공급망용 AI 시장 : 제공별, 전개별, 조직 규모별, 용도별, 최종 이용 산업별, 지역별 - 세계 예측(-2032년)

The global AI in supply chain market is projected to reach USD 50.41 billion by 2032 from USD 13.93 billion in 2025 at a CAGR of 20.2%. The market is growing rapidly as organizations respond to greater supply chain complexity, demand changes, and regular operational disruptions.

Businesses are using AI to improve demand forecasting, manage inventory, and plan logistics. This enables quicker and more accurate decisions. Growth is further boosted by cloud-based systems, real-time data use, and a rise in digital supply chain platforms. The focus on resilience, cost savings, and overall visibility in global supply networks is making AI essential for modern, data-driven supply chain changes across industries.

"Services segment to record highest CAGR during forecast period"

The services segment is expected to grow rapidly in the AI in supply chain market. This growth is driven by the increasing complexity of AI projects and the need for custom solutions. Organizations want specialized services for data preparation, customizing models, integrating systems, and making AI work with their existing ERP, warehouse, and transportation systems. Services are also crucial for managing change, training users, and ongoing optimization to keep AI models accurate as demand patterns and supply conditions shift. As businesses adopt AI across different functions and regions, the demand for consulting, managed services, and performance monitoring is increasing. The move toward cloud-based and subscription-based AI systems also supports long-term service contracts, making services a major growth factor in the AI in supply chain market.

"Large organizations captured largest market share in 2024"

Large organizations held the biggest market share in the AI in supply chain market, thanks to their complicated operations, global supplier networks, and higher budgets for technology. These businesses deal with more challenges around demand changes, balancing inventory, and coordinating logistics across many regions, making AI planning and optimization essential. Large organizations are often early adopters of advanced tools like demand sensing, scenario planning, predictive risk analytics, and real-time supply chain visibility. Their ability to invest in strong data systems, cloud services, and integration across the organization allows for faster and wider AI use. Furthermore, large businesses typically follow long-term digital transformation plans, embedding AI in procurement, manufacturing, and distribution, which reinforces their leading market position.

"Europe to account for a significant share of the AI in supply chain market in 2025"

Europe is expected to hold significant share in the AI in supply chain market due to its well-connected trade networks, strong export-focused industries, and commitment to supply chain resilience. Companies throughout Europe are increasingly turning to AI to manage supplier risks, enhance production planning, and improve coordination across operations in multiple countries. Rising labor costs and workforce shortages are also driving the adoption of AI automation in warehousing, transportation, and demand planning. Additionally, European regulations on traceability, sustainability, and reporting are prompting companies to use AI analytics for compliance and transparency. The growing use of cloud platforms, digital twins, and data-sharing initiatives in the region is further boosting AI adoption on a larger scale. Together, these factors are promoting ongoing use of AI in supply chain operations across Europe, maintaining its strong market presence.

Extensive primary interviews were conducted with key industry experts in the AI in supply chain market to determine and verify the market size for various segments and subsegments gathered through secondary research. The breakdown of primary participants for the report is shown below.

The study contains insights from various industry experts, from component suppliers to Tier 1 companies and OEMs. The break-up of the primaries is as follows:

• By Company Type: Tier 1 - 20%, Tier 2 - 40%, and Tier 3 - 40%
• By Designation: C-level Executives - 20%, Directors - 30%, and Others - 50%
• By Region: North America - 20%, Europe - 30%, Asia Pacific - 40%, and RoW - 10%

The AI in supply chain market is dominated by a few globally established players, such as SAP SE (Germany), Oracle (US), Blue Yonder Group, Inc. (US), Kinaxis Inc. (Canada), Manhattan Associates (US), IBM (US), Microsoft (US), Amazon Web Services, Inc. (US), Anaplan, Inc. (US), and Logility Supply Chain Solutions, Inc. (US).

The study includes an in-depth competitive analysis of these key players in the AI in supply chain market, with their company profiles, recent developments, and key market strategies.

Research Coverage

The report segments the AI in supply chain market based on offering (software, services), deployment (cloud, on-premises, hybrid), organization size (large organizations, small & medium organizations), application (demand planning & forecasting, procurement & sourcing, inventory management, production planning & scheduling, warehouse & transportation management, supply chain risk management, other applications), and end-use industry (retail, healthcare & pharmaceuticals, food & beverages, automotive, logistics & transportation, aerospace & defense, chemicals, electronics & semiconductor, energy & utilities, manufacturing, other end-use industries). It also discusses the market's drivers, restraints, opportunities, and challenges. It gives a detailed view of the market across four main regions (North America, Europe, Asia Pacific, and Rest of the World [RoW]). The report includes an ecosystem analysis of key players.

Key Benefits of Buying the Report

• Analysis of key drivers (growing implementation of big data and AI technologies, need for enhanced visibility in supply chain processes, rapid AI integration to improve customer satisfaction, shift toward cloud-based supply chain solutions, emphasis on supply chain resilience and risk mitigation post-disruptions), restraints (shortage of skilled workforce, security and data privacy concerns, high implementation and integration costs), opportunities (surge in demand for intelligent business processes and automation, improved operational efficiency with AI, emergence of generative AI for real-time decision intelligence), challenges (difficulties in seamless data integration, inconsistent data quality and availability)
• Service Development/Innovation: Detailed insights into upcoming technologies, research & development activities, and launches in the AI in supply chain market
• Market Development: Comprehensive information about lucrative markets through the analysis of the AI in supply chain market across varied regions
• Market Diversification: Exhaustive information about new software and services, untapped geographies, recent developments, and investments in the AI in supply chain market
• Competitive Assessment: In-depth assessment of market shares, growth strategies, and product offerings of leading players, such as SAP SE (Germany), Oracle (US), Blue Yonder Group, Inc. (US), Kinaxis Inc. (Canada), Manhattan Associates (US), IBM (US), Microsoft (US), Amazon Web Services, Inc. (US), Anaplan, Inc. (US), and Logility Supply Chain Solutions, Inc. (US)

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 STUDY OBJECTIVES
• 1.2 MARKET DEFINITION AND SCOPE
• 1.3 STUDY SCOPE
  • 1.3.1 MARKETS COVERED
  • 1.3.2 INCLUSIONS AND EXCLUSIONS
  • 1.3.3 YEARS CONSIDERED
• 1.4 CURRENCY CONSIDERED
• 1.5 UNITS CONSIDERED
• 1.6 STAKEHOLDERS
• 1.7 SUMMARY OF CHANGES

2 EXECUTIVE SUMMARY

• 2.1 MARKET HIGHLIGHTS AND KEY INSIGHTS
• 2.2 KEY MARKET PARTICIPANTS: MAPPING OF STRATEGIC DEVELOPMENTS
• 2.3 DISRUPTIVE TRENDS IN AI IN SUPPLY CHAIN MARKET
• 2.4 HIGH-GROWTH SEGMENTS
• 2.5 REGIONAL SNAPSHOT: MARKET SIZE, GROWTH RATE, AND FORECAST

3 PREMIUM INSIGHTS

• 3.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI IN SUPPLY CHAIN MARKET
• 3.2 AI IN SUPPLY CHAIN MARKET, BY OFFERING
• 3.3 AI IN SUPPLY CHAIN MARKET, BY DEPLOYMENT AND ORGANIZATION SIZE
• 3.4 AI IN SUPPLY CHAIN MARKET, BY APPLICATION
• 3.5 AI IN SUPPLY CHAIN MARKET, BY END-USE INDUSTRY
• 3.6 AI IN SUPPLY CHAIN MARKET, BY COUNTRY

4 MARKET OVERVIEW

• 4.1 INTRODUCTION
• 4.2 MARKET DYNAMICS
  • 4.2.1 DRIVERS
    • 4.2.1.1 Growing implementation of big data and AI technologies
    • 4.2.1.2 Need for enhanced visibility in supply chain processes
    • 4.2.1.3 Rapid AI integration to improve customer satisfaction
    • 4.2.1.4 Shift toward cloud-based supply chain solutions
    • 4.2.1.5 Emphasis on supply chain resilience and risk mitigation post-disruptions
  • 4.2.2 RESTRAINTS
    • 4.2.2.1 Shortage of skilled workforce
    • 4.2.2.2 Security and data privacy concerns
    • 4.2.2.3 High implementation and integration costs
  • 4.2.3 OPPORTUNITIES
    • 4.2.3.1 Surge in demand for intelligent business processes and automation
    • 4.2.3.2 Improved operational efficiency with AI
    • 4.2.3.3 Emergence of generative AI for real-time decision intelligence
  • 4.2.4 CHALLENGES
    • 4.2.4.1 Difficulties in seamless data integration
    • 4.2.4.2 Inconsistent data quality and availability
• 4.3 INTERCONNECTED MARKETS AND CROSS-SECTOR OPPORTUNITIES
  • 4.3.1 INTERCONNECTED MARKETS
  • 4.3.2 CROSS-SECTOR OPPORTUNITIES
• 4.4 STRATEGIC MOVES BY TIER-1/2/3 PLAYERS
  • 4.4.1 MARKET DYNAMICS

5 INDUSTRY TRENDS

• 5.1 INTRODUCTION
• 5.2 PORTER'S FIVE FORCES ANALYSIS
  • 5.2.1 INTENSITY OF COMPETITIVE RIVALRY
  • 5.2.2 BARGAINING POWER OF SUPPLIERS
  • 5.2.3 BARGAINING POWER OF BUYERS
  • 5.2.4 THREAT OF SUBSTITUTES
  • 5.2.5 THREAT OF NEW ENTRANTS
• 5.3 MACROECONOMIC INDICATORS
  • 5.3.1 INTRODUCTION
  • 5.3.2 GDP TRENDS AND FORECAST
  • 5.3.3 TRENDS IN FOOD & BEVERAGES INDUSTRY
  • 5.3.4 TRENDS IN RETAIL INDUSTRY
• 5.4 VALUE CHAIN ANALYSIS
• 5.5 ECOSYSTEM ANALYSIS
• 5.6 PRICING ANALYSIS
  • 5.6.1 INDICATIVE PRICING ANALYSIS OF KEY PLAYERS, BY DEPLOYMENT, 2024
    • 5.6.1.1 Indicative pricing analysis of deployment types offered by key players, 2024
  • 5.6.2 AVERAGE SELLING PRICE TREND, BY REGION, 2021-2024
• 5.7 KEY CONFERENCES AND EVENTS, 2025-2027
• 5.8 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
• 5.9 INVESTMENT AND FUNDING SCENARIO
• 5.10 CASE STUDY ANALYSIS
  • 5.10.1 SAP SE LEVERAGES AI TO BOOST FIELD SERVICE PRODUCTIVITY AND DISPATCH EFFICIENCY
  • 5.10.2 WALMART IMPLEMENTS AI-ENHANCED SUPPLY CHAIN OPERATIONS TO DRIVE EFFICIENCY AND CUSTOMER EXPERIENCE
  • 5.10.3 AEON OPTIMIZES LOCAL ASSORTMENTS WITH BLUE YONDER CATEGORY MANAGEMENT
  • 5.10.4 ACCEL IMPROVES WAREHOUSE PRODUCTIVITY BY 35% WITH BLUE YONDER CLOUD MIGRATION
  • 5.10.5 INTEL CORPORATION BRINGS GRAPHICS PROCESSING UNIT TO VEHICLE COCKPIT
  • 5.10.6 IBM AND NABP DEVELOP BLOCKCHAIN-BASED PLATFORM TO ENHANCE DRUG SUPPLY CHAIN SECURITY
  • 5.10.7 UNIPER SE ENHANCES ENERGY OPERATIONS WITH MICROSOFT COPILOT
  • 5.10.8 NORGREN STREAMLINES SUPPLY CHAIN WITH SAP SE INTEGRATED SOLUTIONS
  • 5.10.9 TERADYNE ENHANCES SUPPLY CHAIN EFFICIENCY WITH C.H. ROBINSON WORLDWIDE'S INTEGRATED LOGISTICS SOLUTIONS
• 5.11 IMPACT OF 2025 US TARIFF - AI IN SUPPLY CHAIN MARKET
  • 5.11.1 INTRODUCTION
  • 5.11.2 KEY TARIFF RATES
  • 5.11.3 PRICE IMPACT ANALYSIS
  • 5.11.4 IMPACT ON COUNTRIES/REGIONS
    • 5.11.4.1 US
    • 5.11.4.2 Europe
    • 5.11.4.3 Asia Pacific
  • 5.11.5 IMPACT ON END-USE INDUSTRIES

6 TECHNOLOGICAL ADVANCEMENTS, AI-DRIVEN IMPACT, PATENTS, INNOVATIONS, AND FUTURE APPLICATIONS

• 6.1 KEY EMERGING TECHNOLOGIES
  • 6.1.1 MACHINE LEARNING
  • 6.1.2 NATURAL LANGUAGE PROCESSING
  • 6.1.3 COMPUTER VISION
• 6.2 COMPLEMENTARY TECHNOLOGIES
  • 6.2.1 INTERNET OF THINGS (IOT) & IIOT SENSORS
  • 6.2.2 CLOUD COMPUTING & EDGE COMPUTING
• 6.3 ADJACENT TECHNOLOGIES
  • 6.3.1 ROBOTIC PROCESS AUTOMATION (RPA)
  • 6.3.2 SMART FACTORIES
  • 6.3.3 5G & NEXT-GENERATION CONNECTIVITY
• 6.4 TECHNOLOGY/PRODUCT ROADMAP
  • 6.4.1 SHORT-TERM (2025-2027) | AI-AUGMENTED VISIBILITY & AUTOMATION SCALING
  • 6.4.2 MID-TERM (2027-2030) | HETEROGENEOUS INTEGRATION & DESIGN ECOSYSTEM EXPANSION
  • 6.4.3 LONG-TERM (2030-2035+) | UNIVERSAL RECONFIGURABLE COMPUTING & SYSTEM-LEVEL CONVERGENCE
• 6.5 PATENT ANALYSIS

7 REGULATORY LANDSCAPE

• 7.1 INTRODUCTION
  • 7.1.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
  • 7.1.2 STANDARDS
  • 7.1.3 GOVERNMENT REGULATIONS

8 CUSTOMER LANDSCAPE AND BUYER BEHAVIOR

• 8.1 DECISION-MAKING PROCESS
• 8.2 KEY STAKEHOLDERS INVOLVED IN BUYING PROCESS AND THEIR EVALUATION CRITERIA
  • 8.2.1 KEY STAKEHOLDERS IN BUYING PROCESS
  • 8.2.2 BUYING CRITERIA
• 8.3 ADOPTION BARRIERS AND INTERNAL CHALLENGES
• 8.4 UNMET NEEDS OF VARIOUS END-USE INDUSTRIES

9 AI IN SUPPLY CHAIN MARKET, BY OFFERING

• 9.1 INTRODUCTION
• 9.2 SOFTWARE
  • 9.2.1 RISING SUPPLY CHAIN COMPLEXITY TO DRIVE ADOPTION OF AI IN SUPPLY CHAIN SOFTWARE
• 9.3 SERVICES
  • 9.3.1 MANAGED SERVICES
    • 9.3.1.1 Enterprise shift from AI pilots to scaled deployments to drive demand for managed services
  • 9.3.2 PROFESSIONAL SERVICES
    • 9.3.2.1 Critical role in business innovation to drive demand for professional services

10 AI IN SUPPLY CHAIN MARKET, BY DEPLOYMENT

• 10.1 INTRODUCTION
• 10.2 CLOUD
  • 10.2.1 DEMAND FOR SCALABLE AND CONTINUOUSLY UPDATED PLATFORMS TO DRIVE CLOUD AI ADOPTION IN SUPPLY CHAINS
• 10.3 ON-PREMISES
  • 10.3.1 NEED FOR DATA SOVEREIGNTY AND REGULATORY COMPLIANCE TO DRIVE ON-PREMISES AI DEPLOYMENT
• 10.4 HYBRID
  • 10.4.1 NEED FOR FLEXIBLE AI DEPLOYMENT ACROSS CLOUD AND ON-PREMISES TO DRIVE HYBRID ADOPTION

11 AI IN SUPPLY CHAIN MARKET, BY ORGANIZATION SIZE

• 11.1 INTRODUCTION
• 11.2 LARGE ORGANIZATIONS
  • 11.2.1 GLOBAL SUPPLY CHAIN SCALE AND COMPLEXITY TO DRIVE AI ADOPTION AMONG LARGE ORGANIZATIONS
• 11.3 SMALL & MEDIUM ORGANIZATIONS
  • 11.3.1 NEED FOR FASTER, DATA-DRIVEN DECISIONS TO DRIVE AI ADOPTION IN SUPPLY CHAINS FOR SMALL & MEDIUM ORGANIZATIONS

12 AI IN SUPPLY CHAIN MARKET, BY APPLICATION

• 12.1 INTRODUCTION
• 12.2 DEMAND PLANNING & FORECASTING
  • 12.2.1 RISING DEMAND VOLATILITY TO DRIVE ADOPTION OF AI-POWERED SUPPLY CHAIN SOLUTIONS FOR FORECASTING
• 12.3 PROCUREMENT & SOURCING
  • 12.3.1 GROWING NEED TO AUTOMATE PURCHASING DECISIONS AND OPTIMIZE SOURCING STRATEGIES TO DRIVE AI ADOPTION IN PROCUREMENT
• 12.4 INVENTORY MANAGEMENT
  • 12.4.1 GROWING PRESSURE TO MINIMIZE MANUAL ERRORS AND OPERATIONAL COSTS TO BOOST AI-BASED INVENTORY MANAGEMENT ADOPTION
• 12.5 PRODUCTION PLANNING & SCHEDULING
  • 12.5.1 RISING FOCUS ON THROUGHPUT OPTIMIZATION AND DOWNTIME REDUCTION TO DRIVE AI ADOPTION IN PRODUCTION SCHEDULING
• 12.6 WAREHOUSE & TRANSPORTATION MANAGEMENT
  • 12.6.1 SHIFT TOWARD AUTOMATED WAREHOUSES AND INTELLIGENT LOGISTICS NETWORKS TO DRIVE DEMAND FOR AI IN SUPPLY CHAIN EXECUTION
• 12.7 SUPPLY CHAIN RISK MANAGEMENT
  • 12.7.1 FOCUS OF ENTERPRISES ON RESILIENCE AND CONTINUITY TO DRIVE DEPLOYMENT OF AI-DRIVEN SUPPLY CHAIN RISK SOLUTIONS
• 12.8 OTHER APPLICATIONS

13 AI IN SUPPLY CHAIN MARKET, BY END-USE INDUSTRY

• 13.1 INTRODUCTION
• 13.2 RETAIL
  • 13.2.1 EXPANSION OF AI ACROSS RETAIL PLANNING, INVENTORY OPTIMIZATION, AND LOGISTICS EXECUTION TO DRIVE MARKET
• 13.3 HEALTHCARE & PHARMACEUTICALS
  • 13.3.1 NEED TO REDUCE SHORTAGES AND IMPROVE FORECAST ACCURACY TO DRIVE AI ADOPTION IN HEALTHCARE & PHARMACEUTICAL SUPPLY CHAIN
• 13.4 FOOD & BEVERAGES
  • 13.4.1 NEED TO REDUCE WASTE AND IMPROVE INVENTORY TURNOVER TO DRIVE AI ADOPTION IN FOOD & BEVERAGES SUPPLY CHAIN
• 13.5 AUTOMOTIVE
  • 13.5.1 SURGE IN DEMAND FOR ELECTRIC AND SOFTWARE-DEFINED VEHICLES TO DRIVE MARKET
• 13.6 LOGISTICS & TRANSPORTATION
  • 13.6.1 NEED FOR REAL-TIME SHIPMENT VISIBILITY TO ACCELERATE AI ADOPTION IN LOGISTICS & TRANSPORTATION
• 13.7 AEROSPACE & DEFENSE
  • 13.7.1 MISSION-CRITICAL PRODUCTION SCHEDULES TO ACCELERATE AI INTEGRATION IN AEROSPACE & DEFENSE SUPPLY CHAIN
• 13.8 CHEMICALS
  • 13.8.1 NEED TO MANAGE COMPLEX, TIGHTLY COUPLED CHEMICAL PRODUCTION NETWORKS TO DRIVE AI ADOPTION IN SUPPLY CHAIN
• 13.9 ELECTRONICS & SEMICONDUCTOR
  • 13.9.1 RAPID TECHNOLOGY TRANSITIONS AND MULTI-TIER SUPPLIER DEPENDENCE TO FUEL MARKET GROWTH
• 13.10 ENERGY & UTILITIES
  • 13.10.1 RISING INFRASTRUCTURE COMPLEXITY TO DRIVE AI INTEGRATION IN ENERGY & UTILITIES SUPPLY CHAIN
• 13.11 MANUFACTURING
  • 13.11.1 DEMAND FOR REAL-TIME PRODUCTION COORDINATION TO DRIVE AI-ENABLED SUPPLY CHAIN TRANSFORMATION IN MANUFACTURING
• 13.12 OTHER END-USE INDUSTRIES

14 AI IN SUPPLY CHAIN MARKET, BY REGION

• 14.1 INTRODUCTION
• 14.2 NORTH AMERICA
  • 14.2.1 US
    • 14.2.1.1 Integration of robotics and AI to transform supply chain productivity
  • 14.2.2 CANADA
    • 14.2.2.1 National AI strategy and ecosystem partnerships to propel intelligent supply chain transformation
  • 14.2.3 MEXICO
    • 14.2.3.1 Manufacturing expansion and cross-border trade to propel market growth
• 14.3 EUROPE
  • 14.3.1 GERMANY
    • 14.3.1.1 Automotive sector investments to strengthen AI-driven supply chain efficiency
  • 14.3.2 UK
    • 14.3.2.1 Government initiatives and policy support to drive AI adoption across supply chains
  • 14.3.3 FRANCE
    • 14.3.3.1 Life sciences and pharmaceutical expansion to boost AI deployment in supply chain ecosystem
  • 14.3.4 ITALY
    • 14.3.4.1 Government focus on accelerating industrial digital transformation across supply chains to drive market
  • 14.3.5 REST OF EUROPE
• 14.4 ASIA PACIFIC
  • 14.4.1 CHINA
    • 14.4.1.1 AI-powered logistics and e-commerce ecosystems to propel supply chain efficiency and automation
  • 14.4.2 JAPAN
    • 14.4.2.1 AI-driven forecasting and automation to enhance resilience and efficiency in pharmaceutical supply chains
  • 14.4.3 SOUTH KOREA
    • 14.4.3.1 Digital twin and robotics integration to optimize electronics manufacturing supply chains
  • 14.4.4 INDIA
    • 14.4.4.1 Public-private partnerships to drive AI innovation and intelligent supply chain development
  • 14.4.5 REST OF ASIA PACIFIC
• 14.5 REST OF THE WORLD
  • 14.5.1 MIDDLE EAST & AFRICA
    • 14.5.1.1 Government AI strategies and smart logistics investments to drive supply chain modernization
    • 14.5.1.2 GCC
    • 14.5.1.3 Rest of Middle East & Africa
  • 14.5.2 SOUTH AMERICA
    • 14.5.2.1 Industrial automation and AI-driven manufacturing to enhance supply chain efficiency

15 COMPETITIVE LANDSCAPE

• 15.1 OVERVIEW
• 15.2 KEY PLAYER COMPETITIVE STRATEGIES/RIGHT TO WIN, 2021-2025
• 15.3 REVENUE ANALYSIS, 2021-2024
• 15.4 MARKET SHARE ANALYSIS, 2024
• 15.5 COMPANY VALUATION AND FINANCIAL METRICS
• 15.6 BRAND/PRODUCT COMPARISON
• 15.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2024
  • 15.7.1 STARS
  • 15.7.2 EMERGING LEADERS
  • 15.7.3 PERVASIVE PLAYERS
  • 15.7.4 PARTICIPANTS
  • 15.7.5 COMPANY FOOTPRINT: KEY PLAYERS, 2024
    • 15.7.5.1 Company footprint
    • 15.7.5.2 Regional footprint
    • 15.7.5.3 Offering footprint
    • 15.7.5.4 Deployment footprint
    • 15.7.5.5 Organization size footprint
    • 15.7.5.6 Application footprint
    • 15.7.5.7 End-use industry footprint
• 15.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2024
  • 15.8.1 PROGRESSIVE COMPANIES
  • 15.8.2 RESPONSIVE COMPANIES
  • 15.8.3 DYNAMIC COMPANIES
  • 15.8.4 STARTING BLOCKS
  • 15.8.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2024
    • 15.8.5.1 Detailed list of key startups/SMEs
    • 15.8.5.2 Competitive benchmarking of key startups/SMEs
• 15.9 COMPETITIVE SCENARIO
  • 15.9.1 PRODUCT LAUNCHES/ENHANCEMENTS
  • 15.9.2 DEALS

16 COMPANY PROFILES

• 16.1 KEY PLAYERS
  • 16.1.1 SAP SE
    • 16.1.1.1 Business overview
    • 16.1.1.2 Products/Services/Solutions offered
    • 16.1.1.3 Recent developments
      • 16.1.1.3.1 Product launches/enhancements
      • 16.1.1.3.2 Deals
    • 16.1.1.4 MnM view
      • 16.1.1.4.1 Key strengths
      • 16.1.1.4.2 Strategic choices
      • 16.1.1.4.3 Weaknesses & competitive threats
  • 16.1.2 ORACLE
    • 16.1.2.1 Business overview
    • 16.1.2.2 Products/Services/Solutions offered
    • 16.1.2.3 Recent developments
      • 16.1.2.3.1 Product launches/enhancements
      • 16.1.2.3.2 Deals
    • 16.1.2.4 MnM view
      • 16.1.2.4.1 Key strengths
      • 16.1.2.4.2 Strategic choices
      • 16.1.2.4.3 Weaknesses & competitive threats
  • 16.1.3 BLUE YONDER GROUP, INC.
    • 16.1.3.1 Business overview
    • 16.1.3.2 Products/Services/Solutions offered
    • 16.1.3.3 Recent developments
      • 16.1.3.3.1 Product launches/enhancements
      • 16.1.3.3.2 Deals
    • 16.1.3.4 MnM view
      • 16.1.3.4.1 Key strengths
      • 16.1.3.4.2 Strategic choices
      • 16.1.3.4.3 Weaknesses & competitive threats
  • 16.1.4 MANHATTAN ASSOCIATES
    • 16.1.4.1 Business overview
    • 16.1.4.2 Products/Services/Solutions offered
    • 16.1.4.3 Recent developments
      • 16.1.4.3.1 Product launches/enhancements
      • 16.1.4.3.2 Deals
    • 16.1.4.4 MnM view
      • 16.1.4.4.1 Key strengths
      • 16.1.4.4.2 Strategic choices
      • 16.1.4.4.3 Weaknesses & competitive threats
  • 16.1.5 KINAXIS INC.
    • 16.1.5.1 Business overview
    • 16.1.5.2 Products/Services/Solutions offered
    • 16.1.5.3 Recent developments
      • 16.1.5.3.1 Product launches/enhancements
      • 16.1.5.3.2 Deals
    • 16.1.5.4 MnM view
      • 16.1.5.4.1 Key strengths
      • 16.1.5.4.2 Strategic choices
      • 16.1.5.4.3 Weaknesses & competitive threats
  • 16.1.6 IBM
    • 16.1.6.1 Business overview
    • 16.1.6.2 Products/Services/Solutions offered
    • 16.1.6.3 Recent developments
      • 16.1.6.3.1 Deals
  • 16.1.7 MICROSOFT
    • 16.1.7.1 Business overview
    • 16.1.7.2 Products/Services/Solutions offered
    • 16.1.7.3 Recent developments
      • 16.1.7.3.1 Deals
  • 16.1.8 AMAZON WEB SERVICES, INC.
    • 16.1.8.1 Business overview
    • 16.1.8.2 Products/Services/Solutions offered
    • 16.1.8.3 Recent developments
      • 16.1.8.3.1 Product launches/enhancements
      • 16.1.8.3.2 Deals
  • 16.1.9 ANAPLAN, INC.
    • 16.1.9.1 Business overview
    • 16.1.9.2 Products/Services/Solutions offered
    • 16.1.9.3 Recent developments
      • 16.1.9.3.1 Product launches/enhancements
      • 16.1.9.3.2 Deals
  • 16.1.10 LOGILITY SUPPLY CHAIN SOLUTIONS, INC.
    • 16.1.10.1 Business overview
    • 16.1.10.2 Products/Services/Solutions offered
    • 16.1.10.3 Recent developments
      • 16.1.10.3.1 Product launches/enhancements
      • 16.1.10.3.2 Deals
• 16.2 OTHER PLAYERS
  • 16.2.1 GEP
  • 16.2.2 COUPA
  • 16.2.3 O9 SOLUTIONS, INC.
  • 16.2.4 ALIBABA CLOUD
  • 16.2.5 ALTANA
  • 16.2.6 PROJECT44
  • 16.2.7 RESILINC CORPORATION
  • 16.2.8 FOURKITES, INC.
  • 16.2.9 FERO.AI
  • 16.2.10 INFOR
  • 16.2.11 CONVECT.AI, INC.
  • 16.2.12 LVRG, INC.
  • 16.2.13 EVERSTREAM ANALYTICS
  • 16.2.14 RELEX SOLUTIONS
  • 16.2.15 E2OPEN, LLC

17 RESEARCH METHODOLOGY

• 17.1 RESEARCH DATA
  • 17.1.1 SECONDARY DATA
    • 17.1.1.1 Major secondary sources
    • 17.1.1.2 Key data from secondary sources
  • 17.1.2 PRIMARY DATA
    • 17.1.2.1 List of primary interview participants
    • 17.1.2.2 Breakdown of primaries
    • 17.1.2.3 Key data from primary sources
    • 17.1.2.4 Key industry insights
  • 17.1.3 SECONDARY AND PRIMARY RESEARCH
• 17.2 MARKET SIZE ESTIMATION
  • 17.2.1 BOTTOM-UP APPROACH
    • 17.2.1.1 Approach to estimate market size using bottom-up analysis (supply side)
  • 17.2.2 TOP-DOWN APPROACH
    • 17.2.2.1 Approach to estimate market size using top-down analysis (demand side)
• 17.3 MARKET BREAKDOWN AND DATA TRIANGULATION
• 17.4 RESEARCH ASSUMPTIONS
• 17.5 RISK ASSESSMENT
• 17.6 RESEARCH LIMITATIONS

18 APPENDIX

• 18.1 DISCUSSION GUIDE
• 18.2 KNOWLEDGESTORE: MARKETSANDMARKETS' SUBSCRIPTION PORTAL
• 18.3 CUSTOMIZATION OPTIONS
• 18.4 RELATED REPORTS
• 18.5 AUTHOR DETAILS