The Artificial Intelligence In Supply Chain Market Size was valued at USD 48.22 Billion in 2023 and is expected to reach USD 91.65 Billion by 2031 and grow at a CAGR of 8.36 % over the forecast period 2024-2031.
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.
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Drivers:
The growing need for advanced data analytics capabilities to optimize supply chain operations is driving the adoption of AI in supply chain management.
The AI adoption is improve customer service and satisfaction.
AI-powered automation and predictive analytics help in reducing operational costs by optimizing inventory management, logistics, and production processes.
AI enables real-time decision-making by providing actionable insights based on real-time data, thus improving agility and responsiveness in supply chain management.
AI-driven supply chain management can improve customer experience by ensuring on-time deliveries, personalized recommendations, and efficient order fulfillment.
The Growing demand for data analytics tools is Helps companies to adopt AI in managing their supply chains with efficiently. With the Implementing AI-driven analytics, businesses can increase valuable insights into their operations, allowing to optimize processes, forecast demand accurately, helps to reduce inventory costs, and improve overall performance of supply chain. This Reflects a strategic shift towards data-driven decision-making and operational excellence in supply chain management.
Restraints:
Implementing AI in the supply chain requires significant initial investment in technology infrastructure, training, and integration.
The Handling large volumes of sensitive data in the supply chain raises concerns about data security, privacy, and compliance with regulations such as GDPR and CCPA.
There is a shortage of skilled professionals with expertise in AI, to effective utilization of AI in the supply chain.
The Integration of AI into supply chain operations Requires High initial investments in various areas. This includes upgrading existing technology infrastructure to support AI applications effectively, providing comprehensive training programs for employees to understand and utilize AI tools efficiently, and ensuring seamless integration of AI systems with existing supply chain management platforms. These investments are Important to use the full potential of AI in optimizing supply chain processes, improving decision-making, and enhancing overall operational performance.
Opportunities:
AI can enable predictive maintenance of supply chain assets such as machinery and vehicles, reducing downtime and improving asset utilization.
AI-powered analytics can provide real-time visibility into the entire supply chain, enabling better inventory management, demand forecasting, and risk mitigation.
AI-driven analytics can help in creating personalized supply chains tailored to individual customer preferences, leading to increased customer satisfaction and loyalty.
AI-based risk assessment and mitigation strategies can enhance supply chain resilience against disruptions such as natural disasters, geopolitical events, and market fluctuations.
Challenges:
Ensuring high-quality data inputs for AI algorithms is a challenge, as supply chain data can be complex, fragmented, and prone to errors, leading to inaccurate insights and decisions.
Integrating AI solutions from different vendors and ensuring interoperability with existing systems can be challenging.
Scaling AI applications across complex supply chain networks with multiple stakeholders, geographies, and business units requires robust infrastructure and scalable algorithms.
This crisis Between Russia-Ukraine caused disruptions in the supply of crucial commodities and raw materials, resulting in Growing prices and shortages. Russia plays a Major role as a major supplier of essential minerals like platinum-group elements, titanium, and nickel, which are vital for various technologies including semiconductor production. The conflict has contributed to price surges in critical components like palladium, essential for catalytic converters, and has also led to a decrease in projected light vehicle sales across Europe. The global market of food commodity has been significantly Impacted by the war, particularly in Russia and Ukraine. Russia and Ukraine are the countries which contributes 29 percent of global wheat exports and 17 percent of global corn exports. Because of the conflict, Chicago wheat futures grew to a record high of $13.50 per bushel in the first week of March, a increase from the previous price of less than $8.00 per bushel on February 21st. This Increasing in prices reflects the impact of the war on the supply and pricing of essential food commodities worldwide.
This crisis has shed light on the intricate interconnectedness of global economies and the complexities inherent in supply chains. Many businesses rely directly or indirectly on suppliers from Russia, exposing vulnerabilities in supply chain networks beyond the primary suppliers.
Within the AI-driven supply chain market landscape, the crisis has prompted companies to identify weak links in their value chains and develop adaptable supply chain strategies. The disruption has impacted tech demand, with anticipated contractions in local market demand within Russia and Ukraine. Companies are advised to formulate actionable plans enabling them to anticipate and respond to market disruptions effectively. Leveraging AI and machine learning for enhanced visibility and risk management can empower companies to navigate uncertainties and uphold the seamless flow of goods, capital, and information throughout their supply chains.
The economic downturn has both positive and negative effects on AI in Supply Chain Market. there's a greater need for efficiency and Dependance, driving AI adoption. The AI solutions have been proven to reduce logistics costs, improve inventory management, and enhance service levels. This has resulted in a growing market for AI in supply chains, with significant growth expected in the forecast period Because of the advancements in AI technologies like machine learning and computer vision.
By Offering
Hardware
Software
Services
On The Basis of Offering, the software segment dominates the market with a holding share of more than 35% due to Various factors. software offerings in the AI supply chain market encompass a wide range of solutions such as predictive analytics, demand forecasting, inventory optimization, and logistics management, addressing critical needs across the supply chain.
By Technology
Machine Learning
Natural Language Processing
Context-aware Computing
Computer Vision
By Application
Fleet Management
Supply Chain Planning
Warehouse Management
Virtual Assistant
Risk Management
Freight Brokerage
Others
On the Basis of Application, The Supply Chain Planning segment dominant market share of more than 25% due to its critical role in optimizing supply chain operations. Businesses across industries Depends on supply chain planning applications powered by AI to forecast demand accurately, optimize inventory levels, streamline production schedules, and manage supplier relationships effectively. These AI-driven planning tools enable companies to enhance forecasting accuracy, reduce lead times, minimize stockouts, and improve the supply chain resilience. the scalability and customization options offered by supply chain planning solutions make them highly adaptable for various business needs, contributing to their widespread adoption and market leadership.
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By End-User Industry
Automotive
Aerospace
Manufacturing
Retail
Healthcare
Consumer-packaged Goods
Food and Beverages
Others
The North American Region is Dominates the AI in Supply Chain market With holding the share of More than 32%, the market is driven by the presence of developed economies that are actively looking to enhance their supply chain solutions. The presence of major players in the industry and a strong inclination towards adopting advanced technology contribute to North America's market dominance.
The Europe holds the second-largest share of AI in Supply Chain market. Because of the region's youthful and tech-savvy population, as well as the increasing dependance of the Internet of Things (IoT). the German market is growing with the largest share, while the UK market is experiencing the most rapid growth within Europe.
The Asia Pacific AI in Supply Chain Market is projected to grow with the highest compound annual growth rate from 2024 to 2031. This growth is Due to the region's Continuously growing economy, tech-savvy population, rising IoT adoption, increasing disposable income, and expanding applications of computer vision technology. The integration of artificial intelligence solutions and services into supply chain operations is also on the rise, supported by the region's digitalization efforts and enhanced connectivity infrastructure.
REGIONAL COVERAGE:
North America
US
Canada
Mexico
Europe
Eastern Europe
Poland
Romania
Hungary
Turkey
Rest of Eastern Europe
Western Europe
Germany
France
UK
Italy
Spain
Netherlands
Switzerland
Austria
Rest of Western Europe
Asia Pacific
China
India
Japan
South Korea
Vietnam
Singapore
Australia
Rest of Asia Pacific
Middle East & Africa
Middle East
UAE
Egypt
Saudi Arabia
Qatar
Rest of the Middle East
Africa
Nigeria
South Africa
Rest of Africa
Latin America
Brazil
Argentina
Colombia
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
Actyv.ai, a leading enterprise SaaS company based in Singapore, is at the forefront of innovation in the B2B BNPL and insurance sectors. In March 2023, Actyv.ai announced a strategic partnership with PwC India to drive the adoption of embedded finance in supply chain ecosystems for their clients. This collaboration aims to provide clients with access to essential embedded financial and insurance products, while leveraging artificial intelligence to unlock growth opportunities within the global supply chain ecosystem.
At the Sapphire conference in Orlando, Florida in May 2023, Actyv.ai unveiled the SAP Business AI solution. Through a partnership with Microsoft, Actyv.ai will integrate its solutions with Microsoft 365 Copilot and Azure OpenAI to empower clients to enhance their logistical capabilities and equip their workforce to tackle future logistical challenges.
In November 2021, Microsoft introduced cutting-edge supply chain and manufacturing technologies, further highlighting the importance of innovation in the industry.
| Report Attributes | Details |
|---|---|
| Market Size in 2024 | USD 48.22 Billion |
| Market Size by 2031 | USD 91.65 Billion |
| CAGR | CAGR 8.36% From 2024 to 2031 |
| Base Year | 2023 |
| Forecast Period | 2024-2031 |
| Historical Data | 2020-2022 |
| 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 (US, Canada, Mexico), Europe (Eastern Europe [Poland, Romania, Hungary, Turkey, Rest of Eastern Europe] Western Europe] Germany, France, UK, Italy, Spain, Netherlands, Switzerland, Austria, Rest of Western Europe]), Asia Pacific (China, India, Japan, South Korea, Vietnam, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (Middle East [UAE, Egypt, Saudi Arabia, Qatar, Rest of Middle East], Africa [Nigeria, South Africa, Rest of Africa], Latin America (Brazil, Argentina, Colombia, 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. |
Ans. The projected market size for the Artificial Intelligence In Supply Chain Market is USD 91.65 billion by 2031.
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. Industry Flowchart
3. Research Methodology
4. Market Dynamics
4.1 Drivers
4.2 Restraints
4.3 Opportunities
4.4 Challenges
5. Impact Analysis
5.1 Impact of Russia-Ukraine Crisis
5.2 Impact of Economic Slowdown on Major Countries
5.2.1 Introduction
5.2.2 United States
5.2.3 Canada
5.2.4 Germany
5.2.5 France
5.2.6 UK
5.2.7 China
5.2.8 Japan
5.2.9 South Korea
5.2.9 India
6. Value Chain Analysis
7. Porter’s 5 Forces Model
8. Pest Analysis
9. AI governance Market, By Offering
9.1 Introduction
9.2 Trend Analysis
9.3 Hardware
9.4 Software
9.5 Services
10. AI governance Market, By Technology
10.1 Introduction
10.2 Trend Analysis
10.3 Machine Learning
10.4 Natural Language Processing
10.5 Context-aware Computing
10.6 Computer Vision
11. AI governance Market, By Application
11.1 Introduction
11.2 Trend Analysis
11.3 Fleet Management
11.4 Supply Chain Planning
11.5 Warehouse Management
11.6 Virtual Assistant
11.7 Risk Management
11.8 Freight Brokerage
11.9 Others
12. AI governance Market, By End-user Industry
12.1 Introduction
12.2 Trend Analysis
12.3 Automotive
12..4 Aerospace
12.5 Manufacturing
12.6 Retail
12.7 Healthcare
12.8 Consumer-packaged Goods
12.9 Food and Beverages
12.10 Others
13. Regional Analysis
13.1 Introduction
14.2 North America
14.2.1 USA
14.2.2 Canada
14.2.3 Mexico
14.3 Europe
14.3.1 Eastern Europe
14.3.1.1 Poland
14.3.1.2 Romania
14.3.1.3 Hungary
14.3.1.4 Turkey
14.3.1.5 Rest of Eastern Europe
14.3.2 Western Europe
14.3.2.1 Germany
14.3.2.2 France
14.3.2.3 UK
14.3.2.4 Italy
14.3.2.5 Spain
14.3.2.6 Netherlands
14.3.2.7 Switzerland
14.3.2.8 Austria
14.3.2.9 Rest of Western Europe
14.4 Asia-Pacific
14.4.1 China
14.4.2 India
14.4.3 Japan
14.4.4 South Korea
14.4.5 Vietnam
14.4.6 Singapore
14.4.7 Australia
14.4.8 Rest of Asia Pacific
14.5 The Middle East & Africa
14.5.1 Middle East
14.5.1.1 UAE
14.5.1.2 Egypt
14.5.1.3 Saudi Arabia
14.5.1.4 Qatar
14.5.1.5 Rest of the Middle East
14.5.2 Africa
14.5.2.1 Nigeria
14.5.2.2 South Africa
14.5.2.3 Rest of Africa
14.6 Latin America
14.6.1 Brazil
14.6.2 Argentina
14.6.3 Colombia
14.6.4 Rest of Latin America
15. Company Profiles
15.1 IBM Corporation.
15.1.1 Company Overview
15.1.2 Financials
15.1.3 Products/ Services Offered
15.1.4 SWOT Analysis
15.1.5 The SNS View
15.2 Intel Corporation
15.2.1 Company Overview
15.2.2 Financials
15.2.3 Products/ Services Offered
15.2.4 SWOT Analysis
15.2.5 The SNS View
15.3 Logility, Inc.
15.3.1 Company Overview
15.3.2 Financials
15.3.3 Products/ Services Offered
15.3.4 SWOT Analysis
15.3.5 The SNS View
15.4 Micron Technology, Inc.
15.4 Company Overview
15.4.2 Financials
15.4.3 Products/ Services Offered
15.4.4 SWOT Analysis
15.4.5 The SNS View
15.5 Microsoft Corporation
15.5.1 Company Overview
15.5.2 Financials
15.5.3 Products/ Services Offered
15.5.4 SWOT Analysis
15.5.5 The SNS View
15.6 NVIDIA Corporation.
15.6.1 Company Overview
15.6.2 Financials
15.6.3 Products/ Services Offered
15.6.4 SWOT Analysis
15.6.5 The SNS View
15.7 Oracle Corporation.
15.7.1 Company Overview
15.7.2 Financials
15.7.3 Products/ Services Offered
15.7.4 SWOT Analysis
15.7.5 The SNS View
15.8 SAP SE.
15.8.1 Company Overview
15.8.2 Financials
15.8.3 Products/ Services Offered
15.8.4 SWOT Analysis
15.8.5 The SNS View
15.9 Xilinx, Inc.
15.9.1 Company Overview
15.9.2 Financials
15.9.3 Products/ Services Offered
15.9.4 SWOT Analysis
15.9.5 The SNS View
15.10 Samsung Electronics.
15.10.1 Company Overview
15.10.2 Financials
15.10.3 Products/ Services Offered
15.10.4 SWOT Analysis
15.10.5 The SNS View
16. Competitive Landscape
16.1 Competitive Benchmarking
16.2 Market Share Analysis
16.3 Recent Developments
16.3.1 Industry News
16.3.2 Company News
16.3.3 Mergers & Acquisitions
17. USE Cases and Best Practices
18. Conclusion
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