1、In collaboration with IBM Institute for Business Value|Research InsightsCloud-enabled manufacturingOperations and IT leaders turn ambition into advantage2IBM can help manufacturers leverage hybrid cloud,AI,and automation to achieve new levels of business agility.We help you set your direction based

2、on a time-tested Industry 4.0 reference architecture and industry standards,achieve scale by consistently deploying advanced shop-floor technologies on an open platform,and unleash optimum value by selecting manufacturing process use cases to address immediate needs.For more information,visit AWS ca

3、n helpAWS helps leading manufacturers transform their operations with the most advanced set of cloud solutions,including machine learning,IoT,robotics,and analytics.AWS allows you to focus your resources on optimizing production,creating new smart products,and improving operational efficiencies acro

4、ss the value chain,not on the infrastructure to make it happen.For more information,visit IBM can help1Nearly half(48%)of surveyed manufacturers indicate they can harness more value from cloud.Companies need to pivot from a focus on cost savings for isolated cloud use cases to an end-to-end,outcome-

5、driven cloud strategy.Innovative manufacturers are leveraging cloud to build a foundation for digital dexterity.A subgroup of top performers have implemented a data-driven culture 1.7 times more than their closest peer group,positioning them to embrace emerging technologies that drive operational tr

6、ansformation.Cloud-enabled digital technologies such as AI and IoT catalyze reinvention.Leading manufacturers are modernizing both how they work and the tech tools they use while investing in the digital skills of their workforce to elevate performance and production.Advanced digital technologies un

7、derpinned by cloud can power manufacturing transformation.Key takeaways2Capturing clouds potentialAs the Industry 4.0 era evolves,manufacturing organizations have been steadily embracing cloud computing,with most reporting significant implementation progress in 2022.1 But recent insights from the IB

8、M Institute for Business Value(IBM IBV)and Amazon Web Services(AWS)suggest that many manufacturing organizations may not be optimizing the valueand opportunityof cloud as the cornerstone for digital transformation.In our global survey of manufacturers,only half(52%)of their IT executives say their o

9、rganizations are harnessing clouds benefits.What is holding them back?Three reasons stand out in our research:A surprisingly low number of manufacturing workloads have been migrated to the cloud,hindering advanced operational initiatives where cloud can be a key enabler.Some manufacturers lack integ

10、rated technology strategies that include cloud,AI,IoT,and application modernization for manufacturing activities.Some respondents have focused strictly on cost savings versus additional business outcomes,such as improving performance and increasing value across core manufacturing operations.The less

11、on for manufacturers?Merely adopting cloud for simple lift-and-shift workloads or standalone use cases is not enough.A more outcome-driven approach can help them realize benefits such as boosting productivity,quality,machine availability,and sustainability,as well as accelerating engineering efforts

12、 and product lifecycle management.Organizations are tackling the next phase of complex technology-powered initiativesincluding supply chain orchestration,quality analysis and resolution,materials and production optimization,and predictive monitoring of assets.And they are learning that these require

13、 integration of data,security,and exponential technologies,with the cloud as the foundation to make innovation possibleand powerful.In fact,our research has shown that combining cloud computing with these other levers of business transformation can generate 13 times greater benefits than cloud alone

14、.23FIGURE 1Manufacturers maturity in leveraging data and digital technologies is defining how they unlock clouds deeper value.Without a more strategic,value-driven approach to cloud,digital transformation in manufacturing becomes more challenging.To explore how manufacturing organizations can unleas

15、h more value from cloud and the advanced technologies it enables,we analyzed survey responses from both manufacturing and IT executives at more than 1,100 manufacturing companies worldwide to assess their organizations digital technology maturity and data maturity.Respondents work in automotive,elec

16、tronics,downstream oil and gas,chemicals,metals,and industrial machinery(see“Study approach and methodology”on page 32).Our analysis resulted in four archetypes(see Figure 1):Source:IBM Institute for Business Value Constrained Operators:behind their peers in both digital technology and data manageme

17、nt Digital Enthusiasts:committed to digital transformation but lagging in their data practices Data-focused Deciders:invested in data management but lacking technology enablement Transformational Optimizers:leveraging data and technology to drive success.High digitaltechnology maturityLow digital te

18、chnology maturityHigh data maturityLow data maturity27%of respondentsTOTransformational Optimizers 27%of respondentsDDData-focusedDeciders 22%of respondentsCOConstrainedOperators24%of respondentsDEDigitalEnthusiasts FIGURE 1Manufacturers maturity in leveraging data and digital technologies is defini

19、ng how they unlock clouds deeper value4We then pinpointed five traits that distinguish Transformational Optimizers,positioning them to outperform the other groups in key performance metrics and achieve cloud-driven benefits:A modern cloud platform A robust data foundation Digital technology integrat

20、ion New ways of working Business outcomes linked to cloud.This report dives deeper into each of these attributes,describing the archetypes efforts in each area to support their operational priorities.An action guide offers a three-step plan for moving forward based on a manufacturers maturity in dig

21、ital technologies and data management.3 in 4 of application/system workloads for manufacturing-related operations have not been migrated to cloud3 in 5 manufacturing and IT leaders say their organizations do not focus on business outcomes of technology initiatives3 in 4 of organizations have not est

22、ablished integrated technology strategies across cloud,AI,and application modernization for manufacturing activitiesWhy have only half of manufacturing organizations harvested business outcomes from cloud?IT Q.What percentage of your applications/systems workloads have been migrated from the data ce

23、nter(s)to your cloud estate?IT Q.Describe your organizations technology strategies for the following activities.Manufacturing Q and IT Q.To what extent do you agree with the following statements:IT and manufacturing focus on the business outcomes of technology initiatives;percentages show responses

24、of 4 and 5 on a 5-point scale where 1=strongly disagree and 5=strongly agree.5Traits that transform manufacturing Trait#1A modern cloud platformDigital transformation is facilitated by hybrid cloud,which combines and unifies public cloud,private cloud,and on-premises environments to create a single,

25、flexible,cost-optimal IT infrastructure that enables organizations to process data where it makes the most sense.3 It enables real-time data collected from sensors,devices,and machines on the factory floor to be used by other factory assets,as well as shared across other components in the enterprise

26、 software stack,including ERP and other business management software.4 Similarly,cloud supports the required IT workloads,such as operational technology(OT)-IT integration,edge analytics,OT security,and both new and traditional applica-tions.Data from different manufacturing operations can be centra

27、lized,allowing cross-factory insights,KPI comparison,and optimization.5 In addition to basic cloud infrastructure advantages,more than 60%of executives in our survey say that advanced cloud capabilities such as containers,portability,and DevSecOps are an imperative for success.But for many manufactu

28、rers,their current cloud architecture insufficiently supports most of their primary initiatives,making it difficult to orchestrate the multiple digital technologies required for implementing these priorities(see Figure 2).For instance,predictive management of assets might require the cloud,IoT,AI,an

29、d 5G.Manufacturing quality root cause needs the cloud,IoT,AI,computer vision,and edge computing.Without the cloud underpinning the other technologies,these initiatives could stall or even fail.6The CEO Global C-suite Study:CEO decision-making in the age of AI CEO decision-making in the age of AI:Act

30、 with intention.IBM Institute for Business Value.June 2023.https:/ibm.co/c-suite-study-ceo Clouds next leap Clouds next leap:How to create transformational business value for energy and resources.IBM Institute for Business Value.August 2022.https:/ibm.co/cloud-transformation-energy-resourcesManufact

31、uring 4.0 Manufacturing 4.0:From data to decisions.IBM Institute for Business Value.May 2022.https:/ibm.co/manufacturing-4-0About Research Insights Research Insights are fact-based strategic insights for business executives on critical public-and private-sector issues.They are based on findings from

32、 analysis of our own primary research studies.For more information,contact the IBM Institute for Business Value at .IBM Institute for Business ValueFor two decades,the IBM Institute for Business Value has served as the thought leadership think tank for IBM.What inspires us is producing research-back

33、ed,technol-ogy-informed strategic insights that help leaders make smarter business decisions.From our unique position at the intersection of business,technology,and society,we survey,interview,and engage with thousands of executives,consumers,and experts each year,synthesizing their perspectives int

34、o credible,inspiring,and actionable insights.FIGURE 2Executives report their cloud architecture is inadequate for some of their most important technology initiatives.Supply orchestrationManufacturing quality root causeMaterials optimizationProduction optimizationPredictive asset monitoring and perfo

35、rmance managementManufacturing quality resolutionTransportation optimization58%52%54%GapSupportive48%51%SupportiveSupportive40%51%GapGapGap44%50%57%55%49%46%50%Importance of operational technology initiatives versus having a supportive cloud architecture in place*Importance of initiativeCloud archit

36、ecture supportive*A gap is defined as a percentage point difference of more than 5%.Manufacturing Q.How important are the following operational technology initiatives to your organization?Percentages show responses of 4 and 5 on a 5-point scale where 1=not at all important and 5=extremely important.

37、IT Q.To what extent does your cloud architecture support your operational initiatives?Percentages show responses of 4 and 5 on a 5-point scale where 1=not at all and 5=to a very large extent.7Transformational Optimizers have made the most progress in implementing cloud technologies to support advanc

38、ed operational initiatives(see Figure 3).Take supply orchestration for examplea critical area given that a National Association of Manufacturers survey found nearly 80%of manufacturers cited supply chain disruptions as their number-one business challenge.6 Transformational Optimizers report their cl

39、oud architecture supports supply orchestration 1.5 times more often than peers.They are gaining real-time tracking to monitor and manage the flow of materials and track work in progress and finished goods.With this insight,they can prevent inventory issues by intervening when an issue occurs.Manufac

40、turing executives estimate that optimized supply orchestration can yield 37%lower supply chain costs.Likewise,Transformational Optimizers report their cloud architecture supports manufacturing quality root cause initiatives 1.4 times more often than peers.The ability to identify problems or defects

41、in manufacturing processes and automate rectification translates to determining the cause of a problem faster and mitigating recurring issues.Executives estimate this focus can reduce the cost impact of poor quality by 57%.Transformational Optimizers are also better positioned for predictive managem

42、ent of assetsa priority that executives say can increase asset availability by 52%.Using data and analytics,predictive capabilities help facilitate asset utilization and avoid costly downtime and repairs.8DEManufacturing quality root causeMaterials optimizationProduction optimizationPredictive asset

43、 monitoring and performance managementSupply orchestration60%38%36%43%62%45%42%Manufacturing quality resolutionTransportation optimization48%52%48%51%54%42%49%56%47%35%58%48%26%34%45%50%38%46%52%47%DEDEDDDDDDDDCOCOCOCOCOCOCODEDEDEDDDDDD DETransformational Optimizers Data-focused Deciders Digital Ent

44、husiasts Constrained Operators Percent that say their cloud architecture supports these operational technology initiativesTOTOTOTOTOTOTOFIGURE 3Transformational Optimizers claim a more mature cloud architecture to support operational technology initiatives.IT Q.To what extent does your cloud archite

45、cture support your operational initiatives?Percentages show responses of 4 and 5 on a 5-point scale where 1=not at all and 5=to a very large extent.9Volkswagen transforms manufacturing and logistics7Case studiesTo transform its automotive manufacturing and logistics processes,the Volkswagen Group bu

46、ilt the Volkswagen Industrial Cloud on AWS,which uses AWS IoT services to connect data from machines,plants,and systems across more than 120 factory sites.The Volkswagen Industrial Cloud aims to yield a 30%increase in productivity,30%decrease in factory costs,and save over$1 billion in supply chain

47、costs.The Group is also using AWS to expand beyond manufacturing into ride-sharing services,connected vehicles,and immersive,virtual car-shopping experiences to shape the future of mobility.IBM Systems Manufacturing scales AI value by combining hybrid cloud with edge computing8Rather than build an i

48、solated AI solution,IBM Systems Manufacturing combined hybrid cloud with edge computing to scale the value of AI across the global manufacturing enterprise.It deployed a first-of-its-kind AI visual inspection system on assembly lines in plants in Canada,Hungary,Mexico,and the US.The solution leverag

49、es cloud and edge computing to eliminate bandwidth and latency issues that arise from running AI inferencing in a data center.The AI models are deployed to edge devices where image data is processed,enabling the company to detect anomalies and act on them in real time.AI models and edge devices are

50、managed from a central location through the cloud,an automated process that reduces software maintenance costs by 20%.Compared to a human inspector,AI automation reduced inspection times from 10 minutes to one minute in one use case.10Trait#2A robust data foundationManufacturers have more than enoug

51、h data to fuel far-reaching operational changes,but approximately 90%of that data stagnates in isolated systems.9 Cloud computing flips the script,enabling manufacturers to cultivate a culture where high-quality data is democratized and employees are skilled in digital technologies.Data from equipme

52、nt,processes,and systems feeds deeper insights that drive continuous process improvement.Transformational Optimizers demonstrate the greatest data maturity,having implemented a data-driven culture 1.7 times more than their closest peerData-focused Decidersand 2.9 times more than Constrained Operator

53、s.These leaders are leveraging the cloud and other technologies to strengthen data management practices(see Figure 4).For example,nearly two-thirds(63%)of Transformational Optimizers have teams of data experts who are skilled in cloud services,and they have near real-time capabilities to update data

54、 repositories.This helps ensure that employees can tap into the most current data for insights that power improved factory operations.11FIGURE 4Cloud underpins strong data management practices to sharpen factory operations.IT Q.To what extent does your manufacturing organization use the following da

55、ta management practices?Percentages show responses of 4 and 5 on a 5-point scale where 1=not at all and 5=to a very large extent.DENear-to real-time updates to data repositoriesAPIs are used for internal data-sharing activitiesAPIs are exposed to share data with an ecosystem of third partiesSensitiv

56、e data has been migrated and encrypted in cloudTeams of data experts are proficient with cloud services63%43%51%52%26%38%40%31%36%51%33%40%43%49%25%29%49%40%TOTOTOTOTODEDDDDCOCOCOCOCODEDEDD45%DDDEDDTransformational Optimizers Data-focused Deciders Digital Enthusiasts Constrained Operators Percent th

57、at are implementing these data management practices12Case studyPanasonic Connect conquers complexity with shop-floor analytics10To support chip manufacturers adapting to new semiconductor packaging trends,Panasonic Connect has infused advanced analytics into two process control solutions that have e

58、merged as the companys first smart-factory offerings.The first solution created an advanced plasma dicera specialized tool for more precise cutting and processing of semiconductor wafersby fully automating the“recipe”generation,which determines the optimal combination of decisions on variables that

59、affect the process.This solution reduced the development cycle time by as much as 30%.The second solution optimized plasma cleaner machine performance through smarter,data-driven maintenance practices.The combination of reduced unnecessary maintenance,proactive parts ordering,and fewer machine outag

60、es helped decrease maintenance costs for manufacturing customers by 50%.Data-driven maintenance practices helped decrease maintenance costs for manufacturing customers by 50%.13Trait#3Digital technology integrationManufacturers recognize the importance of digital technologies to their initiatives.Io

61、T sensors monitor plant production,energy consumption,inventory,and asset maintenance.Additive manufacturingalso known as 3D printingenables creation of bespoke parts and supports agile design changes.AI helps automate manufacturing production processes and improve quality control,while the growth o

62、f generative AI opens the door to even more advanced AI use cases(see Perspective,“Anticipating the boost from generative AI”on page 17).These technologies,when deployed in concert,propel innovation.The cloud enables that integration.Transformational Optimizers are integrating the cloud with enablin

63、g technologies to a greater extent than peers in all areas except AI,where Data-focused Deciders are likely capitalizing on their commitment to data(see Figure 5).14FIGURE 5Cloud platforms enable integration of digital technologies to spur innovation.IT Q.To what extent do your cloud platforms integ

64、rate with the following digital technologies in your manufacturing organization?Percentages show responses of 4 and 5 on a 5-point scale where 1=not at all and 5=to a very large extent.DE57%Robotic process automationAdditive manufacturing(3D)AIRobotsInternet of Things66%53%63%57%48%39%Edge computing

65、5G51%42%46%60%59%43%49%56%40%25%45%35%30%32%38%46%45%48%26%DE TOTOTOTOTODDTOTODE DDDDDDCOCOCOCOCOCOCODEDEDEDDDDDE55%DDTransformational Optimizers Data-focused Deciders Digital Enthusiasts Constrained Operators Percent that are integrating these digital technologies with cloud platforms15One technolo

66、gy that fuses the power of IoT and both traditional and generative AI for enormous potential benefits to the manufacturing industry is digital twins.Offering a virtual representation of a system across its lifecycle and updated from real-time data,digital twins use simulation,machine learning,and re

67、asoning to strengthen decision-making and drive efficiency,innovation,and competitiveness.11 Transformational Optimizers are using digital twins dramatically more than their peers(see Figure 6).FIGURE 6Leading manufacturers use digital twins to combine real-time simulation and controls.FIGURE 6Leadi

68、ng manufacturers use digital twins to combine real-time simulation and controls.Use of digital twins in manufacturing operationsProduction optimizationQuality managementPredictive maintenance56%62%60%42%47%38%25%48%38%33%49%23%Transformational Optimizers Data-focused Deciders Digital Enthusiasts Con

69、strained Operators Manufacturing Q.To what extent has your organization used digital twins in the following areas of your manufacturing operations?Percentages show responses of 4 and 5 on a 5-point scale where 1=not at all and 5=to a very large extent.16Similarly,Transformational Optimizers report h

70、eightened security readiness through the cloud(see Figure 7).They recognize that the combination of AI and the cloud is critical to defending against cyberthreats.As IT and OT become more intertwined,the OT network and connected OT devices are increasingly exposed to security risks,while remote acce

71、ss to OT networks by outside vendors further expands vulnerabil-ities.In fact,IBM X-Force reported that manufacturing continued to be the top attacked industry in 2022.12 FIGURE 7Transformational Optimizers are building cyber resilience with robust security practices.IT Q.To what extent has your man

72、ufacturing organization adopted the following security practices?Percentages show responses of 4 and 5 on a 5-point scale where 1=not at all and 5=to a very large extent.FIGURE 7Transformational Optimizers are building cyber resilience with robust security practices.Adoption of security practicesRob

73、ust OT/industrial control system(ICS)asset inventory is developedIncidents are managed with a unified AI automation across environmentsA mature OT or ICS patch management program is in place 63%62%58%47%39%44%49%35%43%40%29%26%Transformational Optimizers Data-focused Deciders Digital Enthusiasts Con

74、strained Operators 17PerspectiveAnticipating the boost from generative AI in manufacturingOur study reveals that manufacturing executives expect generative AI to improve manufacturing processes across a range of areas(see figure).Four significant pillars of impact include:Production quality and opti

75、mization.Generative AI systems can ingest a large amount of production data and proactively detect quality issues in production.The combination of IoT and generative AI can identify real-time anomalies and optimize production accordingly,ultimately improving overall equipment effectiveness.Sourcing

76、and procurement.Off the factory floor,generative AI can assist with vendor discovery and evaluation,pricing,supply chain risk assessment,and contracts.Predictive maintenance.With asset sensors continu-ously monitoring variables such as temperature,flow,and pressure,generative AI models can leverage

77、the data to recognize the normal operational behavior of equipment and then identify deviations to predict and rectify equipment issues.Product design and development.An array of alternatives for products,parts,components,and/or materials can be created by generative AI models.Using variables specif

78、ied by engineers such as cost and operational criteria,generative AI algorithms can help create entirely new,innovative designs.62%Quality management1Sourcing/procurement plans58%2Production optimization55%3Predictive maintenance52%4Identification,design,and development of products/parts/components/

79、materials50%5Operations where executives expect generative AI to have an impact Manufacturing Q.Where do you see generative AI impacting your manufacturing operations?Percentages show responses of 4 and 5 on a 5-point scale where 1=very low and 5=very high.18Doosan Digital Innovation protects invest

80、ment in digital transformation13Case studiesDoosan Digital Innovation(DDI)embraced the idea that an effective,comprehensive cybersecurity program should be the foundation of digital transfor-mation.To that end,the company identified and mapped appropriate roles and responsibilities of its staff work

81、ing within the security infrastructure.DDI also consolidated its regional security operation centers(SOCs)to a unified,global SOC that delivers 24x7 monitoring and protection.To control the operations of the global SOC,DDI updated its core security infrastructure.The team enhanced the companys proac

82、tive security incident and event management efforts,deploying technol-ogies to oversee endpoint detection and response and delivering AI-based automation that further streamlines threat responses.As a result,the company accelerated threat reactions,cutting approximately 85%from response times.SRAM d

83、rives innovation with next-generation manufacturing14To improve the cycling experience,SRAM,a bicycle component manufacturer,has embraced the use of new materials and advanced manufacturing techniques.Working with AWS and its partner Autodesk,SRAM is leveraging generative design,which is a form of A

84、I that uses cloud computing to speed time to design and time to market while optimizing performance.Using generative design tools,SRAM can now generate multiple concepts at the beginning of the project and then evaluate each to choose the ones most promising to be produced using additive manufacturi

85、ng(3D printing).This approach enabled them to produce a part that was twice as strong and 20%lighter in less time with fewer resources.19Trait#4New ways of workingTransformational Optimizers have radically changed how their organizations work by:Investing in digital and data skills Training their em

86、ployees in digital technologies Redefining the relationship between manufacturing and IT Establishing an operating model for their cloud operations.They outperform their peers in each area and gain the added benefit of making traditionally mundane factories more appealing to tech workers.Nurturing d

87、igital and technology skillsWhile each archetype is actively investing in technology skills,Transformational Optimizers are ahead in all areas(see Figure 8).They sense the urgency of having employees who can put intelligent automation,data,and digital technologies to work.Three in five say they are

88、training their employees with digital technologies and intelligent machines/devices,compared to less than half of the other archetypes.CODECloud deployment and migrationRobotic process automation developmentDevOpsData scienceCloud security65%61%61%47%55%42%33%53%30%39%42%48%39%30%44%34%TOTOTOTOTODED

89、DCOCOCOCODEDEDD45%DDDEDD43%DDTransformational Optimizers Data-focused Deciders Digital Enthusiasts Constrained Operators Percent that are investing in these skills to support digital initiativesFIGURE 8Manufacturing organizations are investing in their workforces to close the digital skills gap.IT Q

90、.To what extent has your organization invested in the following skills to support digital initiatives in manufacturing?Percentages show responses of 4 and 5 on a 5-point scale where 1=not at all and 5=to a very large extent.20Notably,all archetypes have significant room to improve their workforces d

91、ata science skills,which support optimization of products,simulation,and automation.Filling this need will become even more difficult as cross-industry demand for data scientists is projected to climb 36%from 2021 to 2031.15Creating synergy between manufacturing and ITEssential to implementing manuf

92、acturings OT priorities is a shared understanding between IT and manufacturing executives.About three in five manufacturing executives from each of the four archetypes agree that they collaborate effectively with their organizations CIO/CTO.Likewise,IT leaders agree that they work effectively with t

93、he Chief Manufacturing Officer/Head of Manufacturing.Where Transformational Optimizers differentiate themselves is the effectiveness of their manufacturing executives relationships with their Chief Information Security Officers(CISOs)and their IT executives relationships with manufacturing maintenan

94、ce managers.They report effective collaboration with CISOs and maintenance managers far more than peers.The CISO partnership is essential to provide control over technology and devices and help ensure a safe OT environment.Transformational Optimizers recognize that manufacturing transformation is a

95、team effort that requires synchronization across key activities;it cannot be successful at just a leader level.Establishing cloud operating modelsTransformational Optimizers embrace modernization of their operating models to empower new ways of working.As the bedrock for data-driven operations,cloud

96、 creates the opportunity for cultural change where teams are entrusted with decisions and collaborate more effectively(see Figure 9).For example,small teams assume responsibility for end-to-end operational tasks.Cross-functional collaboration supports infrastructure development and autonomous decisi

97、on-making about how best to deliver business outcomes.21FIGURE 9Cloud enables a culture of empowerment and collaboration.IT Q.To what extent are the following practices applied to your cloud operations?Percentages show responses of 4 and 5 on a 5-point scale where 1=not at all and 5=to a very large

98、extent.Cross-functional teams are responsible for maintaining and evolving infrastructureTeams are allowed autonomy to make decisions about the best way to deliver good outcomesTeams are incentivized to collaborate across business and technology functions by sharing costs and profitabilitySmall team

99、s are responsible for identified end-to-end operational tasks 68%38%31%70%52%56%64%52%34%40%37%30%62%39%TOTOTOTODEDDCOCOCODEDD47%DDDEDDCODE36%Transformational Optimizers Data-focused Deciders Digital Enthusiasts Constrained Operators Percent that are applying these practices to cloud operations22Geo

100、rgia-Pacific optimizes manufacturing production16Case studiesFor Georgia-Pacific,a wood products,pulp,and paper company,getting valuable manufacturing insights was a challenge because the organization relied on disparate sources to collect and analyze data on material quality,moisture content,temper

101、ature,machine calibration,and other features.The company chose to create a new analytics solution based in the AWS Cloud.Georgia-Pacific established a central data lake and streams real-time structured and unstructured data from manufacturing equipment to it for analysis.The solution enabled the com

102、pany to optimize key manufacturing processes in many of its facilities,helping to increase profits by millions,predict equipment failure 60-90 days in advance to reduce downtime,run more production lines in a predictable manner,and produce highest-quality products at the fastest possible rates.Ritta

103、l leverages a managed edge appliance for industrial analytics17For manufacturers,operational data is often trapped in traditional OT architectures.Rittal GmbH&Co.,a manufacturer of electrical and IT enclosures,is resolving the issue by adopting German Edge Clouds ONCITE,an open,industrial edge appli

104、ance powered by AI-based analytics and hybrid cloud.Deployed in the plant at the network edge to avoid latency,ONCITE includes a set of production optimization tools such as smart manufacturing operations management,a manufacturing execution system,an industrial IoT framework,and visual inspection.R

105、ittal uses ONSITE to manage 250 networked production machines that generate up to 18 terabytes of data each day.After combining real-time IoT data from factory stations with product information from the ERP system,ONCITE analyzes the data in near-real time.As a result,managers can quickly visualize

106、the status of production and gain insights into how to improve.23Trait#5 Business outcomes linked to cloudTo experience clouds deeper benefits as an enabler of exponential technologies,manufacturing leaders must purposefully pursue its value.That means building a business case with clear outcomes an

107、d adopting ongoing,disciplined cloud financial management,also known as FinOps.FinOps provides visibility into how and where cloud services are needed and being used,what they cost,and what business benefits they deliver.Transformational Optimizers demonstrate the importance of bringing the finance

108、function to the table(see Figure 10).They understand finance can facilitate three critical tasks:Creating a coherent financial justification for cloud investments Becoming a system of record for tracking cloud needs,usage,and cost Connecting cloud investments to quantifiable business outcomes.Transf

109、ormational Optimizers leverage real-time data to enable process measurement and reporting that yields insight into cloud benefits.With this knowledge of cloud costs and usage,organizations can better account for their cloud spending,helping avoid a legacy of“cloud waste”or overspending on cloudthat

110、plagues many organizations.1824FIGURE 10Manufacturing leaders quantify the value of cloud by collaborating with the finance function.IT Q.To what extent has your organization implemented the following to support cloud investments in manufacturing?Percentages show responses of 4 and 5 on a 5-point sc

111、ale where 1=not at all and 5=to a very large extent.FIGURE 10Manufacturing leaders quantify the value of cloud by collaborating with the finance function.Collaboration between manufacturing and financeFinance is intimately involved in the development of cloud business casesA cross-functional team(in

112、cluding finance,engineering,manufacturing,and IT)forecasts and allocates cloud costs and manages cloud budgetsReal-time data shows how and where cloud services are being used,what they cost,and the business benefits they deliver53%59%52%42%48%47%41%38%39%37%40%43%Transformational Optimizers Data-foc

113、used Deciders Digital Enthusiasts Constrained Operators 25Toyota creates a smarter,more digital factory19Case studyAt Toyota Indiana,the company is preparing its East plant for continuous-run operations where minimizing downtime and having zero defects is critical.To enable the next-gen maintenance

114、worker,the company has consolidated multiple IT tools supporting equipment maintenance into a common platform.It implemented a cloud-based enterprise asset management system that contextualizes and integrates a programmable logic controller(PLC),sensor,and existing manufac-turing data such as work o

115、rders,and uses AI to gain better insights.The solution allows a team member to see the health of the equipment and its components,monitor for abnormal activities,and use predictive solutions to shift maintenance work from reactive to proactive.A cloud-based enterprise asset management system plus AI

116、-powered analytics helps shift maintenance work from reactive to proactive.26 To help manufacturers move forward in their journey with cloud and advanced technologies and harness deeper value,weve put together a three-step plan.Action guide01Assess yourselfNext steps depend on your maturity in digit

117、al technologies and data.Your well-considered answers to the following questions help determine your current state and the organizational archetype with which you most closely align.Digital maturity Do you align cloud technologies to deliver business outcomes in manufacturing operations?Do you inves

118、t in machine learning/AI skills to support digital initiatives in manufacturing?Do you view digital technologies such as IoT,robotic process automation,additive manufacturing,AI,and computer vision as critical in advancing manufacturing objectives?Have you integrated cloud services with digital tech

119、nologies in your manufacturing organization?Data maturity Do you actively invest in data-driven architecture?Do you encourage your teams to experiment with available data?What is your maturity level in using a data mesh or data fabric?Do you invest in database(manage,store,access data)skills to supp

120、ort digital initiatives in manufacturing?2702Draft a blueprintRegardless of your starting point,you should address three core priorities to help boost cloud benefits.Charter a formal scope that defines the purpose and goals of your cloud-driven transformation efforts.Include the use cases to impleme

121、nt,noting that some are easier than others(for example,engineering shift to cloud is easier,overall equipment effectiveness OEE optimization is harder,supply chain orchestration is most difficult).Create an integrated technology strategy to support multiple use cases.Define the architectural require

122、ments and decisions and operational design of cloud solutions with total cost of ownership,return on investment,and business outcomes.Several business case tools exist to help accelerate the migration to cloud.Organize data into an“information architecture”that aligns with the various levels of manu

123、facturing systems.Action guideDesign and build internal workflows and processes around FinOps.Establish an organizational home for FinOps capabilities with executive buy-in.Develop governance through a responsibility assignment matrix with resources from finance,IT,engineering,and business.Define KP

124、Is to measure FinOps success.Identify cloud cost management tools to help monitor,measure,and control cloud spend,budgeting,forecasting,and chargebacks.This allows you to reflect variable costing,agile scenario planning,and incentives for common cloud objectives.Provide visibility into cloud benefit

125、s based on data by enabling process measurement and reporting.Determine cloud cost allocation.Build a data repository of cloud costs and usage.Maintain a cloud budget.28Action guide03Optimize your effortsThe unique structure of each organization dictates many possibilities.Weve constructed an impact

126、-focused guide for each of the four archetypes;even the leading operators can get better.Transformational Optimizers Capitalize on the cloud environment to continuously improve business outcomes and increase competitive advantage.Finalize the adoption of cloud applications to achieve full steady sta

127、te.Automate discovery,linking,semantic enrichment,and understanding of business-ready data.Leverage the robust data foundation to support high-priority initiatives,such as supply chain,materials optimization,and product quality.Implement capabilities to govern the lifecycle of AI models to identify

128、and eliminate drift and bias.Pursue the manufacturing quality resolution(cloud,IoT,AI)initiative.A manufacturing quality system should automatically enforce the use of approved materials.With deviations,reports are automatically generated,and traceability is in place.Closed-loop corrective and preve

129、ntive action tracking systems integrated with the quality system enable the identification and resolution of quality issues.Invest in the transportation optimization(cloud,IoT,AI,robots)operational initiative.Transportation management systems link with ERPs,monitor freight and fleet status and movem

130、ents,and track carbon emissions.Continuously challenge the“status quo”to drive innovation.Leverage process mining tools to identify improvement opportunities.Encourage experimentation across all teams.Add data and digital technology skills.Implement an organization model and enabling technologies to

131、 scale up initiatives across the enterprise.29Action guide03 Optimize your effortsData-focused Deciders Deploy advanced operational initiatives that require integration of data,security,and exponential technologies in the cloud.Accelerate adoption of a cloud foundation to expedite achievement of bus

132、iness outcomes.Implement a data lakehouse as a unified repository to support analytical and AI workloads.Adopt AI/analytics and automation technologies to support plant operators decision-making in complex manufacturing processes.Focus on supply orchestration(cloud,IoT,AI,edge).Data integration is r

133、equired for supply-and-demand visibility and planning.Control towers connect with IoT sensors,AI analytics,ERP,transportation management systems,and warehouse management systems.Use AI and automation technologies to redefine how work is done,aiming to optimize productivity.Add data,digital technolog

134、y,cloud security,and cloud deployment skills.Leverage AI to predict and optimize business performance with proactive actions.Digital Enthusiasts Use a cloud environment as the foundational enabler to achieve business outcomes.Leverage a cloud foundation to continue adoption of cloud technologies,aim

135、ing to achieve steady state in the near term.Establish a standardized data architecture,data commonality,and governance to engender trust in data.Deploy a data foundation at the enterprise and industrial edge,including data fabric to contextualize data from multiple sources and harness data as an as

136、set.Process real-time data to calculate KPIs and other leading indicators to anticipate and prevent problems.Implement initiatives to drive OEE improvement.Focus on supply chain orchestration.Empower front-line workers with the information they need to make better informed decisions.Add data,digital

137、 technology,and cloud deployment skills.Refine the operating model to include small teams to manage end-to-end cloud operations.Collect data to calculate and track KPIs that quantify business outcomes.Constrained Operators Define and implement a cloud strategy to facilitate achievement of desirable

138、business outcomes.Establish a cloud foundation to enable deployment of digital technologies from the industrial edge to public cloud.Establish a standardized data architecture,data commonality,and governance to engender trust in data.Establish IoT capabilities to capture real-time data from OT/facto

139、ries for enterprise applications.Integrate data,security,and exponential technologies to accelerate digital transformation.Automate security,privacy,and usage policies enforcement to reduce cyber risks to supervisory control and data acquisition(SCADA)and industrial control systems.Enhance employee

140、understanding of intelligent machines.Add data and digital technology skills.Define metrics and KPIs,baseline current operations,and define targets to achieve the desirable outcomes enabled by technologies deployed in the cloud.30Jos Favilla Director and Global Industry 4.0 Leader,Global Manufacturi

141、ng and Energy IndustriesIBM T HabibVice President,Global Energy and Natural Resources and Industrial Sector LeaderIBM C leads Industry 4.0 globally for IBM across all manufacturing-related industries including the definition of the strategy,offerings,partnerships,and go to market.He has over 35 year

142、s of experience helping global clients drive major business transformation programs.Wendy BauerVice President and General Manager,Manufacturing and Automotive Amazon Web S the global automotive and manufacturing organization,Wendy is responsible for supporting the worlds largest automotive original

143、equipment manufacturers(OEMs),suppliers,and cross-segment manufacturing companies to accelerate their digital transformation journeys while maximizing value creation.Wendy spent more than 20 years in the automotive industry in leadership roles across both OEMs and tier 1 suppliers.Her experience spa

144、ns sales,product strategy and business development,engineering,purchasing,and quality.Wendy has been recognized as a Software-Defined Vehicle Innovator Leader by MotorTrend(2023),as one of the top 100 Leading Women in the North America Auto Industry(2020),and a Rising StarOEM and Supplier(2016)by Au

145、tomotive News.Zahid is global industry leader for energy and resources,global industrial sector leader,and vice president in IBM Consulting.He is responsible for all industry solutions at scale and go-to-market strategies.He has more than 35 years of experience in management consulting,program manag

146、ement of capital projects,ERP transformation,AI and IoT solutions,trading systems implementations,business process transformation,and enterprise application integration.About the authors31Spencer Lin Global Research Leader,Chemicals,Petroleum,and Industrial ProductsIBM Institute for Business V a glo

147、bal research leader,Spencer is responsible for market insights,thought leadership development,competitive intelligence,and primary research on industry agendas and trends.He has more than 25 years of experience in financial management and strategy consulting.Noriko SuzukiGlobal Research Leader,Autom

148、otive,Electronics,Energy and Utilities IndustriesIBM Institute for Business V is responsible for developing thought leadership for the automotive,electronics,and energy industries.She has more than 20 years of experience working with global manufacturing customers on technology strategies and implem

149、entation.Her recent expertise includes Industry 4.0,digital transformation of operations,mobility solutions,and sustainable transportation.About the authorsScot WlodarczakHead of Industrial MarketingAmazon Web S manages AWSs manufacturing industry marketing efforts.He has over 25 years of experience

150、 in manufacturing operations with companies such as Cisco and Rockwell Automation.He focuses on marketing to industrial customers on their digital transformation journey and bridging the gap between IT and operations.32Study methodology and approachFor the study methodology section in the back matte

151、rIndustriesEnterprise size(annual revenues)Automotive(OEM/Supplier)16%16%Chemicals17%18%Downstream,Oil&Gas(Refining)ElectronicsIndustrial Machinery/Heavy Components16%16%Metals$250$500 M$500+M$1 B$1+B$5 B12.7%20.6%20.6%$5+B$20 B31.7%$20 B14.4%Note:Due to rounding,percentages total slightly below 100

152、%These executives come from different industries and organizations of diverse sizes.All data is self-reported.In cooperation with Oxford Economics,the IBM Institute for Business Value and AWS surveyed 1,171 manufacturing companies in 21 countries from June to July 2023.Two surveys were conducted wit

153、h each company as part of this effort.IT leadership:executives significantly involved in defining or implementing cloud computing strategies for the manufacturing area.We collected responses from Chief Information Officers,Chief Technology Officers,and heads of IT.Manufacturing leadership:executives

154、 significantly involved in defining or implementing their manufacturing organizations technologies.We collected responses from Chief Manufacturing Officers or equivalent,Vice Presidents/Directors of Manufacturing/Production,and Plant Managers.In terms of data analysis,we clustered organizations surv

155、eyed based on their capabilities in two dimensions:Digital maturity:alignment of cloud to deliver business outcomes in manufacturing operations,investment in machine learning/AI skills to support digital initiatives in manufacturing,importance of digital in advancing manufacturing objectives(compute

156、r vision),and integration of cloud platforms with digital technologies in manufacturing organization(computer vision).Data maturity:data-driven architecture,data mesh or data fabric,and database(manage,store,access data)skills investment to support digital initiatives in manufacturing.This yielded f

157、our distinct archetypes:Transformational Optimizers stand out in their capabilities across the two dimensions.Digital Enthusiasts place a relative focus on advancing their digital agenda and have extended capabilities in digital and data,albeit far less than the Transformational Optimizers.Data-focu

158、sed Deciders are far along with data capabilities but have not made enough progress on digital.Constrained Operators lag behind the other archetypes in digital and data.By comparing the performance and practices of the archetypes,we were able to identify the activities that distinguish each group.Th

159、ese findings help ascertain the pillars of progress needed for each archetype.33Related reportsThe CEO Global C-suite Study:CEO decision-making in the age of AICEO decision-making in the age of AI:Act with intention.IBM Institute for Business Value.June 2023.https:/ibm.co/c-suite-study-ceo Clouds ne

160、xt leapClouds next leap:How to create transformational business value for energy and resources.IBM Institute for Business Value.August 2022.https:/ibm.co/cloud-transformation-energy-resourcesManufacturing 4.0Manufacturing 4.0:From data to decisions.IBM Institute for Business Value.May 2022.https:/ib

161、m.co/manufacturing-4-0IBM Institute for Business ValueFor two decades,the IBM Institute for Business Value has served as the thought leadership think tank for IBM.What inspires us is producing research-backed,technology-informed strategic insights that help leaders make smarter business decisions.Fr

162、om our unique position at the intersection of business,technology,and society,we survey,interview,and engage with thousands of executives,consumers,and experts each year,synthesizing their perspectives into credible,inspiring,and actionable insights.To stay connected and informed,sign up to receive

163、IBVs email newsletter at can also find us on LinkedIn at https:/ibm.co/ibv-linkedin.The right partner for a changing worldAt IBM,we collaborate with our clients,bringing together business insight,advanced research,and technology to give them a distinct advantage in todays rapidly changing environmen

164、t.About AWSFor over 15 years,Amazon Web Services has been the worlds most comprehensive and broadly adopted cloud offering.Today,we serve millions of customers,from the fastest-growing startups to the largest enterprises,across a myriad of industries in practically every corner of the globe.Weve had

165、 the opportunity to help these customers grow their businesses through digital transformation efforts enabled by the cloud.In doing so,we have worked closely with the C-suite,providing a unique vantage point to see the diverse ways executives approach digital transformationthe distinct thought proce

166、sses across C-suite roles,their attitudes and priorities,obstacles to progress,and best practices that have resulted in the most success.About Research InsightsResearch Insights are fact-based strategic insights for business executives on critical public-and private-sector issues.They are based on f

167、indings from analysis of our own primary research studies.For more information,contact the IBM Institute for Business Value at .34Notes and sources1 Favilla,Jos,Spencer Lin,and Marcelo Svio.Manufacturing 4.0:From data to decisions.IBM Institute for Business Value.May 2022.https:/ibm.co/manufacturing

168、-4-02 Payraudeau,Jean-Stphane,Anthony Marshall,and Jacob Dencik.Unlock the business value of hybrid cloud:How the Virtual Enterprise drives revenue growth and innovation.IBM Institute for Business Value.July 2021.https:/ibm.co/hybrid-cloud-business-value3“What is Hybrid Cloud?”IBM website.Accessed N

169、ovember 10,2023.https:/ is Industry 4.0?”IBM website.Accessed August 22,2023.https:/ 4.0 architecture for manufacturing.”IBM website.Accessed August 22,2023.https:/ 2022 3rd Quarter Manufacturers Outlook Survey.National Association of Manufacturers.Accessed August 31,2023.https:/www.nam.org/2022-3rd

170、-quarter-manufacturers-outlook-survey/7“Volkswagen Group.”AWS website.Accessed August 7.2023.https:/ Ouyang,Christene.“Edge computing and hybrid cloud:scaling AI within manufacturing.”IBM Blog.August 3,2021.https:/ Weber,Austin.“The Big Data Dilemma.”Assembly Magazine.August 24,2021.https:/ semicond

171、uctor engineers conquer complexity with shop-floor analytics.”IBM website.October 2022.https:/ is a digital twin?”IBM website.Accessed August 23,2023.https:/ X-Force Threat Intelligence Index 2023.IBM Security.February 2023.https:/ cyberthreats demand new approaches.”IBM website.October 2022.https:/

172、 transforms cycling with Autodesk on AWS,delivering components that are 20 percent lighter and 2x stronger.”AWS website.Accessed November 9,2023.https:/ Occupational Outlook Handbook.US Bureau of Labor Statistics.Accessed August 31,2023.https:/www.bls.gov/ooh/math/data-scientists.htm16“Georgia-Pacif

173、ic Optimizes Processes,Saves Millions of Dollars Yearly Using AWS.”AWS website.Accessed November 10,2023.https:/ Kremer,Bernd and Andreas Zerfas.“Data is the engine:Powering a smart manufacturing edge appliance.”IBM Blog.May 18,2021.https:/ Flexera 2023 State of the Cloud.Flexera.Accessed March 14,2

174、023.https:/ “Manufacturing operations management with IBM Maximo Application Suite.”IBM website.Accessed July 28,2023.https:/ Copyright IBM Corporation 2023IBM Corporation New Orchard Road Armonk,NY 10504Produced in the United States of America|November 2023IBM,the IBM logo,and IBM X-Force are trade

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177、AR PURPOSE AND ANY WARRANTY OR CONDITION OF NON-INFRINGEMENT.IBM products are warranted according to the terms and conditions of the agreements under which they are provided.This report is intended for general guidance only.It is not intended to be a substitute for detailed research or the exercise

178、of professional judgment.IBM shall not be responsible for any loss whatsoever sustained by any organization or person who relies on this publication.The data used in this report may be derived from third-party sources and IBM does not independently verify,validate or audit such data.The results from the use of such data are provided on an“as is”basis and IBM makes no representations or warranties,express or implied.W4KDMG2QUSEN-02