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Artificial Intelligence in Supply Chain Market

Artificial Intelligence in Supply Chain Market Size, Share & Segmentation by Offering (Hardware, Software, and Services), by Technology (Machine Learning, Natural Language Processing, Context-aware Computing, and Computer Vision), by Application (Fleet Management, Supply Chain Planning, Warehouse Management, Virtual Assistant, Risk Management, Freight Brokerage, and Others), by End-user Industry(Automotive, Aerospace, Manufacturing, Retail, Healthcare, consumer-packaged Goods, Food and Beverages, and Others ),by Regions and Global Market Forecast 2022-2028

Report Id: SNS/ICT/2503 | July 2022 | Region: Global | 135 Pages

Report Scope & Overview:

The Artificial Intelligence (AI) in Supply Chain Market size was valued at USD 2246.32 Million in 2021 and is expected to reach USD 35471.36 Million by 2028, and grow at a CAGR of 48.32 % over the forecast period 2022-2028.

Artificial intelligence (AI) is a technology that allows robots, software, and systems to compete in some respects with human intelligence and behavior. At the heart of AI is a system that employs complex algorithms to assess data and execute many jobs. Artificial intelligence has several uses in the supply chain, including data extraction, data analysis, supply and demand planning, and autonomous vehicle operation. It also has access to warehouse operations in order to optimize product sending, receiving, storing, picking, and management.

Artificial Intelligence in Supply Chain Market

Logistics improvement is accomplished through improving warehouse operations and distribution. AI-powered supply-chain management solutions assist firms in improving their performance and quality. Transparency from beginning to finish, demand forecasting models, dynamic planning optimization, integrated business planning, and physical flow automation are just a few of the vital features. This helps to construct effective prediction models and analysis, which aids in the study of supply chain causes and repercussions.

Data from IoT devices and other sources acquired from in-transit supply chain vehicles may offer a lot of information on the health and durability of the expensive equipment necessary to keep commodities flowing through supply networks. Machine learning provides maintenance recommendations and failure forecasts based on historical and real-time data. This allows businesses to remove cars from the supply chain before performance concerns result in a backlog of delays. AI-powered, self-driving cars will be deployed throughout supply chains in the future, thanks to advances in supply chain innovation. The present data mining and analysis capabilities of these platforms will continue to enhance the cost and efficiency of an increasingly complicated global supply chain.

MARKET DYNAMICS:

KEY DRIVERS:

  • Big Data is expanding.

  • Greater visibility and transparency in supply chain data and processes are desired.

  • AI adoption to improve customer service and satisfaction.

RESTRAINTS:

  • There are a limited number of AI experts.

OPPORTUNITY:

  • Cloud-based Supply Chain Solutions' Growing Influence.

  • Expanding Demand for Intelligent Business Processes and Automation.

  • Improving Manufacturing Industry Operational Efficiency.

CHALLENGES:

  • Difficulties in Integrating Data from Multiple Sources.

  • Concerns About Data Privacy.

IMPACT OF COVID-19:

The Just-in-time supply chain is one of the primary enterprises affected by COVID-19 in the worldwide market. All manufacturing enterprises are experiencing difficulty procuring materials needed to manufacture items and shipping their products to retailers and distributors. COVID-19, unlike other interruptions, affects every area of supply networks. Many nations banned the import and export of products and services due to government consequences like as lockdowns, resulting in a scarcity of consumer goods resources.

To ensure the delivery of important items, supply chain firms are taking all necessary precautions for employee safety, such as slowing down manufacturing lines, assigning personnel to designated work zones, and cleaning equipment between shifts. Because of the COVID-19 situation, all firms are focusing on shifting production back to the nation of origin, reducing income loss.

MARKET ESTIMATION:

The market is classified into Fleet Management, Supply Chain Planning, Warehouse Management, Virtual Assistant, and Others. The Artificial Intelligence in Supply Chain Market was led by the supply chain planning sector. This industry's growth may be attributed to the rising need for enhanced factory scheduling and production planning, as well as increased agility and optimization of supply chain decision-making. Furthermore, automating current procedures and workflows to reimagine the supply chain planning paradigm is contributing to the growth of this industry.

Artificial intelligence in the supply chain may be classified as machine learning, natural language processing, context-aware computing, and computer vision, depending on the technology. The market for computer vision is likely to expand faster. The increasing acceptance of computer vision for autonomous or semiautonomous applications in different sectors such as manufacturing and automotive is fueling this technology's rise in artificial intelligence in the supply chain industry.

The automobile sector dominates the Supply Chain Market for Artificial Intelligence. The rapidly increasing global vehicle sector is to thank for this segment's rise. The retail industry accounted for the second-largest part of the entire Supply Chain Artificial Intelligence Market. This is due to an increase in consumer retail goods demand.

KEY MARKET SEGMENTS:

On The Basis of Offering

  • Hardware

  • Software

  • Services

On The Basis of Technology

  • Machine Learning

  • Natural Language Processing

  • Context-aware Computing

  • Computer Vision

On The Basis of Application

  • Fleet Management

  • Supply Chain Planning

  • Warehouse Management

  • Virtual Assistant

  • Risk Management

  • Freight Brokerage

  • Others 

On The Basis of End-user Industry

  • Automotive

  • Aerospace

  • Manufacturing

  • Retail

  • Healthcare

  • Consumer-packaged Goods

  • Food and Beverages

  • Others

Artificial Intelligence in Supply Chain Market

REGIONAL ANALYSIS:

North America dominates artificial intelligence in the supply chain market and will maintain its dominance throughout the projected period due to the presence of important companies as well as developed economies concentrating on strengthening current supply chain solutions. During the projection period, Asia-Pacific will continue to have significant increases and will have the highest CAGR. This is due to the existence of a youthful and tech-savvy populace in this region, as well as the increasing prevalence of the internet of things.

REGIONAL COVERAGE:

  • North America

    • USA

    • Canada

    • Mexico

  • Europe

    • Germany

    • UK

    • France

    • Italy

    • Spain

    • The Netherlands

    • Rest of Europe

  • Asia-Pacific

    • Japan

    • south Korea

    • China

    • India

    • Australia

    • Rest of Asia-Pacific

  • The Middle East & Africa

    • Israel

    • UAE

    • South Africa

    • Rest of Middle East & Africa

  • Latin America

    • Brazil

    • Argentina

    • Rest of Latin America

KEY PLAYERS:

The major key players are Amazon Web Services, Inc., IBM Corporation, Intel Corporation, Logility, Inc., Micron Technology, Inc., Microsoft Corporation, NVIDIA Corporation, Oracle Corporation, SAP SE, Xilinx, Inc.

Artificial Intelligence in Supply Chain Market Report Scope
Report Attributes Details
Market Size in 2021 USD 2246.32 Million
Market Size by 2028 USD 35471.36 Million
CAGR CAGR 48.32% From 2022 to 2028
Base Year 2021
Forecast Period 2022-2028
Historical Data 2017-2020
Report Scope & Coverage Market Size, Segments Analysis, Competitive  Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook
Key Segments • by Offering (Hardware, Software, and Services)
• by Technology (Machine Learning, Natural Language Processing, Context-aware Computing, and Computer Vision)
• by Application (Fleet Management, Supply Chain Planning, Warehouse Management, Virtual Assistant, Risk Management, Freight Brokerage, and Others)
• by End-user Industry (Automotive, Aerospace, Manufacturing, Retail, Healthcare, consumer-packaged Goods, Food and Beverages, and Others)
Regional Analysis/Coverage North America (USA, Canada, Mexico), Europe
(Germany, UK, France, Italy, Spain, Netherlands,
Rest of Europe), Asia-Pacific (Japan, South Korea,
China, India, Australia, Rest of Asia-Pacific), The
Middle East & Africa (Israel, +D11UAE, South Africa,
Rest of Middle East & Africa), Latin America (Brazil, Argentina, Rest of Latin America)
Company Profiles  Amazon Web Services, Inc., IBM Corporation, Intel Corporation, Logility, Inc., Micron Technology, Inc., Microsoft Corporation, NVIDIA Corporation, Oracle Corporation, SAP SE, Xilinx, Inc.
Key Drivers •Big Data is expanding.
•Greater visibility and transparency in supply chain data and processes are desired.
Market Restraints •There are a limited number of AI experts.


Frequently Asked Questions (FAQ) :

Ans: - The Artificial Intelligence in Supply Chain Market size was valued at USD 2246.32 Million in 2021.

Ans: - Greater visibility and transparency in supply chain data and processes are desired and AI adoption to improve customer service and satisfaction.

Ans: - The segments covered in the Artificial Intelligence in Supply Chain Market report for study are on the Basis of Offering, Technology, Application, and End-user Industry.

Ans: - North America dominates artificial intelligence in the supply chain market and will maintain its dominance throughout the projected period.

Ans: - The primary growth tactics of Artificial Intelligence in Supply Chain market participants include merger and acquisition, business expansion, and product launch.


Table of Contents

 

1. Introduction

1.1 Market Definition

1.2 Scope

1.3 Research Assumptions

 

2. Research Methodology

 

3. Market Dynamics

3.1 Drivers

3.2 Restraints

3.3 Opportunities

3.4 Challenges

 

4. Impact Analysis

4.1 COVID 19 Impact Analysis

4.2 Impact of the Ukraine- Russia war

 

5. Value Chain Analysis

 

6. Porter’s 5 forces model

 

7.  PEST Analysis

 

8. Artificial Intelligence in Supply Chain Market Segmentation, by Offering

8.1 Hardware

8.2 Software

8.3 Services

 

9. Artificial Intelligence in Supply Chain Market Segmentation, by Technology

9.1 Machine Learning

9.2 Natural Language Processing

9.3 Context-aware Computing

9.4 Computer Vision

 

10. Artificial Intelligence in Supply Chain Market Segmentation, by Application

10.1 Fleet Management

10.2 Supply Chain Planning

10.3 Warehouse Management

10.4 Virtual Assistant

10.5 Risk Management

10.6 Freight Brokerage

10.7 Others 

 

11. Artificial Intelligence in Supply Chain Market Segmentation, by End-user Industry

11.1 Automotive

11.2 Aerospace

11.3 Manufacturing

11.4 Retail

11.5 Healthcare

11.6 Consumer-packaged Goods

11.7 Food and Beverages

11.8 Others

 

12. Regional Analysis

12.1 Introduction

12.2 North America

12.2.1 USA

12.2.2 Canada

12.2.3 Mexico

12.3 Europe

12.3.1 Germany

12.3.2 UK

12.3.3 France

12.3.4 Italy

12.3.5 Spain

12.3.6 The Netherlands

12.3.7 Rest of Europe

12.4 Asia-Pacific

12.4.1 Japan

12.4.2 South Korea

12.4.3 China

12.4.4 India

12.4.5 Australia

12.4.6 Rest of Asia-Pacific

12.5 The Middle East & Africa

12.5.1 Israel

12.5.2 UAE

12.5.3 South Africa

12.5.4 Rest

12.6 Latin America

12.6.1 Brazil

12.6.2 Argentina

12.6.3 Rest of Latin America

 

13. Company Profiles

13.1 Amazon Web Services, Inc.

13.1.1 Financial

13.1.2 Products/ Services Offered

13.1.3 SWOT Analysis

13.1.4 The SNS view

13.2 IBM Corporation

13.3 Intel Corporation

13.4 Logility, Inc.

13.5 Micron Technology, Inc.

13.6 Microsoft Corporation

13.7 NVIDIA Corporation

13.8 Oracle Corporation

13.9 SAP SE

13.10 Xilinx, Inc.

 

14. Competitive Landscape

14.1 Competitive Benchmarking

14.2 Market Share Analysis

14.3 Recent Developments

 

15. Conclusion

An accurate research report requires proper strategizing as well as implementation. There are multiple factors involved in the completion of good and accurate research report and selecting the best methodology to compete the research is the toughest part. Since the research reports we provide play a crucial role in any company’s decision-making process, therefore we at SNS Insider always believe that we should choose the best method which gives us results closer to reality. This allows us to reach at a stage wherein we can provide our clients best and accurate investment to output ratio.

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The 5 steps process:

Step 1: Secondary Research:

Secondary Research or Desk Research is as the name suggests is a research process wherein, we collect data through the readily available information. In this process we use various paid and unpaid databases which our team has access to and gather data through the same. This includes examining of listed companies’ annual reports, Journals, SEC filling etc. Apart from this our team has access to various associations across the globe across different industries. Lastly, we have exchange relationships with various university as well as individual libraries.

Secondary Research

Step 2: Primary Research

When we talk about primary research, it is a type of study in which the researchers collect relevant data samples directly, rather than relying on previously collected data.  This type of research is focused on gaining content specific facts that can be sued to solve specific problems. Since the collected data is fresh and first hand therefore it makes the study more accurate and genuine.

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Part 1 wherein we interview the KOLs of major players as well as the upcoming ones across various geographic regions. This allows us to have their view over the market scenario and acts as an important tool to come closer to the accurate market numbers. As many as 45 paid and unpaid primary interviews are taken from both the demand and supply side of the industry to make sure we land at an accurate judgement and analysis of the market.

This step involves the triangulation of data wherein our team analyses the interview transcripts, online survey responses and observation of on filed participants. The below mentioned chart should give a better understanding of the part 1 of the primary interview.

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