Summary

The AI in supply chain market is projected to grow from USD 9.15 billion in 2024 and is expected to reach USD 40.53 billion by 2030, growing at a CAGR of 28.2% from 2024 to 2030. Al has improved customers satisfaction toward consumer products. This improvement benefits the organization by maintaining sales tracking and hence garnering more customers. The machine learning techniques that involve deep analytics and real-time monitoring significantly enhance the supply chain visibility of the businesses and hence enable them to deliver better customer experiences and maintain the pace within the delivery timelines. Therefore, market players are employing Al-based supply chain management solutions to increase efficiency and productivity.
“The cloud segment in the AI in supply chain market to witness higher growth rate during the forecast period.”
Cloud segment is mainly driven by cloud-based solutions that are increasingly being adopted by small and medium enterprises, primarily because they provide the flexibility, scalability, and cost-effectiveness features that the organizations require. In addition, the speed in developing sophisticated security solutions for cloud-based deployment offers answers to issues that existed on data privacy and thus attract businesses seeking to adopt AI without investing in big premises-based infrastructure.
“The US is expected to hold the largest market size in the North America region during the forecast period.”
The US companies face pressure and competition to reduce costs while maintaining high levels of customer service. AI supply chain solutions allow for the automation of tasks, analyzing big data, and generating actionable insights that might make efficiency, transparency, and agility inside the supply chain more efficient. There has been a manufacturing and logistics labor shortfall in the US. The use of AI helps to eliminate a human workforce so that activities relate more to higher-value tasks requiring expertise and experience. Further, the US is a leading country in AI research and development. This encourages the development of advanced AI solutions specifically for supply chain applications.

• By Company Type: Tier 1 – 20%, Tier 2 – 35%, and Tier 3 – 45%
• By Designation: C-level Executives – 15%, Directors –20%, and Others – 65%
• By Region: North America –20%, Europe – 15%, Asia Pacific– 60%, and RoW – 5%
Players profiled in this report are SAP SE (Germany), Oracle (US), Blue Yonder Group, Inc. (US), Kinaxis Inc. (Canada), Manhattan Associates (US), NVIDIA Corporation (US), Advanced Micro Devices, Inc. (US), Intel Corporation (US), Micron Technology, Inc. (US), Qualcomm Technologies, Inc. (US), SAMSUNG (South Korea), IBM (US), Microsoft (US), Amazon Web Services, Inc. (US), Google (US), Anaplan, Inc. (US), Logility Supply Chain Solutions, Inc. (US), Coupa (US), O9 Solutions, Inc. (US), Alibaba Group Holding Limited (China), FedEx Corporation (US), Deutsche Post AG (Germany), ServiceNow (US), Project44 (US), Resilinc Corporation (US), FourKites, Inc. (US), RELEX Solutions (Finland), C.H. Robinson Worldwide, Inc. (US), e2open, LLC (US), FERO.Ai (UAE) among a few other key companies in the AI in supply chain ecosystem.
Report Coverage
The report defines, describes, and forecasts the AI in supply chain market based on offering, deployment, organization size, application, end-use industry, and region. It provides detailed information regarding drivers, restraints, opportunities, and challenges influencing the growth of the AI in supply chain market. It also analyzes competitive developments such as acquisitions, product launches, expansions, and actions carried out by the key players to grow in the market.
Reasons to Buy This Report
The report will help the market leaders/new entrants in the market with information on the closest approximations of the revenue for the overall AI in supply chain market and the subsegments. The report will help stakeholders understand the competitive landscape and gain more insight to position their business better and plan suitable go-to-market strategies. The report also helps stakeholders understand the pulse of the market and provides them with information on key drivers, restraints, opportunities, and challenges.
The report will provide insights into the following pointers:
• Analysis of key drivers (Big data enhance supply chain efficiency through data-driven decision making) restraints (Shortage of skilled workforce)
opportunities (Surge in increasing demand for intelligent business processes and automation), and challenges (Difficulties in data integration from multiple sources) of the AI in supply chain market.
• Product development /Innovation: Detailed insights on upcoming technologies, research & development activities, and new product launches in the AI in supply chain market.
• Market Development: Comprehensive information about lucrative markets; the report analyses the AI in supply chain market across various regions.
• Market Diversification: Exhaustive information about new products launched, untapped geographies, recent developments, and investments in the AI in supply chain market.
• Competitive Assessment: In-depth assessment of market share, growth strategies, and offering of leading players like SAP SE (Germany), Oracle (US), Blue Yonder Group, Inc. (US), Kinaxis Inc. (Canada), Manhattan Associates (US) among others in the AI in supply chain market.

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Table of Contents

1 INTRODUCTION 24
1.1 STUDY OBJECTIVES 24
1.2 MARKET DEFINITION 24
1.3 STUDY SCOPE 25
1.3.1 MARKET SEGMENTATION 25
1.3.2 INCLUSIONS AND EXCLUSIONS 26
1.4 YEARS CONSIDERED 26
1.5 CURRENCY CONSIDERED 27
1.6 UNITS CONSIDERED 27
1.7 STAKEHOLDERS 27
1.8 SUMMARY OF CHANGES 27
2 RESEARCH METHODOLOGY 28
2.1 RESEARCH DATA 28
2.1.1 SECONDARY DATA 29
2.1.1.1 List of secondary sources 29
2.1.1.2 Key data from secondary sources 29
2.1.2 PRIMARY DATA 30
2.1.2.1 List of interview participants 30
2.1.2.2 Breakdown of primary interviews 30
2.1.2.3 Key data from primary sources 31
2.1.2.4 Insights from industry experts 32
2.1.3 SECONDARY AND PRIMARY RESEARCH 32
2.2 MARKET SIZE ESTIMATION 33
2.2.1 BOTTOM-UP APPROACH 33
2.2.1.1 Approach to estimate market size using bottom-up analysis
(supply side) 33
2.2.2 TOP-DOWN APPROACH 35
2.2.2.1 Approach to estimate market size using top-down analysis
(demand side) 35
2.3 DATA TRIANGULATION 36
2.4 RESEARCH ASSUMPTIONS 37
2.5 RESEARCH LIMITATIONS 37
2.6 RISK ASSESSMENT 38
3 EXECUTIVE SUMMARY 39
 
4 PREMIUM INSIGHTS 44
4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI IN SUPPLY CHAIN MARKET 44
4.2 AI IN SUPPLY CHAIN MARKET, BY OFFERING 44
4.3 AI IN SUPPLY CHAIN MARKET, BY DEPLOYMENT 45
4.4 AI IN SUPPLY CHAIN MARKET, BY ORGANIZATION SIZE 45
4.5 NORTH AMERICA: AI IN SUPPLY CHAIN MARKET, BY DEPLOYMENT AND COUNTRY 46
4.6 GLOBAL AI IN SUPPLY CHAIN MARKET, BY COUNTRY 46
5 MARKET OVERVIEW 47
5.1 INTRODUCTION 47
5.2 MARKET DYNAMICS 47
5.2.1 DRIVERS 48
5.2.1.1 Growing implementation of big data and AI technologies 48
5.2.1.2 Need for enhanced visibility in supply chain processes 48
5.2.1.3 Rapid AI integration to improve customer satisfaction 48
5.2.1.4 Shift toward cloud-based supply chain solutions 48
5.2.2 RESTRAINTS 49
5.2.2.1 Shortage of skilled workforce 49
5.2.2.2 Security and data privacy concerns 49
5.2.3 OPPORTUNITIES 50
5.2.3.1 Surge in demand for intelligent business processes and automation 50
5.2.3.2 Improved operational efficiency with AI 50
5.2.4 CHALLENGES 51
5.2.4.1 Difficulties in seamless data integration from multiple sources 51
5.3 VALUE CHAIN ANALYSIS 52
5.4 ECOSYSTEM ANALYSIS 53
5.5 TRENDS AND DISRUPTIONS IMPACTING CUSTOMER BUSINESS 55
5.6 TECHNOLOGY ANALYSIS 55
5.6.1 KEY TECHNOLOGIES 55
5.6.1.1 Machine Learning 55
5.6.1.2 Natural Language Processing 55
5.6.1.3 Computer Vision 56
5.6.2 COMPLEMENTARY TECHNOLOGIES 56
5.6.2.1 Internet of Things 56
5.6.3 ADJACENT TECHNOLOGIES 56
5.6.3.1 Robotic Process Automation 56
5.6.3.2 Internet of Things 56
5.6.3.3 Edge Computing 56
5.7 INVESTMENT AND FUNDING SCENARIO 57
 
5.8 PORTER’S FIVE FORCES ANALYSIS 58
5.8.1 INTENSITY OF COMPETITIVE RIVALRY 59
5.8.2 BARGAINING POWER OF SUPPLIERS 59
5.8.3 BARGAINING POWER OF BUYERS 59
5.8.4 THREAT OF SUBSTITUTES 59
5.8.5 THREAT OF NEW ENTRANTS 59
5.9 KEY STAKEHOLDERS AND BUYING CRITERIA 60
5.9.1 KEY STAKEHOLDERS IN BUYING PROCESS 60
5.9.2 BUYING CRITERIA 61
5.10 CASE STUDY ANALYSIS 62
5.10.1 INTEL CORPORATION BRINGS GRAPHICS PROCESSING UNIT TO VEHICLE COCKPIT 62
5.10.2 IBM AND NABP DEVELOP BLOCKCHAIN-BASED PLATFORM TO ENHANCE DRUG SUPPLY CHAIN SECURITY 62
5.10.3 UNIPER SE ENHANCES ENERGY OPERATIONS WITH MICROSOFT COPILOT 63
5.10.4 NORGREN STREAMLINES SUPPLY CHAIN WITH SAP SE INTEGRATED SOLUTIONS 63
5.10.5 TERADYNE ENHANCES SUPPLY CHAIN EFFICIENCY WITH C.H. ROBINSON WORLDWIDE’S INTEGRATED LOGISTICS SOLUTIONS 64
5.11 TRADE ANALYSIS 64
5.11.1 IMPORT SCENARIO (HS CODE 854231) 64
5.11.2 EXPORT SCENARIO (HS CODE 854231) 66
5.12 PATENT ANALYSIS 68
5.13 KEY CONFERENCES AND EVENTS, 2024–2025 70
5.14 REGULATORY LANDSCAPE 71
5.14.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 71
5.14.2 REGULATORY STANDARDS 73
5.14.3 GOVERNMENT REGULATIONS 74
5.15 PRICING ANALYSIS 75
6 AI IN SUPPLY CHAIN MARKET, BY OFFERING 76
6.1 INTRODUCTION 77
6.2 SOFTWARE 78
6.2.1 INCLINATION TOWARD SMART AUTOMATION TO DRIVE MARKET 78
6.3 SERVICES 80
6.3.1 MANAGED SERVICES 82
6.3.1.1 Extensive use in supply chain management to drive market 82
6.3.2 PROFESSIONAL SERVICES 83
6.3.2.1 Critical role in business innovation to drive market 83
 
7 AI IN SUPPLY CHAIN MARKET, BY DEPLOYMENT 84
7.1 INTRODUCTION 85
7.2 CLOUD 86
7.2.1 GROWING POPULARITY DUE TO SIGNIFICANT ADVANTAGES TO DRIVE MARKET 86
7.3 ON-PREMISES 87
7.3.1 COMPLIANCE WITH STRINGENT REGULATORY REQUIREMENTS TO DRIVE MARKET 87
7.4 HYBRID 88
7.4.1 NEED FOR CLOUD SCALABILITY AND ON-PREMISES CONTROL TO DRIVE MARKET 88
8 AI IN SUPPLY CHAIN MARKET, BY ORGANIZATION SIZE 90
8.1 INTRODUCTION 91
8.2 LARGE ORGANIZATION 92
8.2.1 RAPID AI INTEGRATION ACROSS GLOBAL SUPPLY CHAIN NETWORKS TO DRIVE MARKET 92
8.3 SMALL & MEDIUM ORGANIZATION 92
8.3.1 ADVENT OF SCALABLE AND COST-EFFECTIVE AI SOLUTIONS TO DRIVE MARKET 92
9 AI IN SUPPLY CHAIN MARKET, BY APPLICATION 93
9.1 INTRODUCTION 94
9.2 DEMAND PLANNING & FORECASTING 95
9.2.1 REAL-TIME DATASET PROCESSING CAPACITY TO DRIVE MARKET 95
9.3 PROCUREMENT & SOURCING 96
9.3.1 AUTOMATION OF DATA-DRIVEN DECISION-MAKING TO DRIVE MARKET 96
9.4 INVENTORY MANAGEMENT 96
9.4.1 NEED FOR STEADY FLOW OF SUPPLIES AND FINISHED GOODS TO DRIVE MARKET 96
9.5 PRODUCTION PLANNING & SCHEDULING 97
9.5.1 ENHANCED SCHEDULING AND INVENTORY MANAGEMENT WITH AI ALGORITHMS TO DRIVE MARKET 97
9.6 WAREHOUSE & TRANSPORTATION MANAGEMENT 97
9.6.1 AI-DRIVEN DEMAND FORECASTING AND ROUTE OPTIMIZATION CAPABILITIES TO DRIVE MARKET 97
9.7 SUPPLY CHAIN RISK MANAGEMENT 98
9.7.1 ABILITY TO MITIGATE POTENTIAL DISRUPTIONS TO DRIVE MARKET 98
9.8 OTHER APPLICATIONS 98
 
10 AI IN SUPPLY CHAIN MARKET, BY END-USE INDUSTRY 99
10.1 INTRODUCTION 100
10.2 RETAIL 102
10.2.1 RAPID ADOPTION OF AI TO ENHANCE CUSTOMER EXPERIENCE TO DRIVE MARKET 102
10.3 HEALTHCARE & PHARMACEUTICALS 103
10.3.1 INCREASED FUNDING TO ENHANCE OPERATIONAL EFFICIENCY TO DRIVE MARKET 103
10.4 FOOD & BEVERAGES 105
10.4.1 EXTENSIVE USE OF AI IN SUPPLY CHAIN TO PREDICT DEMAND TO DRIVE MARKET 105
10.5 AUTOMOTIVE 106
10.5.1 SURGE IN DEMAND FOR ELECTRIC AND AUTONOMOUS VEHICLES TO DRIVE MARKET 106
10.6 LOGISTICS & TRANSPORTATION 108
10.6.1 IMPLEMENTATION OF CLOUD-BASED SOLUTIONS TO REDUCE COSTS TO DRIVE MARKET 108
10.7 AEROSPACE & DEFENSE 110
10.7.1 GOVERNMENT INITIATIVES TO STRENGTHEN NATIONAL SECURITY TO DRIVE MARKET 110
10.8 CHEMICALS 111
10.8.1 NEED FOR PROCESS OPTIMIZATION IN SUPPLY CHAIN TO DRIVE MARKET 111
10.9 ELECTRONICS & SEMICONDUCTOR 113
10.9.1 RISE IN TECHNOLOGICAL INNOVATIONS TO DRIVE MARKET 113
10.10 ENERGY & UTILITIES 115
10.10.1 NEED FOR EFFICIENT ENERGY UTILIZATION TO DRIVE MARKET 115
10.11 MANUFACTURING 116
10.11.1 INCORPORATION OF INTELLIGENT SYSTEMS TO AUTOMATE OPERATIONS TO DRIVE MARKET 116
10.12 OTHER END-USE INDUSTRIES 118
11 AI IN SUPPLY CHAIN MARKET, BY REGION 120
11.1 INTRODUCTION 121
11.2 NORTH AMERICA 122
11.2.1 MACROECONOMIC OUTLOOK 123
11.2.2 US 128
11.2.2.1 Increasing adoption of technology infrastructure and growth initiatives by US government to drive market 128
11.2.3 CANADA 129
11.2.3.1 Rising investments to boost adoption of AI across industries 129
11.2.4 MEXICO 130
11.2.4.1 Government initiatives to boost manufacturing capabilities in Mexico 130
 
11.3 EUROPE 131
11.3.1 MACROECONOMIC OUTLOOK 131
11.3.2 GERMANY 136
11.3.2.1 Increasing adoption of AI to drive market growth 136
11.3.3 UK 137
11.3.3.1 Continuous investments and initiatives by UK government to bolster growth 137
11.3.4 FRANCE 138
11.3.4.1 AI initiatives and investments to push French market forward 138
11.3.5 REST OF EUROPE 139
11.4 ASIA PACIFIC 140
11.4.1 MACROECONOMIC OUTLOOK 140
11.4.2 CHINA 145
11.4.2.1 Government initiatives and rising investments to drive market growth 145
11.4.3 JAPAN 146
11.4.3.1 Growth in investments and government initiatives to drive innovation 146
11.4.4 SOUTH KOREA 147
11.4.4.1 Government investments in artificial intelligence to accelerate market growth 147
11.4.5 INDIA 148
11.4.5.1 Rapid surge in development and adoption of AI technologies to propel market 148
11.4.6 REST OF ASIA PACIFIC 149
11.5 REST OF THE WORLD 150
11.5.1 MACROECONOMIC OUTLOOK 150
11.5.2 MIDDLE EAST & AFRICA 154
11.5.2.1 Commitment to digital transformation and technological innovation to drive growth 154
11.5.2.2 GCC 155
11.5.2.3 Rest of Middle East & Africa 156
11.5.3 SOUTH AMERICA 157
11.5.3.1 Growing interest of private enterprises to boost market 157
12 COMPETITIVE LANDSCAPE 158
12.1 OVERVIEW 158
12.2 KEY PLAYER STRATEGIES/RIGHT TO WIN 158
12.3 REVENUE ANALYSIS, 2019–2023 161
12.4 MARKET SHARE ANALYSIS, 2023 161
12.5 COMPANY VALUATION AND FINANCIAL METRICS 164
12.6 BRAND/PRODUCT COMPARISON 165
12.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023 166
12.7.1 STARS 166
12.7.2 EMERGING LEADERS 166
12.7.3 PERVASIVE PLAYERS 166
12.7.4 PARTICIPANTS 166
12.7.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023 168
12.7.5.1 Company footprint 168
12.7.5.2 Offering footprint 169
12.7.5.3 Deployment footprint 170
12.7.5.4 Organization size footprint 171
12.7.5.5 Application footprint 172
12.7.5.6 End-use industry footprint 173
12.7.5.7 Region footprint 174
12.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023 175
12.8.1 PROGRESSIVE COMPANIES 175
12.8.2 RESPONSIVE COMPANIES 175
12.8.3 DYNAMIC COMPANIES 175
12.8.4 STARTING BLOCKS 175
12.8.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023 177
12.8.5.1 Detailed list of key startups/SMEs 177
12.8.5.2 Competitive benchmarking of key startups/SMEs 178
12.8.5.2.1 Competitive benchmarking, by offering and region 178
12.8.5.2.2 Competitive benchmarking, by application and deployment 179
12.8.5.2.3 Competitive benchmarking, by end-use industry and organization size 180
12.9 COMPETITIVE SCENARIO 181
12.9.1 PRODUCT LAUNCHES/DEVELOPMENTS 181
12.9.2 DEALS 185
13 COMPANY PROFILES 194
13.1 KEY PLAYERS 194
13.1.1 SAP SE 194
13.1.1.1 Business overview 194
13.1.1.2 Products/Services/Solutions offered 196
13.1.1.3 Recent developments 197
13.1.1.3.1 Deals 197
13.1.1.4 MnM view 197
13.1.1.4.1 Key strengths/Right to win 197
13.1.1.4.2 Strategic choices 197
13.1.1.4.3 Weaknesses/Competitive threats 197
13.1.2 ORACLE 198
13.1.2.1 Business overview 198
13.1.2.2 Products/Services/Solutions offered 199
13.1.2.3 Recent developments 200
13.1.2.3.1 Product launches/developments 200
13.1.2.3.2 Deals 200
13.1.2.4 MnM view 201
13.1.2.4.1 Key strengths/Right to win 201
13.1.2.4.2 Strategic choices 201
13.1.2.4.3 Weaknesses/Competitive threats 201
13.1.3 BLUE YONDER GROUP, INC. 202
13.1.3.1 Business overview 202
13.1.3.2 Products/Services/Solutions offered 202
13.1.3.3 Recent developments 203
13.1.3.3.1 Deals 203
13.1.3.4 MnM view 204
13.1.3.4.1 Key strengths/Right to win 204
13.1.3.4.2 Strategic choices 204
13.1.3.4.3 Weaknesses/Competitive threats 204
13.1.4 KINAXIS INC. 205
13.1.4.1 Business overview 205
13.1.4.2 Products/Services/Solutions offered 206
13.1.4.3 Recent developments 207
13.1.4.3.1 Product launches/developments 207
13.1.4.3.2 Deals 207
13.1.4.4 MnM view 208
13.1.4.4.1 Key strengths/Right to win 208
13.1.4.4.2 Strategic choices 208
13.1.4.4.3 Weaknesses/Competitive threats 208
13.1.5 MANHATTAN ASSOCIATES 209
13.1.5.1 Business overview 209
13.1.5.2 Products/Services/Solutions offered 210
13.1.5.3 Recent developments 211
13.1.5.3.1 Product launches/developments 211
13.1.5.3.2 Deals 211
13.1.5.4 MnM view 211
13.1.5.4.1 Key strengths/Right to win 211
13.1.5.4.2 Strategic choices 211
13.1.5.4.3 Weaknesses/Competitive threats 212
13.1.6 NVIDIA CORPORATION 213
13.1.6.1 Business overview 213
13.1.6.2 Products/Services/Solutions offered 214
13.1.6.3 Recent developments 216
13.1.6.3.1 Product launches/developments 216
13.1.6.3.2 Deals 217
 
13.1.7 ADVANCED MICRO DEVICES, INC. 218
13.1.7.1 Business overview 218
13.1.7.2 Products/Services/Solutions offered 219
13.1.7.3 Recent developments 220
13.1.7.3.1 Product launches/developments 220
13.1.7.3.2 Deals 221
13.1.8 INTEL CORPORATION 222
13.1.8.1 Business overview 222
13.1.8.2 Products/Services/Solutions offered 223
13.1.8.3 Recent developments 226
13.1.8.3.1 Product launches/developments 226
13.1.9 MICRON TECHNOLOGY, INC. 227
13.1.9.1 Business overview 227
13.1.9.2 Products/Services/Solutions offered 229
13.1.9.3 Recent developments 229
13.1.9.3.1 Deals 229
13.1.10 QUALCOMM TECHNOLOGIES, INC. 231
13.1.10.1 Business overview 231
13.1.10.2 Products/Services/Solutions offered 232
13.1.10.3 Recent developments 233
13.1.10.3.1 Product launches/developments 233
13.1.10.3.2 Deals 234
13.1.11 SAMSUNG 235
13.1.11.1 Business overview 235
13.1.11.2 Products/Services/Solutions offered 236
13.1.11.3 Recent developments 237
13.1.11.3.1 Product launches/developments 237
13.1.11.3.2 Deals 238
13.1.12 IBM 239
13.1.12.1 Business overview 239
13.1.12.2 Products/Services/Solutions offered 240
13.1.12.3 Recent developments 241
13.1.12.3.1 Deals 241
13.1.13 MICROSOFT 242
13.1.13.1 Business overview 242
13.1.13.2 Products/Services/Solutions offered 243
13.1.13.3 Recent developments 244
13.1.13.3.1 Deals 244
 
13.1.14 AMAZON WEB SERVICES, INC. 245
13.1.14.1 Business overview 245
13.1.14.2 Products/Services/Solutions offered 246
13.1.14.3 Recent developments 247
13.1.14.3.1 Product launches/developments 247
13.1.14.3.2 Deals 248
13.1.15 GOOGLE 249
13.1.15.1 Business overview 249
13.1.15.2 Products/Services/Solutions offered 250
13.1.15.3 Recent developments 251
13.1.15.3.1 Product launches/developments 251
13.1.15.3.2 Deals 251
13.1.16 ANAPLAN, INC. 252
13.1.16.1 Business overview 252
13.1.16.2 Products/Services/Solutions offered 252
13.1.16.3 Recent developments 253
13.1.16.3.1 Deals 253
13.2 OTHER PLAYERS 254
13.2.1 LOGILITY SUPPLY CHAIN SOLUTIONS, INC. 254
13.2.2 COUPA 255
13.2.3 O9 SOLUTIONS, INC. 256
13.2.4 ALIBABA GROUP HOLDING LIMITED 257
13.2.5 FEDEX CORPORATION 258
13.2.6 DEUTSCHE POST AG 259
13.2.7 SERVICENOW 260
13.2.8 PROJECT44 261
13.2.9 RESILINC CORPORATION 262
13.2.10 FOURKITES, INC. 263
13.2.11 RELEX SOLUTIONS 264
13.2.12 C.H. ROBINSON WORLDWIDE, INC. 265
13.2.13 E2OPEN, LLC 266
13.2.14 FERO.AI 267
14 APPENDIX 268
14.1 DISCUSSION GUIDE 268
14.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 272
14.3 CUSTOMIZATION OPTIONS 274
14.4 RELATED REPORTS 274
14.5 AUTHOR DETAILS 275