Generative AI in Fulfillment & Logistics Market Research Report Forecast Till 2032
풀필먼트 & 물류용 생성 AI 시장 규모는 예측 기간 중 43.6%의 CAGR로 성장할 것으로 예측되고 있습니다.
물류 산업에서 생성형 AI의 이점이 증가하고 엔드 투 엔드 공급망 최적화에서 생성형 AI의 잠재력이 증가함에 따라 생성형 AI가 풀필먼트 & 물류 시장의 발전을 촉진하는 주요 시장 성장 촉진요인으로 작용하고 있습니다.
자동차 산업은 생성형 AI에 의해 재편되고 있으며, 자동차에 지능을 주입하여 개인의 취향과 요구에 따라 맞춤형 운전 경험을 창출하고 있습니다. 이러한 자동차는 외관, 디스플레이 및 제어를 변경할 수 있으며 사용자의 선택에 따라 완전히 사용자 정의 가능한 운전 경험을 제공합니다. 컴퓨터 비전 및 LiDAR와 같은 기술을 통해 생성된 AI는 차량이 주변 상황을 이해하도록 돕습니다. 또한 AI 시스템은 주변의 상세한 3D 지도를 생성하여 차량이 장애물, 보행자 및 다른 차량을 보다 정확하게 발견할 수 있도록 돕습니다. 또한 생성형 AI는 자율주행차가 실시간 의사결정을 내릴 수 있도록 돕습니다. 이러한 시스템은 다양한 운전 상황을 시뮬레이션하고 속도를 줄이거나, 속도를 높이거나, 다른 차선으로 이동하는 등 안전한 경로를 보장하기 위해 이상적인 응답을 할 수 있습니다.
지역별 인사이트
북미 시장은 2022년 54.0%의 점유율을 보였습니다. 이 지역의 풀필먼트 & 물류용 생성 AI 산업은 규모가 크고, 풀필먼트 및 물류 분야에서 생성형 AI의 인지도가 높아지고 있습니다. 기업이 자재 취급 및 운송 작업의 효율성, 생산성, 안전성을 향상시키기 위해 생성형 AI를 활용함에 따라 향후 수년간 크게 성장할 것으로 예상됩니다.
세계의 풀필먼트 & 물류용 생성 AI 시장을 조사했으며, 시장의 정의와 개요, 시장 성장에 대한 영향요인 및 시장 기회의 분석, 시장 규모의 추이·예측, 각종 구분·지역별 내역, 경쟁 환경, 주요 기업의 개요 등을 정리하여 전해드립니다.
목차
제1장 주요 요약
제2장 시장 개요
제3장 조사 방법
제4장 시장 역학
- 촉진요인
- 물류 업계에서 생성 AI의 이점 확대
- 엔드 투 엔드 공급망 최적화에서 생성 AI의 잠재 능력 확대
- AI와 기계학습(ML)의 급속한 기술 진보
- 억제요인
- 고액의 초기 투자가 필요
- 데이터 보안과 프라이버시 리스크
- 법률 및 규제 준수 문제
- 기회
- E-Commerce의 급증과 고객 기대의 증가
- 물류 기업 전체에서 최적화와 자동화에 대한 요구의 증가
- 물류 업무 전체에서 예지보전(PDM)의 수요
- 과제
- 도입과 확장성
- 모델 트레이닝과 최적화
- 해석 가능성과 설명 가능성
- 실시간 적응과 동적 환경
- 동향
- 자율주행차와 로봇
- 스마트 계약과 블록체인 기술
- 퍼스널라이즈 고객 경험
- 실시간 데이터 분석과 의사결정
- AI 구동형 시뮬레이션과 모델링
- 로봇공학과 자동화
제5장 시장 요인 분석
- 밸류체인 분석
- Porter's Five Forces 모델
- SWOT 분석
- PEST 분석
- 사용 사례 분석
- 규제 상황 분석
- 물류와 공급망에서 AI의 역할
- 투자수익률(ROI)을 고려한 AI의 활용
- 식별형 AI와 생성형 AI가 공급망에 미치는 영향
제6장 풀필먼트 & 물류용 생성 AI 시장 : 제공별
제7장 풀필먼트 & 물류용 생성 AI 시장 : 유형별
- VAE(VARIATIONAL AUTOENCODER)
- GAN(GENERATIVE ADVERSARIAL NETWORKS)
- RNN(RECURRENT NEURAL NETWORKS)
- 장단기 기억(LSTM) 네트워크
제8장 풀필먼트 & 물류용 생성 AI 시장 : 용도별
- 창고 운영, 최적화, 관리
- 공급망 오퍼레이션
- 수요 예측
- 운송과 루트 최적화
- 그린 공급망
- 생산계획/스케줄
- 위기 관리
- 공급업체 관리
- 조달
- 계약 분석
- 기타
- 예측 정비
- 물류 네트워크 설계
- 재고 관리
- 부정 탐지
- 고객 서비스 업무
- 자율형 로봇
- 데이터 분석과 리포트
- 기타
제9장 풀필먼트 & 물류용 생성 AI 시장 : 산업별
- 개요
- 자동차
- 의약품·헬스케어
- 반도체·일렉트로닉스
- 소매·E-Commerce
- 식품 및 음료
- 기타
제10장 풀필먼트 & 물류용 생성 AI 시장 : 지역별
- 개요
- 북미
- 유럽
- 아시아태평양
- 중동 및 아프리카
- 남미
제11장 경쟁 구도
- 개요
- 경쟁 벤치마킹
- 시장 점유율 분석
- 주요 개발
제12장 기업 개요
- MICROSOFT CORPORATION
- SECONDMIND
- WAREDOCK ESTONIA LLC
- LEEWAYHERTZ
- SAP SE
- DHL GROUP
- C3.AI, INC.
- BLUE YONDER
- XENONSTACK.AI
- OSA COMMERCE
- DAT SOLUTIONS LLC
- IBM CORPORATION
- ORACLE CORPORATION
- ITREX GROUP
- ACCENTURE
KSA
Market Overview
Generative AI in Fulfillment & Logistics Market is anticipated to register a CAGR of 43.6% during the review period. Growing advantages of generative AI for logistics industry and growing capability of generative AI across start to finish production network advancement are the key market drivers boosting the development of the Generative AI in Fulfillment and Logistics market.
The car climate is being reshaped by generative AI, which is instilling intelligence in vehicles and producing customized driving encounters that adjust to individual preferences and requests. The time of unbending and normalized interfaces is finished; these vehicles may now change their looks, shows, and controls, providing a completely customisable driving experience that consistently coordinates with the client's decisions. Through innovation, for example, PC vision and LiDAR, generative AI upholds vehicles in comprehending their surroundings. AI frameworks can create detailed 3D guides of the surroundings, allowing vehicles to detect impediments, walkers, and different vehicles with more noteworthy exactness. Generative AI helps independent vehicles in making ongoing choices. These frameworks might mimic various driving conditions and make ideal responses, for example, whether to slow down, speed up, or move to another lane, to guarantee safe route.
Market Segmentation
Based on offering, the Generative AI in Fulfillment & Logistics Market is bifurcated into Solution and Services. Based on type, the market is categorized into Variational Autoencoder (VAE), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) Networks.
Based on application, the Generative AI in Fulfillment & Logistics Market is classified into Warehouse Operations, Optimization and Management, Supply Chain Operations, Predictive Maintenance, Logistics Network Design, Inventory Management, Fraud Detection, Customer Service Operations, Autonomous Robotics, Data Analytics & Reporting, and Others.
Based on industrial vertical, the Market is categorized into Automotive, Pharmaceutical & Healthcare, Semiconductors & Electronics, Retail & E-Commerce, Food & Beverages, and Others.
Regional Insights
The North America Generative AI in Fulfillment and Logistics market represented ~54.0% in 2022. the region's enormous Generative AI in Fulfillment and Logistics industries, as well as the growing notoriety of Generative AI in Fulfillment and Logistics. The utilization of Generative AI in Fulfillment and Logistics is supposed to fill fundamentally in the coming a very long time as organizations try to further develop effectiveness, efficiency, and wellbeing in their material handling and transportation tasks.
The US, Canada, and Mexico make up the three nation portions that contain the North American market for generative AI in fulfillment and logistics. Using man-made consciousness to deliver original material and arrangements is known as "generative AI. With regards to fulfillment and logistics in the US, generative AI can be utilized in multiple ways.
The European Generative AI in Fulfillment and Logistics market has been partitioned, by country, into Germany, the UK, France, Spain, Italy, Nordics, Balkans, and the rest of Europe. Generative AI has arisen as a vital device in optimizing fulfillment and logistics in Europe. The innovation can help robotize and streamline processes, decrease human mistake, and work on generally functional effectiveness.
Major Players
The major players in the market are Microsoft Corporation, Secondmind, Waredock Estonia LLC, SAP SE, Blue Yonder, OSA Commerce, DAT Solutions LLC, IBM Corporation, Oracle Corporation, ITRex Group, Accenture, LEEWAYHERTZ, and DHL Group Industries Corporation.
TABLE OF CONTENTS
1 EXECUTIVE SUMMARY
2 MARKET INTRODUCTION
- 2.1 DEFINITION
- 2.2 SCOPE OF THE STUDY
- 2.3 RESEARCH OBJECTIVE
- 2.4 MARKET STRUCTURE
3 RESEARCH METHODOLOGY
- 3.1 OVERVIEW
- 3.2 DATA FLOW
- 3.2.1 DATA MINING PROCESS
- 3.3 PURCHASED DATABASE:
- 3.4 SECONDARY SOURCES:
- 3.4.1 SECONDARY RESEARCH DATA FLOW:
- 3.5 PRIMARY RESEARCH:
- 3.5.1 PRIMARY RESEARCH DATA FLOW:
- 3.5.2 PRIMARY RESEARCH: NUMBER OF INTERVIEWS CONDUCTED
- 3.5.3 PRIMARY RESEARCH: REGIONAL COVERAGE
- 3.6 APPROACHES FOR MARKET SIZE ESTIMATION:
- 3.6.1 REVENUE ANALYSIS APPROACH
- 3.7 DATA FORECASTING
- 3.7.1 DATA FORECASTING TECHNIQUE
- 3.8 DATA MODELING
- 3.8.1 MICROECONOMIC FACTOR ANALYSIS:
- 3.8.2 DATA MODELING:
- 3.9 TEAMS AND ANALYST CONTRIBUTION
4 MARKET DYNAMICS
- 4.1 INTRODUCTION
- 4.2 DRIVERS
- 4.2.1 GROWING BENEFITS OF GENERATIVE AI FOR LOGISTICS INDUSTRY
- 4.2.2 GROWING POTENTIAL OF GENERATIVE AI ACROSS END-TO-END SUPPLY CHAIN OPTIMIZATION
- 4.2.3 RAPID TECHNOLOGICAL ADVANCEMENTS INTO ARTIFICIAL INTELLIGENCE (AI) AND MACHINE LEARNING (ML)
- 4.3 RESTRAINTS
- 4.3.1 HIGH INITIAL INVESTMENTS REQUIRED
- 4.3.2 DATA SECURITY AND PRIVACY RISKS
- 4.3.3 LEGAL AND REGULATORY COMPLIANCE ISSUES
- 4.4 OPPORTUNITY
- 4.4.1 SURGE IN E-COMMERCE AND RISING CUSTOMER EXPECTATIONS
- 4.4.2 GROWING NEED FOR OPTIMIZATION AND AUTOMATION ACROSS LOGISTIC COMPANIES
- 4.4.3 DEMAND FOR PREDICTIVE MAINTENANCE (PDM) ACROSS LOGISTICS OPERATIONS
- 4.5 CHALLENGES
- 4.5.1 DEPLOYMENT AND SCALABILITY
- 4.5.2 MODEL TRAINING AND OPTIMIZATION
- 4.5.3 INTERPRETABILITY AND EXPLAINABILITY
- 4.5.4 REAL-TIME ADAPTATION AND DYNAMIC ENVIRONMENTS
- 4.6 TRENDS
- 4.6.1 AUTONOMOUS VEHICLES AND ROBOTICS
- 4.6.2 SMART CONTRACTS AND BLOCKCHAIN TECHNOLOGY
- 4.6.3 PERSONALIZED CUSTOMER EXPERIENCES
- 4.6.4 REAL-TIME DATA ANALYSIS AND DECISION-MAKING
- 4.6.5 AI-DRIVEN SIMULATION AND MODELING
- 4.6.6 ROBOTICS AND AUTOMATION
5 MARKET FACTOR ANALYSIS
- 5.1 VALUE CHAIN ANALYSIS
- 5.1.1 DATA SOURCES
- 5.1.2 AI DEVELOPMENT AND RESEARCH
- 5.1.3 SOLUTION PROVIDERS
- 5.1.4 END USER
- 5.2 PORTER'S FIVE FORCES MODEL
- 5.2.1 THREAT OF NEW ENTRANTS
- 5.2.2 BARGAINING POWER OF SUPPLIERS
- 5.2.3 THREAT OF SUBSTITUTES
- 5.2.4 BARGAINING POWER OF BUYERS
- 5.2.5 INTENSITY OF RIVALRY
- 5.3 MARKET SWOT ANALYSIS
- 5.4 MARKET PEST ANALYSIS
- 5.5 USE CASE ANALYSIS
- 5.5.1 OPTIMIZING SUPPLY CHAINS FOR REAL-TIME DECISION-MAKING
- 5.5.2 DEMAND FORECASTING
- 5.5.3 SUPPLY CHAIN OPTIMIZATION
- 5.5.4 SUPPLIER RISK ASSESSMENT
- 5.5.5 ANOMALY DETECTION
- 5.5.6 PRODUCT DEVELOPMENT
- 5.5.7 SALES AND OPERATIONS PLANNING
- 5.5.8 PRICE OPTIMIZATION
- 5.5.9 TRANSPORTATION AND ROUTING OPTIMIZATION
- 5.5.10 INVENTORY MANAGEMENT
- 5.5.11 FINANCIAL OPTIMIZATION IN SUPPLY CHAIN
- 5.6 REGULATORY LANDSCAPE ANALYSIS
- 5.7 ROLE OF AI IN LOGISTICS AND SUPPLY CHAIN
- 5.8 NAVIGATING AI WITH RETURN ON INVESTMENT (ROI)
- 5.9 IMPLICATIONS OF DISCRIMINATIVE AI VERSUS GENERATIVE AI ON SUPPLY CHAIN
6 GLOBAL GENERATIVE AI IN FULFILLMENT & LOGISTICS MARKET, BY OFFERING
- 6.1 INTRODUCTION
- 6.2 SOLUTION
- 6.3 SERVICES
- 6.3.1 PROFESSIONAL SERVICES
- 6.3.2 MANAGED SERVICES
7 GLOBAL GENERATIVE AI IN FULFILLMENT & LOGISTICS MARKET, BY TYPE
- 7.1 INTRODUCTION
- 7.2 VARIATIONAL AUTOENCODER (VAE)
- 7.3 GENERATIVE ADVERSARIAL NETWORKS (GANS)
- 7.4 RECURRENT NEURAL NETWORKS (RNNS)
- 7.5 LONG SHORT-TERM MEMORY (LSTM) NETWORKS
8 GLOBAL GENERATIVE AI IN FULFILLMENT & LOGISTICS MARKET, BY APPLICATION
- 8.1 INTRODUCTION
- 8.2 WAREHOUSE OPERATIONS, OPTIMIZATION AND MANAGEMENT
- 8.3 SUPPLY CHAIN OPERATIONS
- 8.3.1 DEMAND FORECASTING
- 8.3.2 TRANSPORTATION AND ROUTE OPTIMIZING
- 8.3.3 GREEN SUPPLY CHAINS
- 8.3.4 PRODUCTION PLANNING/ SCHEDULING
- 8.3.5 RISK MANAGEMENT
- 8.3.6 SUPPLIER MANAGEMENT
- 8.3.7 SOURCING
- 8.3.8 CONTRACT ANALYSIS
- 8.3.9 OTHERS
- 8.4 PREDICTIVE MAINTENANCE
- 8.5 LOGISTICS NETWORK DESIGN
- 8.6 INVENTORY MANAGEMENT
- 8.7 FRAUD DETECTION
- 8.8 CUSTOMER SERVICE OPERATIONS
- 8.9 AUTONOMOUS ROBOTICS
- 8.10 DATA ANALYTICS & REPORTING
- 8.11 OTHERS
9 GLOBAL GENERATIVE AI IN FULFILLMENT AND LOGISTICS MARKET, BY INDUSTRY VERTICAL
- 9.1 OVERVIEW
- 9.2 AUTOMOTIVE
- 9.3 PHARMACEUTICAL & HEALTHCARE
- 9.4 SEMICONDUCTORS & ELECTRONICS
- 9.5 RETAIL & E-COMMERCE
- 9.6 FOOD & BEVERAGES
- 9.7 OTHERS
10 GLOBAL GENERATIVE AI IN FULFILLMENT & LOGISTICS MARKET, BY REGION
- 10.1 OVERVIEW
- 10.2 NORTH AMERICA
- 10.2.1 US
- 10.2.2 CANADA
- 10.2.3 MEXICO
- 10.3 EUROPE
- 10.3.1 GERMANY
- 10.3.2 UK
- 10.3.3 FRANCE
- 10.3.4 ITALY
- 10.3.5 SPAIN
- 10.3.6 REST OF EUROPE
- 10.4 ASIA PACIFIC
- 10.4.1 CHINA
- 10.4.2 INDIA
- 10.4.3 JAPAN
- 10.4.4 SOUTH KOREA
- 10.4.5 REST OF ASIA PACIFIC
- 10.5 MIDDLE EAST & AFRICA
- 10.5.1 SAUDI ARABIA
- 10.5.2 UAE
- 10.5.3 SOUTH AFRICA
- 10.5.4 REST OF MIDDLE EAST & AFRICA
- 10.6 SOUTH AMERICA
- 10.6.1 BRAZIL
- 10.6.2 ARGENTINA
- 10.6.3 REST OF SOUTH AMERICA
11 COMPETITIVE LANDSCAPE
- 11.1 OVERVIEW
- 11.2 COMPETITIVE BENCHMARKING
- 11.3 MARKET SHARE ANALYSIS
- 11.4 KEY DEVELOPMENT IN THE GLOBAL GENERATIVE AI IN FULFILLMENT AND LOGISTICS
- 11.4.1 PRODUCT LAUNCHES/SERVICE
- 11.4.2 PARTNERSHIP & COLABORATION
- 11.4.3 EXPANSIONS/ JOINT VENTURE & INVESTMENTS
- 11.4.4 OTHER RECENT DEVELOPMENT
12 COMPANY PROFILE
- 12.1 MICROSOFT CORPORATION
- 12.1.1 COMPANY OVERVIEW
- 12.1.2 FINANCIAL OVERVIEW
- 12.1.3 PRODUCTS OFFERED
- 12.1.4 KEY DEVELOPMENTSS
- 12.1.5 SWOT ANALYSIS
- 12.1.6 KEY STRATEGIES
- 12.2 SECONDMIND
- 12.2.1 COMPANY OVERVIEW
- 12.2.2 FINANCIAL OVERVIEW
- 12.2.3 PRODUCTS OFFERED
- 12.2.4 KEY DEVELOPMENTSS
- 12.2.5 SWOT ANALYSIS
- 12.2.6 KEY STRATEGIES
- 12.3 WAREDOCK ESTONIA LLC
- 12.3.1 COMPANY OVERVIEW
- 12.3.2 FINANCIAL OVERVIEW
- 12.3.3 PRODUCTS OFFERED
- 12.3.4 KEY DEVELOPMENTS
- 12.3.5 SWOT ANALYSIS
- 12.4 LEEWAYHERTZ
- 12.4.1 COMPANY OVERVIEW
- 12.4.2 FINANCIAL OVERVIEW
- 12.4.3 PRODUCTS OFFERED
- 12.4.4 KEY DEVELOPMENTS
- 12.4.5 SWOT ANALYSIS
- 12.5 SAP SE
- 12.5.1 COMPANY OVERVIEW
- 12.5.2 FINANCIAL OVERVIEW
- 12.5.3 PRODUCTS OFFERED
- 12.5.4 KEY DEVELOPMENTSS
- 12.5.5 SWOT ANALYSIS
- 12.5.6 KEY STRATEGIES
- 12.6 DHL GROUP
- 12.6.1 COMPANY OVERVIEW
- 12.6.2 FINANCIAL OVERVIEW
- 12.6.3 PRODUCTS OFFERED
- 12.6.4 KEY DEVELOPMENTS
- 12.6.5 SWOT ANALYSIS
- 12.6.6 KEY STRATEGIES
- 12.7 C3.AI, INC.
- 12.7.1 COMPANY OVERVIEW
- 12.7.2 FINANCIAL OVERVIEW
- 12.7.3 PRODUCTS OFFERED
- 12.7.4 KEY DEVELOPMENTSS
- 12.7.5 SWOT ANALYSIS
- 12.7.6 KEY STRATEGIES
- 12.8 BLUE YONDER
- 12.8.1 COMPANY OVERVIEW
- 12.8.2 FINANCIAL OVERVIEW
- 12.8.3 PRODUCTS OFFERED
- 12.8.4 KEY DEVELOPMENTSS
- 12.8.5 SWOT ANALYSIS
- 12.8.6 KEY STRATEGIES
- 12.9 XENONSTACK.AI
- 12.9.1 COMPANY OVERVIEW
- 12.9.2 FINANCIAL OVERVIEW
- 12.9.3 PRODUCTS OFFERED
- 12.9.4 KEY DEVELOPMENTS
- 12.9.5 SWOT ANALYSIS
- 12.9.6 KEY STRATEGIES
- 12.10 OSA COMMERCE
- 12.10.1 COMPANY OVERVIEW
- 12.10.2 PRODUCTS OFFERED
- 12.10.3 KEY DEVELOPMENTS
- 12.10.4 SWOT ANALYSIS
- 12.10.5 KEY STRATEGIES
- 12.11 DAT SOLUTIONS LLC
- 12.11.1 COMPANY OVERVIEW
- 12.11.2 FINANCIAL OVERVIEW
- 12.11.3 PRODUCTS OFFERED
- 12.11.4 KEY DEVELOPMENTS
- 12.11.5 SWOT ANALYSIS
- 12.11.6 KEY STRATEGIES
- 12.12 IBM CORPORATION
- 12.12.1 COMPANY OVERVIEW
- 12.12.2 FINANCIAL OVERVIEW
- 12.12.3 PRODUCTS OFFERED
- 12.12.4 KEY DEVELOPMENTS
- 12.12.5 SWOT ANALYSIS
- 12.12.6 KEY STRATEGIES
- 12.13 ORACLE CORPORATION
- 12.13.1 COMPANY OVERVIEW
- 12.13.2 FINANCIAL OVERVIEW
- 12.13.3 PRODUCTS OFFERED
- 12.13.4 KEY DEVELOPMENTS
- 12.13.5 SWOT ANALYSIS
- 12.13.6 KEY STRATEGIES
- 12.14 ITREX GROUP
- 12.14.1 COMPANY OVERVIEW
- 12.14.2 FINANCIAL OVERVIEW
- 12.14.3 PRODUCTS OFFERED
- 12.14.4 KEY DEVELOPMENTS
- 12.14.5 SWOT ANALYSIS
- 12.15 ACCENTURE
- 12.15.1 COMPANY OVERVIEW
- 12.15.2 FINANCIAL OVERVIEW
- 12.15.3 PRODUCTS OFFERED
- 12.15.4 KEY DEVELOPMENTSS
- 12.15.5 SWOT ANALYSIS
- 12.15.6 KEY STRATEGIES
