A comprehensive analysis of the Global Artificial Intelligence in Supply Chain Market is been done in this intelligence report. It includes the investigations done on the past progress, ongoing market scenarios, and future prospects. An accurate data of the products, strategies and market shares of leading companies in this particular market is mentioned. The report also analyzes innovative business strategies; value added factors and business opportunities with Detailed Porter’s Five Forces Analysis. The Artificial Intelligence in Supply Chain market report presents a 360-degree overview of the competitive scenario of the Global market. The report further projects the size and valuation of the global market in the coming forecast period. The report also presents a thorough qualitative and quantitative data affecting to the projected impact of these factors on market’s future growth prospects.
The Global Artificial Intelligence in Supply Chain Market is expected to reach USD 10,787.2 million by 2025 from USD 21.21 million in 2017 and is projected to grow at a CAGR of 8.1% in the forecast period of 2018 to 2025.
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KEY HIGHLIGHTS OF THE ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MARKET REPORT:
- Get enlightened information available on the Artificial Intelligence in Supply Chain market worldwide.
- Identify growth in various segments and investment opportunities. Benchmark fulfillment adjacent the key adversary.
- The main criterion related to Artificial Intelligence in Supply Chain industry area including the overall structure of the product, numbers of applications, price, demand, and supply are enfolded in this report.
- The research of emerging Artificial Intelligence in Supply Chain market section and extant industry segments will aid the investors or novice in formulating and developing business strategies.
- Facilitates decision making in view of noteworthy and gauging information also the drivers and limitations available of the market.
- Global Artificial Intelligence in Supply Chain Market share assessments for the regional and country level segments.
COMPANIES COVERED
- NVIDIA,
- IBM,
- Intel Corporation,
- Xilinx,
- Samsung Electronics,
- Samsung Electronics America,
- Micron Technology,
- Microsoft,
- Amazon,
- SAP,
- Oracle,
- Logility,
- LLamasoft, Inc.,
- ClearMetal,
- Splice Machine,
- Cainiao Networks,
- FedEx,
- DHL,
- C.H. Robinson,
- E2open,
- RELEX Solutions,
- Teknowlogi,
- Presenso,
- Fraight AI,
- FedEx Supply Chain,
- Deutsche Post,
- Alibaba Group,
- SoftBank Group,
- Baidu, Inc.,
- Salesforce,
- Interactions LLC,
- Lexalytics, Inc.,
- FedEx Express Canada,
- Next IT,
- RetailNext,
- Sentient Technologies,
- ViSenze – AI for Visual Commerce,
- Amazon Web Services and others.
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TABLE OF CONTENTS
PART 01: EXECUTIVE SUMMARY
PART 02: SCOPE OF THE REPORT
PART 03: RESEARCH METHODOLOGY
PART 04: MARKET LANDSCAPE
- Market ecosystem
- Market characteristics
- Market segmentation analysis
PART 05: PIPELINE ANALYSIS
- Pipeline analysis
PART 06: MARKET SIZING
- Market definition
- Market sizing
- Market size and forecast
PART 07: FIVE FORCES ANALYSIS
- Bargaining power of buyers
- Bargaining power of suppliers
- Threat of new entrants
- Threat of substitutes
- Threat of rivalry
- Market condition
PART 08: MARKET SEGMENTATION
- Segmentation
- Comparison
- Market opportunity
PART 09: CUSTOMER LANDSCAPE
PART 10: REGIONAL LANDSCAPE
- Geographical segmentation
- Regional comparison
- North America
- South America
- Europe
- MEA
- APAC
- Market opportunity
PART 11: DECISION FRAMEWORK
PART 12: DRIVERS AND CHALLENGES
- Market drivers
- Market challenges
PART 13: MARKET TRENDS
PART 14: VENDOR LANDSCAPE
- Overview
- Landscape disruption
PART 15: VENDOR ANALYSIS
- Vendors covered
- Vendor classification
- Market positioning of vendors
PART 16: APPENDIX
- List of abbreviations
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Market Segmentation
Based on application
- Fleet management,
- Supply chain planning,
- Warehouse management,
- Virtual assistant,
- Risk management,
- Freight brokerage and others
Based on technology
- Machine learning,
- Natural language processing (NLP),
- Context-aware computing and
- Computer vision
Based on industry
- Automotive,
- Aerospace,
- Manufacturing,
- Retail,
- Healthcare,
- Consumer-packaged goods,
- Food & beverages and others
Based on offering
- Hardware,
- Software and
- Services
By Geography
- Asia Pacific (China, South Korea, Japan, India, Australia, Singapore, Thailand, Indonesia, Malaysia, Philippines, Rest of Asia-Pacific).
- North America (US, Canada, Mexico).
- Europe (Russia, Turkey, Belgium, Netherlands, Switzerland, Spain, France, Germany, Italy, UK, Rest of Europe.
- South America (Brazil, Argentina, Rest of South America).
- Middle East and Africa (South Africa, Egypt, Saudi Arabia, United Arab Emirates, Israel, Rest of Middle East and Africa).
Competitive Analysis
The global artificial intelligence in supply chain market is fragmented and the major players have used various strategies such as new product launches, expansions, agreements, joint ventures, partnerships, acquisitions, and others to increase their footprints in this market in order to sustain in long run. The report includes market shares of artificial intelligence in supply chain market for global, Europe, North America, Asia Pacific and South America.
In 2016 artificial intelligence and machine learning emerging as the next big technology has seen a race for artificial intelligence, with a number of acquisitions and large technology vendors such as IBM, Google and Amazon are launching a new artificial intelligence-enabled product.
In 2017, Teknowlogi Launches a first cloud-based solution built on artificial intelligence for the logistics industry. It is a next-generation platform category, representing a much-needed advancement for traditional industry logistics system.
Market Drivers and Restraints:
- Growth of big data
- Demand for greater visibility and transparency in supply chain data and processes
- Adoption of AI to improve consumer services and satisfaction.
- High Information and data storage capacity
- High computing power and parallel processing capabilities
- Lack of people power
- Data inefficiency in building proper AI algorithms.
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