Artificial Intelligence In Supply Chain Market
Artificial Intelligence in Supply Chain - Global Market Outlook (2019-2027)
According to Stratistics MRC, the Global Artificial Intelligence in Supply Chain Market is accounted for $1,078.68 million in 2019 and is expected to reach $18,861.79 million by 2027 growing at a CAGR of 43.0% during the forecast period. Increasing demand for big data and demand for greater visibility and transparency are the major factors propelling the market growth. However, the limited number of AI experts and difficulties in data integration from multiple sources are hampering the market growth.
Artificial Intelligence (AI) is increasingly relied upon to improve operational efficiency and effectiveness of a broad spectrum of industries. Supply Chain Management (SCM) is a critical logistics function for the most modern enterprise. AI is being integrated into SCM solutions to improve everything from process automation to provide greater visibility into static and real-time data. AI in the SCM market also improves related management information systems. Accordingly, the AI in the supply chain management market represents a substantial opportunity for many software-driven and data-oriented companies. Modern supply chains represent complex systems of organizations, people, activities, information, and resources involved in moving a product or service from supplier to customer.
Based on the end-user, the consumer-packaged-goods segment is going to have a lucrative growth during the forecast period owing to the consistent proliferation of the e-commerce sector across emerging economies. The use of AI in the supply chain can offer profitable drop-shipping features such as providing product tracking, inventory management, and warehouse management.
By geography, Asia Pacific is going to have a lucrative growth during the forecast period due to the growing demand for AI-based business solutions for automation across several operational areas including supply chain management. Furthermore, increasing adoption of modern technologies including machine learning and natural language processing, government initiatives towards the adoption of advanced technologies, and rapid digitalization are also contributing to the fastest growth in the Asia Pacific region.
Some of the key players profiled in the Artificial Intelligence in Supply Chain Market include NVIDIA, IBM, Intel, Xilinx, Samsung Electronics, Micron Technology, Microsoft, Amazon, SAP, Oracle, Logility, LLamasoft, Inc., ClearMetal, Splice Machine, and Cainiao Network.
Offerings Covered:
• Hardware
• Software
• Services
Technologies Covered:
• Machine Learning
• Natural Language Processing (NLP)
• Context-Aware Computing
• Computer Vision
• Cognitive Computing
Applications Covered:
• Fleet Management
• Supply Chain Planning
• Warehouse Management
• Virtual Assistant
• Risk Management
• Freight Brokerage
• Planning & Logistics
• Inventory Management
• Operational Procurement
• Supplier Relationship Management
End Users Covered:
• Automotive
• Aerospace
• Manufacturing
• Retail
• Healthcare
• Consumer-Packaged Goods
• Food & Beverages
• Energy & Power
Regions Covered:
• North America
o US
o Canada
o Mexico
• Europe
o Germany
o UK
o Italy
o France
o Spain
o Rest of Europe
• Asia Pacific
o Japan
o China
o India
o Australia
o New Zealand
o South Korea
o Rest of Asia Pacific
• South America
o Argentina
o Brazil
o Chile
o Rest of South America
• Middle East & Africa
o Saudi Arabia
o UAE
o Qatar
o South Africa
o Rest of Middle East & Africa
What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2018, 2019 2020, 2024, and 2027
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements
Free Customization Offerings:
All the customers of this report will be entitled to receive one of the following free customization options:
• Company Profiling
o Comprehensive profiling of additional market players (up to 3)
o SWOT Analysis of key players (up to 3)
• Regional Segmentation
o Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
o Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances
Table of Contents
1 Executive Summary
2 Preface
2.1 Abstract
2.2 Stake Holders
2.3 Research Scope
2.4 Research Methodology
2.4.1 Data Mining
2.4.2 Data Analysis
2.4.3 Data Validation
2.4.4 Research Approach
2.5 Research Sources
2.5.1 Primary Research Sources
2.5.2 Secondary Research Sources
2.5.3 Assumptions
3 Market Trend Analysis
3.1 Introduction
3.2 Drivers
3.3 Restraints
3.4 Opportunities
3.5 Threats
3.6 Technology Analysis
3.7 Application Analysis
3.8 End User Analysis
3.9 Emerging Markets
3.10 Impact of Covid-19
4 Porters Five Force Analysis
4.1 Bargaining power of suppliers
4.2 Bargaining power of buyers
4.3 Threat of substitutes
4.4 Threat of new entrants
4.5 Competitive rivalry
5 Global Artificial Intelligence in Supply Chain Market, By Offering
5.1 Introduction
5.2 Hardware
5.2.1 Processors
5.2.1.1 Microprocessing Unit (MPU)
5.2.1.2 Graphics Processing Unit (GPU)
5.2.1.3 Field Programmable Gate Arrays (FPGA)
5.2.1.4 Application Specific Integrated Circuit (ASIC)
5.2.2 Memory
5.2.3 Network
5.3 Software
5.3.1 AI Platforms
5.3.1.1 Application Program Interface (API)
5.3.1.2 Machine Learning Framework
5.3.1.3 Chatbots
5.3.1.4 Deep Learning Software
5.3.2 AI Solutions
5.4 Services
5.4.1 Deployment & Integration
5.4.1.1 On Premise
5.4.1.2 On Cloud
5.4.2 Support & Maintenance
6 Global Artificial Intelligence in Supply Chain Market, By Technology
6.1 Introduction
6.2 Machine Learning
6.2.1 Supervised Learning
6.2.2 Unsupervised Learning
6.2.3 Reinforcement Learning
6.3 Natural Language Processing (NLP)
6.4 Context-Aware Computing
6.5 Computer Vision
6.6 Cognitive Computing
7 Global Artificial Intelligence in Supply Chain Market, By Application
7.1 Introduction
7.2 Fleet Management
7.3 Supply Chain Planning
7.4 Warehouse Management
7.5 Virtual Assistant
7.6 Risk Management
7.7 Freight Brokerage
7.8 Planning & Logistics
7.9 Inventory Management
7.10 Operational Procurement
7.11 Supplier Relationship Management
8 Global Artificial Intelligence in Supply Chain Market, By End User
8.1 Introduction
8.2 Automotive
8.3 Aerospace
8.4 Manufacturing
8.5 Retail
8.6 Healthcare
8.7 Consumer-Packaged Goods
8.8 Food & Beverages
8.9 Energy & Power
9 Global Artificial Intelligence in Supply Chain Market, By Geography
9.1 Introduction
9.2 North America
9.2.1 US
9.2.2 Canada
9.2.3 Mexico
9.3 Europe
9.3.1 Germany
9.3.2 UK
9.3.3 Italy
9.3.4 France
9.3.5 Spain
9.3.6 Rest of Europe
9.4 Asia Pacific
9.4.1 Japan
9.4.2 China
9.4.3 India
9.4.4 Australia
9.4.5 New Zealand
9.4.6 South Korea
9.4.7 Rest of Asia Pacific
9.5 South America
9.5.1 Argentina
9.5.2 Brazil
9.5.3 Chile
9.5.4 Rest of South America
9.6 Middle East & Africa
9.6.1 Saudi Arabia
9.6.2 UAE
9.6.3 Qatar
9.6.4 South Africa
9.6.5 Rest of Middle East & Africa
10 Key Developments
10.1 Agreements, Partnerships, Collaborations and Joint Ventures
10.2 Acquisitions & Mergers
10.3 New Product Launch
10.4 Expansions
10.5 Other Key Strategies
11 Company Profiling
11.1 NVIDIA
11.2 IBM
11.3 Intel
11.4 Xilinx
11.5 Samsung Electronics
11.6 Micron Technology
11.7 Microsoft
11.8 Amazon
11.9 SAP
11.10 Oracle
11.11 Logility
11.12 LLamasoft, Inc.
11.13 ClearMetal
11.14 Splice Machine
11.15 Cainiao Network
List of Tables
1 Global Artificial Intelligence in Supply Chain Market Outlook, By Region (2018-2027) ($MN)
2 Global Artificial Intelligence in Supply Chain Market Outlook, By Offering (2018-2027) ($MN)
3 Global Artificial Intelligence in Supply Chain Market Outlook, By Hardware (2018-2027) ($MN)
4 Global Artificial Intelligence in Supply Chain Market Outlook, By Processors (2018-2027) ($MN)
5 Global Artificial Intelligence in Supply Chain Market Outlook, By Memory (2018-2027) ($MN)
6 Global Artificial Intelligence in Supply Chain Market Outlook, By Network (2018-2027) ($MN)
7 Global Artificial Intelligence in Supply Chain Market Outlook, By Software (2018-2027) ($MN)
8 Global Artificial Intelligence in Supply Chain Market Outlook, By AI Platforms (2018-2027) ($MN)
9 Global Artificial Intelligence in Supply Chain Market Outlook, By AI Solutions (2018-2027) ($MN)
10 Global Artificial Intelligence in Supply Chain Market Outlook, By Services (2018-2027) ($MN)
11 Global Artificial Intelligence in Supply Chain Market Outlook, By Deployment & Integration (2018-2027) ($MN)
12 Global Artificial Intelligence in Supply Chain Market Outlook, By Support & Maintenance (2018-2027) ($MN)
13 Global Artificial Intelligence in Supply Chain Market Outlook, By Technology (2018-2027) ($MN)
14 Global Artificial Intelligence in Supply Chain Market Outlook, By Machine Learning (2018-2027) ($MN)
15 Global Artificial Intelligence in Supply Chain Market Outlook, By Supervised Learning (2018-2027) ($MN)
16 Global Artificial Intelligence in Supply Chain Market Outlook, By Unsupervised Learning (2018-2027) ($MN)
17 Global Artificial Intelligence in Supply Chain Market Outlook, By Reinforcement Learning (2018-2027) ($MN)
18 Global Artificial Intelligence in Supply Chain Market Outlook, By Natural Language Processing (NLP) (2018-2027) ($MN)
19 Global Artificial Intelligence in Supply Chain Market Outlook, By Context-Aware Computing (2018-2027) ($MN)
20 Global Artificial Intelligence in Supply Chain Market Outlook, By Computer Vision (2018-2027) ($MN)
21 Global Artificial Intelligence in Supply Chain Market Outlook, By Cognitive Computing (2018-2027) ($MN)
22 Global Artificial Intelligence in Supply Chain Market Outlook, By Application (2018-2027) ($MN)
23 Global Artificial Intelligence in Supply Chain Market Outlook, By Fleet Management (2018-2027) ($MN)
24 Global Artificial Intelligence in Supply Chain Market Outlook, By Supply Chain Planning (2018-2027) ($MN)
25 Global Artificial Intelligence in Supply Chain Market Outlook, By Warehouse Management (2018-2027) ($MN)
26 Global Artificial Intelligence in Supply Chain Market Outlook, By Virtual Assistant (2018-2027) ($MN)
27 Global Artificial Intelligence in Supply Chain Market Outlook, By Risk Management (2018-2027) ($MN)
28 Global Artificial Intelligence in Supply Chain Market Outlook, By Freight Brokerage (2018-2027) ($MN)
29 Global Artificial Intelligence in Supply Chain Market Outlook, By Planning & Logistics (2018-2027) ($MN)
30 Global Artificial Intelligence in Supply Chain Market Outlook, By Inventory Management (2018-2027) ($MN)
31 Global Artificial Intelligence in Supply Chain Market Outlook, By Operational Procurement (2018-2027) ($MN)
32 Global Artificial Intelligence in Supply Chain Market Outlook, By Supplier Relationship Management (2018-2027) ($MN)
33 Global Artificial Intelligence in Supply Chain Market Outlook, By End User (2018-2027) ($MN)
34 Global Artificial Intelligence in Supply Chain Market Outlook, By Automotive (2018-2027) ($MN)
35 Global Artificial Intelligence in Supply Chain Market Outlook, By Aerospace (2018-2027) ($MN)
36 Global Artificial Intelligence in Supply Chain Market Outlook, By Manufacturing (2018-2027) ($MN)
37 Global Artificial Intelligence in Supply Chain Market Outlook, By Retail (2018-2027) ($MN)
38 Global Artificial Intelligence in Supply Chain Market Outlook, By Healthcare (2018-2027) ($MN)
39 Global Artificial Intelligence in Supply Chain Market Outlook, By Consumer-Packaged Goods (2018-2027) ($MN)
40 Global Artificial Intelligence in Supply Chain Market Outlook, By Food & Beverages (2018-2027) ($MN)
41 Global Artificial Intelligence in Supply Chain Market Outlook, By Energy & Power (2018-2027) ($MN)
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.
List of Figures
RESEARCH METHODOLOGY
We at ‘Stratistics’ opt for an extensive research approach which involves data mining, data validation, and data analysis. The various research sources include in-house repository, secondary research, competitor’s sources, social media research, client internal data, and primary research.
Our team of analysts prefers the most reliable and authenticated data sources in order to perform the comprehensive literature search. With access to most of the authenticated data bases our team highly considers the best mix of information through various sources to obtain extensive and accurate analysis.
Each report takes an average time of a month and a team of 4 industry analysts. The time may vary depending on the scope and data availability of the desired market report. The various parameters used in the market assessment are standardized in order to enhance the data accuracy.
Data Mining
The data is collected from several authenticated, reliable, paid and unpaid sources and is filtered depending on the scope & objective of the research. Our reports repository acts as an added advantage in this procedure. Data gathering from the raw material suppliers, distributors and the manufacturers is performed on a regular basis, this helps in the comprehensive understanding of the products value chain. Apart from the above mentioned sources the data is also collected from the industry consultants to ensure the objective of the study is in the right direction.
Market trends such as technological advancements, regulatory affairs, market dynamics (Drivers, Restraints, Opportunities and Challenges) are obtained from scientific journals, market related national & international associations and organizations.
Data Analysis
From the data that is collected depending on the scope & objective of the research the data is subjected for the analysis. The critical steps that we follow for the data analysis include:
- Product Lifecycle Analysis
- Competitor analysis
- Risk analysis
- Porters Analysis
- PESTEL Analysis
- SWOT Analysis
The data engineering is performed by the core industry experts considering both the Marketing Mix Modeling and the Demand Forecasting. The marketing mix modeling makes use of multiple-regression techniques to predict the optimal mix of marketing variables. Regression factor is based on a number of variables and how they relate to an outcome such as sales or profits.
Data Validation
The data validation is performed by the exhaustive primary research from the expert interviews. This includes telephonic interviews, focus groups, face to face interviews, and questionnaires to validate our research from all aspects. The industry experts we approach come from the leading firms, involved in the supply chain ranging from the suppliers, distributors to the manufacturers and consumers so as to ensure an unbiased analysis.
We are in touch with more than 15,000 industry experts with the right mix of consultants, CEO's, presidents, vice presidents, managers, experts from both supply side and demand side, executives and so on.
The data validation involves the primary research from the industry experts belonging to:
- Leading Companies
- Suppliers & Distributors
- Manufacturers
- Consumers
- Industry/Strategic Consultants
Apart from the data validation the primary research also helps in performing the fill gap research, i.e. providing solutions for the unmet needs of the research which helps in enhancing the reports quality.
For more details about research methodology, kindly write to us at info@strategymrc.com
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