacity to absorb the initial costs of these projects,particularly the costs associated with replacing obsolete mana

10、gement software.A Shift in Priorities Toward Data ManagementConcerns and initiatives are shifting to place greater emphasis on data management.However,projects involving data exploitation using Generative AI remain rare.The main challenges for industrial companies are to ensure maturity in foundatio

11、nal areas,combining digitalized production monitoring with process control through artificial intelligence,while continuing to innovate in emerging technologies such as real-time monitoring and advanced analysis via industrial data lakes that centralize data.These projects generate additional needs,

12、leading notably to the renewal of operational information systems.Accelerating the Move to ActionIncreased emphasis is being placed on these areas.This is evident,on one hand,in concrete projects aimed at optimizing resource management(energy,water,etc.),particularly through the deployment of inform

13、ation systems for energy monitoring and carbon management.On the other hand,the integration of the human factor and employee satisfaction have become crucial elements for successful implementation.GLOBAL MATURITYDATA/AISUSTAINABILITY AND SOCIAL RESPONSIBILITYMethodologyMethodologyEditorialEditorialK

14、ey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContacts701.MONITORING OF INDUSTRY 4.0 TRENDS AND DIGITAL MATURITY OF COMPANIESMethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messa

15、ges1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContacts8Industry 4.0:A Steady and Contrasting ProgressionIndustry 4.0 still appears as an obvious choice for industrialists.The expertise in Industry 4.0 solutions continues to grow(+8%since 2023).In a more

16、challenging economic context(inflation,rising energy prices),22%of respondents indicate slowdowns in the implementation of their Industry 4.0 projects.Paradoxically,25%of respondents state that this context has led them to accelerate investments in their Industry 4.0 projects,where the projects allo

17、w them to generate ROI.For example,value can be quickly generated in projects such as robotization or energy consumption management.17%of companies are able to deploy Industry 4.0 solutions at the initially planned pace,an improvement since 2023(+10%):the subject remains complex to implement on a la

18、rge scale.My organization is fully mature in the deployment of Industry 4.0 solutions(technological and organizational basis)True22%Quite True44%False4%Rather False30%MethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3

19、.CSRAcknowledgementsAcknowledgementsContactsContacts9Industrial performance and employee engagement,major drivers for 4.0 initiatives This year,we note the increasing importance of issues related to employee roles(improving their satisfaction and enhancing collaboration)and decarbonization challenge

20、s.The inclusion of the human factor and CSR in these issues gives rise to the concept of Industry 5.0,which aims to define digitalization programs for industrial operations that are resilient,sustainable,and human-centered.9%9%9%11%11%13%19%What challenges are Industry 4.0 initiatives addressing in

21、your organization?-8%-8%-7%-1%-3%Change since 2023Optimization of operational performanceImprove my employees satisfactionImprove traceabilityStrengthen product/service innovation in the long termAccelerate decarbonizationStrengthen collaborationImprove peoples safetyConfirmed trend:Optimizing opera

22、tional performance(improving quality and reducing costs)is the primary challenge for manufacturers in their Industry 4.0 initiatives.An example of a lever for optimizing business processes is the cross-analysis of industrial data from various sources.Examples:predictive maintenance,automated quality

23、 control through machine vision,etc.Ergonomics,well-being,and employee engagement:Employee satisfaction ranks second.It is an important factor in enriching tasks,improving job interest,or reducing hardship.Examples:ergonomics,cobots,augmented reality-assisted training,etc.Improving traceability comp

24、letes the podium once again.Sector-specific standards and regulatory constraints(ISO 9001 Standard,Food Hygiene Regulations,etc.)drive these investments.Examples:raw material tracking,advanced quality control,batch management,etc.MethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey me

25、ssagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContacts10Industries are not yet fully capable of deploying Industry 4.0 initiatives on their current infrastructureFor 21%of the surveyed industrial companies,the existing infrastructure is fu

26、lly capable of supporting digital transformation projects(a decrease of 6%since 2023).This decline is explained by the need to adapt technological foundations to accommodate new projects focused on Data and AI exploitation.56%of industrial companies need to make minor adjustments to adapt their infr

27、astructure to new initiatives.This figure is stable:+9%since 2023.For large groups,these infrastructure modernization projects are often lengthy to implement.This can be explained by the complexity and heterogeneity of the existing infrastructure of multisite groups.Modernizing infrastructure is a f

28、undamental step to ensure the success of Industry 4.0 projects.Initiatives must rely on an infrastructure that is:Reliable,with a high-performance communication network and significant availabilityRobust,with substantial data storage and processing capacities(data lakes,relational databases,etc.)Sec

29、ure,with access control and compliance with cybersecurity requirementsModular,with the ability to support various use cases(on-premise,cloud,etc.)Aligned with business and IT roadmaps to ensure its longevity over timeIs your current infrastructure capable of supporting the deployment of new Industry

30、 4.0 projects?21%56%23%The existing infrastructure is operationaland flexibleUpdates(minor or major)are necessaryThe infrastructure has technologicalobsolescenceMthodologieMthodologieEditorialEditorialConcepts clsConcepts clsMessages clsMessages cls1.Gnralits1.Gnralits 2.Data /IA2.Data /IA3.RSE3.RSE

31、RemerciementsRemerciementsContactsContacts11The expertise in data management(connectivity and data management)ranks first in our survey.This topic is also progressing compared to last year,unlike other non-data-related themes.These developments confirm the importance of upstream thinking when implem

32、enting data models and optimizing data-centric tools.For example,to compare indicators from multiple sites,standardized collection and inter-compatibility of models between factories are required.A global priority on data managementGrands GroupesETI&PMEMid-sized and small businesses opt for more fle

33、xible and accessible mobile work tools,which allow them to quickly improve their operational efficiency.Investments are primarily focused on tools with easily achievable ROI.55%67%68%78%ConnectivitOutil de travail en mobilitData managementSI dexploitationLarge corporations have been able to invest i

34、n operational information systems with more resources over the long term.These complex applications are now mature and well-integrated.80%77%69%68%54%47%Level of expertise of the manufacturers by themeVS 2023ConnectivityDatamanagementOperational information systemMobility toolsAutomationAlgorithmics

35、+6%+24%-14%-15%-13%+13%MethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContacts76%84%88%92%SI dexploitationData managementConnectivitOutil de travail en mobilitConnectivit

36、yMobility toolsOperational information systemConnectivityMobility toolsOperational information systemData managementLarge companiesSmall,medium and intermediate size companies12Expertise in connectivity is making strong progress,up 11%since 2023.Connectivity is at the core of the interconnection of

37、objects,machines,and systems.The collection and analysis of real-time data allows for improved efficiency of industrial processes and informed decision-making.Connectivity:The Foundation for Data CollectionA number of constraints must be addressed to successfully carry out a connectivity project:Pre

38、cise description of the data usage case to choose the most appropriate solutionData capture from legacy systems not designed for communication on an industrial networkCompatibility with all industrial protocols:OPC UA,MQTT,etc.Data management ensuring model interoperabilityRedundant systems to avoid

39、 single points of failureContinuous monitoring of the availability of collection toolsMethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContacts44%33%18%14%37%40%33%41%15%18

40、%31%38%4%9%18%7%0%10%20%30%40%50%60%70%80%90%100%2024202320222021Expertise in ConnectivityOprationnelPartiellement MatrisA ltudeInexplorOperationalPartially masteredUnder studyUnexplored13Interfacing with management information systems,a key element of digital continuityManagement information system

41、s(ERP)are the basis for centralizing company data and processes.It is therefore essential to be able to interface with these to ensure digital continuity.Interfaces between management IS(ERP)and industrial IS(MES,QMS,etc.)make it possible to:Avoid data loss and re-entriesSynchronize the flow of info

42、rmation in real time36%45%16%3%0%10%20%30%40%50%60%70%80%90%100%2024Maturity of interfacing with management information systems(ERP)OprationnelPartiellement MatrisA ltudeInexplor29%48%44%43%22%9%5%ETI/PMEGrand GroupeIn order to successfully interface different information systems,it is necessary to

43、ensure:Definition of a functional core model,defining responsibilities for data processingHarmonization of data modelsRationalization of technical resources(middleware)between solutionsMethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /

44、AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContactsOperationalPartially MasteredUnder studyUnexplored14Mobility,a technology losing momentumThe level of expertise in mobile work tools is decreasing(-8%in 2024).However,these technologies have seen increasing adoption in previous ye

45、ars.Several factors can explain this decline:Increasing automation and intelligent IT interfaces reduce manual tasks and the need for human intervention,thus deprioritizing mobility projectsTechnology perceived as less innovative and deprioritized in favor of investments in data and AI,which promise

46、 a better ROIDurability and maintenance costs of mobile devices that wear out quickly and lack of reinvestment reduce the perceived effectiveness of mobility solutionsMethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3

47、.CSRAcknowledgementsAcknowledgementsContactsContacts31%39%41%33%38%44%37%43%25%15%18%21%6%2%4%3%0%10%20%30%40%50%60%70%80%90%100%2024202320222021OprationnelPartiellement MatrisA ltudeInexplorOperationalPartially MasteredUnder studyUnexploredExpertise in mobile work toolsMobile technologies in factor

48、ies remain a lever for optimizing industrial operations:Real-time access for operators to production data and necessary documentation Enhanced training and real-time assistance for complex tasks,through overlaying instructions directly in the users field of vision Increased collaboration and communi

49、cation between teams and departments15A global decline in maturity on Operational Information SystemsEvolution of the overall maturity of Operational Information SystemsMaturity by functional categories of Information Systems in 202418%29%28%43%39%44%24%13%25%19%22%15%27%21%13%16%19%19%31%37%34%22%2

50、0%22%Sustainable Solution inOperationRenewal in ProgressInitial DeploymentNo Solution Deployed49%39%33%15%13%36%31%44%21%5%4%10%0%10%20%30%40%50%60%70%80%90%100%202220232024OperationalPartially MasteredUnder ReviewUnexploredQuality control and energy consumption monitoring solutions have been progre

51、ssing since 2023:QMS and LIMS:+12%EMS,BMS,energy flexibility:+5%These results align with the measured challenges of industrials in improving production quality and accelerating decarbonization.However,there remains a disparity in the adoption of energy consumption monitoring tools:32%for large group

52、s compared to 6%for SMEs/ETIs.Indeed,SMEs/ETIs are not yet subject to the same regulations,which require better control of energy consumption,as large groups.High-value use cases require accessing and processing large amounts of structured data.This demand for digital continuity necessitates the ren

53、ewal of Operational Information Systems,which is why we observe a 16%decrease in sustainable solutions in operation since 2022.MaintenanceProduction ManagementQuality ControlLogistics ManagementEngineeringEnergy Consumption ManagementMethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKe

54、y messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContacts16Actors in the industrial sectors are increasingly implementing energy consumption management tools,such as an EMS(Energy Management System)or a BMS(Building Management System).Thi

55、s reflects a heightened awareness of the issues related to decarbonization and the reduction of energy consumption.These technologies enhance resilience to energy crises and the variability of energy prices.However,for some actors in the industry(food and pharmaceutical sectors),the deployment of th

56、ese tools appears to be less of a priority.Since their energy consumption is lower,they generate a lower return on investment.Operational priorities vary according to industry sectorsIT systems deployed based on industry sectorsThe deployment of quality control information systems is cross-functiona

57、l across all sectors,particularly to ensure regulatory compliance,customer satisfaction,and to reduce costs and non-conformities.Agri-foodPharmaceuticalEnergy31%36%38%Process Industry Heavy Industry33%42%42%33%50%50%8%10%50%8%31%17%Solution prennePremier dploiement en coursSustainable solutionInitia

58、l deployment in progressDeployment of energy consumption management toolsAgri-food and PharmaceuticalProcess industry and heavy industryEnergyMethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAckno

59、wledgementsContactsContactsMaintenanceQuality controlLogistics managementQuality controlEnergy consumption managementEngineeringProduction managementMaintenanceQuality control17Algorithmics:a step towards decision automationThe implementation of an algorithmic use case relies on a detailed understan

60、ding of the data model in order to define the operating rules.This approach is different from new AI technologies(such as generative AI)which infer operating rules based on learning from a large amount of data.The expertise in algorithmics has significantly increased(+12%since 2023).Algorithmics con

61、tributes to the development of recommendations from information systems:Predictive analyses(predictive maintenance,etc.)Process optimization(self-regulation of production parameters,etc.)Big data analysisDecision automationMethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesK

62、ey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContacts21%11%4%25%23%33%35%41%29%19%25%34%0%10%20%30%40%50%60%70%80%90%100%202420232022Expertise in algorithmicsOprationnelPartiellement MatrisA ltudeInexplorOperationalPartially masteredUnder studyU

63、nexplored18Hosting strategy:a strategic axis to evaluate carefullyThe two main types of hosting,on-premise and cloud,each have specific advantages.25%44%50%Flexibilit delinfrastructureMaintenanceet gestion desressourcesPrixCriteria for choosing cloud hosting35%52%70%MatrisetechniqueRapidit de lacons

64、ultatitiondes donnesConfidentialitdes donnnesCriteria for choosing on-premises hostingSmall and medium-sized enterprises prefer on-premise hosting to maintain full control over their data and for faster data access.Conversely,large enterprises favor hybrid hosting to have a flexible and scalable arc

65、hitecture while also optimizing costs.Indeed,consumption commitment agreements with cloud solution providers help reduce data center operation costs.Large corporations also have a greater capacity to internalize cloud expertise.14%43%43%37%55%9%CloudHybrideSur siteETI&PMEGrand groupeChoosing the typ

66、e of hosting based on company sizeThe hybrid model combines the advantages of the cloud with those of on-premise infrastructure,offering companies a flexible infrastructure.Each of these solutions presents benefits:Cloud:better risk distribution,rapid scalability,and greater computing capacity On-pr

67、emise:ability to handle use cases that require data processing close to the machines for real-time decision-makingMethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContactsC

68、loud Hybrid On-premise Mid-sized company&SMELarge groupInfrastructure flexibilityMaintenance and resource managementPriceTechnical expertiseSpeed of data retrievalData confidentiality19Main Challenges in Cybersecurity:Obsolescence Management:Managing vulnerabilities induced in the industrial IT infr

69、astructureLack of Skills:Shortage of resources and skills in cybersecurity,particularly in industrial cybersecurity at the convergence of IT and OTDifficulties in Finding the Right Level of Governance and Investment:Difficulty in establishing cybersecurity governance at the appropriate group level,w

70、hich hinders project funding due to a lack of visibility on ROIIndustrial Cybersecurity:An Essential Requirement73%of industrials claim to have reached maturity in integrating industrial cybersecurity criteria within their organization.96%71%57%The majority of respondents indicate that cybersecurity

71、 is widely integrated into all industrial activities,especially for large corporations.These companies are more exposed to cyberattacks due to:The potential impact of service interruptions on their global operationsThe high value of their dataTheir aging industrial infrastructureBreakdown by company

72、 sizeSMEsMid-sized companies Large GroupsMethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContacts20What is your view on the maturity of the technical foundation for deploy

73、ing Industry 4.0 initiatives?And how do you approach these Industry 4.0 project roadmaps?“Generally speaking,we must avoid taking shortcuts when launching a new 4.0 initiative,because while it may save a little time and money at start-up,it could seriously compromise scalability.And in most of the i

74、ndustry 4.0 use cases Ive observed,scalability is the factor that enables the estimated return on investment to be achieved in the valorisation phase.It is essential to move from Proof of Concept(POC)to Proof of Value(POV),by demonstrating the concrete economic value rather than the technical feasib

75、ility of a use case,because 4.0 technologies are already sufficiently mature.It is therefore extremely important to invest more or less massively in layers of enabling technologies.The idea is to build strong foundations,a robust technological base that can cover a wide variety and typology of use c

76、ases.Next,it is also important to ensure that by design,these platforms are sufficiently modular,so that they can be shared across multiple use cases as well as various business units within the group.We can imagine iterations on the POCs in several phases depending on the level of automation achiev

77、ed.We start with information gathering and the construction of dashboards for real-time data visualization on the shop floor,before evolving towards a more predictive model.Then,we reach more advanced levels of prescription,automation,and regulation.These phases can initially focus on specific use c

78、ases on the shop floor,eventually leading to a complete optimization of the industrial process.Finally,one of the major obstacles I observe concerns the heterogeneity and aging of the shop floor,whether at the level of industrial IT or even OT.Compliance,particularly from a cybersecurity perspective

79、 of the shop floor,is something that can sometimes cost more than the implementation of the use case itself,and therefore all this requires significant investments.“Pierre RAYMONDWhat are the other major obstacles you can identify today in the implementation of 4.0 solutions?“The first obstacle we i

80、dentify is the support from the hierarchy for the 4.0 project.The second obstacle we may encounter,particularly in SMEs and mid-sized companies,is the lack of resources and sometimes even the lack of qualified resources suitable for the deployment of digital solutions in the industry.Finally,one las

81、t obstacle I identify is the gap that can exist today between digital and industry,and the perception of value which can be completely different on each side.”Daniel BLENGINOVERBATIMSTrend monitoring Industry 4.0Pierre RAYMONDHead of digital supply chain&manufacturing solutions,Saint GobainDaniel BL

82、ENGINOCEO,Visionairy MethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContacts21Given your contact with many industrialists,what is your vision of Industry 4.0?“In France,S

83、MEs have a significantly lower level of digital intensity compared to the European average;23%of French companies use at least one Cloud solution,which is almost half the European average(39%);6%of French companies have already adopted AI solutions,2 points less than the EU average.The 4.0 initiativ

84、e has been a success,as it has expanded the scope of Industry 4.0 to SMEs and mid-sized companies,although some still have a level of digital intensity significantly below the European average.This automation is essential for reindustrialization as it increases productivity and compensates for labor

85、 shortages,improves workplace safety,and reduces operational costs.Today,it is crucial to consider this automation not only as a step towards Industry 4.0 but also as preparation for 5.0,integrating artificial intelligence as the next step.”Vincent MOULIN WRIGHTVERBATIMSTrend monitoring Industry 4.0

86、Philippe MUTRICYDirector of Studies Evaluation and Foresight,BpifranceVincent MOULIN WRIGHTManaging Director,France IndustrieCan we imagine a collaboration between SMEs/mid-sized companies and large groups in the realization of Industry 4.0 projects?“For initiatives to be effective and function prop

87、erly in the industry,there must be a sharing of strategic decisions made by principals within the sector.A complete integration of their strategic vision is necessary,including supply chains and subcontractors.”Philippe MUTRICYMethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messa

88、gesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContacts2202.DATA MANAGEMENT AND AI:ACCELERATED GROWTHMethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAckno

89、wledgementsAcknowledgementsContactsContacts2331%46%18%5%OprationnelPartiellementMatrisA ltudeInexplore31%of respondents feel that they are up and running with their data management,but 23%have no dedicated solutions.Building a solid foundation for the utilization of industrial dataLevel of proficien

90、cy in data managementCompanies that have mostly internalized their data skills consider themselves more mature in terms of exploiting their industrial data.This dynamic is also illustrated by the emergence of new data-related professions such as Data Steward,Data Miner,Data Ethicist,Machine Learning

91、 Engineer,and Data Protection Officer.Level of proficiency in Data Management according to the distribution of skills61%36%39%64%ComptencesmajoritairementinternalisesComptencesmajoritairementexternalisesMaturit forteMaturit faibleTo ensure a solid foundation for leveraging their data,industrial comp

92、anies must work on the following aspects:QUALITY MAINTENANCEANALYSISGOVERNANCEINTEROPERABILITY with industrial protocolsPRESERVATIONMethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgement

93、sContactsContactsOperationalPartially controlledUnder studyUnexploredSkills mostly internalizedSkills mostly externalizedHigh maturityLow maturity24Establishing the right roles,processes,and a company culture around data usage allows for effective structuring,exploitation,and integration of data int

94、o decision-making processes(analysis,continuous improvement,etc.)and helps to derive more value.As in 2023,the lack of data/AI culture and skills remains the main barrier for industries in exploiting their data.Barriers to fully exploiting the potential of industrial data19%Data security21%Difficult

95、y finding the right IT tools29%Difficulty demonstrating ROI35%Poor data quality39%Difficulty accessing data49%Lack of data/AI culture and skillsMethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAck

96、nowledgementsContactsContacts25Generative AI is generally at the same level across all functional areas:MaintenanceProductionQuality,traceabilityR&D&DesignSupply Chain-Logistics42%36%15%6%1%Generative AI:The 2024 Trend That Hasnt(Yet)Conquered the IndustryWhat is the current level of use of generati

97、ve AI technologies within your organizations value chain?Level 4:Generative AI implemented in industrial processesLevel 3:Generative AI integrated into some processesLevel 2:PoCs(Proofs of Concept)completed or in progressLevel 1:Scoping in progressLevel 0:No use of generative AI36%of large industria

98、l groups are at least at the experimental stage compared to 10%for SMEs and mid-sized companies.To ensure a successful PoC,the foundations of a generative AI project must be based on:A quality and representative datasetContext on data insertionIterative fine-tuning on increasing volumes of dataTo su

99、ccessfully interface between different IS,it is necessary to ensure:The definition of a functional core model that defines responsibilities for data processingThe harmonization of data modelsThe rationalization of technical means(Middleware)between solutionsMethodologyMethodologyEditorialEditorialKe

100、y conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContactsExamples of AI and Generative AI use cases for operations:Maintenance:guided machine maintenance,creation of virtual sensorsProduction:energy consumption man

101、agement,automated report generation,service load forecastingQuality:consolidation of customer feedback,monitoring and analysis of quality incidentsR&D&Design:optimization of the design-to-manufacturing process,optimization of manufacturing parametersSupply Chain:management and scheduling of supplies

102、,optimization of the supply chain26What can explain the growing importance of data-related topics and what can they bring to the industry?“What is clearly apparent is that the core of the subject and digital transformation is data,and having data that is of high quality and can circulate freely with

103、in the company to be used in the best possible way.We realize in all companies,and even between companies,that it is the basis for exchanging information and making decisions that will support the entire movement of reindustrialization,decarbonization,etc.What is remarkable about the ongoing digital

104、 revolution,and particularly the availability of this data,is that we are able to make worlds that were very siloed within companies communicate with each other.Data management is a major topic in Industry 4.0 to derive the best value from all use cases.”Ludovic DONATIVERBATIMSData Management and AI

105、How are data-related and generative AI topics integrated into the industrial sector?“According to Bpifrance Le Lab surveys,only 3%of small and medium-sized enterprises(SMEs)use generative AI regularly,and 12%use it occasionally.It becomes clear that without specific use cases,business leaders do not

106、 prioritize significant investment in generative AI.They are aware that it is the future,but due to the difficulty of having precise use cases,we are more,at this stage,at a level of awareness and monitoring rather than on massive investment plans.Nevertheless,there is a growing awareness of the wea

107、lth and value creation that can be derived from all this data,as long as it is well optimized,structured,exploited,and protected.”Philippe MUTRICYWhat is your vision on the use of data in the industry?“To reindustrialize France,the adoption of these new technologies and artificial intelligence is vi

108、tal,and involves several projects.First,a modernization project,particularly on infrastructure.Then,we need to accelerate the use and analysis of data by using new solutions.Less than 40%of companies worldwide gain significant benefits from artificial intelligence,even though the industry is the lar

109、gest source of data in the world,and that demonstrates that we dont know how to use it.We need to accelerate data architectures to make better use of it.We need to focus not only on collecting data,but also on processing it in the right way to feed algorithms.What is very important is to access the

110、real-time use of this data,which obviously leads to a significant productivity gain.”Vincent MOULIN WRIGHTLudovic DONATIVice-President of AfnetPhilippe MUTRICYDirector of Evaluation,Studies,and Foresight,BpifranceVincent MOULIN WRIGHTManaging Director,France IndustrieMethodologyMethodologyEditorialE

111、ditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContacts27What are the priority and highlighted topics around data and possibly AI today?“Mainly those that generate the most hard savings.Being quite prag

112、matic,we are really focused on projects that allow for optimization of product quality and energy consumption,and that can especially have a significant impact on the roadmap,particularly related to carbon impact,which is also crucial.What is essential is to consider an approach that is very iterati

113、ve over time.We cannot start directly by managing a fully automated industrial process with AI.However,we are moving towards this automation,this prescription,and automated regulation loop.And it also comes with significant financial benefits that generate ROI.”Pierre RAYMONDVERBATIMSData Management

114、 and AIFrom your perspective,what use cases are gaining the most traction in the field?“The industry remains very pragmatic and crystallized around the three pillars:cost,quality,and time.Topics are driven by the hierarchy when there is a return on investment(ROI)and true pragmatism in the implement

115、ation of these solutions.Indeed,it is necessary to find a use case,but with a better understanding of what technology allows.We could also find more and thus have more levers of competitiveness to modernize and accelerate the management of operations.”Daniel BLENGINOWhat other challenges should be a

116、nticipated when launching this type of project to ensure a successful implementation?“First,there is cybersecurity.When we talk about AI,we talk about data and its processing,and often about the Cloud as well.The second issue is education around AI.AI brings something complementary and,for example,w

117、ill allow for more variability,whereas it is often thought that it will replace.Finally,there is the regulatory aspect,especially in sectors like pharmaceuticals.Indeed,AI is intended to evolve with data over time.The more data evolves,the more AI evolves and becomes efficient,which continuously imp

118、roves this AI model.This provides consistency and operational robustness over time.”Daniel BLENGINOPierre RAYMONDHead of digital supply chain&manufacturing solutions,Saint GobainDaniel BLENGINOCEO,Visionairy MethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.Ge

119、neral1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContacts2803.SUSTAINABILITY AND SOCIAL RESPONSIBILITY:AT THE HEART OF CONCERNSMethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSR

120、AcknowledgementsAcknowledgementsContactsContacts29Driven by regulatory impetus,the deployment of CSR action plans is accelerating further for large French companiesThe imminent enforcement of regulations is driving the professionalization of reporting and the deployment of CSR roadmaps.50%Large comp

121、anies have begun deploying their CSR action plans44%Industrials have begun deploying their CSR action plansCSRDEntre en Entry into force for companies with 500+employees,sales of 50m&+25M sales obligation to publish a certified annual non-financial report on ESG topics(environment,social,governance,

122、etc)Gradual implementation of the CSRD for all publicly listed companies with activities within the European UnionEnergy and Climate Law French adaptation of the Paris Agreement objectives(carbon neutrality):40%reduction in fossil energy compared to 2012.Cessation of electricity production from coal

123、 in 2022.Mandatory installation of solar panels or other renewable energy sourcesEuropean Climate Law Reduction of 55%in greenhouse gas emissions compared to 1990 for EU member states 42.5%of renewable energy in the European energy mix 310 million tons of CO2 absorbed by natural carbonExcerpt from t

124、he regulatory commitments to which states and industrial companies are subject+14%vs 2022+11%vs PME/ETI2024202520272030MethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsCont

125、acts30Labeling Sustainability Initiatives:A Value-Generating Methodological Framework?The SBTi Label Establishes Itself as the Benchmark for Supporting Corporate Decarbonization.Launched in 2015,the Science Based Targets Initiative(SBTi)helps companies align their climate goals with the Paris Agreem

126、ent to limit warming to 1.5C.Companies must set greenhouse gas(GHG)emission reduction targets based on scientific data,mandatorily including scopes 1 and 2 of the Greenhouse Gas Protocol,and scope 3 if it exceeds 40%of total emissions.SBTi-labeled companies worldwide(2x vs 2022),including 400 French

127、 companiesCAC 40 companies have targets aligned with the SBTi frameworkSBTi Monitoring Report 2023-Science Based Targets InitiativeWhy do industries choose to get labeled?44%64%68%82%RenforcerlinnovationSe mettre enconformitrglementaireRduire les cotsAccder denouveaux marchsLabeling sustainability i

128、nitiatives sets emission reduction targets,encouraging industries to adopt concrete measures to achieve them.These measures are sometimes already underway with their existing transformation programs(deployment of EMS,QMS,etc.).Knowledge of these labels is essential,as it allows to highlight a dual o

129、bjective of performance and decarbonization,making possible to:Enhance Industry 4.0 projects within CSR initiativesFacilitate funding for digitalization initiativesIncluding a decarbonization component in an Industry 4.0 project helps accelerate ROI by opening up the possibility of obtaining public

130、funding.+550073%MethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContactsStrengthen innovationComply with regulationsReduce costsAccess new markets315%8%5%13%19%22%12%50%29

131、%36%50%55%51%51%62%28%66%56%45%32%30%27%26%22%Optimisation de la consommation dnergieMise en place dune dmarche circulaireMesure et pilotage du bilan carbone Rduction de la non qualit Eco-conception des produitsNouvel outil de productionGain de performance industrielleStockage/valorisation carbone S

132、olution non retenueSolution en cours de dploiementSolution dployeThe performance optimization levers used by industrial companies also allow them to decarbonize production.The performance optimization levers used by industrial companies also allow them to decarbonize production.To accelerate their d

133、ecarbonization,industrial companies rely on levers already used in digitalization programs to optimize their industrial performance(reduction of non-quality,renewal of production tools,waste recovery,etc.).The rationalization of energy consumption is the most used lever and fits into this dual objec

134、tive of performance optimization and decarbonization.Levers that have accelerated significantly over the past year:PROJECT PROGRESSTransformation projects of the production system within large groups+13%Ongoing projects aimed at reducing non-quality+19%Waste recovery projects and/or implementation o

135、f a circular approach+22%Carbon storage/valorization projects+16%MethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContactsEnergy consumption optimizationImplementation of a

136、 circular approachMeasurement and management of the carbon footprintReduction of non-qualityEco-design of productsNew production toolIndustrial performance improvementCarbon storage/utilizationSolution not retainedSolution in progressSolution deployed32Energy:a key focus area for industrial companie

137、s to minimize their carbon impactsThe management of energy supply and consumption is now an essential part of CSR roadmaps because:Little dependent on upstream and downstream factors,and thus within the control of industrial companies in the short termTechnical solutions are available and can be dep

138、loyed quickly(1 year)Encouraged by financial and regulatory issues(e.g.,ISO 5001 on energy performance)Energy management solutions are more favored than carbon management tools.Selection of technical solutions for energy management6%8%23%33%59%40%48%42%35%52%29%25%0%10%20%30%40%50%60%70%80%90%100%Mi

139、se en place de la sobrit/efficacit nergtiqueMesure et pilotage de lnergieIntgration au mix nergtique de nouvelles sources dnergielectrification du parc machineSolution non retenueSolution en cours de dploiementSolution dployeImplementation of Energy Sobriety/EfficiencyMeasurement and Control of Ener

140、gyIntegration of New Energy Sources into the Energy MixElectrification of the Machine Fleet+8%Energy consumption monitoring or carbon emissions management solutions since 2023.45%Industrial companies are deploying carbon emissions management solutionsIndustrial companies are deploying energy consump

141、tion monitoring solutions69%92%of large groups64%of large groupsMethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContactsSolution not retainedSolution not retainedSolution

142、deployed33By combining Industry 4.0 and the human factor,Industry 5.0 is part of an HR and market positioning strategyBy adopting an approach that takes the human factor into account,industrial companies reconcile economic performance,technological innovation,and social responsibility.This strengthe

143、ns their position in a world where environmental and social expectations are increasingly high and standardized.These concepts give rise to the notion of Industry 5.0,which redefines digitalization programs towards resilient,sustainable,and human-centered industrial operations.2Access new markets3Im

144、prove employee engagementTOP 3 sustainable transformation challenges for industrial companies(%of respondents who consider the challenge important)1Enhance the companys project82%84%77%MethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /

145、AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContacts34A positive impact of 4.0 initiatives on employees and attractiveness21%6%4%30%20%19%Industry 4.0 goes beyond transforming industrial processes:Improves the employee experience by developing their skills and enriching their tasks

146、Increases employee satisfaction,making companies more attractiveEnhances employer imageThese benefits can translate into better overall company performance.What is the impact of Industry 4.0 projects on the members of your organizations?Improved job satisfaction thanks to more efficient processesRed

147、uced employee motivation:resistance to change,adaptation difficulties,etc.Increased concerns about job security Development of new employee skills Greater attractiveness to new employees Increased team collaboration and commitmentMethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey me

148、ssagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContacts35Skills development:a strategic and significant focus areaData management,management information systems(MIS),and operations skills are mostly internalized because they require experti

149、se of the functional domain and a fine understanding of the business.Industrial cybersecurity and algorithm skills are outsourced because they are more dynamic and require constant updating.Robotics skills are weakly internalized in smaller companies because they do not require replication.However,l

150、arge industrial companies,especially in the automotive sector,massively replicate robotics in factories and therefore prefer to develop strong internal expertise.59%66%67%69%74%79%Automatisation&RobotisationAlgorithmieCyberscuritindustrielleSI dExploitationSI de GestionArchitecture etmanagementde la

151、 donneInternalization of skills by functional domainVS 2023+8%+2%+11%-7%+7%+1%MethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContactsAutomation&Robotics AlgorithmicsIndus

152、trial CybersecurityOperational Information SystemsManagement Information SystemsData Architecture and Management36Public funding mechanisms are mainly sought for R&D and innovation projects.The Research Tax Credit(CIR)is the main funding mechanism(In 2024,more than 15,000 companies will benefit from

153、 it,for a total of 7.6 billion euros).It encourages companies of all sectors and sizes in France to invest in research.However,other sources of funding still need to be promoted to industrial companies.France 2030,the national funding framework program,particularly encourages and supports data-relat

154、ed initiatives(data space,usage,platform development),but only 31%of respondents seek funding for this type of projectProject financing:opportunities to seizeSurveyed industrial companies are aware of and seek public fundingDecarbonization is an essential condition for accessing public funding.Since

155、 the Climate and Resilience laws of August 22,2021,and No.2023-973 of October 23,2023,relating to the green industry,public authorities have strengthened the consideration of environmental criteria in the allocation of aid.For what type of project do you mobilize public funding?86%31%36%43%43%69%Imp

156、lementation of production meansTraining employees on toolsResource savingsR&D or innovation actionsData developmentMethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContacts

157、37Daniel BLENGINOCEO,Visionairy Ludovic DONATIVice-President of AfnetHow have the use and sharing of data enabled progress in CSR?“The issue of traceability is extremely important.We need to be able to trace not only all operational data,but also all impacts in terms of consumption.In the case of me

158、tals,for example,we need to be able to trace CO2 impacts,impacts linked to the use of resources and local spin-offs.This involves a great deal of data management,knowing where the data is located,how it can be measured,ensuring that it is reliable and how it is made available to the companys custome

159、rs and stakeholders.One of the aims of Industry 4.0 and data management is clearly to meet the challenges of the energy transition.”Ludovic DONATIVERBATIMSSustainability and responsibilityNew technologies such as AI can profoundly change business processes and professions.What have you seen on this

160、subject?“Today,factories are finding it hard to recruit for highly repetitive tasks.Some plants have set up production islands for operators.Instead of working on a repetitive task,operators will be allowed to pilot a small island of machines and digital solutions autonomously.By increasing the oper

161、ators precision,we reduce the number of rejects,which not only improves environmental impact,but also relieves the operators workload.AI will not replace work,but on the contrary,will transform it.”Daniel BLENGINO MethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messag

162、es1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContacts38What environmental impacts need to be considered when implementing digitalization projects?“In this process of digital transformation,we need to be able to identify a return on the environment,i.e.to

163、 see what the impact will be in terms of decarbonization of the digital solutions proposed in the overall portfolio.This would enable decisions to be taken on the launch of digital projects not only in terms of ROI,but also in terms of whether the project will have a positive impact on the companys

164、carbon footprint,or on the contrary be neutral,or even negative,because it implies deploying a huge amount of hardware and equipment,and retrieving very large volumes of data that need to be stored.”Ludovic DONATI Ludovic DONATIVice-President of AfnetPhilippe MUTRICYDirector of Evaluation,Studies,an

165、d Foresight,BpifranceVincent MOULIN WRIGHTManaging Director,France Industrie“Todays threats to Industry 4.0 are not just the traditional economic ones:strikes,energy costs,transport,and so on.What can stop a factory is a flood,or a drought decree if the factory uses water in its production processes

166、.These are very concrete problems that need to be addresses and which are groups together under the heading of“adaptation to climate change”.The study on reindustrialization published last May by Bpifrance Le Lab,reveals that this issue is given too little consideration by company managers when choo

167、sing where to locate their plant:only 3%say they are considering it.Company managers must also take all these environmental factors into account when making strategic decisions.“Philippe MUTRICY“An important aspect of the digital transition is that it is obviously a gas pedal of the environmental tr

168、ansition that is required of all companies.Today,industry is one of the few sectors in the French economy with a positive balance sheet and an extremely ambitious trajectory.Digitization will enable us to accelerate the environmental transition by processing data more finely and providing real-time

169、data.However,we need to remain cautious when it comes to planning and locating datacenters and digitization in general,since it may also contribute to increasing electricity requirements.This acceleration needs to be planned,particularly in terms of infrastructure.These past and future digital trans

170、itions raise the question of the sustainability of investments.Industry needs public support,especially when extra-financial ROI cannot be financed spontaneously.”Vincent MOULIN WRIGHT How are CSR issues integrated into the industrial sector?VERBATIMSSustainability and responsibilityMethodologyMetho

171、dologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContacts39GlossaryKey ConceptsIndustry 4.0:A concept referring to the contribution of new technologies and,more broadly,digitalization to i

172、mprove industrial performance and transform operational methods.Industry 5.0:Industry that fully integrates environmental and social issues at the heart of its priorities.Socles informatiques industriels:Encompasses all industrial infrastructures(network,connectivity,cybersecurity,data)underlying th

173、e technologies implemented by Industry 4.0.Socles informatiques industriels:Encompasses all industrial infrastructures(network,connectivity,cybersecurity,data)underlying the technologies implemented by Industry 4.0.Sustainability:Economic,social,and environmental development that meets the needs of

174、the present without compromising the ability of future generations to meet their own needs.Other DefinitionsResearch Tax Credit:Tax Credit for Research and development expenses.CMS(Carbon Management System):Tool for measuring,tracking,and managing greenhouse gas emissions.Datalake:Storage system tha

175、t allows for the retention of large amounts of raw data for later analysis.Data management:Collecting,storing,organizing,and protecting data to ensure accessibility,reliability,and security.EMS(Energy Management System):Solution technologique permettant de surveiller,Technological solution for monit

176、oring,controlling,and optimizing energy consumption.ERP:Enterprise Resource Planning.CMMS:Computerized Maintenance Management System.BMS(Building Management System):Building Management System:supervision and control of a buildings technical systems(heating,ventilation,lighting,etc.).AI:Artificial In

177、telligence.Industrial infrastructure:Low-level technological foundation,not highly differentiated by sector,with a common goal of being state-of-the-art to support Industry 4.0 initiatives(Data management,OT,network,cybersecurity,Data platform,IoT,etc.).IoT(Internet of Things):Network of interconnec

178、ted physical objects equipped with sensors,software,and communication technologies,enabling data collection,exchange,and analysis for various applications.IT(Information Technology):Set of tools,devices,systems,and processes used to collect,store,process,transmit,and manage data and information with

179、in an organization.LIMS(Laboratory Information Management System):Management of data and processes in a laboratory.MES:Manufacturing Execution System.PCA/PRA(Business Continuity Plan,Disaster Recovery Plan):Plan developed by an organization to ensure the availability and continuity of its operations

180、 in case of major disruptions.PoC:Proof of Concept.PLM:Product Lifecycle Management.QMS(Quality Management System):Automation of quality management processes.ROI(Return On Investment):Financial indicator that measures the return on an investment.Greenhouse gas emission scopes:Scope 1:Direct emission

181、s from a companys activities.Scope 2:Indirect emissions associated with the energy purchased and used by the company.Scope 3:Indirect emissions not under the companys control(suppliers,etc.).SI:Information Systems.SMS(Sustainability Management System):Organized structure helping companies plan,imple

182、ment,monitor,and improve sustainability performance.WMS:Warehouse Management System.40FRANCE INDUSTRIE CONTRIBUTORSJean-Philippe ThierryVincent Moulin WrightLA FRENCH FAB CONTRIBUTORFranois-xavier De ThieulloyPhilippe MutricyStphane NdourJulie SzaniawskiEXTERNAL CONTRIBUTORSDaniel BlenginoLudovic Do

183、natiPierre RaymondLE HUB FRANCE IA CONTRIBUTORS Caroline ChopinaudMehdi TrikiAcknowledgementsWAVESTONE CONTRIBUTORSOlivier FontanilleAntony RanqueLucie VarletJosselin KiefelPierre BarangerAlexandre BeguinLouis BordronLucas BourgueAntoine de PouillyMariam DiombanaLouis DirlikHala HafiMehdi HarrouchMa

184、rgaux IderneClestine LorphelinValentine ObertHortense PhanEmma PraudAxel StriglCorentin ThibertMethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContacts41Contact our expert

185、sOlivier FontanilleAssociate PartnerAntony RanqueSenior ManagerLucie VarletConsultantJosselin KiefelConsultantDiscover our latest publications2024 CSR BarometerJune 27,2024,Generative Artificial Intelligence:2024 Radar of French“GenAI”startupsMay 16,20242024 Radar of energy performance solutions for industryApril 8,2024MethodologyMethodologyEditorialEditorialKey conceptsKey conceptsKey messagesKey messages1.General1.General 2.Data /AI2.Data /AI3.CSR3.CSRAcknowledgementsAcknowledgementsContactsContacts