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Global Artificial Intelligence in Supply Chain Market Research Report: Forecast (2022-27)

By Application (Fleet Management, Supply Chain Planning, Warehouse Management, Virtual Assistant, Machine Utilization, Others (Risk Management, Freight Brokerage, etc.)), By Technology (Machine Learni... ... logy (Machine Learning (Supervised Learning, Unsupervised Learning, Reinforcement), Natural Language Processing (NLP), Context-Aware Computing, Deep Learning, Computer Vision), By End User (Automotive, Aerospace, Retail, Food & Beverages, Others (Healthcare, Energy, etc.)), By Product Offering (Hardware (Processor, Networking, Memory), Software (AI Solutions, AI Platform), Services (Deployment & Integration, Support & Maintenance)), By Deployment Type (On-premise, Cloud), By Region (North America, South America, Europe, The Middle East & Africa, Asia-Pacific) Read more

ICT & Electronics
Jun 2022
Pages 180
Report Format: PDF, Excel, PPT

Market Definition

Artificial Intelligence is the simulation of human intelligence displayed by machines, where machines can be utilized for problem-solving, learning, or even making decisions. In addition, supply chain management solutions based on AI provide visibility & scalability into warehouse operations, logistics channels, and resource management, among others, through the ability to analyze a massive volume of data. It has numerous benefits, such as accurate inventory management, warehouse efficiency enhancement, and reduced operational costs. Therefore, the increasing demand for AI-based services during warehouse operations would considerably escalate the market growth in the coming years.

Market Insights

The Global Artificial Intelligence in the Supply Chain market is anticipated to grow at a CAGR of around 37.89% during the forecast period, 2022-27. The factors responsible for the market growth are the increasing adoption of AI-based services in warehouse automation for improving accuracy & security, the rising count of AI-based technology start-ups, and the dire need for automating supply chain routes, among others. Additionally, the vast pool of data collected by the operational tasks in the warehouses has augmented the use of AI-driven tools which would help the facility owners to improve inventory management efficiency, thereby enhancing productivity.

Furthermore, the low demand of the goods may lead to a stockpile of unsold stocks in the warehouses, which increases the burden of maintaining it. Therefore, demand forecasting through AI integration can help organizations to manufacture products accordingly & accurately with minimum waste of goods. This is mainly important during a global supply chain crisis, which was felt during the COVID-19 pandemic. Also, the application of AI for time-series forecasting helps an organization in planning the inventory & logistics based on the insights derived from the seasonal sales cycles.

Report Coverage	Details
Study Period	Historical Data: 2017-20
	Base Year: 2021
	Forecast Period: 2022-27
CAGR (2022-2027)	37.89%
Regions Covered	North America: The US, Canada, Mexico
	South America: Brazil, Argentina, Rest of South America
	Europe: Germany, France, Italy, The UK, Spain, Rest of Europe
	Asia-Pacific: China, Japan, India, South Korea, Rest of Asia Pacific
	Middle East & Africa: UAE, South Africa, Saudi Arabia, Rest of Middle East & Africa
Key Companies Profiled	Nvidia Corporation, Google LLC, SAP SE, SAS, Amazon Web Services (AWS), Verizon Communications Inc, AT&T Inc, Cisco Systems, Dell, Qlik, Others
Unit Denominations	USD Million/Billion

Moreover, data such as previous sales history, macroeconomic situations, product reviews, and sales promotions, among others, is fed into the AI model to find the last year’s data. Therefore, the trained AI model has the ability to provide demand estimates during the forecast period. In addition, modern AI algorithms such as Long Short-term Memory networks (LSTMs) & autoencoders have proved to help generate new product information forecasts. Thus, the expanding AI algorithms for forecasting demand & supply globally have bolstered the need for AI in the supply chain during the forecast period.

Key Trend in the Market

Increasing Demand for Intelligent Business Processes & Automation to Escalate the Market Growth

Due to the rising need for a variety of warehouse automation operations, businesses, including IBM & Amazon Web Services (AWS), among others, have created a supply chain intelligence suite with an integrated suite that offers AI-based supply chain optimization & automation solutions. These solutions improve supply chain processes, increase agility, and optimize warehouse operations. Furthermore, an intelligent automation system is made to encourage cooperation by fusing disparate data sets to help you produce actionable insights, smarter workflows, and intelligent automation, which leads to quicker problem-solving & more effective supply chain operations. Therefore, the growing technological advancement globally would propel the demand for AI in the supply chain in the coming years.

Impact of Industry 4.0 on the Global Artificial Intelligence in the Supply Chain Market

The industrial 4.0 revolution catalyzed the development of new automation technologies, robotics, Internet of Things (IoT), Big data, machine learning (ML), artificial intelligence, as well as for analytics, which has resulted in the rapid adoption of technology in the warehouse operations. Therefore, with the advent of Industry 4.0, supply chain mechanisms are transforming by adopting digitization, automation, and centralized business intelligence systems. Notably, these technologies mainly depend on cloud computing to bring agility in services & quicker response.

Furthermore, the incorporation of AI & IoT in industrial activities is changing the pace of the transformation in supply chain management (SCM). As a result, the development in the supply chain is visible at every level of manufacturing, procurement, logistics, warehousing, and fulfillment, which has made companies with integrated digital supply chain functions far more efficient than their predecessor.

Introduction
1. Research Process
2. Assumptions
3. Market Segmentation
4. Market Definition
Executive Summary
Global Artificial Intelligence in Supply Chain Market Start-Up Ecosystem, 2017-2022
1. Entrepreneurial Activity
2. Year on Year Funding Received
3. Funding Received by Top Companies
4. Key Investors Active in the Market
5. Series Wise Funding Received
  1. Seed Funding
  2. Angel Investing
  3. Venture Capitalists (VC) Funding
  4. Others
Impact of Covid-19 on the Global Artificial Intelligence in the Supply Chain Market
Impact of Industry 4.0 on the Global Artificial Intelligence in the Supply Chain Market
Global Artificial Intelligence in Supply Chain Market Trends & Insights
Global Artificial Intelligence in Supply Chain Market Dynamics
1. Drivers
2. Challenges
Global Artificial Intelligence in Supply Chain Market Hotspots & Opportunities
Global Artificial Intelligence in Supply Chain Market Government Regulations & Policies, 2017-2022
Global Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
1. Market Size & Analysis
  1. By Revenues (USD Million)
2. Market Share & Analysis
  1. By Application
    1. Fleet Management
    2. Supply Chain Planning
    3. Warehouse Management
    4. Virtual Assistant
    5. Machine Utilization
    6. Others (Risk Management, Freight Brokerage, etc.)
  2. By Technology
    1. Machine Learning
      1. Supervised Learning
      2. Unsupervised Learning
      3. Reinforcement
    2. Natural Language Processing (NLP)
    3. Context-Aware Computing
    4. Deep Learning
    5. Computer Vision
  3. By End User
    1. Automotive
    2. Aerospace
    3. Retail
    4. Food & Beverages
    5. Others (Healthcare, Energy, etc.)
  4. By Product Offering
    1. Hardware
      1. Processor
        
        Graphics Processing Unit (GPU)
        
        Vision Processing Unit (VPU)
        
        Field-Programmable Gate Array (FPGA)
        
        Application-Specific Integrated Circuit (ASIC)
        
        Others (Tensor Processing Unit (TPU), Microprocessor (MPU)
      2. Networking
      3. Memory
    2. Software
      1. AI Solutions
      2. AI Platform
        
        Application Program Interface (API)
        
        Machine Learning Framework
    3. Services
      1. Deployment & Integration
      2. Support & Maintenance
  5. By Deployment
    1. On-Premise
    2. Cloud
  6. By Region
    1. North America
    2. South America
    3. Europe
    4. The Middle East & Africa
    5. Asia Pacific
North America Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
1. Market Size & Analysis
  1. By Revenues (USD Million)
2. Market Share & Analysis
  1. By Application
  2. By Technology
  3. By End User
  4. By Product Offering
  5. By Deployment
  6. By Country
    1. The US
    2. Canada
    3. Mexico
3. The US Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
  1. Market Size & Analysis
    1. By Revenues
  2. Market Share & Analysis
    1. By End User
    2. By Product Offering
    3. By Deployment
4. Canada Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
  1. Market Size & Analysis
    1. By Revenues
  2. Market Share & Analysis
    1. By End User
    2. By Product Offering
    3. By Deployment
5. Mexico Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
  1. Market Size & Analysis
    1. By Revenues
  2. Market Share & Analysis
    1. By End User
    2. By Product Offering
    3. By Deployment
South America Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
1. Market Size & Analysis
  1. By Revenues (USD Million)
2. Market Share & Analysis
  1. By Application
  2. By Technology
  3. By End User
  4. By Product Offering
  5. By Deployment
  6. By Country
    1. Brazil
    2. Argentina
    3. Rest of South America
3. Brazil Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
  1. Market Size & Analysis
    1. By Revenues
  2. Market Share & Analysis
    1. By End User
    2. By Product Offering
    3. By Deployment
4. Argentina Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
  1. Market Size & Analysis
    1. By Revenues
  2. Market Share & Analysis
    1. By End User
    2. By Product Offering
    3. By Deployment
Europe Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
1. Market Size & Analysis
  1. By Revenues (USD Million)
2. Market Share & Analysis
  1. By Application
  2. By Technology
  3. By End User
  4. By Product Offering
  5. By Deployment
  6. By Country
    1. Germany
    2. The UK
    3. Italy
    4. Spain
    5. France
    6. Rest of Europe
3. Germany Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
  1. Market Size & Analysis
    1. By Revenues
  2. Market Share & Analysis
    1. By End User
    2. By Product Offering
    3. By Deployment
4. The UK Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
  1. Market Size & Analysis
    1. By Revenues
  2. Market Share & Analysis
    1. By End User
    2. By Product Offering
    3. By Deployment
5. Italy Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
  1. Market Size & Analysis
    1. By Revenues
  2. Market Share & Analysis
    1. By End User
    2. By Product Offering
    3. By Deployment
6. Spain Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
  1. Market Size & Analysis
    1. By Revenues
  2. Market Share & Analysis
    1. By End User
    2. By Product Offering
    3. By Deployment
7. France Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
  1. Market Size & Analysis
    1. By Revenues
  2. Market Share & Analysis
    1. By End User
    2. By Product Offering
    3. By Deployment
The Middle East & Africa Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
1. Market Size & Analysis
  1. By Revenues (USD Million)
2. Market Share & Analysis
  1. By Application
  2. By Technology
  3. By End User
  4. By Product Offering
  5. By Deployment
  6. By Country
    1. The UAE
    2. Saudi Arabia
    3. South Africa
    4. Rest of Middle East & Africa
3. The UAE Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
  1. Market Size & Analysis
    1. By Revenues
  2. Market Share & Analysis
    1. By End User
    2. By Product Offering
    3. By Deployment
4. Saudi Arabia Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
  1. Market Size & Analysis
    1. By Revenues
  2. Market Share & Analysis
    1. By End User
    2. By Product Offering
    3. By Deployment
5. South Africa Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
  1. Market Size & Analysis
    1. By Revenues
  2. Market Share & Analysis
    1. By End User
    2. By Product Offering
    3. By Deployment
Asia-Pacific Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
1. Market Size & Analysis
  1. By Revenues (USD Million)
2. Market Share & Analysis
  1. By Application
  2. By Technology
  3. By End User
  4. By Product Offering
  5. By Deployment
  6. By Country
    1. China
    2. India
    3. Japan
    4. South Korea
    5. Rest of Asia Pacific
3. China Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
  1. Market Size & Analysis
    1. By Revenues
  2. Market Share & Analysis
    1. By End User
    2. By Product Offering
    3. By Deployment
4. India Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
  1. Market Size & Analysis
    1. By Revenues
  2. Market Share & Analysis
    1. By End User
    2. By Product Offering
    3. By Deployment
5. Japan Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
  1. Market Size & Analysis
    1. By Revenues
  2. Market Share & Analysis
    1. By End User
    2. By Product Offering
    3. By Deployment
6. South Korea Artificial Intelligence in Supply Chain Market Outlook, 2017-2027F
  1. Market Size & Analysis
    1. By Revenues
  2. Market Share & Analysis
    1. By End User
    2. By Product Offering
    3. By Deployment
Global Artificial Intelligence in Supply Chain Market Key Strategic Imperatives for Success & Growth
Competitive Outlook
1. Competition Matrix
  1. Application Portfolio
  2. Brand Specialization
  3. Target Markets
  4. Target Applications
  5. Research & Development
  6. Strategic Alliances
  7. Strategic Initiatives
2. Company Profiles ((Business Description, Application Segments, Business Segments, Financials, Strategic Alliances/ Partnerships, Future Plans)
  1. Hardware Providers
    1. Nvidia Corporation
    2. Google LLC
    3. Intel Corporation
    4. Advanced Micro Devices (AMD)
    5. Micron Technology Inc.
    6. Cerebras Systems Inc.
  2. Software Providers
    1. SAP SE
    2. SAS
    3. Amazon Web Services (AWS)
    4. IBM
    5. Microsoft Azure
    6. Oracle
  3. Telecommunication Data Providers
    1. Verizon Communications Inc.
    2. AT&T Inc.
    3. BT Group
    4. The Nippon Telegraph and Telephone Corporation (NTT)
    5. Vodafone Group Plc
    6. Deutsche Telekom AG
    7. Reliance Industries Limited
  4. Networking Solution Provider
    1. Cisco Systems
    2. Dell
    3. VMware, Inc.
    4. Schneider Electric SE
  5. Data Analytics
    1. Qlik
    2. Alteryx
    3. Salesforce
Disclaimer

MarkNtel Advisors follows a robust and iterative research methodology designed to ensure maximum accuracy and minimize deviation in market estimates and forecasts. Our approach combines both bottom-up and top-down techniques to effectively segment and quantify various aspects of the market. A consistent feature across all our research reports is data triangulation, which examines the market from three distinct perspectives to validate findings. Key components of our research process include:

1. Scope & Research Design At the outset, MarkNtel Advisors define the research objectives and formulate pertinent questions. This phase involves determining the type of research—qualitative or quantitative—and designing a methodology that outlines data collection methods, target demographics, and analytical tools. They also establish timelines and budgets to ensure the research aligns with client goals.

2. Sample Selection and Data Collection In this stage, the firm identifies the target audience and determines the appropriate sample size to ensure representativeness. They employ various sampling methods, such as random or stratified sampling, based on the research objectives. Data collection is carried out using tools like surveys, interviews, and observations, ensuring the gathered data is reliable and relevant.

3. Data Analysis and Validation Once data is collected, MarkNtel Advisors undertake a rigorous analysis process. This includes cleaning the data to remove inconsistencies, employing statistical software for quantitative analysis, and thematic analysis for qualitative data. Validation steps are taken to ensure the accuracy and reliability of the findings, minimizing biases and errors.

4. Data Forecast and FinalizationThe final phase involves forecasting future market trends based on the analyzed data. MarkNtel Advisors utilize predictive modeling and time series analysis to anticipate market behaviors. The insights are then compiled into comprehensive reports, featuring visual aids like charts and graphs, and include strategic recommendations to inform client decision-making

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