Report Scope & Overview:
The Artificial Intelligence (AI) in Supply Chain Market size was valued at USD 3.32 Billion in 2022 and is expected to reach USD 77.81 Billion by 2030, and grow at a CAGR of 48.32% over the forecast period 2023-2030.
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.
To get more information on Artificial Intelligence (AI) in Supply Chain Market - Request Sample Report
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.
Big Data is expanding.
Greater visibility and transparency in supply chain data and processes are desired.
AI adoption to improve customer service and satisfaction.
There are a limited number of AI experts.
Cloud-based Supply Chain Solutions' Growing Influence.
Expanding Demand for Intelligent Business Processes and Automation.
Improving Manufacturing Industry Operational Efficiency.
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.
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
On The Basis of Technology
Natural Language Processing
On The Basis of Application
Supply Chain Planning
On The Basis of End-user Industry
Food and Beverages
Need any customization research on Artificial Intelligence (AI) in Supply Chain Market - Enquiry Now
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.
Rest of Europe
Rest of Asia-Pacific
The Middle East & Africa
Rest of Middle East & Africa
Rest of Latin America
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. & Other Players
Oracle Corporation - Company Financial Analysis
|Market Size in 2022
|USD 3.32 Billion
|Market Size by 2030
|USD 77.81 Billion
|CAGR 48.32% From 2023 to 2030
|Report Scope & Coverage
|Market Size, Segments Analysis, Competitive Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook
|• 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)
|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)
|Amazon Web Services, Inc., IBM Corporation, Intel Corporation, Logility, Inc., Micron Technology, Inc., Microsoft Corporation, NVIDIA Corporation, Oracle Corporation, SAP SE, Xilinx, Inc.
|•Big Data is expanding.
•Greater visibility and transparency in supply chain data and processes are desired.
|•There are a limited number of AI experts.
Ans: - The Artificial Intelligence in Supply Chain Market size was valued at USD 3.32 Billion in 2022.
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
1.1 Market Definition
1.3 Research Assumptions
2. Research Methodology
3. Market Dynamics
4. Impact Analysis
4.1 COVID-19 Impact Analysis
4.2 Impact of Ukraine- Russia war
4.3 Impact of ongoing Recession
4.3.2 Impact on major economies
18.104.22.168 United Kingdom
22.214.171.124 South Korea
126.96.36.199 Rest of the World
5. Value Chain Analysis
6. Porter’s 5 forces model
7. PEST Analysis
8. Artificial Intelligence in Supply Chain Market Segmentation, by Offering
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
11. Artificial Intelligence in Supply Chain Market Segmentation, by End-user Industry
11.6 Consumer-packaged Goods
11.7 Food and Beverages
12. Regional Analysis
12.2 North America
12.3.6 The Netherlands
12.3.7 Rest of Europe
12.4.2 South Korea
12.4.6 Rest of Asia-Pacific
12.5 The Middle East & Africa
12.5.3 South Africa
12.6 Latin America
12.6.3 Rest of Latin America
13. Company Profiles
13.1 Amazon Web Services, Inc.
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
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.
Each report that we prepare takes a timeframe of 350-400 business hours for production. Starting from the selection of titles through a couple of in-depth brain storming session to the final QC process before uploading our titles on our website we dedicate around 350 working hours. The titles are selected based on their current market cap and the foreseen CAGR and growth.
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.
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.
We at SNS Insider have divided Primary Research into 2 parts.
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.
Part 2: In this part of primary research the data collected via secondary research and the part 1 of the primary research is validated with the interviews from individual consultants and subject matter experts.
Consultants are those set of people who have at least 12 years of experience and expertise within the industry whereas Subject Matter Experts are those with at least 15 years of experience behind their back within the same space. The data with the help of two main processes i.e., FGDs (Focused Group Discussions) and IDs (Individual Discussions). This gives us a 3rd party nonbiased primary view of the market scenario making it a more dependable one while collation of the data pointers.
Step 3: Data Bank Validation
Once all the information is collected via primary and secondary sources, we run that information for data validation. At our intelligence centre our research heads track a lot of information related to the market which includes the quarterly reports, the daily stock prices, and other relevant information. Our data bank server gets updated every fortnight and that is how the information which we collected using our primary and secondary information is revalidated in real time.
Step 4: QA/QC Process
After all the data collection and validation our team does a final level of quality check and quality assurance to get rid of any unwanted or undesired mistakes. This might include but not limited to getting rid of the any typos, duplication of numbers or missing of any important information. The people involved in this process include technical content writers, research heads and graphics people. Once this process is completed the title gets uploader on our platform for our clients to read it.
Step 5: Final QC/QA Process:
This is the last process and comes when the client has ordered the study. In this process a final QA/QC is done before the study is emailed to the client. Since we believe in giving our clients a good experience of our research studies, therefore, to make sure that we do not lack at our end in any way humanly possible we do a final round of quality check and then dispatch the study to the client.
The Cash Flow Management Market size was USD 0.6 billion in 2022 and is expected to Reach USD 3.8 billion by 2030 and grow at a CAGR of 26.0 % over the forecast period of 2023-2030
The Sales Performance Management Market size was USD 2.1 billion in 2022 and is expected to Reach USD 6.8 billion by 2030 and grow at a CAGR of 15.4% over the forecast period of 2023-2030
The Retail Cloud Market size was valued at USD 34.57 Bn in 2022 and is expected to reach USD 148.37 Bn by 2030, and grow at a CAGR of 19.97% over the forecast period 2023-2030.
The Geospatial Analytics Market size was valued at USD 71.89 billion in 2022 and is expected to grow to USD 186.43 billion by 2030 and grow at a CAGR of 12.65 % over the forecast period of 2023-2030.
The Serverless Architecture Market size was USD 12 billion in 2022 and is expected to Reach USD 65.7 billion by 2030 and grow at a CAGR of 23.7 % over the forecast period of 2023-2030
The In-Memory Computing Market size was USD 15.2 billion in 2022 and is expected to Reach USD 52.6 billion by 2030 and grow at a CAGR of 16.8% over the forecast period of 2023-2030.
Hi! Click one of our member below to chat on Phone