The AI in supply chain management market size was US$ 3.5 Billion in 2023 and is likely to grow at a dynamic CAGR of 30.3% in the long run to reach US$ 22.7 Billion in 2030.
In August 2024, Korber AG acquired Mercurygate International Inc. to deliver a comprehensive, innovative, and scalable supply chain execution portfolio by utilizing Mercurygate’s capabilities of transportation management systems (TMS).
In March 2024, Blue Yonder acquired One Network Enterprises to strengthen its position as a leading provider of end-to-end supply chain solutions.
In March 2024, ArcBest entered into a partnership with NVIDIA to improve supply chain management with new AI 'omniverse' products and services.
Software solutions can be customized to various supply chain functions, including demand forecasting, inventory management, transportation optimization, and quality control.
The software can easily scale up or down to meet changing business needs, making it a flexible and cost-effective solution.
AI software offers powerful analytics and insights critical for optimizing supply chain operations. Predictive analytics, real-time monitoring, and machine learning algorithms aid organizations in making data-driven decisions, improving forecasting, and enhancing operational efficiency.
ML is inherently data-driven, making it well-suited for supply chain applications, which depend on large amounts of data to make informed decisions.
ML can be scaled to handle large datasets and complex models, ensuring that it can meet the needs of even the largest businesses.
NLP's ability to automate, analyze, and enhance textual data processing is driving its rapid growth in the AI supply chain management market. NLP-powered chatbots provide automated customer support by answering questions and resolving issues.
Supply chain planning deals with the major challenges of supply chain management, including demand variability, complexity, cost efficiency, risk management, and customer expectations.
AI algorithms can analyze data to predict future demand, helping businesses optimize inventory and production planning.
Supply chain planning is projected to experience rapid growth due to increasing complexity in global trade and the necessity for efficient, resilient supply chains to fulfill evolving customer demands and mitigate risks.
The manufacturing industry is at the forefront of accepting Industry 4.0 initiatives, which highlight the use of AI, IoT, and automation to develop smart factories. These technologies improve productivity, flexibility, and efficiency, leading to increased AI adoption in supply chain management.
Manufacturing creates large amounts of data, which AI can use to spot trends, optimize processes, and improve decision-making.
The retail industry is expected to register the fastest growth in the upcoming years due to factors such as the rapid expansion of e-commerce, evolving consumer preferences, the necessity for real-time inventory management, and the digital transformation of the retail industry.
Additionally, key players in the industry, such as Microsoft Corporation, Inc., Oracle Corporation, Amazon.com Inc., and IBM Corporation, contributed to this share.
Asia-Pacific’s rapid economic growth, e-commerce expansion, large consumer market, increased adoption of advanced technologies, complex supply chains, and the need for cost optimization, resilience, and sustainability are the key reasons behind the region’s fastest growth.
Increasing adoption of deep learning and Natural Language Processing (NLP) technologies for applications in automotive, retail, and manufacturing industries in the Asia-Pacific region.
The report segments and analyzes the market in the most detailed manner to provide a panoramic view of the market.
The vital data/information provided in the report can play a crucial role for market participants as well as investors in the identification of the low-hanging fruits available in the market as well as to formulate growth strategies to expedite their growth process.
This report offers high-quality insights and is the outcome of a detailed research methodology comprising extensive secondary research, rigorous primary interviews with industry stakeholders, and validation and triangulation with this Research’s internal database and statistical tools.
More than 1,000 authenticated secondary sources, such as company annual reports, fact books, press releases, journals, investor presentations, white papers, patents, and articles, have been leveraged to gather the data.
We conducted more than 15 detailed primary interviews with market players across the value chain in all four regions and industry experts to obtain both qualitative and quantitative insights.
COVID-19 Impact
The pandemic has accelerated the adoption of AI in supply chain management, enabling businesses to build more resilient supply chains through improved demand forecasting, risk management, and contingency planning. The pandemic highlighted the need for resilient supply chains capable of adapting to disruptions. The adoption of AI in supply chain management as businesses sought to improve resilience, visibility, and efficiency in the face of disruptions.Recent Market JVs and Acquisitions:
A decent number of strategic alliances, including M&As, JVs, etc. have been performed over the past few years:In August 2024, Korber AG acquired Mercurygate International Inc. to deliver a comprehensive, innovative, and scalable supply chain execution portfolio by utilizing Mercurygate’s capabilities of transportation management systems (TMS).
In March 2024, Blue Yonder acquired One Network Enterprises to strengthen its position as a leading provider of end-to-end supply chain solutions.
In March 2024, ArcBest entered into a partnership with NVIDIA to improve supply chain management with new AI 'omniverse' products and services.
By Offering Type
Software is expected to maintain its indisputable lead and is likely to grow at the fastest rate for the AI in supply chain management market during the forecast period
The AI in the supply chain management market is segmented into hardware, software, and services.Software solutions can be customized to various supply chain functions, including demand forecasting, inventory management, transportation optimization, and quality control.
The software can easily scale up or down to meet changing business needs, making it a flexible and cost-effective solution.
AI software offers powerful analytics and insights critical for optimizing supply chain operations. Predictive analytics, real-time monitoring, and machine learning algorithms aid organizations in making data-driven decisions, improving forecasting, and enhancing operational efficiency.
By Technology Type
Machine learning's adaptability, maturity, and data-driven approach establish it as the leading technology of the market
The market is segmented into machine learning (ML), natural language processing (NLP), context-aware computing, and computer vision.ML is inherently data-driven, making it well-suited for supply chain applications, which depend on large amounts of data to make informed decisions.
ML can be scaled to handle large datasets and complex models, ensuring that it can meet the needs of even the largest businesses.
NLP's ability to automate, analyze, and enhance textual data processing is driving its rapid growth in the AI supply chain management market. NLP-powered chatbots provide automated customer support by answering questions and resolving issues.
By Application Type
Supply chain planning is expected to remain the forerunner of the market throughout the forecast period
The market is segmented into fleet management, supply chain planning, warehouse management, virtual assistant, risk management, freight brokerage, and others.Supply chain planning deals with the major challenges of supply chain management, including demand variability, complexity, cost efficiency, risk management, and customer expectations.
AI algorithms can analyze data to predict future demand, helping businesses optimize inventory and production planning.
Supply chain planning is projected to experience rapid growth due to increasing complexity in global trade and the necessity for efficient, resilient supply chains to fulfill evolving customer demands and mitigate risks.
By End-User Type
Manufacturing is projected to remain the dominant, whereas retail is expected to be the fastest-growing end-user of the market during the forecast period
The market is segmented into automotive, aerospace, logistics, manufacturing, retail, healthcare, consumer-packaged goods, food & beverages, and others.The manufacturing industry is at the forefront of accepting Industry 4.0 initiatives, which highlight the use of AI, IoT, and automation to develop smart factories. These technologies improve productivity, flexibility, and efficiency, leading to increased AI adoption in supply chain management.
Manufacturing creates large amounts of data, which AI can use to spot trends, optimize processes, and improve decision-making.
The retail industry is expected to register the fastest growth in the upcoming years due to factors such as the rapid expansion of e-commerce, evolving consumer preferences, the necessity for real-time inventory management, and the digital transformation of the retail industry.
Regional Analysis
North America is expected to remain the largest market for AI in supply chain management whereas Asia-Pacific is projected to witness the fastest growth during the forecast period
The region's significant share can be attributed to the presence of developed economies that prioritize enhancing existing supply chain solutions.Additionally, key players in the industry, such as Microsoft Corporation, Inc., Oracle Corporation, Amazon.com Inc., and IBM Corporation, contributed to this share.
Asia-Pacific’s rapid economic growth, e-commerce expansion, large consumer market, increased adoption of advanced technologies, complex supply chains, and the need for cost optimization, resilience, and sustainability are the key reasons behind the region’s fastest growth.
Increasing adoption of deep learning and Natural Language Processing (NLP) technologies for applications in automotive, retail, and manufacturing industries in the Asia-Pacific region.
Key Players
The market is consolidated with the presence of a few players. Most of the major players compete in some of the governing factors, including price, product & service offerings, regional presence, etc. The following are the key players in the AI in supply chain management market.Here is the list of the Top Players (Based on Dominance)
- Microsoft Corporation, Inc.
- Oracle Corporation
- SAP SE
- IBM Corporation
- Amazon.com Inc.
- Google LLC
Report Features
This report provides market intelligence most comprehensively. The report structure has been kept such that it offers maximum business value. It provides critical insights into market dynamics and will enable strategic decision-making for existing market players as well as those willing to enter the market. The following are the key features of the report:- Market structure: Overview, industry life cycle analysis, supply chain analysis.
- Market environment analysis: Growth drivers and constraints, Porter’s five forces analysis, SWOT analysis.
- Market trend and forecast analysis.
- Market segment trend and forecast.
- Competitive landscape and dynamics: Market share, Service portfolio, New Product Launches, etc.
- COVID-19 impact.
- Attractive market segments and associated growth opportunities.
- Emerging trends.
- Strategic growth opportunities for the existing and new players.
- Key success factors.
The AI in supply chain management market is segmented into the following categories:
AI in Supply Chain Management Market, by Class Type
- Hardware (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
- Software (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
- Services (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
AI in Supply Chain Management Market, by Technology Type
- Machine learning (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
- Natural Language Processing (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
- Context-Aware Computing (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
- Computer Vision (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
AI in Supply Chain Management Market, by Application Type
- Fleet Management (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
- Supply Chain Planning (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
- Warehouse Management (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
- Virtual Assistant (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
- Risk Management (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
- Freight Brokerage (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
- Others (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
AI in Supply Chain Management Market, by End-User Type
- Automotive (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
- Aerospace (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
- Logistics (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
- Manufacturing (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
- Retail (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
- Healthcare (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
- Consumer-packaged Goods (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
- Food & Beverages (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
- Others (Regional Analysis: North America, Europe, Asia-Pacific, and RoW)
AI in Supply Chain Management Market, by Region
- North America (Country Analysis: The USA, Canada, and Mexico)
- Europe (Country Analysis: France, The UK, Russia, Italy, and Rest of Europe)
- Asia-Pacific (Country Analysis: China, Japan, India, South Korea, and Rest of Asia-Pacific)
- Rest of the World (Country Analysis: Brazil, Argentina, and Others)
Research Methodology
This strategic assessment report from this research provides a comprehensive analysis that reflects today’s AI in supply chain management market realities and future market possibilities for the forecast period.The report segments and analyzes the market in the most detailed manner to provide a panoramic view of the market.
The vital data/information provided in the report can play a crucial role for market participants as well as investors in the identification of the low-hanging fruits available in the market as well as to formulate growth strategies to expedite their growth process.
This report offers high-quality insights and is the outcome of a detailed research methodology comprising extensive secondary research, rigorous primary interviews with industry stakeholders, and validation and triangulation with this Research’s internal database and statistical tools.
More than 1,000 authenticated secondary sources, such as company annual reports, fact books, press releases, journals, investor presentations, white papers, patents, and articles, have been leveraged to gather the data.
We conducted more than 15 detailed primary interviews with market players across the value chain in all four regions and industry experts to obtain both qualitative and quantitative insights.
Table of Contents
Overview of Research Scope, Methodology, and Data Collection
2. AI in Supply Chain Management Market Environment Analysis
3. AI in Supply Chain Management Market Assessment (2018-2030) (US$ Million)
4. AI in Supply Chain Management Market Segment Analysis (2018-2030) (US$ Million)
5. Competitive Analysis
6. Strategic Growth Opportunities
7. Company Profile of Key Players
8. Appendix
Companies Mentioned
- Advanced Micro Devices, Inc. (Xilinx, Inc.)
- Amazon.com Inc.
- C3.ai, Inc.
- Cainiao Intelligent Logistics Holdings Limited / Alibaba Cloud
- Coupa Software Inc. (LLamasoft, Inc.)
- Deutsche Post AG (DHL Group)
- FedEx Corporation
- Google LLC
- IBM Corporation
- Intel Corporation
- Logility (American Software, Inc.)
- Micron Technology, Inc.
- Microsoft Corporation, Inc.
- NVIDIA Corporation
- Oracle Corporation
- Project44, Inc. (ClearMetal, Inc.)
- Samsung Electronics Co., Ltd.
- SAP SE
Methodology
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