Generative AI in Fulfillment & Logistics Market Research Report Forecast Till 2032

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.

Product Code: MRFR/ICT/17289-CR

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