世界经济论坛：释放制造业中人工智能的价值.pdf

Unlocking Value from Artificial Intelligence in ManufacturingW H I T E P A P E RD E C E M B E R 2 0 2 2In collaboration with MEXT Technology CenterContentsForewordExecutive summaryIntroduction1 Unlocking value in manufacturing through AI2 Shedding light on common barriers to industrial AI adoption3 A collection of AI applications in manufacturing4 A step-by-step approach to implementing scalable industrial AI applicationsConclusionContributorsEndnotes345681117212225Cover:Jian Fan,Getty Images Inside:Getty Images 2022 World Economic Forum.All rights reserved.No part of this publication may be reproduced or transmitted in any form or by any means,including photocopying and recording,or by any information storage and retrieval system.DisclaimerThis document is published by the World Economic Forum as a contribution to a project,insight area or interaction.The findings,interpretations and conclusions expressed herein are a result of a collaborative process facilitated and endorsed by the World Economic Forum but whose results do not necessarily represent the views of the World Economic Forum,nor the entirety of its Members,Partners or other stakeholders.Unlocking Value from Artificial Intelligence in Manufacturing2ForewordTrkiye has established itself as a key global player in advanced manufacturing and aims to boost its position through Fourth Industrial Revolution technologies.In recent decades,the country has made significant efforts to position itself as a global innovation hub,excelling in developing state-of-the-art technologies in ground-breaking companies in various fields.Artificial intelligence(AI)technology applications are part of this effort.In principle,AI could unlock more than$13 trillion in the global economy and boost GDP by 2%per year.1 However,companies struggle to tap into the value that AI applications can create.This paper seeks to uncover the hidden potential of AI in the manufacturing sector and the respective end-to-end systems by providing practical use cases and critical enablers to help harness its potential.Coupled with the energy crisis and material shortages facing the world,manufacturing players need to go beyond traditional operating methods to drive efficiency and sustainability.The twin challenges of technological progress and socio-political distress call for new forms of cooperation that respond to heightened demand for localization while recognizing the drivers of connectivity that shape global impact.Acknowledging this,the Centre for the Fourth Industrial Revolution in Trkiye mandated by the Ministry of Industry and Technology and established by the Turkish Employers Association of Metal Industries(MESS)joined the World Economic Forums Centre for the Fourth Industrial Revolution Network,the foremost platform helping leaders anticipate emerging technologies and drive their inclusive and sustainable adoption.The network links on-the-ground experience and action with global network-based collaboration,learning and scaling.This white paper is an output of the ongoing partnership between the Forums Platform for Shaping the Future of Advanced Manufacturing and Value Chains and Platform for Shaping the Future of Technology Governance:Artificial Intelligence and Machine Learning,the Centre for the Fourth Industrial Revolution Affiliate in Trkiye and MESS.It highlights case studies from organizations on the impact,feasibility and scalability of AI in manufacturing.It identifies several opportunities and lessons from the community on how to increase operational efficiency,sustainability and workforce engagement in manufacturing and value chains by using AI.We hope this report will provide decision-makers with a better understanding of how to unlock the untapped potential of industrial artificial intelligence(AI).We look forward to collaborating with you to deploy these technologies responsibly.Unlocking Value from Artificial Intelligence in ManufacturingDecember 2022zgr Burak Akkol Chairman,Turkish Employers Association of Metal IndustriesJeremy Jurgens Managing Director,World Economic ForumUnlocking Value from Artificial Intelligence in Manufacturing3Executive summaryRecent global developments and an ever-growing list of shocks and disruptions have put further strain on already shaken global value chains.The complexity of current challenges impacting manufacturing and value chains calls for the need to go beyond the traditional means of driving productivity to uncover the next wave of value for businesses,the workforce and the environment.Artificial intelligence(AI)is a crucial enabler of industry transformation,opening new ways to address business problems and unlock innovation while driving operational performance,sustainability and inclusion.Even though the impact of AI applications on manufacturing processes is known,the full opportunity from their deployment is still to be uncovered due to a number of organizational and technical roadblocks.Recognizing this need,the Centre for the Fourth Industrial Revolution Trkiye,together with the World Economic Forums Platform for Shaping the Future of Advanced Manufacturing and Value Chains and Platform for Shaping the Future of Technology Governance:Artificial Intelligence and Machine Learning,convened industry,technology and academic experts to shed light on these challenges and propose a step-by-step approach to overcome them.The consultations revealed six main challenges hindering the adoption and scaling of AI applications in manufacturing:1.A mismatch between AI capabilities and operational needs2.The absence of a strategic approach and leadership communication3.Insufficient skills at the intersection of AI and operations4.Data availability and the absence of a data governance structure5.A lack of explainable AI models in manufacturing6.Significant customization efforts across manufacturing use casesThe consultations show that leading manufacturers have successfully overcome the challenges mentioned above,implementing a variety of AI applications and achieving a positive impact on operational performance,sustainability and workforce engagement,mainly in six areas:health and safety,quality,maintenance,production processes,the supply chain,and energy management.While opportunities enabled by AI in manufacturing are promising and attracting many leaders,organizations are looking for a common framework that outlines how to implement AI solutions and ensure a successful return on investment.Based on the consultations,this white paper presents one step-by-step process as an example of how it is possible to overcome barriers,using the AI Navigator2 developed by the INC Invention Center as a reference:Phase 0:Initiation to build the fundamentals strategy,data and workforcePhase 1:Ideation to identify potential use cases and conduct a pre-selectionPhase 2:Assessment to select use cases and identify priorities via gap analysisPhase 3:Feasibility to complete all required tests and studiesPhase 4:Implementation,which requires iteration and piloting using agile project managementMoving forward,the World Economic Forum and the Centre for the Fourth Industrial Revolution Trkiye will continue to work closely with stakeholders in the Centre for the Fourth Industrial Revolution Network and across industries to accelerate the journey to capture value from AI in manufacturing globally.It will offer the Turkish Employers Association of Metal Industries(MESS)Technology Centre as a unique testing and collaboration system for businesses to pilot new AI applications and foster a collaborative approach among a diverse group of stakeholders to ensure the right AI capabilities are built in manufacturing and rolled out worldwide.Unlocking Value from Artificial Intelligence in Manufacturing4IntroductionCompanies across value chains are now facing an energy crisis and material and key component shortages,even as they are still recovering from and adapting to COVID-19 impacts.The complexity of the challenges impacting operations calls for the need to go beyond the traditional means of driving productivity to uncover the next wave of value and address sustainability and workforce challenges.Artificial intelligence(AI)can enable a new era in the digital transformation journey,offering tremendous potential to transform industries to gain greater efficiency,sustainability and workforce engagement by generating new insights from large amounts of data.However,despite this promising value creation potential,the deployment of AI in manufacturing and value chains is still below expected levels.Based on a global survey conducted over the last four years of more than 3,000 companies across industries and geographies,a growing number of companies recognize the business imperative to improve their AI competencies:70%of respondents understand how AI can generate business value 59%have an AI strategy in place 57%affirm that their companies are piloting or deploying AI.Despite these trends,only 1 in 10 companies believe they generate significant financial benefits with AI.3 While manufacturers acknowledge the importance and urgency of embedding AI in their processes and while leading companies have already internalized it in their business processes,many are becoming disillusioned with their efforts to capture value from it and lag in developing the right AI capabilities.Understanding the purpose and role of AI is key to solving manufacturing challenges.With a problem-oriented approach,AI efforts can be linked to clear business targets,giving business units and business functions a joint interest in making the transformation successful.4This white paper sheds light on the benefits that can be achieved through industrial AI and the successful AI applications implemented across industries,lessons learned and tangible impacts.Consultations conducted with the multistakeholder initiative community find that industrial AI helps people work in a smarter,safer and more efficient way.However,to unlock its full potential,companies require an understanding of current barriers to adoption and a structured approach to overcome them.Therefore,this paper also presents one example of a step-by-step guide to successfully implementing scalable industrial AI use cases.Unlocking Value from Artificial Intelligence in Manufacturing5Unlocking value in manufacturing through AI1The artificial intelligence(AI)revolution allows the conversion of large amounts of data into actionable insights and predictions that can provide impetus to data-driven processes.Manufacturing companies capture value from AI using different mechanisms,the most common being eliminating redundant work,solving existing problems and revealing hidden value by analysing and recognizing patterns in data.AI is applied to augment tasks such as classification,continuous estimation,clustering,optimization,anomaly detection,rankings,recommendations and data generation to solve industrial problems.5 Consultations with senior executives from the World Economic Forums Platform for Shaping the Future of Advanced Manufacturing and Value Chains and Platform for Shaping the Future of Technology Governance:Artificial Intelligence and Machine Learning,as well as members and partners of the Centre for the Fourth Industrial Revolution Trkiye,find that AI can help drive a step-change in manufacturing,yielding significant benefits in three categories(figure 1):Operational performance by automating and optimizing routine processes and tasks,increasing productivity and operational efficiencies,improving quality(e.g.reducing defects,forecasting unwanted failures)and optimizing production parameters Sustainability by optimizing material and energy usage,increasing energy efficiencies,reducing scrap rates and extending machine lifespans Workforce augmentation by guiding the decision-making process and parameter setting,enhancing the accuracy of predictions and forecasting,reducing repetitive tasks and increasing human-robot interactionsAI applications in manufacturing help increase operational performance,drive the sustainability agenda and empower the workforce.Unlocking Value from Artificial Intelligence in Manufacturing6Performance(e.g.yield optimization)Throughput(e.g.fewer unwanted breakdowns,decreased lead time)Quality(e.g.fewer process defects and failure rates)Business uptime(e.g.productive time and capacity)Operational performanceDecision-making and planning supportCollaborationPrediction and forecasting accuracyTask automationWorkforce augmentationRisk(e.g.feedback mechanism to avoid incidents and alarms)Material efficiencyEnergy efficiency(e.g.energy savings and thermal efficiency)Machine lifetimeScrap rate and used materialSustainabilityDimensions of value creation with AI in manufacturingFIGURE 1Unlocking Value from Artificial Intelligence in Manufacturing7Shedding light on common barriers to industrial AI adoption2Implementing AI solutions requires continuous project management efforts,expectation management andthe necessary resources.Despite this potential,companies have not yet fully realized the vision of AI-powered manufacturing systems.To unlock the untapped value of industrial AI,pinpointing the source of a companys struggles and defining the roadblocks open a new path to think through and derive the right solutions to overcome them.As the barriers to AI adoption stem mainly from organizational,strategic and technical components,understanding them will help identify a pathway to implement scalable AI applications.Consultations with the community of over 35 senior operations executives,technology experts and academics have identified six challenges hindering the adoption of AI in manufacturing and value chains(figure 2).Barriers to AI adoption in manufacturingFIGURE 2Mismatch between AI capabilities and operational needsAbsence of a strategic approach and leadership communicationInsufficient skills at the intersection of AI and operationsData availability and absence of a data governance structureLack of explainable AI models in manufacturingSignificant customization efforts across manufacturing use casesUnlocking Value from Artificial Intelligence in Manufacturing8Manufacturers have often selected AI projects based on existing technical capabilities instead of focusing on the impact on business operations.The match between business pain points and AI technologies is not always thoroughly considered.Therefore,AI solutions may be technically feasible but fail to solve a relevant,impactful problem in operations.This causes a mismatch of expectations and hinders their wider adoption in manufacturing.Building a solid business case with a problem-oriented approach that clearly defines business needs and evaluating the value of an AI solution compared to alternative solutions are the first steps in overcoming that barrier to adoption and scale.Mismatch between AI capabilities and operational needsA clear company-wide AI strategy and communication pl