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1、Supply chain networksin the age of generative AI:Turning promise into performanceThe annual Accenture Pulse of Change Index found that technology rose to the top of the list of business disruptors in 2023,catapulted by advances in generative AI.The technology is unique in its ability to impact the e

2、ntire value chain,reinventing every part of an organization and resetting the performance frontier.Based on our Technology Vision 2024,95 percent of executives agree that generative AI will compel their organization to modernize its technology architecture.The good news for supply chain leaders is t

3、his exciting revolution in machine learning is creating an array of possibilities for reinventing work in their domain too.In this paper,we present a comprehensive and forward-thinking explorationof the opportunities across the end-to-end supply chain.We see applicationsin everything from sourcing a

4、nd planning,through manufacturing and fulfillment,to aftersales and service.We also see significant value in cross-functional outcomes like supply chain sustainability,resilience,talent management,and customer-centricity.Can organizations realize this value today?We believe they can.But it means app

5、roaching generative AI not merely as just another technology implementation.Its an enterprise transformation,with implications for the way an organization thinks about its data,talent,and ways of working.Not to mention the critical importance of implementing generative AI responsibly and securely.By

6、 embracing this broader change,supply chain leaders can fully capitalize on the age of generative AI.And drive innovation across supply chain networks that deliver better outcomes for business,for people,and for the planet.Its an exciting journey.And its one I look forward to supporting our clients

7、on in the months and years ahead.Kris TimmermansGlobal Lead Supply Chain&Operations2Generative AI is booming.Since ChatGPT launched in late 2022 the technology has taken the world by storm.Across industries and business functions,companies are looking to explore the possibilities and capitalize on t

8、he transformative potential of the creative side of AI.The sheer number of possible applications has captured the attention of business leaders.Our research found that 97 percent of senior executives agree that generative AI foundation models (also known as Large Language Models or LLMs)will be tran

9、sformative for their company.And 100 percent anticipate changes to the workforce.2Why is this good news for supply chain leaders?LLM capabilities are not limited to coding,content creation or marketing.They also hold immense promise across the end-to-end supply chain network.Theres value to be gaine

10、d in everything from new product development,procurement and planning,manufacturing and logistics,to after sales and services.Accenture analysis suggests 43 percent of all working hours across the entire supply chain function will be impacted with generative AI either automating activities(29 percen

11、t)or significantly augmenting the work of human employees(14 percent).1 Given the sheer scale of the global supply chain workforce,the potential cumulative value for businesses is massive.3All C-suite leaders are grappling with fundamental questions.How much of the hype around generative AI is real?

12、Can its promise be turned into scalable solutions?Which use cases can deliver real value today?And how do leaders get the data and the organization ready to capitalize on the opportunity?Generative AI excels in language-related activities,as well explore in the following sections of this paper.Howev

13、er,supply chain leaders must also recognize that,while its incredibly powerful at what it does well,its not suited to every task.In particular,supply chain activities that are more focused on numerical processing or require greater levels of complex reasoning will see less direct impact.Its why we a

14、lso recommend viewing generative AI in its broader context as part of a continuum of automation capabilities that include traditional process automation and classical machine learning models,as well as LLMs.Theres broad consensus about generative AIs potential,and many organizations are actively exp

15、erimenting.But our Pulse of Change Quarterly survey suggests only one in three has so far made a significant investment.Theres real value on the table across the end-to-end supply chain.But reaping the benefits requires a profound shift in the way an organization thinks about creating value and gett

16、ing work done.It means approaching generative AI not merely as the latest in a long line of software implementations,but rather as an enterprise transformation,with a clear focus on end-to-end business capabilities and implications for areas like data,people,ways of working,processes and responsible

17、 adoption.Why?Our view?Success with generative AI means getting the data ready,getting people ready,and getting the enterprise ready4Generative AI-powered reinvention helps bridge the gap from the linear supply chains of the past to the truly interconnected,intelligent supply chain networks of the f

18、uture.Building on previous advances in supply chain management artificial intelligence,generative AI offers a range of new capabilities.56Contextual understanding.Supply chain managers can use generative AI to make better decisions based on contextualized insights from unstructured data sources that

19、 were previously inaccessible.Examples include improving forecasting by scanning huge numbers of public online sources to identify the root causes of future demand.Or embedding generative AI into a Supply Chain Control Tower to enhance the way users interact with data,improving explainability and tr

20、ust.Generative AI can also be combined with existing process automation to significantly streamline supply chain activities.Conversational capabilities.Supply chain workers can use generative AI to gain access to tailored insights and automations based on chatbot interactions in everyday language.Th

21、at might include asking a chatbot to help find a specific spare part and create a call-off or spot buy to a preferred supplier if its not available.Other applications include auto-generating documents like purchase orders,training and upskilling manufacturing workers and maintenance troubleshooting.

22、Content generation.Generative AI offers the promise of creating relevant,context-specific text,code,images or insights on demand and on an industrial scale.Today,applications in sourcing and procurement are the most robust,such as auto-generated vendor-specific insights(KPIs,market trends,demand for

23、ecasts)to support contract renewal negotiations with suppliers,as well as contextualized business operational performance metrics.Together with existing AI,machine learning models and workplace platforms,these capabilities will allow companies to optimize and elevate supply chain operations,solve pr

24、essing supply chain challenges,and,ultimately,ensure supply chain networks have a more positive impact on business,people and the planet.For Chief Supply Chain Officers(CSCOs),generative AIs promise extends all the way across supply chain network operations,from designing and planning through to aft

25、ersales and service.Accentures analysis indicates that,in total,a massive 58 percent of the 122 supply chain processes analyzed,can be reimagined.378Design and engineer In domains like model-based systems engineering,LLMs will increasingly augment and accelerate the work of designers.By tapping into

26、 historical data,generative AI solutions will quickly generate new designs and models,saving time and reducing repetitive effort,especially during design iterations.Packaging design is a good example.The need to consider multiple factors sustainability,ease of transport,durability,regulations,brandi

27、ng,and more eats into time and limited resources.At the same time,documenting and retrieving packaging information becomes increasingly difficult with large product portfolios.Generative AI can serve up multiple design concepts(in 2D or 3D)as well as proposing suitable packaging copy and marketing b

28、ased on summarized design information.Human co-workers can then review these concepts to ensure compliance with product and regulatory requirements.The applications for biopharmaceutical companies are particularly powerful.Terray Therapeutics is using generative AI to revolutionize small-molecule dr

29、ug discovery.Its COATI foundation model for chemistry translates chemical structures into numerical representations,allowing generative AI to design novel optimized molecules.4PlanMany CSCOs have already implemented advanced analytics solutions to augment and optimize supply chain planning activitie

30、s.However,the complexity of the insights these tools produce,and the need for specialist expertise in making them actionable,means they can often be challenging to use in practice.Generative AI promises to revolutionize access to insights,not only in supply chain planning but also in areas like netw

31、ork design optimization.Through simple-to-use interfaces,employees can query optimization recommendations in everyday language and receive explanations they can easily understand and action.This opens up critical insights to a much larger number of supply chain workers,while also improving trust in

32、data and accelerating speed to action for domain experts.At the same time,generative AI can be used to bring a broader set of unstructured data sources(such as market reports,news results and social media)into forecasting calculations.It also supports more collaborative and streamlined ways of worki

33、ng across sales and operations planning instantly summarizing meeting action points,comparing plans with actual outcomes,building dashboards of key metrics,even generating draft plans themselves.This will free up planners valuable time for more strategic tasks.9SourceToday,sourcing and procurement t

34、eams grapple with challenges stemming from inefficient,manual processes,diverse categories,and system integration issues.While teams often spend significant time on strategy alignment,sourcing,and data reconciliation,generative AI presents an opportunity to streamline operations,bridge information g

35、aps,and improve access to a broader array of data sources,enabling faster insights and simplified processes.It also opens up the possibility of hyper-automation,where different forms of automation including existing machine learning algorithms and process automations as well as generative AI are lin

36、ked together as part of an increasingly autonomous system at scale.This promises to free teams for more valuable work and enhance overall efficiency.Consider how retail giant Carrefour is using generative AI to enhance its internal purchasing processes.The company is developing a solution that will

37、help employees complete everyday tasks more quickly,including drafting invitations to tender and analyzing quotes.5 1011Imagine if every business user had an assistant buyer powered by generative AI.When they needed to buy something,the assistant could guide them to the right buying channel,support

38、any call off or spot buy,and,if needed,connect with a professional buyer to handle the purchase.Text-heavy activities like contract generation also stand to gain significantly.A generative AI solution can be applied to large volumes of unstructured procurement information,such as historical contract

39、s,procurement policies and product specifications,to identify common patterns and requirements.This allows it to instantly produce a first draft of a new contract,which teams can then review and enhance using their procurement expertise.What about RFP drafting?Fine-tuned on historical RFI,RFP and RF

40、Q information,generative AI can not only draft RFx documentation,but also review and compare the responses that are returned by suppliers.Upstream procurement activities like supplier discovery and category management also stand to gain from generative AIs ability to rapidly summarize a wide array o

41、f market intelligence insights.Accenture has built a smart sourcing and contracting tool using generative AI.It helps sourcing managers with supplier negotiations by analyzing business requirements,historical contracts,and bidding patterns to suggest suitable sourcing strategies.The tool also sugges

42、ts terms and conditions to help ensure best-in-class contracts result from negotiations.MakeIf companies can bring their IT data together with their operating and engineering data,generative AI will help them achieve a consistent level of quality and operational excellence in their manufacturing ope

43、rations,particularly in areas such as asset maintenance and empowering the workforce with actionable predictive insights.It can also offer new insights into product design and quality control.In plant management,for example,asset maintenance teams often grapple with complex processes and large volum

44、es of asset-specific documentation.Generative AI can be used to digest all this information and summarize it into a series of logical steps as part of a work order.It means expert know-how is unlocked and democratized across the workforce improving not only operational performance but also job satis

45、faction.What about maintenance planning?Many companies in heavy industries are moving towards risk-based inspections(RBI)to unlock value.But planning for these inspections,plus preventive maintenance and operator care routines,is still a manual,human-intensive and repetitive process.It needs highly

46、skilled field engineers to create planning documents,as well as subject matter experts to review them.However,generative AI can write precision maintenance job plans for equipment classes or specific equipment with high accuracy and completeness.That significantly reduces the time needed to create a

47、nd review key planning documents.12As data availability and trust improves,generative AI will also be increasingly applied to the wealth of insights in operational digital twins,expediting diagnoses and root cause analyses.And the combination of classical and generative AI offers the promise of sign

48、ificantly streamlining access to predictive maintenance insights,real-time data analysis and failure diagnostics by making the information more consumable through easy-to-use Q&A interfaces.Quality control and compliance,too,stand to gain.Even companies in heavily regulated industries like pharmaceu

49、ticals are exploring how generative AI can be applied to multiple data sources to identify irregularities in cold chain management and auto-populate compliance documentation for review by human experts.Generative AI can also draft technical publications with accurate content,significantly reducing a

50、uthoring efforts.In aerospace,for instance,it can accelerate the production of legally mandated technical documentation such as work/assembly/repair instructions,user manuals,warranty information and instructions for use(IFUs).FulfillToday,supply chain disruption is everywhere.Its forcing companies

51、to transform supply chain operations for greater resiliency,relevancy,and responsibility.Leaders are focused on improving forecasting while implementing Transportation Management Systems(TMS),Warehouse Management Systems(WMS)and Warehouse Automation/Robotics to drive up agility and efficiency.By lay

52、ering generative AI onto the broader data maturity and automation agenda,companies can achieve significant gains in fulfillment.That includes enhancing hyper-personalized customer experiences and extracting new revenue opportunities from insights based on large volumes of omnichannel data.Fulfillmen

53、t operators can also use generative AI to suggest ways to optimize transportation management and improve forecasting by considering a broader range of factors from unstructured information(such as weather forecasts and competitor activity).Consider how an LLM-powered import/export document generator

54、 could transform shipping and export processes.Generative AI can be applied to a comprehensive collection of multi-modal unstructured information,including historical internal records and governmental regulations,across various formats,including PDFs and tablets.Shipping and export documents can the

55、n be automatically populated for human experts to review and verify,reducing opportunities for error while saving time and manual effort.13ServiceThe goal of providing services rather than just products is becoming a reality for many companies.However,the service space is typically still highly frag

56、mented,with assets and resources distributed regionally and globally.Its also heavily reliant on coordination with other parts of the supply network.Not only that but executing a service-oriented strategy requires a far more proactive approach to forecasting and responding to individual customer nee

57、ds.Its why generative AI can have a game-changing impact.Its ability to scan vast amounts of information across a broader range of data sources including unstructured data that was previously difficult to process offers the promise of deeper insights.From geographic locations to weather conditions a

58、nd from customer lifestyles to individual usage patterns,these can be combined with classical AI techniques to enable truly one-to-one service experiences on a global scale.An example?Look at how Accenture helped one major automotive company use generative AI to enhance customer support.By creating

59、an intelligent incident resolution copilot to summarize incidents,detect known issues,recommend resolutions and compose customer responses,were helping customer support agents access contextualized information and resolve incidents faster.14Cross-functional value on the tableFor CSCOs,the generative

60、 AI era promises a wealth of additional benefits that cut across individual supply chain functions.SustainabilityCompanies are under pressure to increase their supply chain sustainability and report on their corporate responsibility commitments more accurately.However,with information dispersed acro

61、ss a multitude of sources and sustainability categories,teams are faced with an almost insurmountable challenge collecting and analyzing the data.The work is slow and requires intense manual effort from subject matter experts.Its no surprise,then,that 63 percent of CEOs say the lack of ESG data meas

62、urement across the value chain is a key challenge.6 But generative AI offers solutions.For example,we worked with one global pharmaceutical company to accelerate supply chain decarbonization efforts.The companys teams had spent years painstakingly compiling data on how many suppliers had science-bas

63、ed targets(SBTs).We built a generative AI solution capable of delivering near-instant insights by trawling through thousands of supplier websites.After one hour,the company had reliable intelligence confirming it had already exceeded its supplier SBT target.Generative AI has many other use cases in

64、sustainability,including generating prioritized decarbonization roadmaps for individual companies and enhancing Scope 3 emissions reporting.Today,for example,accurately matching company spending to emissions is time-consuming and laborious work.Accenture developed a generative AI solution able to si

65、ft through millions of lines of spend data,across multiple languages,and automatically map each line item to relevant emissions factors,which procurement teams can then review.A process that once took days can now be completed in minutes.16Intelligent ways of workingOne of generative AIs most revolu

66、tionary aspects is the way it lets people interact with unstructured data more easily and comprehensively.One way to think of it is as a“superpowered navigation system”for language-based activities,providing near-instant access to consumable data insights that help people accomplish tasks faster and

67、 more effectively.This will empower supply chain leaders and their teams to reinvent the way work gets done.For instance,generative AIs ability to shift unstructured data on a superhuman scale helps demand planning and supply chain resilience teams unlock insights into market trends and developments

68、.Examples include the rapid analysis of market data to understand and predict pricing changes of raw materials,understand consumer reaction to promotional activity,and connect the dots between global disruption events and supplier lead times.Accenture created a generative AI powered market watcher t

69、ool for commodities.Its designed to help business analysts at oil and gas companies as they make purchasing decisions.The tool ingests a broad range of both structured and unstructured data and outputs key metrics in numerical formats for further analysis,saving time and effort while also enriching

70、model outputs with expanded data sources.1718ResilienceWhen it comes to managing disruption in supply chains which has cost businesses$1.6 trillion in missed revenue opportunities over the last 2 years,according to recent research one of the key challenges for CSCOs is knowing who their n-tier suppl

71、iers are and assessing if theyre a potential source of risk and vulnerability.Understanding the full configuration of these supplier networks is a critical prerequisite for increasing supply chain resilience.Generative AI can support these efforts by augmenting existing AI-powered solutions that ana

72、lyze structured data(such as trading reports)with the analysis of much larger volumes of unstructured data(such as news sources,videos,chatting traffic,etc)to produce deeper insights into the supplier network.Procurement teams can also use generative AI chatbot interfaces to make those insights more

73、 accessible,helping them collaborate with suppliers to understand where priority risks exist and make more effective sourcing decisions.An example?Accenture built an N-tier Supply Chain Navigator powered by OpenAI GPT.It helps procurement managers analyze supplier network data by providing real-time

74、 insights,answering specific queries,and facilitating data-driven decision-making.Employees can quickly and easily query the tool to identify supply network vulnerabilities such as suppliers with geographic ties to conflict areas or locations experiencing natural disasters.Customer-centricityGenerat

75、ive AIs ability to provide accurate,easy-to-use chatbot interfaces has many applications in building a more customer-centric supply chain network.Take product design,for example.Generative AI can analyze a broad range of unstructured customer feedback,such as online product reviews and social media

76、sentiment,much faster.This can then be channeled back into product design workflows,allowing for rapid feedback loops between customer demand and product development.Companies can use LLMs in conjunction with classical AI to transform service-related call center experiences.Examples include predicti

77、ng customer intent and creating a tailored tone of voice especially important when handling complaints.LLMs can also be used to summarize calls,generate action points,and draft customer responses,freeing up employees to focus on bringing human creativity and empathy to customer actions where they ca

78、n add most value.Whats more,each new customer interaction serves as additional context for AI models,improving the relevance and quality of outputs and thus customer retention.Generative AI chatbots can also allow customers and employees to explore complex technical product documentation faster and

79、more easily.For example,Accenture developed a generative AI solution for managing technical documentation,such as product manuals and guides.It not only allows companies to draft these documents faster,but also then query and summarize them in plain language,meaning readers can find and consume the

80、information they need almost instantly.19Unlocking talentFor the first time in history,were embracing a generation of technology that is“human by design”.Generative AIs effectiveness hinges on human input to drive quality outputs whether thats something straightforward,like drafting an email,or comp

81、lex,like a financial forecast.These more human-centered processes will reinvent work across the entire value chain.By synthesizing data,comprehending natural language,and converting unstructured data into actionable insights,generative AI is democratizing business process redesign,empowering everyon

82、e from frontline workers to lab scientists to design professionals to reshape their own workflows and make language-based work faster and easier.Generative AI is also being used to produce tailored learning materials,to help onboard and upskill new team members.However,nearly half of organizations t

83、hat are leaders in reinvention recognize that processes across the value chain will require significant changes in order to realize the opportunity for generative AI to accelerate economic value,increase productivity and drive business growth,while also fostering more creative and meaningful work fo

84、r people.7CSCOs also see key challenges in sourcing and retaining skilled talent.For example,32 percent see talent scarcity,due to skill gaps or unawareness,as a major barrier in utilizing generative AI.And 36 percent believe workers will not fully embrace generative AI due to a lack of technologica

85、l understanding.8 However,most workers(82 percent)believe they do grasp the technology.And 94 percent are confident they can develop the needed skills.920How to get startedAs CSCOs embark on their generative AI transformations,there are several key success factors to bear in mind.22The good news?Gen

86、erative AI can itself be applied to an organizations data pipelines to accelerate digital maturity.Companies can use it to automatically synthesize and extract knowledge from their supply chain data,including dramatically simplifying and maximizing the use of unstructured data.This creates a circula

87、r pathway that uses LLMs to mine and process supply chain data,which can then be supplied to supply chain use cases,including those supported by generative AI itself.Companies are understandably cautious about supplying external generative AI solutions with business-critical manufacturing,purchasing

88、 and other supply chain information.Strict data retention and privacy policies and trustworthy security guardrails are therefore vital.CSCOs will need to weigh up the relative risks and rewards of using their proprietary data to enhance LLM outputs in each use case.Working with partners who can guar

89、antee data security and provide sandboxed generative AI solutions is one way of safeguarding data in supply chain implementations.Given the large amounts of data needed to customize and optimize LLMs,a mature enterprise data strategy is a critical prerequisite for a generative AI transformation.Thos

90、e with strong supply chain data capabilities have an important head start over their peers.However,many companies are still wrestling with the challenge of increasing their data and digital maturity across their supply chain networks.Now,theyll need to take this further by extending their data lifec

91、ycle management to include large volumes of unstructured mixed-modality data(meeting transcripts,technical documents,video,audio,images,and more),as well as prompt engineering pipelines and new“ModelOps”ways of working.23From potentially biased and harmful outcomes,to question marks over accuracy,“s

92、upply chain cannot hallucinate”and user trust,to security and data vulnerabilities,generative AI represents a unique shift in the business risk landscape.Thats why its essential to take a responsible approach to supply chain implementations from the very start.Employees,customers and supply chain pa

93、rtners all need to trust that any AI implementation is fair,secure and reliable.Accenture believes strongly in leading by example when it comes to responsibility.Its why weve been pioneering our responsible AI framework for the best part of a decade.Updated for generative AI and built on four key pi

94、llars principles and governance;risk,policy and control;technology;and people,culture and training our framework has been scaled to over 700,000 people in our organization worldwide.24While generative AI is not about replacing people or jobs,it will have an increasingly central role in day-to-day wo

95、rk.Accenture analysis indicates that,in seven of 15 supply chain network occupations including purchasing managers and buyers,production,planning and expediting clerks,industrial production managers,logisticians,and others more than half of all working hours will be impacted by the technology throug

96、h varying degrees of automation and augmentation.Its incumbent on both supply chain leaders and their workforces to understand and plan for this reinvention of work on two dimensions:which tasks can be automated or augmented,and which people need to be upskilled to make use of generative AI.By analy

97、zing these factors,companies can map out the different levels of impact on their people and develop the right upskilling programs.Work time distribution by occupation and potential LLMs impactOrdered by their employment levels in the US in 2022 Note:Estimates are based on Human+Machine identificatio

98、n of work tasks exposure to impact of generative AI.Source:Accenture Research based on US BLS May 2023 and O*Net.2526%35%25%29%9%37%32%57%30%36%32%30%41%31%19%14%19%10%9%7%6%21%15%19%10%24%24%34%22%18%8%8%35%25%6%9%47%18%29%38%32%26%13%47%5%51%39%29%37%78%48%10%22%17%12%20%12%58%0%20%40%60%80%100%He

99、avy and Tractor-Trailer Truck DriversShipping,Receiving,and Inventory ClerksFirst-Line Supervisors of Production andFirst-Line Supervisors of Transportation andInspectors,Testers,Sorters,Samplers,andDriver/Sales WorkersBuyers and Purchasing AgentsProduction,Planning,and Expediting ClerksIndustrial P

100、roduction ManagersTransportation,Storage,and Distribution ManagersLogisticiansCargo and Freight AgentsProcurement ClerksPurchasing ManagersWeighers,Measurers,Checkers,and Samplers,Higher potential for automationLower potential for automation or augmentationHigher potential of augmentationNon-languag

101、e tasksOur analysis finds that the roles for production,planning and expediting clerk and procurement clerk have the highest potential impact from generative AI 72 percent and 75 percent of their time respectively.This significant potential for transformation,however,does not necessarily equate to j

102、ob losses.Rather,it indicates that a considerable portion of their work could be augmented by generative AI technologies.For instance,34 percent of procurement clerks tasks could be augmented by generative AI this includes tasks such as evaluating the quality and accuracy of data and determining the

103、 value or price of goods and services.Embracing generative AI would allow these professionals to reallocate their time to more value-added activities,enhancing overall efficiency and productivity in their role.26To reinvent work in a way that drives innovation and enriches the employee experience,co

104、mpanies will not only need to upskill their people in core generative AI skills,but also develop other dimensions such as working with purpose,strengthening trust and supporting emotional,physical and financial health.Accenture research has found that companies that lead in driving reinvention are a

105、lso around twice as likely to be prioritizing the soft skills that are increasingly important to ensuring generative AI adoption and value.11Generative AI can itself be used to identify reinvention priorities for both people and processes.For example,applied to a range of unstructured internal and e

106、xternal information,it can help supply chain planners suggest trends,summarize requirements,understand cross-functional dependencies,capture the employee voice,and identify peoples pain points,sentiment and workplace challenges.Generative AI will help us ideate new ways of operating that are truly i

107、nnovative and dont simply recreate what weve done in the past 27More than ever,generative AI requires companies to build partnerships with the broader technology ecosystem.With every cloud hyperscaler and numerous supply chain platform vendors offering,or developing,their own generative AI solutions

108、,C-suite leaders face an overwhelming number of options.These decisions are intimately tied to the broader enterprise architecture,reinforcing the need for agility and flexibility in the digital core.Whether theyre using an LLM out of the box or fine-tuning it with their own enterprise data,companie

109、s need to understand the relative benefits and implementation complexities of each LLM solution for each different use case.CSCOs have several“no regrets”opportunities that can be realized from ready-made generative AI tools,such as those built into everyday workplace applications like Microsoft Exc

110、el.For example,were working with Microsoft to help organizations adopt and scale the disruptive power of generative AI in the supply chain.12 That includes extending Accentures logistics control tower solution to recognize unstructured data in news alerts,helping operators predict delays and mitigat

111、e disruption.28Weve also reimagined our SynOps for Supply Chain platform on AWS13 to help organizations improve supply chain resilience and customer-centricity.It includes a Supply Chain Digital Assistant that uses generative AI to enhance planning,identify supply chain risks,and augment logistics c

112、ontrol towers.Similarly,Googles key generative AI offerings include intelligent contract visibility and management for procurement organizations and a supply chain copilot for planners supporting custom insights and root cause analysis.More domain-specific solutions,such as those to support asset ma

113、nagement and capital project design,will likely require greater levels of in-house customization.The good news for CSCOs is that the technology is evolving rapidly,with new generative AI models and services continuously entering the market.Examples include Open AIs Assistants API designed to make it

114、 easier for developers to build their own assistive AI apps.14Across supply chain operations,companies should also be considering how generative AI will impact managed services partners,especially in areas like procurement.Increasingly,LLMs will become standard tools in the toolbox across all kinds

115、of managed services,offering significant gains in productivity and user experience.Help-desk functionality is a great example,where LLM-powered chatbots can dramatically enhance response times by fielding,routing and even resolving ever greater numbers of first-level user queries.29Generative AI in

116、supply chain is still largely uncharted territory that presents a huge amount of untapped potential.The challenge for supply chain leaders is to move beyond experimentation and start identifying and scaling up the most impactful use cases.By applying value-driven analytics,supply chain leaders can f

117、ully understand how people are working today and identify where and how generative AI can deliver both business value and better employee experiences.Learning from peers in the broader ecosystem is critically important.Experienced partners understand not only the rapidly evolving technology but also

118、 the unique requirements of supply chain operations.They can help turn promising ideas into scalable solutions that can deliver higher performance in day-to-day work.The combination of generative and traditional AI is opening up a new world of possibility for supply chain networks.Its promising to a

119、ccelerate time to insight and power the data-led decision making that drives greater supply chain sustainability,resilience,cost transformation and customer-centricity.Realizing the value at scale calls for generative AI to be fully integrated into a strong digital core as part of a deliberate strat

120、egy of reinvention spanning data,people,workflows,IT architectures,and responsible adoption.Supply chain leaders that recognize this can act quickly to capitalize on generative AIs rapid acceleration and turn its immense promise into tomorrows new performance frontiers.30Inge OosterhuisManaging Dire

121、ctor Talent and Organization,Industry X and Supply Chain&Operations LeadMark GeorgeManaging Director Supply Chain&Operations,Process Transformation LeadMatias Pollmann-LarsenManaging Director Supply Chain&Operations,Sustainability LeadMichel Van de VeegaeteManaging Director Supply Chain&Operations,P

122、lanning LeadPatricia CornetManaging Director Supply Chain&Operations,Fulfillment LeadRobert FuhrmannSenior Managing Director Supply Chain&Operations,Sourcing and Procurement LeadElsa SansSenior Manager Data&AI,Value StrategyVivek LuthraManaging Director Data and AI Lead,Growth MarketsKeyra MoralesMa

123、naging Director Operations,Supply Chain,Sourcing and Procurement LeadBrad PawlowskiManaging Director Supply Chain&Operations,Technology LeadKristine Renker Managing Director Supply Chain&Operations,Service LeadMaria leads a team of supply chain strategists and innovators driving sustainable growth f

124、or companies.She is passionate about integrating data and technology to improve customer experiences and build resilient,responsible,and transparent supply networks.Maria Rey-MarstonManaging Director Supply Chain&Operations,Innovation LeadJaime leads a global network of data scientists and AI specia

125、lists who help companies use advanced technologies to solve their most pressing supply chain challenges.He has extensive cross-industry experience helping global organizations transform supply chain networks.Jaime R.LagunasManaging Director Data and AI,Supply Chain&Operations LeadStephen MeyerPrinci

126、pal Director Supply Chain&Operations,Research LeadDeepak TantryManager Supply Chain&Operations,ResearchJonathan TipperPrincipal Director Industry X,Generative AI LeadReferences1.Accenture Research analysis based on US BLS May 2023 and O*Net2.Accenture Pulse of Change Quarterly survey,October 2023:ht

127、tps:/ analysis of the impact of gen AI on 122 processes across the Plan,Make,Deliver and Source domains4.Nvidia customer stories,A New Molecular Language for Generative AI in Small-molecule Drug Discovery:https:/ news release,Carrefour Integrates OpenAI Technologies and Launches a Generative AI-powe

128、red Shopping Experience,June 8,2023:https:/ 2021 United Nations Global CompactAccenture CEO Sustainability Study:https:/unglobalcompact.org/library/5976#:text=The%20United%20Nations%20Global%20Compact,to%20address%20the%20climate%20crisis7.Accenture Total Enterprise Reinvention survey,Oct-Nov,20238.

129、Accenture Pulse of Change Quarterly survey,October 2023:https:/ in the age of generative AI,January 2023:https:/ Research analysis based on US BLS May 2023 and O*Net11.Accenture Total Enterprise Reinvention survey,Oct-Nov,202312.Accenture news release,Accenture and Microsoft Expand Collaboration to

130、Help Organizations Accelerate Responsible Adoption of Generative AI,June 21,2023:https:/ Partner Network Blog,How Accenture Reimagines Supply Chain Operations Using SynOps for Supply Chain on AWS,September 25,2023:https:/ blog,New models and developer products announced at DevDay,November 6,2023:htt

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