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1、2022:43%*2023:49%*2024:60%*INDUSTRY 4.0 BAROMETER 2024STUDYUKGuy WilliamsonCEO MHP UK Guy.WThe Industry 4.0 Barometer 2024 and the Executive Summary were published by:MHP Management-und IT-Beratung GmbH in cooperation with the Ludwig-Maximilians-University of Munich.All rights reserved!Reproduction,

2、microfilming,storage and processing in electronic systems are not permitted without the prior consent of the publishers.The contents of this publication are intended for the information of our clients and business partners.They correspond to the authors state of knowledge at the time of publication.

3、For solutions to problems connected to the topic in hand,please refer to the sources indicated in the publication or contact the people named.Opinion pieces reflect the views of the individual authors.March 2024Contact partners internationalCHINAThomas MooserCEO MHP ChinaThomas.MLei YaoAssociated Pa

4、rtnerTechnology ConsultingMHP ChinaGERMANYMarkus WambachCOO and Member of the Board of ManagementMarkus.WUSATobias HoffmeisterCEO MHP AmericasTobias.HGreg ReynoldsSales DirectorGreg.R Marcus Bohlemann Senior Account ExecutiveMHP AmericasUSCNUKGER2 Industry 4.0 Barometer 2024Contact partnersAuthorsAu

5、thorMuriel HerfMHPAuthorNora HagerMHPAuthorTobias SchreiberMHPSponsorProf.Dr.Johann KranzLMUHead of the Chair for DigitalServices and SustainabilityKranzlmu.deSponsorDr.Christina Reich MHPChristina.RExpertDr.Walter HeibeyMHPWalter.HSponsorTimo HaugMHPTimo.HExpertDr.Oliver KelkarMHPOliver.KProject Ma

6、nagerJulian EngelMHPJulian.ESponsorCaspar KoltzeMHPCaspar.K3 ContentForeword 7Summary 8Key results General 10Key results Industrial AI 131.0 Introducing the Industry 4.0 Barometer 2024 141.1 Focus 161.2 Evaluation method 161.3 Interviews and success stories 171.4 Participants 174 Industry 4.0 Barome

7、ter 20242.0 Results of the study 202.1 Status quo of Industry 4.0 222.2 Industrial AI Artificial Intelligence in production 33Interview Bentley Motors Ltd.38Interview German AI Association 42Success Story New Dimensions:Sounce 48Interview Knorr-Bremse Rail Vehicle Systems 52Success Story SEW-EURODRI

8、VE DriveRadar IoT Suite 58Success Story New Dimensions:paint_it 62Interview Deutsche Bahn Project”Ideas Train”653.0 Summary and outlook 704.0 Further information 745 6 Industry 4.0 Barometer 2024Foreword Dear readers,In the face of current global affairs,we must take proactive and decisive action.Af

9、ter all,the challenges before us are complex in nature.Among other things,the past year has been shaped by geopolitical tension.As a result,industrialized nations are striving more than ever for economic independence,which is in turn fueling the war for talents.To tackle this new,strained situation,

10、businesses require innovative,pioneering solutions.Now,more than ever,executives are being forced to make decisions that could have ramifications for years to come.Digitalization is crucial for both the development of innovations and a businesss ability to remain competitive.In what is already our s

11、ixth issue of the MHP Industry 4.0 Barometer,were going to be looking at how matters relating to digitalization have progressed in the industrial sector.Artificial Intelligence(AI)has garnered much attention in both the media and wider society of late,not least due to the ChatGPT app.This topic is a

12、lso becoming increasingly important for our customers in the production sec-tor.As such,we have decided to make Artificial Intelligence our main focus for this years Industry 4.0 Barometer.As the examples below show,integrating established AI solutions into their operations,will allow businesses to

13、access a wide range of optimization opportunities.AI data will enable businesses to conduct efficient analyses,identify complex patterns and generate more accurate forecasts,so they can make(semi-)automated decisions based on the data available.Intelligent analyses can use existing sensor data to au

14、tomatically check quality cri-teria.AI-based smart production management offers the ability to reduce lead times,increase planning stability and make processes more efficient overall.This means AI-assisted solutions can help to boost both quality and efficiency.With the aid of machine learning and a

15、dvanced algorithms,AI-controlled machines can analyze and interpret tasks independently.This enables businesses to reduce the workload repetitive tasks place on their skilled workers,and offer them support when carrying out complex activities.In order to ensure that businesses can benefit from the i

16、ntegration of AI solutions in the future,they will need a clear vision,innovative minds,a willingness to take risks,and all the necessary resources which they can gener-ate either themselves or with the aid of an experienced partner,like MHP.Before I leave your to read on in peace,Id just like to th

17、ank Professor Dr.Johann Kranz of LMU Mnchen Uni-versity,with whom we collaborated for the sixth time to produce this issue of Industry 4.0 Barometer.My special thanks also go to the 856 experts,interviewees and people who answered surveys as part of our study.Together,we strive for pioneering soluti

18、ons that offer a resistant response to crises and a better,digital future.That is,and remains,our purpose:Enabling You To Shape A Better Tomorrow.I wish you all the best for 2024.Yours,Markus WambachCOO and Member of the Board of Management MHP Management-und IT-Beratung GmbH7 SummaryThe Industry 4.

19、0 Barometer 2024 provides a compre-hensive overview of the current status of Industry 4.0 in various different sectors as of 2023.It has been compiled based on surveys and interviews conducted with businesses in Germany,Austria,Switzerland,the United Kingdom,the USA,and China,which asked questions r

20、egarding their initiatives and progress in the digitalization process.The questions focused on the topic of Industrial AI.2023 presented businesses with a number of glob-al challenges,including international tensions,price rises,the ongoing climate change,the skilled labor shortage coming to a head,

21、and the aftermath of the COVID-19 pandemic.As a result,businesses found themselves in an ongoing dilemma,caught between the guarantee of economic security and the resulting pressure to keep outgoings to a minimum on the one hand and,on the other,the need to invest in digitali-zation in order to keep

22、 pace with the competition.The Industry 4.0 Barometer 2024s findings show that,in spite of the difficult circumstances,businesses have managed to make progress in terms of their Industry 4.0 development.In fact,the speed of digital progress has clearly increased further compared to the previous year

23、 the implementation of automation,the inte-gration of autonomous systems and the introduction of digital twins have all picked up pace,for example.Particularly noteworthy are the high Barometer results relating to data analysis capabilities,which is likely a 8 Industry 4.0 Barometer 2024result of th

24、e hype around Artificial Intelligence(AI).This progress provides cause for optimism,as it will lay the foundations for long-term success in the inte-gration of Industrial AI.Most businesses are only at the beginning of this journey,and still some way from capitalizing on the full potential of Artifi

25、cial Intelli-gence in production processes.Many businesses are at least putting their first use cases into practice,in fields such as visual quality assurance.However,a closer look at the results shows that rates of progress and implementation for both Industry 4.0 technologies and Industrial AI var

26、y greatly from one region to the next.Overall,China and the USA are the clear frontrunners when it comes to establishing digi-tal twins,incorporating location technology into sup-ply chains,and using autonomous systems.China has now significantly pulled ahead of the other regions including the USA i

27、n terms of its integration of Industrial AI.Businesses in Europe(Germany,Austria,Switzerland and the UK)are also showing progress;however,they are lagging behind the USA,and espe-cially China,in the use of both Industry 4.0 technolo-gies and Industrial AI.Historically established system landscapes a

28、nd a lack of technological maturity hinder the introduction of Industry 4.0 technologies and Industrial AI.However,the trend shows that these obstacles are starting to fall away,so there remains how that businesses will overcome these challenges eventually.The shortage of skilled labor presents a mo

29、re persis-tent obstruction.In particular,there is a lack of quali-fied workers who possess the skills required to identify and integrate the opportunities AI offers in a business context,and to implement the technology in a prof-itable way.Yet at the same time,Industrial AI in par-ticular has the po

30、tential to assuage the labor shortage to some extent,as AI-based solutions could be used to handle and automate less complex,repetitive and time-consuming tasks.Despite differences between the regions in terms of the spread of Industry 4.0 and Industrial AI,the glob-al progress provides reason to be

31、 confident.As such,it is crucial for businesses in Germany,Austria,Swit-zerland and the UK continue to actively work to keep pace with the international competition.Investments in basic and advanced training initiatives,the promo-tion of partnerships with outside AI experts,and intel-ligent investme

32、nts in scalable IT infrastructures dont just provide a basis for the efficient use of Industrial AI they also help to shape a promising future for digitalization.9 Key resultsGeneral The expansion of Industry 4.0 technologies is progressing apace:Businesses have been progressing well in their use of

33、 Industry 4.0 technologies since 2022,especially in the field of automation.However,European businesses are lagging noticeably behind their global competitors.Data as a USP businesses are recognizing the true value of data:High Barometer results for data analysis capabilities show that businesses ha

34、ve come to recognize how crucial data is in their future.It remains to be seen whether this recognition will lead to tangible strategic action or remain nothing more than an acknowledgement.10 Industry 4.0 Barometer 2024 Chinas Great Firewall obstacle or protective shield?The study indicates that bo

35、th internal and intercompany data access and exchange is seen as a significant obstacle in China.This raises the question of whether the restrictive data retention policy acts more as a barrier to innovation or a protective shield for sensitive business information.The skilled labor shortage and the

36、 advanced training deficit a risk for innovation?Europe is suffering from a striking lack of qualified workers with knowledge of Industry 4.0 technologies.Businesses have stated that they are in significant need of external experts,yet investments in advanced training and relevant courses are insuff

37、icient.This raises the question of whether Europe is equipped to ensure its own future survival.China and the USA pioneers with a drive for experimentation:The dynamic growth of China and the USA as the leading markets for Industry 4.0 is illustrative not only of these countries economic ambitions,b

38、ut also of their willingness to experiment in a way that sets them apart from other nations.European businesses tend to be less strategically nuanced,focusing to an excessive extent on economic feasibility and cost efficiency possibly at the cost of potential for innovation.11 China shows highest us

39、e of AI is this impressive progress driven by cultural acceptance or political support?Chinese businesses are far ahead of their global competitors,using AI-based solutions in their production processes up to twice as frequently as the competition.The progress of AI an opportunity for European busin

40、esses,or a risk?Businesses in Germany,Austria,Switzerland and the UK are lagging some way behind in their use of AI.It remains to be seen whether they can make up this ground over the next few years.Barriers to AI what stands in the way of progress?The introduction of Artificial Intelligence in the

41、production industry promises to boost efficiency,yet there are a number of obstacles that stand in the way of this progress:insufficient technological maturity,high costs up front,the lack of acceptance among users,and a widespread shortage of skilled labor.12 Industry 4.0 Barometer 2024CIOKey resul

42、tsIndustrial AI Dependency on external resources for AI:Around 70 percent of the businesses we surveyed use external AI experts to make up for their current shortfall in AI skills.An innovation-driven partnership between AI experts and company-specific knowledge will be required if the switch to AI

43、is to be successful.Industrial AI is a CIO matter:The study shows that businesses that dont have a Chief Information Officer(CIO)on their Executive Board make slower progress in terms of digitalization and competitiveness this is very important,especially in the era of Industry 4.0 and Industrial AI

44、.13 1.0 Introducing the Industry 4.0 Barometer 202414 Industry 4.0 Barometer 202415 Industry 4.0 continues to grow,and has already become a reality for many businesses.The Industry 4.0 Barom-eter collects information on what this reality looks like in different sectors and regions,how Industry 4.0 i

45、s regarded in each area,and the degree of maturity with which they use the various different Industry 4.0 tech-nologies.It acts as a benchmark for the developmental status and use of different technologies and initiatives.As such,the Industry 4.0 Barometer provides inside into the gaps and areas of

46、potential that currently exist in the Industry 4.0 context.In addition to this,it also shows how businesses can cover these gaps,utilize the potential and further extend their advantage over the competition.1.1 FocusIn order to provide businesses with a well-founded overview of the relevant informat

47、ion,MHP has joined together with the Ludwig-Maximilians-Universitt Mnchen(LMU)University for the sixth time to pro-duce this Industry 4.0 Barometer.The results of this benchmark study outline the status quo of Industry 4.0 activities among businesses in the Germany,Austria and Switzerland region(DAC

48、H),the United Kingdom(UK),the United States of America(USA)and China.Every year,the survey that acts as the basis for the study comprises four topic clusters:1.Technology:Efficient use of Industry 4.0 tech-nologies(supply chain transparency,digital twins,automation and autonomous systems)2.IT integr

49、ation:Increase in the performance of businesses internal IT infrastructure(data analysis and IT security)3.Strategy and goals:The strategic focus of Indus-try 4.0 activities4.Obstacles:Factors with a negative impact on the implementation of Industry 4.0 technologiesIn addition to the above,each stud

50、y also incorporates current digitalization issues for the year in question.In 2024,Industrial AI was chosen as the main focus topic and investigated in more detail.For the purposes of this study,Industrial AI is defined as follows:The term“Industrial AI”refers to the development and use in productio

51、n processes of systems designed to carry out tasks that would normally be performed by human intelligence,such as learning,problem-solv-ing and decision-making.Industrial AI uses algorithms and data to enable machines to perform human cog-nitive functions and adapt to new information.1.2 Evaluation

52、methodFive and seven-stage Likert scales were used to gather responses to the survey questions.In order to ensure clear evaluation results,the participants responses were clustered.In addition to the distribution of the responses,the weighted mathematical average con-verted into a percentage,which w

53、as then used as the Barometer result in the study.Furthermore,for ques-tions on prioritization,the participants were asked to distribute 100 points across the various response options to a statement.For calculation purposes,the five/seven-stage Likert scales were transformed into metric scales with

54、the values 0 to 5 or 0 to 7.The metric scale values were multiplied by the respective frequencies as per the participants question respons-es.After this,the weighted mathematical average was divided by 5 or 7,depending on the scale used,in order to produce a Barometer result of between 0 and 100 per

55、cent.Since the Industry 4.0 Barometer is a periodic survey,this Barometer result acts as a benchmark.In addition to this,the results were also compared against various characteristics of the partici-pants and the businesses they represented.Responses were collected and evaluated anonymously.16 Indus

56、try 4.0 Barometer 2024SMESize of participating businesses55%26%17%55%856Fewer than 1,0001,0009,999More than 9,999Fig.2:Distribution of participants by size of business n=856 USA 204 DACH 203 UK 201 China 248Origin of survey participants1.3 Interviews and success storiesIn addition to evaluating the

57、results of the survey,the Industry 4.0 Barometer also contains interviews with industry experts and MHP success stories on the use of Industry 4.0 technologies in practice this year,like the survey itself,including pieces from the DACH region and the UK.In addition to questions on our main focus top

58、ic,Industrial AI,the interviewees were asked for their personal assessments of the industrys current developmental status in terms of the digital transforma-tion,and also of use cases and digitalization initiatives within their own organizations.We conducted interviews with the following experts:Jul

59、ian Follner,“Ideas Train”Project Manager(Deutsche Bahn AG)Daniel Abbou,Managing Director (German AI Association)Dr.Andy Moore,Chief Data Officer (Bentley Motors Ltd.)Bernhard Winkler,Vice President Production Rail(Knorr-Bremse Rail Vehicle Systems)The MHP success stories present successful use cases

60、 of Industry 4.0 technologies.This year,the concrete use of AI solutions is our focus for this part of the study.In addition to outlining the initial challenges faced by the business in question,the success stories also explain how the business went about implementing its cho-sen solution and provid

61、e details of the key results.The MHP success story paint_it presents a smart solution created by MHP that is already being put into use by German automotive manufacturers.paint_it provides the basis for an AI-assisted quality check in the paint shop that will reduce costs and boost the efficiency of

62、 painting processes.The MHP success story Sounce presents an AI solution that uses noises to recognize and interpret acoustic patterns.Porsche AG is already using this solution in its R&D department to aid with quality checks for chassis bearings.Our final success story is DriveRadar,by drive manufa

63、cturer SEW-EU-RODRIVE.This tool uses machine learning to detect anomalies in drive and automation solutions.1.4 ParticipantsThe results of the Industry 4.0 Barometer 2024 are based on the responses of 856 participants from Ger-man-speaking regions(Germany,Austria and Switzer-land,203 participants),t

64、he United Kingdom(UK,201 participants),the USA(204 participants)and China(248 participants)(Figure 1).Fig.1:Distribution of participants by region n=856The businesses that participated in the survey were het-erogeneous in terms of their size.55 percent of the par-ticipants represented small to mediu

65、m-sized enterprises(SMEs)with fewer than 1,000 employees,26 percent represented businesses with 1,000 to 9,999 employees,and 17 percent represented businesses with more than 9,999 employees(Figure 2).17 Fig.3:Distribution of participants by sector n=856Fig.4:Distribution of participants by departmen

66、tn=856Distribution of participants by sectorDistribution of participants by department13%Information and communications technology20%IT16%Production10%Research and development10%Management and Executive Board10%Logistics8%Sales and aftersalesFinance and accounting11%Automotive OEM and automotive sup

67、pliers11%Traffic and transport10%Mechanical engineering8%Consumer goods(food,pharmacy,etc.)6%Retail and wholesale6%Electrics and electronics5%Construction industry5%Energy and water sector4%Metal fabrication and metalworking21%Other17%OtherHuman resources4%5%The participants were selected from all l

68、evels of compa-ny hierarchy,from the operative basis to Board level.79 percent of the participants can be classified as working at the third level below the Executive Board or lower.The sector with the strongest representation was infor-mation and communications technology(13 percent),followed by th

69、e automotive industry(11 percent,OEMs and suppliers),traffic and transport(11 percent)and mechanical engineering(10 percent)(Figure 3).The departments with the strongest representation were IT(20 percent)and Production(16 percent)(Figure 4).This aligns with the focal points of the Industry 4.0 Barom

70、eter.18 Industry 4.0 Barometer 202419 2.0 Results of the study20 Industry 4.0 Barometer 202421 2.1 Status quo of Industry 4.0Technological innovations play a large role in shaping the dynamic of the global economy,and in the age of digitalization,Industry 4.0 heralds a new era of smart production.Th

71、is extensive paradigm shift in the indus-trial sector combines progressive technologies such as the Internet of Things(IoT),Artificial Intelligence(AI),big data,and cloud computing to increase aspects such as efficiency,flexibility and quality in production.In this context,Industry 4.0 has grown to

72、become a global trend in which more and more businesses are starting to participate.After all,in an era of crises like the climate change,the shortage of skilled labor,geo-political conflicts,inflation,and rising interest rates,businesses are finding that a greater degree of digitali-zation and thus

73、 the successful use of Industry 4.0 is a key factor in successfully overcoming the global chal-lenges they face.Every company has their own meth-ods and strategies for ensuring success against their global competition.The Industry 4.0 Barometer 2024 focuses on the DACH region(Germany,Austria and Swi

74、tzerland),the UK,the USA and China regions that are not only economic heavyweights,but are also pursuing different approaches and facing differ-ent challenges in terms of Industry 4.0.China and the USA are often seen as the pioneers when it comes to the innovative use and implementation of Industry

75、4.0 technologies on a large scale.This assumption needs to be questioned and verified using the results of this study.The study takes a detailed look at the current status of Industry 4.0 in the aforementioned regions,analyzes the key initiatives and developments,and identifies the challenges and op

76、portunities each of the individual regions will have to navigate on their path toward the digital future.2.1.1 Results of the surveyTopic Cluster 1:TechnologySupply chain transparencyThe ability to locate products and components precisely plays an important role in improving the efficiency and trans

77、parency in supply chains.Real-time goods track-ing enables businesses to determine the exact location of the goods,optimize their delivery times,manage their stock more effectively,and respond more quickly to unexpected interruptions in their supply chains.As such,supply chain transparency is seen n

78、ot just as an efficiency booster,but also as a strategic element that improves agility and resistance at every stage of the supply chain.Realizing this,businesses have started to invest more heavily in improving the transparency of their supply chains.54 percent of the participants stated that,at th

79、eir business,they were at least partly able to locate both individual parts and end products.This is a sig-nificant improvement on the previous years figure of 12 percentage points.This trend is also reflected in the Barometer result,which has hit 60 percent in this years survey compared to 43 perce

80、nt(2022)and 49 percent(2023)over the past two years.As such,location tracking technology produced the highest average of all the technologies included in the survey.Sensors for recording and transmitting environmental parameters and status data have also seen an average increase of 18 percent per ye

81、ar since 2022(Figure 5).When comparing individual regions,it becomes clear that the use of location tracking technologies is much more widespread in China and the USA than in Europe.In China,66 percent of participants stated that they could partially or fully track the location of their individual p

82、arts and products,with 64 percent in the USA saying the same.By comparison,only 47 percent of UK participants said the same thing,while the number in the DACH region was a mere 36 percent.Furthermore,only two percent of the busi-nesses surveyed in China neither used nor were plan-ning to use locatio

83、n tracking technology at all.In the DACH region,this figure was 30 percent(Figure 6).Digital twinsThe fact that businesses are prepared to invest more heavily in the digitalization of their supply chains can also be seen from their use of digital twins for sim-ulation,management and optimization pur

84、poses.The use of this technology in Logistics departments is becoming more commonplace.Since 2022,the implementation level for digital twins has risen by an average of 32 percent per year the largest improve-ment in any of the technologies included in the study in this time period(Figure 7).In China

85、,39 percent of participants stated that their Logistics departments made full use of digital twins.22 Industry 4.0 Barometer 2024At my business,we can track the location of all of the individual parts for our products,and also that of all our end products,at every stage of the value chain.0%0%100%10

86、0%Industry 4.0 BarometerIndustry 4.0 BarometerSupply chain transparencyAt my business,we can track the location of all of the individual parts for our products,and also that of all our end products,at every stage of the value chain.Our systems and plants in our Production,Warehouse and Logistics dep

87、artments areequipped with sensors to record and transmit environmental parameters and status data.Fig.5:Technological equipment levels at every stage of the value chain*Barometer result:Weighted mathematical average as a percentage Dont know No usage Future usage planned Practical tests ongoing Part

88、ial usage Full usage Dont know No usage Future usage planned Practical tests ongoing Partial usage Full usage6%15%12%13%30%24%2022:43%*2023:49%*2022:38%*2023:44%*2024:53%*2024:60%*5%21%13%13%28%20%Fig.6:Supply chain transparency by region Partial usage Full usageDACHUSAUKChina7%35%25%29%29%22%39%27%

89、23 0%100%0%100%At my business,we use digital twins of the following aspects to map process and status data for simulation,management and optimization purposes:Our production facilities(e.g.factories,machines,vehicles).Our products.Industry 4.0 BarometerIndustry 4.0 Barometer Dont know No usage Futur

90、e usage planned Practical tests ongoing Partial usage Full usage Dont know No usage Future usage planned Practical tests ongoing Partial usage Full usage9%26%12%14%24%15%2024:51%*2024:48%*2022:43%*2023:39%*2023:35%*2022:32%*7%24%12%14%24%19%0%100%Our entire Logistics operations(incoming and outgoing

91、 goods).Industry 4.0 Barometer Dont know No usage Future usage planned Practical tests ongoing Partial usage Full usage2024:52%*2023:41%*2022:30%*7%22%15%12%23%21%Fig.7:Distribution of digital twins*Barometer result:Weighted mathematical average as a percentage 24 Industry 4.0 Barometer 2024At my bu

92、siness,we use digital twins of our entire Logistics operations(incoming and outgoing goods)to map process and status data for simulation,management and optimization purposes.Fig.8:Distribution of digital twin use by region Partial usage Full usageDACHUSAUKChina20%22%16%33%39%13%21%5%21 percent of pa

93、rticipants in the USA said the same thing,but only 13 percent of those in the UK and just 5 percent of those in the DACH region(Figure 8).As such,this is another area where businesses in China are a clear distance ahead of the rest of the world.25 Industry 4.0 BarometerIndustry 4.0 BarometerWe use m

94、achines and robots that can act autonomously and independently manage and improve themselves.Our systems,plants and devices exchange data automatically,independently and in real time(machine-to-machine communication).Dont know No usage Future usage planned Practical tests ongoing Partial usage Full

95、usage Dont know No usage Future usage planned Practical tests ongoing Partial usage Full usage0%100%3%27%15%16%26%13%2022:33%*2023:38%*2022:49%*2023:50%*2024:57%*2024:46%*0%100%4%16%14%31%21%14%Fig.9:Degree of maturity with regard to automation and autonomous systems*Barometer result:Weighted mathem

96、atical average as a percentage Automation and autonomous systemsCompared to digitalization of the supply chain and the use of digital twins,the use of automation and autonomous systems recorded a lower Barometer result of just 46 percent.However,there has still been huge improvement in this area com

97、pared to previous years:Since 2022,the average Barometer result has risen by 18 percent per year(Figure 9).When asked about the use of machines and robots that act autonomously,and manage or improve themselves,41 percent of participants from both the DACH region and the UK stated that such technol-o

98、gies were not used at their business.28 percent of participants in the USA said the same.In China,however,only 2 percent of participants agreed with the above statement.The participants supplied sim-ilar responses when asked about systems and plants that exchange information autonomously and inde-pe

99、ndently(Figure 10).Once more,China is the front-runner in this area.The implementation of Industry 4.0 technologies clear-ly shows that participants from the USA and China are the leaders in all areas included in the“Technology”topic cluster,while the DACH region and the UK occu-pied the last two pl

100、aces in all areas.These positions are the result of a number of different factors.One of these is political measures:For example,the Chi-nese governments strategic support acts as a driver for innovation,especially for the industrial sector.In turn,this boosts Chinese businesses technological maturi

101、ty and ability to compete.Legislation in the USA provides a liberal framework,giving businesses plenty of space for technological progress,especially in the field of AI.One example of this can be seen in the market volumes for generative AI,where the USA is the market leader ahead of China and will

102、remain so in the medium term.1Because the use of Industry 4.0 technologies is already widespread and has proven successful as a result of these conditions,businesses in these countries are also more willing to innovate and take risks while doing so.As the leading innovation markets,the USA and 1 Gen

103、erative AI USA()26 Industry 4.0 Barometer 2024Fig.10:Degree of maturity with regard to automation and autonomous systems by region Our systems,plants and devices do not exchange data automatically,independently and in real time (machine-to-machine communication).We do not use machines and robots tha

104、t can act autonomously and independently manage and improve themselves.DACH25%41%China2%2%of survey participants in China saidthat,at their business,machines and robots that act,manage themselves and improvethemselves arenot used.USA19%28%UK41%24%ONLYChina are home to two major tech hubs,Silicon Val

105、ley and Shenzhen.Both these locations have a high den-sity of start-ups and high-tech companies whose fer-vor for experimentation and fast market launches for new technologies is a driving force for digitalization in these areas.Differences in innovation culture also work in favor of the development

106、 of Industry 4.0 technologies.Participative management methods are widespread in Europe,whereas management in China is shaped by the collective.In China,employee management is based on a breakdown of the goals for the planned economy.In order to create an effective culture of innovation,intensive ex

107、changes of information and support for the best ideas are essential.After all,the effects of an innovation strategic can only be seen once it has been implemented.Thanks to its culture of innovation,the Chinese collective enjoys high user acceptance with regard to new technologies.At the same time t

108、his model also boosts the specialist knowl-edge of its workers.2Finally,the leapfrogging phenomenon could come into play here.Due to the booming economic growth and the resulting construction of countless new pro-duction facilities,businesses in China are able to opti-mize the design and constructio

109、n of their new plants from the ground up.In other regions,where the pro-duction facilities already exist,legacy systems need to be either integrated or replaced.This can present a significant challenge,especially for companies in the DACH region,as they may be unable to compete with the benefits the

110、ir Chinese peers can offer in terms of price and quality,and are thus increasingly losing mar-ket shares to Chinese businesses as a result.2 Cf.Jrg Macht:China&Innovation.Was der deutsche Mit-telstand von China lernen kann What German SMEs can learn from China,FOM-Edition,Stuttgart,Germany:Springer,

111、2023,p.727327 Our business possesses comprehensive and adequate capabilities to defend against cyber attacks.Fig.11:IT security status Generally agree Generally agree Completely agreeDACHUSAUKChina22%18%23%20%42%25%34%24%29%28%27%17%Topic Cluster 2:IT integrationData analysis capabilitiesData and da

112、ta analysis capabilities are seen as a key factor in remaining successful in a business environ-ment that is in a constant state of flux.This is because the right data and the ability to analyze it in the right ways enable businesses to increase their capacity for innovation,boost their efficiency a

113、nd thus secure their unique selling point as an advantage over their competitors.With this in mind,an increasing number of businesses are starting to recognize the economic importance of data for their future.In particular,the use of Artificial Intelligence that uses large quantities of data to trai

114、n algorithms has gener-ated a huge amount of hype,and led to a public dis-course on the benefits and risks this technology poses.This debate is founded in the fact that,while gener-ative and Industrial AI offer unfathomable potential,the increased use of these technologies also opens the door to a n

115、umber of ethical,social and security con-cerns,such as how such systems will handle sensitive data(e.g.trade secrets,personal data,financial data).Furthermore,AI systems are also highly energy-inten-sive to train and run,as a result of which businesses are being encouraged to invest more in partners

116、hips and ensure an increased level of modularization and compatibility on the side of the provider.For these reasons,the data analysis capabilities of the businesses included in the survey have increased dramatically compared to previous years in all four surveyed areas(personnel skills,technologica

117、l infra-structure,partially or fully automated production pro-cesses,and systematic data collection),and returned the highest Barometer results in this years study.For example,the participants were asked to rate their businesses data analysis capabilities with regard to production processes with par

118、tially and fully automat-ed decision-making(e.g.through use of AI)compared to their direct competition.While the Barometer value for this area was just 36 percent in 2022 and 51 per-cent in 2023,it has risen to 62 percent in this years study(Figure 12).The central importance of data collection and p

119、rocess-ing especially when it comes to the use of Artificial Intelligence is explained in detail in our main focus topic,Industrial AI.IT securityIn the field of IT security,the situation is similar to in previous years.Generally speaking,IT security remains well-established at the businesses that t

120、ook part in the survey.At the same time,the results show once again that the DACH region is lagging significantly behind the other regions,particularly in terms of the capabilities required to defend against cyber attacks:66 percent of participants in the DACH region stated that their business posse

121、ssed compre-hensive and adequate capabilities to defend against cyber attacks.75 percent of participants in the USA,81 percent in the UK and 87 percent in China said the same thing(Figure 11).28 Industry 4.0 Barometer 2024Industry 4.0 BarometerIndustry 4.0 BarometerIndustry 4.0 BarometerIndustry 4.0

122、 BarometerPersonnel skills and abilities for advanced data analysis methods(e.g.data preparation,analytical algorithms,APIs).Production processes with partially and fully automated decision-making(e.g.Artificial Intelligence ormachine learning).Systematic and continuous collection,preparation and an

123、alysis of data at every stage of the value chain.Technological infrastructure for advanced data analysis(pany-wide data platform,analysis and visualization software,algorithm libraries).Dont know Very bad Worse About the same Better Very good Dont know Very bad Worse About the same Better Very good

124、Dont know Very bad Worse About the same Better Very good Dont know Very bad Worse About the same Better Very good2022:54%*2022:36%*2023:51%*2022:56%*2023:60%*2024:71%*2024:62%*2022:52%*2023:62%*2024:72%*2023:62%*2024:71%*0%100%0%100%0%100%*Barometer result:Weighted mathematical average as a percenta

125、ge 0%100%26%39%22%20%39%28%7%8%2%2%3%3%5%6%17%21%30%21%26%38%25%6%2%4%Data analysis capabilitiesPlease rate your business data analysis capabilities compared to your direct competitors with regard to:Fig.12:Degree of maturity for data analysis capabilities29 Increasing economicefficiencyIncreasing t

126、he quality of production and productsAdaptingour production flexibilityTapping into new market and customer segmentsDeveloping new services for existing products0102030405022171614Fig.13:Strategic focus of surveyed businesses in terms of Industry 4.0(The participants were given the ability to assign

127、 a total of 100 points.The results shown here are the averages for each possible response.)32Strategic focus of businesses in terms of Industry 4.0PointsPointsPointsPointsPointsTopic Cluster 3:Strategy and goalsUnlike the other three topic clusters,the“Strategy and goals”cluster allowed the particip

128、ants to rate the stra-tegic focus of their business by allocating 100 points across five possible responses.As in the previous year,increasing economic efficiency was the most import-ant goal for the businesses,with an average points allocation of 32,followed by increasing production/product quality

129、,which was allocated an average of 22 points(Figure 13).Interestingly,the focal points var-ied depending on the industry and region in question.The focus on economic efficiency was much stronger among businesses in the automotive industry than those in other sectors,with a deviation of 13 percent.On

130、 the other hand,participants in the automotive industry rated tapping into new market and custom-er segments much lower,with a deviation of 21 per-cent.When comparing between different regions,it became clear that while chinese businesses made increasing economic efficiency their main focus,they als

131、o placed much more importance than other regions on tapping into new market and customer segments and adapting their production flexibility.30 Industry 4.0 Barometer 2024Topic Cluster 4:ObstaclesAs Industry 4.0 continues to make strides,employee qualification and expertise levels are both becoming i

132、ncreasingly important and forming an obstacle to progress.This is because specific,specialist knowledge is required in order to implement progressive Indus-try 4.0 technology and data analysis capabilities.As a result,these skills are in high demand among busi-nesses and are becoming a factor in com

133、petition.52 percent of the participants in the study named the shortage of skilled labor as the main reason for delays in the implementation of Industry 4.0 technologies.In a nutshell,while further automation reduces the need for skilled workers to carry out repetitive tasks,it also increases the ne

134、ed for qualified personnel in other areas,as these skills are needed to facilitate the introduction of Industry 4.0 technologies.At the same time,the speed at which Industry 4.0 technologies are being integrated could increase,which could lead to even greater qualification gaps.The workload generate

135、d by day-to-day business and the resulting lack of resources was the second most common obstacle to be named by the participants,at 47 percent.47 percent also listed established,legacy IT systems as a major obstacle.Uncertainty regarding ROI,which was the most common perceived obstacle last year aft

136、er being named by 67 percent of partici-pants,is now only seen as an obstacle by 43 percent of those included in the survey(Figure 14).This may be an indication that businesses are increasingly start-ing to recognize that the main challenge presented by these technologies is not their profitability,

137、but rather the availability of qualified human resources.Compared to the previous years,2022 and 2023,all the obstacles named are on a downward trend in terms of their perceived relevance,indicating that businesses are slowly starting to overcome these challenges.This may be the result of improved e

138、mployee qualifications,adjustments to the businesses capacities,or a more reliable assessment of the ROI.It is important to ensure that employees have access to ongoing advanced training,since established job profiles are changing to keep pace with the latest developments in the industry as digitali

139、zation progresses.Another challenge facing businesses is the need to combine the company-spe-cific knowledge of their own employees with the new technological skills the business requires.When comparing between regions,it becomes evident that data exchange and data silos pose significant chal-lenges

140、 for China regarding collaboration,both within businesses and with external partners.57 percent of the Chinese businesses surveyed listed data silos as an obstacle,while only 35 percent in the DACH region,33 percent in the UK and 28 percent in the USA did the same.The same applies to the problem of

141、insuffi-cient data exchange with external partners(see Figure 15).Here,the results show that the efforts being made in China to ensure an efficient exchange of data and promote collaboration may be being impeded by legal regulations,principles of data sovereignty,and tech-nological hurdles.Strict da

142、ta protection regulations,and particularly the Cybersecurity law3,play a key role here.These laws set out strict rules for the handling of personal data,which makes data exchange more dif-ficult.At the same time,China is placing more impor-tance on data sovereignty,which means that data generated in

143、 China also needs to be stored and pro-cessed within the countrys borders.This can lead to difficulties in terms of global data exchange and col-laboration between Chinese businesses.Even with the high level of digitalization among Chinese businesses,there are still variations within the country in

144、terms of the technological standards and platforms used.This may also be partly to blame for limiting collaboration and data exchange within the country.Time will tell whether this restrictive approach to data proves a bar-rier to innovation in China,or instead turns out to be a smart move in the ba

145、ttle for important company data on the global stage.3 Creemers,Rogier;Webster,Graham;Triolo,Paul,”Translation:Cybersecurity Law of the Peoples Republic of China(Effective June 1,2017)”(2018).URL:https:/digichina.stanford.edu/work/translation-cybersecurity-law-of-the-peoples-republic-of-china-ef-fect

146、ive-june-1-2017/(02.02.2024)31 .data silos that make it difficult to implement cross-departmental solutions.30%17%5%DACHUSAUKChina.a lack of consistent data exchange with partners at other stages of the value chain.Fig.14:Obstacles to the introduction of Industry 4.0 technologiesThe introduction of

147、Industry 4.0 technologies at our business is being held back by.Generally agree Generally agree Completely agree.uncertainty regarding ROI.supply chain issues.historically established legacy systems.data silos.a lack of data exchange between partners.listed the shortage of skilled labor as their rea

148、son.difficulties incorporating them into our day-to-day business.Fig.15:Obstacles to the introduction of Industry 4.0 technologies by regionThe introduction of Industry 4.0 technologies at our business is being held back by.43%40%40%41%47%47%3%2%5%4%29%26%25%27%13%14%3%14%4%24%25%13%12%15%35%40%28%2

149、8%33%36%57%56%32 Industry 4.0 Barometer 20242.2 Industrial AI Artificial Intelligence in productionBusinesses are intensifying their efforts to make increased use of Artificial Intelligence(AI)as part of their digital and technological progress.Although the mathematical foundations for AI were laid

150、in the mid-20th century,AI research as we know it today has only become possible thanks to the increasing availability of large quantities of data,increases in processing power and the progress made in the field of complex mathe-matical models and algorithms.As such,AI isnt actu-ally a revolution it

151、 is simply evolution.Nevertheless,the current accessibility of AI as good as represents a revolutionary step forward for many businesses.These businesses are increasingly starting to realize that AI technologies such as machine learning and neural networks are not just interesting in theory they als

152、o offer untold potential for practical use.The integration of AI into production processes is causing a significant transformation in the way companies operate.This development not only makes it possible to auto-mate repetitive and even complex tasks;it also enables businesses to analyze large volum

153、es of data quickly and recognize patterns with a high degree of precision.The resulting benefits can be felt along the entire value chain.Precise forecasts of demand allow businesses to improve their stock management and respond more flexibly to fluctuations in demand.This makes warehouse management

154、 more efficient and reduced storage and logistics costs.At the same time,AI also facilitates early identification of errors and defects,thus significantly reducing waste and scrap.As such,the implementation of AI leads to an increase in product quality,a reduction in production costs,and shorter lea

155、d times.AI facilitates a formidable boost in productivity,as it can be in use almost 24/7.This con-stant availability leads to significant increases in a busi-ness automation levels and overall productivity.In addition to the impressive potential it offers in terms of automating the value chain,AI a

156、lso promises to bring changes to the landscape of work.By taking on human tasks,AI leads to fundamental alterations to job profiles.For instance,offloading repetitive tasks from human workers onto AI frees up skilled workers to focus more on the core activities associated with their role,thus boosti

157、ng their individual productivity.Many companies that are struggling with the shortage of skilled labor are already noticing the potential AI offers as a means of storing knowledge,and thus com-pensating for the current shortage of skilled workers.In order for businesses to effectively put all these

158、potential benefits of Industrial AI into practical use,careful consideration and significant measures are required.Businesses need to identify the specifics of what they need and require from Industrial AI,while also carefully evaluating the opportunities and added value AI-based solutions have to o

159、ffer.It is essential to take into account a whole range of aspects,including monetary,ethical,legal and social considerations.In addition to this,there are certain prerequisites that play a key role in the success of AI projects.These include the availability and quality of the data required and the

160、 qualification levels and acceptance of the business employees with regard to new AI-based solutions.The following survey results will shed some light on how successful businesses have been in this integration process so far.2.2.1 Results of the surveyIs the DACH region becoming the problem child of

161、 Industrial AI?In international comparisons,the DACH region is lagging particularly far behind in terms of successful use of AI in production.The UK is also behind the USA and China in almost every respect.In order to provide a general overview on the use of Industrial AI,the participants were asked

162、 about the use of AI-based solutions in their production pro-cesses.50 percent of the participants said that their business used Industrial AI in its production process-es.In the DACH region,only 20 percent of those sur-veyed said that AI-based solutions were used in their businesses production proc

163、esses.The figure among UK participants was 29 percent.The use of AI-based solutions is much more widespread in the USA,where 46 percent of participants said it was used in produc-tion processes.Meanwhile,the situation in China is truly astounding:94 percent of participants from the Asian country sai

164、d that their business used AI in its production processes(Figure 16).33 DACHChinaUSAUKFig.16:Use of AI-based solutions by regionNoYesDont know20%46%29%94%70%46%58%6%10%8%13%0%Does your business use AI-based solutions(e.g.predictive maintenance,detection of anomalies,autonomous robots)in its producti

165、on processes?Next,the participants rated the success of the AI proj-ects that had been implemented at their business in terms of on-time production,provision of the planned functionality and adherence to budget.The participants in the DACH region and the UK indi-cated that their businesses success i

166、n all three of these areas had been significantly lower than their peers in the UK and China.For example,only 26 percent of the participants from the DACH region and 34 percent of those from the UK were of the opinion that their busi-ness completed its AI projects on time.In China,on the other hand,

167、86 percent stated that they believed this to be the case.The other categories produced similar results.89 percent confirmed that AI projects provided the functionality they had been designed for,and 82 stated that AI projects were completed within budget(Figure 17).There were also relevant differenc

168、es between the regions in terms of how the participants rated the maturity of their businesses AI.While businesses in the DACH region and the UK posted average ratings(Stage 2)businesses in the USA believe they are on the way to establishing AI solutions(Stage 3).Chinese businesses went one step fur

169、ther,stating that their progress in establishing AI was already advanced,and that they were already starting to look at optimization(Stage 4)(Figure 18).34 Industry 4.0 Barometer 2024Fig.17:Satisfaction with the use of AI projects by regionWhen working on Industrial AI projects,our business is very

170、successful at ensuring the project.provides the functionality it was designed for.is completed on time.is completed within the set budget.DACHDACHDACHUSAUSAUSAUKUKUKChinaChinaChina19%18%21%38%39%9%11%2%23%9%5%2%23%21%33%34%40%15%10%1%27%9%6%1%19%22%18%31%39%12%9%8%1%2%18%8%Generally agree Generally

171、agree Completely agree35 How would you rate the level of AI maturity at your business in terms of.4 Alsheiabni,Sulaiman;Cheung,Yen;and Messom,Chris,”Towards An Artificial Intelligence Maturity Model:From Science Fiction To Business Facts”(2019).PACIS 2019 Proceedings.46.https:/aisel.aisnet.org/pacis

172、2019/46Fig.18:Level of AI maturity at businesses by region4DACHUSAUKChinaJust starting out 1.0Still assessing 2.01.52.53.54.51.62.63.64.61.72.73.74.71.82.83.84.81.92.93.94.91.12.13.14.11.22.23.24.21.32.33.34.31.42.43.44.4Still establishing 3.0Still optimizing 4.0Industry leader 5.0.data availability

173、?employee skills?.organizational AI processes?.IT tools and technologies?36 Industry 4.0 Barometer 2024These results paint a concerning picture of Europes status as an industry in terms of its use of Industrial AI.Compared to the USA and China,businesses in the DACH region and the UK seem to be lagg

174、ing behind signification when it comes to the use,maturity lev-el,perception and acceptance of the technology,as a result of which they are not yet able to benefit from Industrial AI to the same degree as their international competitors.However,there is no cause for alarm just yet.It will still be a

175、 long time until we can say for cer-tain what tangible effect a high degree of AI maturity will have on the global competition and the shifting power dynamics between the USA,China and Europe.However,DACH and UK businesses in particular need to start utilizing the manifold potential of Industrial AI

176、 to ensure that they do not risk falling into tech-nological dependency and losing their competitive edge.Making up this ground in the medium to long term will be a challenge due to the range of individual requirements different businesses have when it comes to AI-based solutions.This is clear from

177、practical exam-ples such as that provided in our interview with Dr.Andy Moore,Chief Data Officer of Bentley Motors Ltd.Bentley realized that the company was not yet able to use AI-based quality control solutions across the board to replace human quality controllers in finding faults,because its prod

178、ucts are produced in small volumes and with a high degree of customization.Bernhard Winkler,Vice President of Production Rail at Knorr-Bremse Rail Vehicle Systems,also reported that his company was facing a similar challenge in its produc-tion of brake systems for rail vehicles.In this case,too,the

179、company has looked into the use of AI technology,but rejected the idea due to its low batch numbers and the high level of variance in its production.On the other hand,the company has already successful-ly implemented AI solutions to handle repetitive tasks outside of Production.In summary,while some

180、 busi-nesses are already enjoying the benefits of Industrial AI in selected areas,others still need to assess just how much added value Industrial AI offers them due to their individual requirements and fields of application.Based on the survey results,most businesses in the USA and China seem to ha

181、ve already overcome this hurdle.However,the need to fulfill individual require-ments isnt the only factor impacting on positive ratings of AI maturity levels and the distribution of AI-based solutions political incentives and measures also play a role.Chinas Next Generation Artificial Intelligence D

182、evelopment Plan5,published in 2017,outlines the countrys objective of becoming the lead-ing global center of innovation for AI by 2030.With the Inflation Reduction Act(IRA)6 of 2022,the USA is now also providing businesses with a political incen-tive to establish a positive environment for AI resear

183、ch and development.These initiatives have accelerat-ed the digital transformation,but more than that,they have also proven to be a driving force for the successful integration of AI projects in these regions.This has increased businesses confidence in their AI capabilities and made them more willing

184、 to take risks and experiment in the field of Industrial AI.The suc-cess of these political initiatives in China and the USA shows once again that additional political support for AI is also something worth striving for in Europe,as stressed by Daniel Abbou,Managing Director of the German AI Associa

185、tion.5 Full translation:Chinas New Generation Artificial Intelligence Development Plan(2017)(stanford.edu)6 1/23 The USAs Inflation Reduction Act(IRA)Implications for Europe(in German)(bundesfinanzministerium.de)37 Dr.Andy Moore,Chief Data Officer,Bentley Motors Ltd.InterviewBentley Motors Ltd.Bentl

186、ey Motors Ltd.ProfileBentley Motors,the worlds premier luxury car brand,operates from its Crewe,United Kingdom,headquar-ters,overseeing design,R&D,and production of its five model lines:Continental GT,Continental GT Convert-ible,Flying Spur,Bentayga,and Bentayga EWB.With the combination of fine craf

187、tsmanship,engineering expertise,and cutting-edge technology,Bentley exem-plifies high-value British manufacturing and employs approximately 4,000 dedicated colleagues.The Bentley Beyond100 strategy is focused on sustain-ability,aiming to transform the entire business and establish leadership in Sust

188、ainable Luxury Mobility.This entails a shift from being the largest producer of 12-cylinder petrol engines to having no combustion engines within a decade,reinventing Bentley as a lead-er in Sustainable Luxury Mobility.Dr.Andy Moore,Chief Data Officer Short vitaAndy has been the inaugural Chief Data

189、 Officer at Bentley since November 2022.As part of establishing and delivering the company-wide data strategy,Andy is responsible for data governance,data literacy,the data tech stack,and supporting enablement to get better value from data across the business.Andy has two decades of experience withi

190、n the auto-motive industry,across data,digital transformation,engineering,and program management.Participants:Dr.Andy Moore(Chief Data Officer,Bentley Motors Ltd.),Dr.Christina Reich(MHP),Kitty Wanke(MHP)Dr.Christina Reich(MHP):Can you please give us a brief overview of your responsibilities as Chie

191、f Data Officer at Bentley?Dr.Andy Moore(Bentley):Ive been in the role of Chief Data Officer for just over 12 months now.Its a new role at Bentley,so my first task is to create and embed a data strategy.The strategy covers four pillars:The first pillar is governance:how we can best use,control,and pr

192、otect our data.The second pillar is the data cloud,which is the tech-nology we use to get the most out of our data with visualisation or machine learning products.The third pillar is data literacy.I strongly believe that we need to upskill the business,and that means upskilling people.The fourth pil

193、lar is enablement:how my team can enable Bentley to get more value from data and deliver products that accelerate the use of data across the business.My role also involves building a new team,which is a great opportunity to bring in experienced data scien-tists from other companies,other industries,

194、and our early careers programme.Bentley Motors Ltd.38 Industry 4.0 Barometer 2024Reich:With regard to data literacy,you mentioned your data scientists and your skilled employees.Our results show that 70 percent of companies hire exter-nal AI experts to compensate for the shortage of skilled workers.

195、How is Bentley handling this?Moore:Its important to use a combined approach.We have an early careers programme,and we also focus on upskilling the existing workforce,because nobody knows Bentley like Bentley employees.But its also an opportunity to bring in external experience,which gives us the cha

196、nce to learn from other indus-tries and gain expertise through our partnerships.A multi-tiered approach is key,because building a team of highly experienced AI experts is very expensive,and they may not understand the business at first.The data experts need to understand the use cases,the auto-motiv

197、e industry,and Bentleys pain points,in particular.Reich:At the same time,the automotive industry is facing challenges like increasing raw material pric-es,tighter environmental regulations,and changing market conditions.As a luxury vehicle manufacturer,is Bentley affected by these challenges to the

198、same extent as other manufacturers,and how so?Moore:We have certainly seen a lot of external chal-lenges over the past four years,from Brexit to COVID to semiconductor shortages.COVID affected work-force availability,as well.We need to build greater flexibility into our business model than ever befo

199、re.Regardless of the external challenge,its essential to have the flexibility in your business model to respond to it.And thats where data can come in to help us make data-driven decisions.We could never have pre-dicted COVID,but we can use data to understand how deeply its going to affect us and us

200、e that to inform our response plan.Reich:Are you also using some specific industry 4.0 technologies to proactively address these challenges?Moore:One example is 3D printing.Not so much for car parts because they have to go through strin-gent crash testing,of course,but we can use it for jigs and fix

201、tures that might take a while to machine.That allows us to cope with a shorter life cycle;if one gets damaged,we can print a new one very quickly.And obviously the Internet of Things is relevant.A lot of our machinery is more connected than ever before,and we now have the ability to capture data fro

202、m it.Were using mobile devices more than ever,and thats allowed us to move away from a more paper-driven process.Obviously,robotics has been around for some time in the automotive industry.For a company like Bentley,with a relatively small volume,its always a question of how much we need that.But th

203、ere are certain pro-cesses like fixing the windscreen that rely on precision and repeatability,so they are an ideal task for a robot.At the other end of the spectrum is the sanding of our wood veneers.Theres such artistry involved that a robot could never do it.It is about finding the right balance.

204、And then ulti-mately cloud computing and data will underpin it all,and well see a lot more use cases.Reich:With regard to the transition from combustion engines to electric drives,how does electrification affect Bentleys operations,and which technologies will be key to achieving your operational goa

205、ls in the future?Moore:Part of our Beyond 100 strategy is to move to a 100-percent electric vehicle lineup,which well do at the end of the decade.Well introduce a number of new models over the next six or seven years in order to fulfil that promise.We announced a 2.5 billion pound investment into Be

206、ntley,into the new models and also into the new factory.Well build an entirely new fac-tory within an existing footprint,which allows us to adopt new technologies and shift from a fixed produc-tion line to an AGV-led production line.That will allow us more flexibility in terms of volume,customisatio

207、n,and the build process.Reich:And how does the proliferation of AI in the automotive industry affect the competitiveness of Bentley?Moore:There is a lot of hype around AI,and ChatGPT in particular,recently,but we have to be careful that there is a solid business case behind any implementa-tion.As Be

208、ntley is low-volume,high-value,high-cus-tomisation,there is a balance to strike.Craftsmanship will always remain a core USP,but we might bring in AI as a cobot where it makes sense.Some things can be truly automated,but many cant.Our quality standards are so high that AI cant do the job.A trained hu

209、man operator will spot defects that AI would miss.So an AI camera system could reduce the workload,but it will never replace the trained operator.39 Kitty Wanke(MHP):How would you rate the current status of AI at Bentley?Could you share some exam-ples of successes or milestones youve had so far in y

210、our role?Moore:Right now at Bentley,were in a“test-and-learn”phase.We dont have widespread AI yet because we cant have an AI strategy without a data strategy.One of my tasks as CDO is to ensure that we have a solid foundation of accurate and authoritative data.We have a number of test cases,but they

211、re localised to a specific system.The next step will be to scale that up and combine multiple data sources to get more val-uable outcomes.Our study showed that AI is highly relevant for predictive maintenance,especially in the automotive industry.Wanke:What is the status of predictive maintenance ri

212、ght now?Moore:Its starting to demonstrate the benefits more and more as we gain confidence in the machine learn-ing model.As we start to get real results beyond the predictive results,well adopt it further.Maintenance is a good example,because theres plenty of IoT data for machinery,and we can look

213、for historical patterns that help us predict when we might have issues in the future.Wanke:What challenges do you face in integrating industrial AI,and what ways have you found to miti-gate them?Moore:The first challenge is trust and security.We need to trust the security of any environment that we

214、use for AI.For example,weve closed access to ChatGPT on our company systems because of the risk of confidential data getting into a publicly trained model.We also need to trust the accuracy of the mod-el,as well.Close behind that is the cost of AI when moving it to the cloud.For a company the size o

215、f Bentley,storage cost is not too much of an issue,but compute cost is.There needs to be an ongoing risk-reward evaluation and a solid business case behind any use of AI.Wanke:When you spoke about the four pillars,you also mentioned enablement and the workforce.From your perspective,what impact is A

216、I having on the automotive labour market in terms of skills and work processes?Moore:This is why Im passionate about rolling out the data literacy programme from the shopfloor right through to management.Data affects everybody,whether theyre fully aware of it or not.So we start with the basics and t

217、hen build that knowledge accord-ing to role and need,as well as when new technology comes along.What were seeing now is an acceler-ation of technology adoption faster than anything weve ever seen before.Its key to keep our colleagues upskilled and aware and remove concerns that AI might take over ev

218、eryones job.AI is there to help you be more effective and reduce time spent on repeti-tive,manual jobs.I think that AI will become impor-tant throughout the labour market in the future.Its a challenge to ensure that employees at all levels feel on board with technology.Whilst we will always need cra

219、ftsmen to add the finishing touches to the Bentley,we also need people that are comfortable using data to make data-enabled decisions.Wanke:Our survey found that predictive maintenance is a topic in the automotive industry,particularly for production leads.How do you work together with the productio

220、n lead to decide which use case you want to test or establish?Moore:This is always based on business case and impact,and there are many ideas that we can help scale once the basic building blocks are in place.For example,we can join multiple data sources like factory and field quality indications,th

221、en use natural language sifting and sentiment analysis to get a more holistic view of our quality status.We can also move towards more effective,efficient supply chains as we look to optimise our production with a fully flexible produc-tion line.Another example is using AI to optimise our inventory

222、levels.Theres a risk to both over-and under-stocking,but we can use data more efficiently to help drive those decisions.Feeding a model with a large volume of data can help to get more accurate stock predictionsfar better than someone with an Excel spreadsheet trying to make a best guess.Wanke:Now l

223、ooking into the future:How do you see the future role of AI,especially in your industry?What developments do you expect in the next 5 to 10 years?Moore:Elements of AI and natural language process-ing will become more widespread and built into every-day tools.When people can write code using natural

224、language,that will really open up the opportunity to adapt.People will be able to build an AI cobot very easily,and that will create opportunities for almost 40 Industry 4.0 Barometer 2024Bentley Motors Ltd.everyone to become more efficient at their job.But we obviously also pride ourselves on the c

225、ustomer magic and the hyper-personalised journeys for individ-ual customers.And we can use AI to augment that by bringing together multiple data sources and sug-gesting next best actions to customers.And whenever our agents interact with a customer in any way,theyll be able to know a lot more about

226、the customer and personalise the experience.That will make the brand experience much more magical for our customers in the future.Wanke:Is there anything you want to add or some-thing youd like to say on a topic we havent touched on yet?Moore:Yes,going back to the topic of test and learnI dont belie

227、ve one single solution that solves everything exists anymore,nor should it.We need modular plat-forms and approaches that will remain compatible with new solutions as technology evolves at an une-ven pace.One overarching system that locks in all the data is no longer acceptable in todays world or in

228、 the future.Also,the sharing of data is key,whether across systems or between suppliers and retailers.There not only needs to be a value exchange there to enable it to happen but also the barriers to exchanging data need to be much lower than they have been historically.41 InterviewGerman AI Associa

229、tionGerman AI Association Profile Artificial intelligence is one of the key technologies of our future.The members of the German AI Associa-tion are committed to ensuring that this technology is applied in the spirit of European and democratic values.The goal is digital sovereignty for Europe.To ach

230、ieve this,the Federal Republic of Germany and the EU must become attractive AI locations for entrepre-neurs,where a willingness to take risks is appreciat-ed and the spirit of innovation can thrive in the best conditions.The German AI Association supports AI entrepreneurs by representing their inter

231、ests with regard to policy,business and media.The Associations goal is to create an active,successful,and sustainable AI ecosystem in Germany and Europe.After all,only if the brightest minds and thought leaders decide to teach,conduct research,and base themselves in the European Union will we be abl

232、e to compete successfully against global competition.The German AI Association enables entrepreneurs to learn from each others experiences and transfer these to their own companies.When ideas and information are exchanged in the Associations network,a con-tribution is made towards strengthening inno

233、vative capacity in Germany.Artificial intelligence can only be successful in Germany if it is accepted by the main-stream economy in all sectors.The German AI Associ-ation helps to awaken openness for AI innovations in European companies.Daniel Abbou Short vitaDaniel Abbou has been Managing Director

234、 of the German AI Association since May 1,2020.His areas of responsibility include political and press communi-cation as well as support for funding projects.Daniel Abbou previously founded AI-Hub Europe and advised politicians and companies.He was press spokesman in various ministries of finance an

235、d economics,including spokesman for Ulrich Nubaum,the former Senator of Finance and State Secretary in the Federal Ministry for Economic Affairs.Mr.Abbou held the position of dep-uty government spokesman in the first Kretschmann cabinet in Baden-Wrttemberg.His enthusiasm for digitalization and innov

236、ation stems from his time working as a television and radio journalist focusing on new technologies.Participants:Daniel Abbou(Managing Director,German AI Association),Julian Engel(MHP)Julian Engel(MHP):What does the German AI Asso-ciation stand for and what is your role there?Daniel Abbou(AI Associa

237、tion):The German AI Asso-ciation represents nearly 400 AI companies in Germany.It was founded almost five years ago.As an associa-tion,we hold stakeholder discussions with politicians,primarily the Federal Ministry for Economic Affairs and Climate Action,but also the Federal Ministry for Digital and

238、 Transport,the Federal Chancellery,and the Federal Ministry of Labour and Social Affairs.I am the first Man-aging Director of the German AI Association together with Vanessa Kern.Engel:What role will AI play in Europes future?Abbou:I think the implications of AI in business will be huge.AI can take

239、over the repetitive tasks that we all have to do in our areas of work.The aim is to be able to concentrate on the main tasks at work.For example,a nurses job is to have contact with patients,not fill out Excel lists.Daniel Abbou,Managing Director,German AI AssociationKI Bundesverband42 Industry 4.0

240、Barometer 2024AI makes it possible to concentrate on the core area of an activity.AI will also take over more complex tasks in a particular framework.In the legal field,all recurring points in contract law could be taken over by AI.German tax law,as complex as it is,can also be tackled using AI.Will

241、 it replace tax advisers?No.Will there be tax advisers who wont be using AI in five years?Also no,Id say.There will be a change in cer-tain professional fields,and also in professional fields that dont yet think it will affect them.But this disrup-tion will happen,Im convinced of that.In automation,

242、where robots are installed in factory workshops,its also changed the job of the factory worker.And there will also be changes in jobs that are at an educationally higher level.Engel:In our survey,we made a country comparison between the DACH regions,the UK,the USA and Chi-na,with a focus on industri

243、al AI.What are the main challenges that companies currently have when it comes to implementing or integrating AI?Abbou:It has to be said that,unfortunately,many areas of the German economy are not yet finished with the task of digitalization.AI without digitalization and without data in a company is

244、 difficult.There is skepticism around technology in traditional SMEs.It varies by region,but is especially prevalent in DACH.Business owners have varying access to digitalization and to data.A further problem we find in corporations is that no data sharing takes place even within the cor-poration it

245、self.I would consider that to be the biggest challenge that AI entrepreneurs have when interacting with SMEs and corporations.Engel:In the survey,we also asked about data avail-ability and data quality.Availability is one thing,but sharing is the real obstacle.What was also interesting in our survey

246、 was that qualified employees really are very scarce.Would you also agree with that?Abbou:Yes,definitely.Finding well-qualified staff is a huge problem.Not only for SMEs,but for AI entrepre-neurs too.The lack of qualified employees exists,of course.What makes it particularly complicated is that the

247、need for more and more data scientists is recog-nized within the German education system,but this is still not being addressed by means of any curricu-la or support measures.Its an issue that needs to be tackled.Digital media and knowledge of how to han-dle data,what data actually is,thats something

248、 we should learn in school or at university.Unfortunately that has only been happening to a limited extent up to now.Engel:This makes companies dependent on service providers.There is another challenge:data protec-tion.How do you see Europes role regarding data protection?Abbou:The General Data Prot

249、ection Regulation(GDPR)is not the most popular regulation among AI companies.But its also important to say that most AI companies in Germany dont use the business-to-con-sumer model(B2C)but are mainly in the busi-ness-to-business sector(B2B).Therefore,to a certain extent the data protection problem

250、is not as notice-able as with a B2C business model.But of course,theres still the question of how personal data is used.For example,in health care,a very sensitive area,the GDPR is extremely serious.43 Engel:What impact do climate aspects and sustainabil-ity have on industrial AI?Abbou:One needs to

251、go hand in hand with the other.If I want to do something new,climate calculations will always be part of that.There will be AI models that can minimize the climate impact.Engel:Weve talked a lot about challenges and obsta-cles.Lets look at your success as an association.What milestones would you lik

252、e to mention?Abbou:Weve made it clear to politicians and parts of the business world how relevant AI is,for example for large language models.I can even remember the first conversation with a federal ministry when someone told us it would be a really crazy idea.And three years later we can see what

253、impact its having,with ChatGPT for example.We were one of the first to make this point clear to stakeholders.Its our aim to highlight the opportunities,as Germany and Austria are quite good at focusing on the risks.There are simply not enough data centers in Germany and Europe that are specif-ically

254、 related to AI.We have also made this clear to Europe.In her State of the Union address on Septem-ber 13,2023,Ursula von der Leyen(President of the European Commission)explicitly mentioned AI access capacities for start-ups.You see that if you stick with something long enough,you get something back.

255、That was a wonderful moment for us.Engel:Have there been reports from members report-ing a breakthrough?Abbou:In terms of generative AI,Aleph Alpha and Lengoo obviously need mentioning.They aim to create a large European AI model.They were invited to speak at the German governments closed cabinet me

256、eting in Meseberg.These are our two big success stories.We have few companies in the association that are fil-ing for bankruptcy.For other firms,start-ups,or asso-ciations,a failure rate of 20 to 30 percent is normal.For us,the rate is under 10 percent.And that shows that our companies even if theyr

257、e not world-famous yet are doing a good job and managing to establish themselves in this market.Engel:As part of our survey,we noticed that opti-mism in the US and China is greater than in Europe when it comes to the use of AI-based solutions in com-panies.In China there is a different political app

258、roach and therefore a very different culture than in Europe.It should not be our goal to catch up with China in terms of speed.Its more about preserving our own values and developing in our own way ethically,sus-tainably,and continuously becoming better.Do you share this view?Abbou:Yes,I completely

259、agree with you.The prob-lem is just that,often,people focus on the potentially negative impacts rather than talking about the oppor-tunities.I think both are okay in equal measure.But Ive been to lots of events where people project their fears into AI.Often along the lines of AI is going to take ove

260、r the world,and well end up living in a soci-ety where everything is just virtual.So Im saying that we need to demystify the topic of AI.Nobody should use apocalyptic movies as a projection screen for their own fears about a technology that is not understood.But thats exactly what lots of people are

261、 doing.Fears arise when people dont understand things.It is our mission to allay peoples fears and make AI tangible as a technology.Engel:Finally,could you give us a brief outlook for the next five years?How do you think Germany and Europe will develop?Do you feel were on the right track?What role w

262、ill the German AI Association play?Abbou:We are on the right track.I have an insight into nine different EU countries and I can see that there is strong motivation to address this topic.If the premises are correct,we will have integrated AI into large parts of the German and European economy in five

263、 years.However,we should be careful not to become more dependent on the large US hyperscal-ers.We provide our economic data for free and also pay the hyperscalers to get the results of the models.We mustnt make this mistake.In the next five years it will be crucial not to become dependent.We want to

264、 have our own European systems that are competitive.KI Bundesverband44 Industry 4.0 Barometer 2024KI Bundesverband45 Fig.19:Expectations for the future impact of AI on production processesHow much of an impact do you expect AI to have on production processes in the future(the next 12 years)?Negligib

265、leVery lowLowModerateHighVery highRadical1%4%8%27%32%22%6%Within the next one to two years,businesses expect the introduction of Industrial AI to have a high,perhaps even radical,impact on their production processes.However,there is still no outstanding field of application for the technology.What i

266、mpact do businesses expect Industrial AI to have in the next one to two years,and what are the most important fields of application and driving forces?The participants answered these questions as follows:60 percent of the businesses included in the survey are expecting AI to have a high,or even radi

267、cal,impact on their production processes.27 percent forecast a mod-erate impact,while 13 percent expect the impact to be low to negligible(Figure 19).The results of the question regarding the most import-ant fields of application for Industrial AI show that there is no specific field of application

268、that stands out from the rest.This diversity illustrates once again the fact that the benefits and added value Industrial AI has to offer are highly dependent on the existing conditions at and requirements of the business in question.Nevertheless,it is worth noting two fields of application that wer

269、e rated slightly more import-ant than the others by the participants.14 percent of participants listed quality controls businesses are obviously focusing on using AI to increase the qual-ity of their products.This includes the use of image recognition and sensors and particularly the analy-sis of th

270、e resulting data using machine learning to detect errors at an early stage,optimize production processes,and ultimately improve the quality of the end products.In addition to this,twelve percent of the participants listed resource efficiency as important.Companies are aiming to use AI technology to

271、reduce their energy consumption,make more efficient use of materials,and generally make their production less resource-intensive(Figure 20).The results show that there is no clear leader in terms of important fields of application for businesses using AI.In fact,the real challenge lies in the fact t

272、hat each company needs to assess the potential of AI on its own individual terms,which will help them to identify the best application for their needs.This is the exact prob-lem that makes the use of AI difficult.The theoretical potential of the technology is enormous,but imple-menting it in practic

273、e is much more complicated,and requires an in-depth understanding of the subject.46 Industry 4.0 Barometer 2024Most important fields of application for AI in production processesFig.20:Most important fields of application for AI in production processes(The participants were given the ability to assi

274、gn a total of 100 points.The results shown here are the averages for each possible response.)Quality control Resource efficiency Product design and simulation Employee safety Material flows N/A Supply chain Demand forecasting Assembly/installation Predictive maintenance47 Success Story New Dimension

275、s:SounceIn the continuously developing automotive industry,it is crucial for premium vehicle manufacturers to deliver flawless quality and technical excellence.To this end,the companies conduct rigorous research and development processes(R&D).The integration of top technologies has become a must,whi

276、ch has opened the door for Sounce a product from MHP.Part of the Industrial Cloud Solu-tions portfolio,Sounce uses the information content of acoustic signals to uncover hidden irregularities and devi-ations in examined parts,products,and machines.Sounce allows the identification of anomalies,the cr

277、eation of clus-ters,and the determination of correlations between data points.This enables a significant enhancement of quality standards using artificial intelligence as a key element.In this success story,we look at the utilization of Sounce as part of the R&D process at Porsche and at its abili-t

278、y to yield valuable insights,which have resulted in the improvement of quality standards.On the one hand,the integration of these technologies has made the R&D pro-cesses at Porsche more efficient.On the other hand,there has been an improvement in quality control standards.Read on to find out how So

279、unce has become an essen-tial component in the striving for top quality in the automotive industry,redefining quality assurance and setting new benchmarks.Use caseChassis mounts fulfill four essential functions,which often conflict with each other during the design process.Their purpose is to transm

280、it forces,enable defined move-ments,isolate noise,and dampen vibrations.For dealing with each of these requirements,different types of elasto-mer mounts are built into the chassis.Depending on the type there is also a risk of the chassis mounts producing unwanted noise,such as rattling,squeaking,or

281、creaking.With sophisticated options for acoustic analysis,Sounce offers a transformative solution for an evaluation of these noises on the test stand.From the selection of different mount concepts offered by numerous suppliers at the beginning of a project to measures to ensure series-production qua

282、lity,Sounce supports the vehicle manufac-turer Porsche in making data-based decisions throughout the R&D process.This also means that good noise quality is ensured for the vehicle launch.Initial situation and challengesIn the past,work to mitigate unwanted noise caused by the chassis mounts was main

283、ly conducted during whole-vehicle testing on test tracks with noise and com-fort lanes,with different temperature preconditioning based on the prevalent conditions in the individual sales markets.Ideally,this required prototypes with specific acoustic properties being available at the very beginning

284、 of the R&D process;in reality,these dont tend to be sufficiently mature in terms of development until a lat-er phase.Consequently,there was a need for achieving noise mitigation for the affected chassis components by means of testing on a test stand,taking into consider-ation suitable load cases an

285、d peripheral components.While the overall vehicle assessment is described using both subjective impressions and objective measurement data,the evaluation,documentation of bench tests and decision on whether the noises are relevant to customers or not are purely subjective.ApproachTo allow acoustic s

286、ignals to be utilized with precision,the testing equipment of the vehicle manufacturer had to fulfill one essential requirement:a static test setup with a repet-itive procedure.Realistic conditions and load cases needed to be replicated on the test rig.It was therefore crucial to be able to rely on

287、a consistent testing environment where noise detection could be conducted in a controlled man-ner.With the new setup,acceleration sensors pick up the vibrations.Based on the resulting measured data,the sys-tem creates spectrograms,visual representations of the sound frequencies over time.These spect

288、rograms serve as an enhanced data source that facilitates subsequent anal-ysis by means of algorithms.48 Industry 4.0 Barometer 2024Unsupervised learning/machine learning:In this par-ticular use case,unsupervised machine learning is at the core of the approach,a powerful technology that enables the

289、system to autonomously recognize anomalies in the visualized acoustic signals that are produced in the course of the testing.Without identification or intervention by humans,Sounce can detect deviations from the norm in complex data records by itself,thus ensuring a robust and objective evaluation o

290、f each tested mount.Another advan-tage of using Sounce in this case is that it allows non-stop operation.Thanks to 24/7 availability,Sounce ensures that the testing process can run without interruption,which makes for maximum efficiency.This non-stop operation enables fast and continuous evaluation

291、of supplied parts,providing the manufacturer with a competitive advantage in a sector characterized by rapid innovation.Faster supplier evaluation:This use case offers great potential with respect to the speed of evaluating the quality of bought-in parts.The system analyzes large cloud-based data vo

292、lumes very quickly and provides prompt feedback on the quality of parts from different suppliers.This accelerated evaluation enables the man-ufacturer to make well-founded decisions,optimize its supply chain,and maintain long-term partnerships with reliable suppliers.Functionality and solution The i

293、ntegrated machine learning functions excel in the analysis of unstructured data and subsequent detection of patterns and groupings in the produced clusters.In the course of its unsupervised learning,the system auton-omously clusters similar noises,thus enabling efficient classification of normal and

294、 abnormal acoustic patterns.In these use cases,Sounce can facilitate cause analysis.When a problem is detected,Sounce provides comprehen-sive information,enabling engineers to determine causes that were not evident previously.This allows customers to address arising problems proactively.In the past,

295、the testing process depended strongly on manual intervention and human hearing to detect anomalies,which entailed the risk of human error.The AI-supported acoustic analysis provided by Sounce has automated the process.Transparent data documentation and web-based visualization:All the data generated

296、during the testing process is fully recorded and documented.This compre-hensive data documentation enables simple checking,which means that the customer can track and investigate all the problems that may occur during the testing.The transparent data path also improves reliability and facil-itates c

297、ontinuous improvement initiatives.The Sounce web app offers an intuitive platform for data visualiza-tion.Engineers can easily retrieve the analysis results via interactive graphics and diagrams for subsequent inter-pretation.In addition,the data generated during testing is analyzed in the cloud,mak

298、ing for speedier processing as well as faster access to the results.The results can also be accessed remotely at any time.Results and outlook With the combination of data recording,documenta-tion,visualization,and analysis by Sounce,Porsche has achieved a substantial optimization of its quality assu

299、r-ance processes in Research&Development.Not only has Sounce helped Porsche ensure fast and reliable supplier evaluation,it has also provided a solid basis for collabo-ration with suppliers.WebapplicationCloud ServicesHardwareSounce as a modular solutionIntuitive web application49 Fig.21:Most import

300、ant fields of application for AI in production processes(The participants were given the ability to assign a total of 100 points.The results shown here are the averages for each possible response.)In your opinion,what factors will be the most important driving forces behind the use of AI in producti

301、on?19%Operational efficiencyLower costsHigher qualityGreater reliabilityLabor shortageGreater flexibilityImproved lead timesCustomer demandKnowledge transfer(human to machine)Greater resilience19%12%11%8%8%6%6%6%5%Shifting the perspective from fields of application to the concrete factors that promo

302、te the use of AI,there are two in particular that the participants clearly regard as key driving forces for the introduction of Industrial AI,and that are thus also important criteria when it comes to assessing AI projects:increased operational efficiency and cost reduction.19 percent of the busi-ne

303、sses included in the survey named each of these fac-tors as a key driving force for the integration of AI.In this context,the idea of increased operational efficien-cy refers to the automation of production processes and optimum utilization of resources.Cost reduction covers the reduction of labor c

304、osts,minimization of errors,and the optimization of energy consumption(Figure 21).These results are also reflected in our inter-view with Julian Follner of Deutsche Bahn.Mr.Follner confirmed that cost reduction was the main objective behind the use of AI.He also said that improving the punctuality o

305、f the companys trains played a large role.50 Industry 4.0 Barometer 2024”Courage to implementation then application ideas for I4.0 technologies will become valuable solutions for companies.The time for this is now not tomorrow.GenAI is a good example of this.It is the responsibility of management es

306、pecially in Europe to create the framework for experimentation and speed in the direction of efficiency and!innovation.Both together will secure the future of the company.”Dr.Christian Fiebig PartnerDigital Factory&Supply Chain 51 Bernhard Winkler,Vice President Production Rail,Knorr-Bremse Rail Veh

307、icle Systems Dr.Christina Reich(MHP):Please can you give us a brief overview of your duties as Vice President Produc-tion Rail at Knorr-Bremse AG?Bernhard Winkler(Knorr-Bremse):I work in the Rail Vehicle Systems division at Knorr-Bremse.Im in charge of the Production Rail central function and have g

308、lobal responsibility for Group-wide production-related activ-ities across all plants.This includes typical projects for standardization,site development,the introduction and ongoing development of our production system,industrialization projects and support for location-spe-cific technology projects

309、.It also includes driving for-ward digitalization issues,as well as division-wide governance tasks like performance management and footprint development.So my area of responsibility covers all the more strategic,production-related topics that are not directly related to the daily output of a plant.R

310、eich:Our survey shows that,in the DACH region,most areas of application for artificial intelligence are to be found in quality control and improving resource efficiency.In which areas of production is your focus on AI-based solutions?Winkler:At Knorr-Bremse Rail,we predominantly operate in a typical

311、 high-mix,low-volume business.We also talk about absolutely safety-critical compo-nents.In the direct production area,the quantities and thus also the samples we can use to train AI or an algorithm are small compared to a typical automo-tive supplier.We have found that the algorithms and commonly us

312、ed tools have not yet been designed for the characteristics of this business with comparative-ly smaller quantities and a huge variance.Thats why we have focused more on processes in administrative Knorr-BremseInterviewKnorr-Bremse Rail Vehicle SystemsKnorr-Bremse Rail Vehicle Systems ProfileKnorr-B

313、remse is the global market leader in braking systems and a leading provider of other systems for rail and commercial vehicles.Knorr-Bremses products make a significant contribution to greater safety and energy efficiency on roads and rails around the world.Some 32,600 employees at over 100 locations

314、 in more than 30 countries use their competence and motiva-tion to satisfy customers worldwide with products and services.In 2022,Knorr-Bremse generated sales of EUR 7.1 billion in its two business divisions worldwide.For more than 115 years,the company has been an indus-try innovator,driving forwar

315、d developments in mobili-ty and transportation technologies and taking the lead in connected system solutions.As one of Germanys most successful industrial companies,Knorr-Bremse profits from the key global megatrends of urbaniza-tion,sustainability,digitalization,and mobility.Bernhard Winkler Short

316、 vitaMr.Winkler joined Knorr-Bremse AG as a trainee after completing his studies in engineering and manage-ment in Munich.He held positions in both divisions of the company(Commercial Vehicle Systems and Rail Vehicle Systems)and worked for several years in the industrial engineering department of th

317、e Rail Vehicle Systems division.His responsibilities included coordi-nating Industry 4.0 initiatives with the global plants.Since May 2021,he has headed the Production cen-tral unit of the Rail Vehicle division(Production Rail)and is responsible for performance management and digital manufacturing,i

318、n addition to technology devel-opment and industrialization,production system,test stand construction,and investments and footprint.Participants:Bernhard Winkler(Vice President Production Rail,Knorr-Bremse Rail Vehicle Systems),Dr.Christina Reich(MHP),Dr.Thilo Greshake(MHP),Stephan Mller(MHP)52 Indu

319、stry 4.0 Barometer 2024areas when it comes to AI-based solutions,because we see great potential there and the proportion of repetitive tasks is higher.Reich:What specific use cases are there in your area as regards typical Industry 4.0 solutions?Winkler:We are currently working intensively on connec

320、ting our production assets using an IIoT plat-form,with the initial aim being to have transparency around the status of assets,productivity,and so on at all times.Building on this,the aim is to ultimately create self-regulating control loops in order to reduce manual control effort.In addition,our p

321、lants are work-ing hard on automating a wide range of processes in the direct area,in intralogistics,and in admin areas.Here,we are currently focusing intensively on robotic process automation(RPA).Thats perhaps not a typical AI field,but its the first step towards it for us.For example,were using a

322、 bot for order management.That means all orders are automatically dispatched to the SAP system,irrespective of which channel is used to send them to the plant or service center.Reich:What performance indicators are used at Knorr-Bremse to measure improvements brought about by the use of industrial A

323、I?Winkler:We look at overhead productivity,for exam-ple.That means we examine how many overheads we need in a plant in order to generate output X.More automation means fewer skilled workers are need-ed for some tasks.However,were also noticing that sometimes we need more staff in other functions tha

324、n we did before,such as for data preparation,pro-cess control,and programming and operating bots.Of course,other skills are needed for this,which we have to build up first.Our colleagues in Purchasing are also working very intensively with RPA and are mea-suring indicators such as the number of auto

325、matically processed orders and the automation level of certain subprocesses.Reich:We saw in our survey that approximately 70 percent of companies are hiring external AI experts to compensate for the shortage of skilled workers.How are you handling this at Knorr-Bremse?Winkler:Were taking a two-prong

326、ed approach here.Firstly,were gradually building up internal skills,for example through vacancy filling and further training.This takes time,however,which is why were also trying to scale using external experts.Essentially,in the central unit we are striving to drive forward new approaches,initially

327、 in the form of pilot projects in or with individual plants.In doing so,we ensure that these approaches address real problems,rather than being pure technology studies.If an approach succeeds,its our job to design it in such a way that it can be scaled and transferred to other plants or areas.Reich:

328、What are the most serious challenges you face when integrating industrial AI in the production envi-ronment?What are the barriers that have prevented AI from being used more widely up to now?Winkler:Data quality is a major issue.For example,if the master data is not correct,the algorithm wont work a

329、t some point.That means well reach a limit as regards standardization in the system.Both our SAP landscape and the IT environment for our sup-plementary systems,such as for our quality assurance or process control issues,are very varied.This makes it necessary to keep redefining the interfaces.Reich

330、:What role do safety-critical aspects play in the application of industrial AI in rail systems?Is that also a factor inhibiting its use?Winkler:Currently,we are not yet using AI as the sole means of ensuring quality in production because we have a zero defect policy.Our products are absolutely safet

331、y critical.For example,the brakes on a high-speed train simply have to work there can be no safety issues.We have to make sure at the plant that the product is 100 percent correct.To do that,we would also have to be able to rely on AI.But weve established that our quantities are currently too small

332、for this.We are not at that point yet.Another factor for us in terms of safety-critical aspects is,of course,cybersecurity.What interfaces or system boundaries are open,for example?How may systems be permitted to interact?What can we even roll out anyway,particularly in the cloud?This is currently r

333、eally hindering us from rolling things out or scaling them.Reich:Weve seen that youve made strategic invest-ments in AI start-ups like Rail Vision.The idea behind Rail Vision is driver assistance technology in the form of high-tech sensor systems.It enables trains to detect objects and obstacles over long distances.Please can you tell us a bit more about it?Could it be extended more widely in the