1 INTRODUCTION 16
1.1 OBJECTIVES OF THE STUDY 16
1.2 DEFINITION 16
1.3 STUDY SCOPE 17
1.3.1 MARKETS COVERED 17
1.3.2 YEARS CONSIDERED FOR THIS STUDY 17
1.4 CURRENCY 18
1.5 LIMITATIONS 18
1.6 STAKEHOLDERS 18

2 RESEARCH METHODOLOGY 19
2.1 RESEARCH DATA 19
2.1.1 SECONDARY DATA 20
2.1.1.1 Key data from secondary sources 20
2.1.2 PRIMARY DATA 20
2.1.2.1 Key data from primary sources 21
2.1.2.2 Key industry insights 22
2.1.2.3 Breakdown of primaries 22
2.2 MARKET SIZE ESTIMATION 23
2.2.1 BOTTOM-UP APPROACH 24
2.2.2 TOP-DOWN APPROACH 25
2.3 MARKET BREAKDOWN & DATA TRIANGULATION 26
2.4 RESEARCH ASSUMPTIONS 27

3 EXECUTIVE SUMMARY 28

4 PREMIUM INSIGHTS 34
4.1 ATTRACTIVE OPPORTUNITIES IN THE ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MARKET 34
4.2 ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MARKET, BY PROCESSOR 35
4.3 ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MARKET FOR FLEET MANAGEMENT APPLICATION, BY TECHNOLOGY 35
4.4 ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MARKET IN APAC, BY APPLICATION 36
4.5 ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MARKET, BY COUNTRY 37
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5 MARKET OVERVIEW 38
5.1 INTRODUCTION 38
5.2 MARKET DYNAMICS 38
5.2.1 DRIVERS 39
5.2.1.1 Growth of big data 39
5.2.1.2 Demand for greater visibility and transparency in supply chain data and processes 39
5.2.1.3 Adoption of AI to improve consumer services and satisfaction 39
5.2.2 RESTRAINTS 40
5.2.2.1 Limited number of AI experts 40
5.2.3 OPPORTUNITIES 40
5.2.3.1 Growing impact of cloud-based supply chain solutions 40
5.2.3.2 Increasing demand for intelligent business process and automation 41
5.2.3.3 Improving operational efficiency in manufacturing industry 41
5.2.4 CHALLENGES 41
5.2.4.1 Difficulties in data integration from multiple sources 41
5.2.4.2 Concerns regarding data privacy 42
5.3 CASE STUDIES 43
5.3.1 A LEADING LUXURY VEHICLES MANUFACTURER EMPLOYED IBM WATSON TO IMPROVE CRITICAL PARTS MANAGEMENT. 43
5.3.2 SPLICE MACHINE PARTNERS WITH INTRIGO TO PROVIDE ORDER PROMISING AND SCHEDULING SOLUTION FOR INFINERA 43
5.3.3 UPS CHATBOT NOW AVAILABLE VIA THE GOOGLE ASSISTANT 43
5.3.4 PANALPINA ENGAGED WITH CLEARMETAL FOR PREDICTIVE LOGISTICS 43
5.3.5 CUMMINS USING LLAMASOFT DEMAND GURU FOR DEMAND MODELLING 43

6 ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MARKET, BY OFFERING 44
6.1 INTRODUCTION 45
6.2 HARDWARE 46
6.2.1 PROCESSORS 47
6.2.1.1 MPU 48
6.2.1.2 GPU 49
6.2.1.3 FPGA 49
6.2.1.4 ASIC 49
6.2.2 MEMORY 49
6.2.3 NETWORK 49
6.3 SOFTWARE 50
6.3.1 AI PLATFORMS 52
6.3.1.1 Application program interface (API) 52
6.3.1.2 Machine learning framework 52
6.3.2 AI SOLUTIONS 52
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6.4 SERVICES 53
6.4.1 DEPLOYMENT & INTEGRATION 54
6.4.2 SUPPORT & MAINTENANCE 54

7 ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MARKET, BY TECHNOLOGY 55
7.1 INTRODUCTION 56
7.2 MACHINE LEARNING 57
7.2.1 SUPERVISED LEARNING 59
7.2.2 UNSUPERVISED LEARNING 59
7.2.3 REINFORCEMENT LEARNING 59
7.2.4 OTHERS 59
7.3 NATURAL LANGUAGE PROCESSING (NLP) 60
7.4 CONTEXT-AWARE COMPUTING 62
7.5 COMPUTER VISION 64

8 ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MARKET, BY APPLICATION 66
8.1 INTRODUCTION 67
8.2 FLEET MANAGEMENT 68
8.3 SUPPLY CHAIN PLANNING 69
8.4 WAREHOUSE MANAGEMENT 71
8.5 VIRTUAL ASSISTANT 73
8.6 RISK MANAGEMENT 74
8.7 FREIGHT BROKERAGE 75
8.8 OTHERS 76

9 ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MARKET, BY INDUSTRY 78
9.1 INTRODUCTION 79
9.2 AUTOMOTIVE 80
9.3 AEROSPACE 81
9.4 MANUFACTURING 82
9.5 RETAIL 83
9.6 HEALTHCARE 84
9.7 CONSUMER-PACKAGED GOODS 84
9.8 FOOD & BEVERAGES 86
9.9 OTHERS 87

10 GEOGRAPHIC ANALYSIS 88
10.1 INTRODUCTION 89
10.2 NORTH AMERICA 91
10.2.1 US 95
10.2.2 CANADA 95
10.2.3 MEXICO 96
10.3 EUROPE 96
10.3.1 GERMANY 100
10.3.2 UK 100
10.3.3 FRANCE 101
10.3.4 ITALY 101
10.3.5 SPAIN 101
10.3.6 REST OF EUROPE 101
10.4 APAC 102
10.4.1 CHINA 106
10.4.2 JAPAN 106
10.4.3 SOUTH KOREA 107
10.4.4 INDIA 107
10.4.5 REST OF APAC 108
10.5 REST OF THE WORLD 108
10.5.1 MIDDLE EAST AND AFRICA 110
10.5.2 SOUTH AMERICA 111

11 COMPETITIVE LANDSCAPE 112
11.1 INTRODUCTION 112
11.2 MARKET RANKING ANALYSIS 113
11.3 COMPETITIVE SITUATIONS AND TRENDS 115
11.3.1 PRODUCT LAUNCHES (2016–2018) 115
11.3.2 AGREEMENTS, PARTNERSHIPS, COLLABORATIONS, & CONTRACTS
(2017–2018) 116
11.3.3 MERGERS & ACQUISITIONS (2016–2018) 117
11.3.4 EXPANSION (2016–2018) 118

12 COMPANY PROFILES 119
(Business Overview, Products Offered, Recent Developments, SWOT Analysis, and MnM View)*
12.1 KEY PLAYERS 119
12.1.1 NVIDIA 119
12.1.2 IBM 124
12.1.3 INTEL 127
12.1.4 XILINX 131
12.1.5 SAMSUNG ELECTRONICS 135
12.1.6 MICRON TECHNOLOGY 139
12.1.7 MICROSOFT 142
12.1.8 AMAZON 145
12.1.9 SAP 149
12.1.10 ORACLE 151
12.1.11 LOGILITY 154
12.1.12 LLAMASOFT, INC. 156
12.1.13 CLEARMETAL 158
12.1.14 SPLICE MACHINE 159
12.1.15 CAINIAO NETWORK (ALIBABA) 160
12.1.16 FEDEX 161
12.1.17 DEUTSCHE POST AG DHL 163
12.2 OTHER COMPANIES 164
12.2.1 FRAIGHT AI 164
12.2.2 C. H.ROBINSON 164
12.2.3 E2OPEN 165
12.2.4 RELEX SOLUTION 165
12.2.5 TEKNOWLOGI 166
12.2.6 PRESENSO. 166
*Details on Business Overview, Products Offered, Recent Developments, SWOT Analysis, and MnM View might not be captured in case of unlisted companies.

13 APPENDIX 167
13.1 INSIGHTS FROM INDUSTRY EXPERTS 167
13.2 DISCUSSION GUIDE 168
13.3 KNOWLEDGE STORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 171
13.4 INTRODUCING RT: REAL-TIME MARKET INTELLIGENCE 173
13.5 AVAILABLE CUSTOMIZATION 174
13.6 RELATED REPORTS 174
13.7 AUTHOR DETAILS 175