The AI in Supply Chain Management market encompasses the comprehensive landscape of products, solutions, and services that leverage Artificial Intelligence (AI) technologies to enhance and optimize various aspects of supply chain operations. This market revolves around the integration of AI algorithms, machine learning, predictive analytics, and other advanced technologies to streamline processes such as demand forecasting, inventory management, logistics optimization, and overall supply chain decision-making. Businesses within this market deploy AI-driven tools to improve efficiency, reduce costs, mitigate risks, and adapt to dynamic market conditions. The market caters to a wide range of industries, including manufacturing, retail, logistics, and distribution, offering solutions that vary from demand planning and warehouse automation to real-time visibility and predictive maintenance. As the demand for intelligent and data-driven supply chain solutions grows, the AI in Supply Chain Management market continues to evolve, presenting opportunities for innovation and transformative advancements in the way businesses manage and optimize their supply chains.

Some of the benefits of designing a Artificial Intelligence in Supply Chain Management Market include:

• Demand Forecasting and Planning: Leveraging sophisticated algorithms, AI analyzes historical data, market trends, and external factors to achieve precise demand forecasting. This culminates in the optimization of inventory levels, a reduction in stockouts, and heightened planning accuracy.
• Inventory Optimization: AI facilitates dynamic, real-time inventory management by scrutinizing various factors, including demand variations, supplier performance, and market conditions. The outcome is a decrease in holding costs, minimized excess inventory, and an enhanced overall efficiency in the supply chain.
• Operational Streamlining: AI streamlines routine and time-consuming tasks such as order processing, invoicing, and inventory management, automating these processes to boost operational efficiency. This, in turn, allows human resources to allocate their focus towards more strategic and intricate aspects of supply chain management.
• Advanced Visibility: AI delivers real-time visibility throughout the entire supply chain. This heightened visibility empowers businesses to monitor the movement of goods, track inventory levels, and promptly identify bottlenecks, leading to improved decision-making and responsiveness.
• Predictive Analytics Mastery: AI-powered predictive analytics discerns patterns and trends within extensive datasets, providing organizations with the ability to anticipate potential disruptions and market changes. This foresight is invaluable for proactive decision-making and effective risk mitigation.

Global Artificial Intelligence in Supply Chain Management Market was valued at US $ 5.2 Billion in 2023 and is expected to reach US $ 230.6  Billion by 2032 growing at a CAGR of 52.4% during the forecast period 2024 – 2032.

COVID -19 Impact

The COVID-19 pandemic has left an indelible mark on the AI in the Supply Chain Management Market, reshaping priorities and accelerating the adoption of advanced technologies. The disruptions caused by lockdowns and restrictions exposed vulnerabilities in global supply chains, prompting a surge in demand for AI-driven solutions to enhance resilience and mitigate risks. The imperative for reduced physical contact and increased operational efficiency fueled a heightened interest in automation, with AI becoming instrumental in tasks such as order processing, warehouse management, and last-mile delivery. Predictive analytics gained prominence as organizations sought to navigate uncertainties by leveraging real-time data for anticipating changes in demand and optimizing logistics routes.

The pandemic underscored the critical need for end-to-end visibility across supply chains, driving investments in AI solutions that provide real-time insights into inventory, production, and logistics. Digital transformation initiatives were accelerated, with businesses recognizing the value of AI in creating adaptable, data-driven supply chain processes capable of handling disruptions. The e-commerce surge during lockdowns emphasized the importance of AI applications in demand forecasting, inventory optimization, and last-mile delivery, facilitating the handling of increased online orders.

Moreover, the focus on remote monitoring and collaboration solutions, enabled by AI technologies, became imperative as physical presence in workplaces was restricted. AI-powered remote monitoring offered a means to ensure continuity while adhering to health and safety guidelines. In essence, the pandemic has not only accelerated the integration of AI into supply chain management but has also reshaped the narrative, emphasizing the critical role of these technologies in building resilient, agile, and digitally transformed supply chains for the future.

Factors Driving the Market

Drivers

Advancements in AI technology

The accelerating growth of the AI in the Supply Chain Management Market is intricately tied to continuous advancements in AI technology. These innovations play a pivotal role in reshaping how supply chain operations are conceived and executed, offering businesses unprecedented levels of efficiency and strategic decision-making capabilities. Advanced machine learning algorithms enable precise predictive analytics and demand forecasting, revolutionizing inventory management and production planning. Real-time data processing capabilities empower supply chain professionals with timely insights, fostering agility and responsiveness to dynamic market conditions. Automation of routine tasks, driven by AI, not only enhances operational efficiency but also reduces the risks associated with manual errors. The integration of computer vision and advanced analytics ensures heightened visibility and transparency throughout the supply chain, facilitating real-time tracking and improved decision support. Additionally, AI algorithms optimize logistics and routing, leading to more efficient transportation routes and minimized costs. This continuous innovation cycle, fueled by ongoing research and development, introduces sophisticated decision support systems and novel AI functionalities, creating a dynamic market environment. As businesses increasingly recognize the transformative potential of these advancements, the AI in the Supply Chain Management Market is propelled forward, shaping the future of supply chain management with unparalleled efficiency and adaptability.

Challenges

Data quality and availability

The trajectory of AI in the Supply Chain Management Market is profoundly shaped by the quality and accessibility of data. The reliability of predictive analytics, a cornerstone of AI applications in supply chain management, hinges on the accuracy of data used for forecasting inventory needs and anticipating demand. Optimal decision-making, another critical aspect, relies on high-quality data to furnish accurate insights, empowering professionals to make informed and strategic choices. Data quality is equally vital for the efficiency of AI-driven automation, ensuring smooth processes such as order processing, inventory management, and logistics optimization. Real-time visibility across the supply chain, a key goal of AI implementation, is contingent upon the availability of accurate and timely information. Reliable data supports the establishment of robust performance metrics, facilitates collaboration among stakeholders, and enhances adaptability to changing conditions. Ultimately, customer satisfaction, a paramount consideration, is intricately tied to the precise data that underpins AI applications, ensuring the fulfillment of orders and the optimization of last-mile logistics. As businesses increasingly recognize the pivotal role of data quality, investments in robust data management become integral to unlocking the full potential of AI-driven advancements in supply chain management, fostering growth and innovation in the market.

Trends

Optimizing inventory management

Optimizing inventory management stands as a linchpin for fostering the growth of AI in the Supply Chain Management Market, driving efficiency and strategic value across various dimensions. AI's ability to analyze historical data, predict demand patterns, and dynamically adjust inventory levels leads to efficient resource utilization, reducing carrying costs associated with excess stock and mitigating the risks of stockouts. The precision in demand forecasting, facilitated by AI, enables businesses to align inventory levels with actual demand, minimizing waste and contributing to overall cost reduction and profitability. Real-time tracking and monitoring of inventory enhance supply chain visibility, allowing businesses to respond promptly to market changes and disruptions, fostering agility and responsiveness. The streamlined order fulfillment processes driven by optimized inventory management improve customer satisfaction, encouraging repeat business and long-term brand loyalty. Additionally, AI's adaptability to changing market conditions, its role in dynamic reordering, and its contribution to strategic decision-making position it as a catalyst for supply chain resilience and growth. As businesses increasingly recognize the transformative impact of AI in inventory optimization, the adoption of AI technologies in the Supply Chain Management Market is expected to continue driving efficiency, reducing costs, and enhancing overall competitiveness.

Market Segmentation

By Component

By Component, the global Artificial Intelligence in Supply Chain Management Market is divided into Hardware, Software, Services.

Software is leading sub segment with 58.32% market share and 26.2% CAGR.

Software takes a central role in the AI-driven transformation of the Supply Chain Management Market, emerging as both a leader and the fastest-growing segment for several compelling reasons. Its leading position is grounded in the seamless integration capabilities with existing supply chain management systems, allowing organizations to enhance operations without extensive infrastructure changes. The scalability of AI-powered software further solidifies its leadership, accommodating businesses of varying sizes and complexities, from small enterprises to large corporations. Offering comprehensive solutions covering diverse aspects like demand forecasting, inventory management, and predictive analytics, AI software simplifies implementation, providing an all-encompassing approach for businesses seeking integrated solutions.

The rapid growth of AI software in the Supply Chain Management Market is propelled by a confluence of factors. Increasing awareness of AI's benefits in supply chain management has spurred adoption, as organizations recognize the technology's potential to enhance efficiency, cut costs, and gain a competitive edge. The evolving technological landscape, marked by continuous advancements in machine learning and predictive analytics, contributes to the development of sophisticated AI software solutions that meet the diverse needs of businesses. Intense market competition fosters innovation among software vendors, resulting in feature-rich and specialized solutions that cater to specific industry requirements. Additionally, the customization and tailoring capabilities of AI software, coupled with its cost-effectiveness, make it an attractive choice for organizations seeking precise solutions tailored to their unique supply chain challenges. As businesses increasingly prioritize technology-driven efficiency, the role of AI software in shaping the future of supply chain management is poised to expand, driving continuous growth and evolution in this dynamic market.

By Technology

By Technology, the global Artificial Intelligence in Supply Chain Management Market is divided into Natural Language Processing, Machine Learning, Computer Vision, Context Aware Computing.

Machine learning stands as a cornerstone in the AI-driven transformation of the Supply Chain Management Market with 42.36% market share, taking the lead due to its exceptional capabilities in predictive analytics, dynamic optimization, efficient data processing, and the automation of routine tasks. By harnessing machine learning algorithms, supply chain professionals can make informed decisions based on accurate demand forecasts, optimize various aspects of the supply chain dynamically, handle large datasets with precision, and automate repetitive tasks, resulting in heightened operational efficiency and reduced risks of errors.

In parallel, computer vision emerges as the fastest-growing application with 14.6.2% CAGR, AI in the supply chain domain. Its rapid ascent is attributed to its ability to revolutionize supply chain visibility, enhance quality control in manufacturing and distribution, automate warehouse operations, optimize last-mile delivery processes, and contribute to the overall security and safety of the supply chain. The real-time tracking capabilities of computer vision, coupled with its capacity to detect defects, automate picking and packing in warehouses, and improve last-mile delivery efficiency, position it as a pivotal technology for addressing critical challenges in the modern supply chain landscape. As industries increasingly recognize the transformative potential of these technologies, machine learning continues to lead by providing a foundation for data-driven decision-making, while computer vision takes center stage in driving innovation and efficiency in supply chain operations. The combined impact of these AI applications is reshaping the industry, ushering in a new era of intelligent and responsive supply chain management.

By Application

By Application the Artificial Intelligence in Supply Chain Management Market is divided by Risk Management, Freight Brokerage, Supply Chain Planning, Warehouse Management, Fleet Management, Virtual Assistant, Others.

Supply chain planning emerges as both a leading and rapidly growing application with 28.34% market share and 13.2% CAGR. The intricacy of planning processes, encompassing demand forecasting, inventory management, production planning, and distribution logistics, positions supply chain planning as an ideal domain for AI applications. AI's prowess in analyzing extensive datasets and optimizing decisions in real-time enhances operational efficiency, streamlining processes, reducing lead times, and maximizing resource utilization. Moreover, AI excels in demand forecasting, leveraging advanced algorithms to analyze historical data and market trends, thereby enabling organizations to align production and inventory with precise demand.

The accelerated growth of AI in supply chain planning is fueled by various factors. Market maturation plays a pivotal role, as organizations witness tangible benefits and successes in adopting AI for planning purposes. The increasing complexity of global supply chains, driven by factors like globalization and diverse consumer demands, amplifies the need for sophisticated planning tools. The COVID-19 pandemic, acting as a catalyst, has prompted organizations to prioritize AI solutions for building resilience, scenario planning, and agility within their supply chains. Additionally, the continuous innovation from vendors specializing in supply chain planning solutions, integrating machine learning algorithms and predictive analytics, contributes to the sustained growth of AI applications in this domain. As supply chain planning remains at the forefront of AI adoption, its transformative impact is set to reshape the landscape of supply chain management, driving operational excellence and adaptability in an ever-evolving global market.

By End Use Industries

By End Use Industries the Artificial Intelligence in Supply Chain Management Market is divided by Healthcare, Retail, Automotive, Aerospace, Manufacturing, Food and Beverages, Consumer-packaged Goods, Others.

The manufacturing sector stands as a leader with 34.5% market share, driven by the inherent complexities of its supply chains and a proactive embrace of Industry 4.0 initiatives. The intricate processes involved in manufacturing, from raw material procurement to production and distribution, benefit significantly from AI optimization, leading to enhanced efficiency and cost reduction. The manufacturing industry has been a frontrunner in implementing predictive maintenance strategies through AI, ensuring optimal performance by proactively addressing potential machinery failures. Additionally, AI facilitates quality control by identifying defects in real-time, contributing to maintaining high product standards and minimizing waste.

Conversely, the retail sector has emerged as one of the fastest-growing segments with 12.5% CAGR, in AI adoption for supply chain optimization. Fueled by the surge in e-commerce, retailers leverage AI applications such as demand forecasting, inventory optimization, and last-mile delivery logistics to address the challenges posed by the rapid expansion of online retail. AI plays a pivotal role in enhancing the customer experience in retail, providing personalized recommendations based on consumer behavior analysis and ensuring product availability through advanced inventory management. Moreover, retailers are increasingly turning to AI for better supply chain visibility, allowing real-time tracking of inventory, monitoring of supplier performance, and agile adjustments to market demands. The dynamic nature of the retail industry, characterized by evolving consumer trends, positions AI as an invaluable tool for analyzing market shifts and adapting supply chain strategies accordingly. While manufacturing leads with its intricate processes and Industry 4.0 initiatives, retail's rapid growth is propelled by the demands of an increasingly digital and customer-centric market.

By Region

By region, the global Artificial Intelligence in Supply Chain Management Market is divided into North America, Europe, APAC and Others. Others is further divided into Middle East, Africa and South America.

North America (NA) has emerged as a leader with 34.62% market share, in the adoption of artificial intelligence (AI) within the Supply Chain Management Market. This leadership position is attributed to the region's early adoption of advanced technologies, a mature technology infrastructure, and a strong ecosystem of tech companies, both established and startup. The availability of substantial investment and funding further accelerates the research, development, and deployment of AI solutions in supply chain operations across various industries.

Simultaneously, the Asia-Pacific (APAC) region has gained recognition as the fastest-growing with 16.21% CAGR, AI in supply chain applications. This growth is fueled by the region's expanding economies, increasing complexity in supply chain operations due to business expansion, and proactive government initiatives promoting the adoption of AI. APAC's status as a major manufacturing hub, coupled with the rapid growth of the e-commerce industry, amplifies the demand for AI solutions to optimize logistics, enhance decision-making, and address the challenges posed by intricate supply networks.

Competitive Landscape

The global Artificial Intelligence in Supply Chain Management Market is consolidated with the presence of few major players contributing to the market revenue. This dominance of these major players is driven by their technological expertise, extensive resources, and established brand recognition. These companies typically offered comprehensive and diversified solutions to end use industries.

• Teknowlogis

Teknowlogis stands out as a premier global supplier of supply chain solutions driven by artificial intelligence (AI). Their cutting-edge solutions cater to businesses of varying sizes, facilitating the optimization of supply chains, enhancement of efficiency, and reduction of costs. Teknowlogis' solutions find application across a diverse spectrum of industries, encompassing manufacturers, retailers, distributors, and logistics providers. With a track record marked by proven success, Teknowlogis has consistently assisted businesses in realizing and surpassing their supply chain objectives.

• Fraight AI

Freight AI, established in 2017, is an innovative freight broker powered by artificial intelligence (AI) that is transforming the logistics landscape for businesses. By harnessing AI capabilities, Freight AI automates tasks traditionally handled by human freight brokers, including carrier discovery, rate negotiation, and shipment booking. This approach enables Freight AI to deliver expedited, highly efficient, and economically advantageous freight brokerage services to its clientele.

• LLamasoft, Inc

In the dynamic realm of supply chains, LLamasoft innovates by leveraging the power of artificial intelligence (AI) to enable businesses to attain unparalleled levels of efficiency, resilience, and sustainability. Founded in 2002, LLamasoft has positioned itself as a trusted partner to leading organizations in various industries, furnishing them with the tools and insights necessary to navigate the intricacies of the global market with agility and foresight.

• Cainiao Network
• Deutsche Post AG DHL
• Google
• Salesforce
• Bosch
• Samsung Electronics
• GE Digital
• Siemens
• ABB
• Honeywell
• Micron Technology
• FedEx
• Logility
• Splice Machine
• ClearMetal
• E2Open
• Relex Solution
• Xilinx, Inc.
• Oracle Corporation
• NVIDIA Corporation
• SAP SE
• IBM Corporation
• Logility, Inc.
• Amazon Web Services, Inc.
• Intel Corporation
• Micron Technology, Inc.
• Microsoft Corporation

Recent Developments

• In September 2023, Ocado Group, a leading British online grocery retailer, forms a strategic alliance with Intel to revolutionize its supply chain operations. The collaboration focuses on harnessing the power of artificial intelligence (AI) to streamline inventory management, automate warehouse processes, and elevate the overall efficiency of the delivery system.
• In August 2023, IBM introduces a cutting-edge solution called IBM Sterling Supply Chain Insights with AI. This cloud-based offering seamlessly integrates AI with supply chain data, delivering instantaneous insights and predictive analytics. Businesses leveraging this solution gain the ability to swiftly identify potential disruptions, fine-tune inventory levels, and enhance the overall performance of their supply chain.
• Microsoft takes center stage in June 2023 by unveiling Azure Cognitive Services for Supply Chain. This suite of AI-powered solutions is designed to transform supply chain dynamics, offering tools for forecasting, inventory management, and logistics optimization. By automating tasks, enhancing efficiency, and cutting costs, Microsoft's innovative suite provides businesses with a powerful toolkit for navigating the complexities of modern supply chain management.
• In December 2022, Google Cloud introduces Vertex AI Supply Chain Management, an innovative suite of AI-driven solutions tailored for supply chain forecasting, demand planning, and inventory optimization. This comprehensive suite empowers businesses to enhance decision-making regarding inventory levels, production schedules, and logistics, fostering improved operational efficiency.
• In October 2022, Oracle makes waves with the announcement of Oracle Supply Chain Management Cloud with AI. This cloud-based solution harnesses the power of AI to automate tasks, streamline processes, and elevate decision-making capabilities throughout the supply chain. Businesses leveraging this technology gain a competitive edge by optimizing operations and fostering more informed decision-making.
• In August 2022, SAP takes a leap forward by launching SAP Supply Chain Intelligence, powered by SAP Leonardo. This suite of AI-driven solutions is designed to provide comprehensive insights into supply chain dynamics, offering tools for visibility, risk management, and collaboration. By enabling businesses to identify and proactively address supply chain disruptions, enhance communication, and optimize overall performance, SAP's innovative suite stands at the forefront of intelligent supply chain management.

Artificial Intelligence in Supply Chain Management Market Scope

Table of Contents

1 INTRODUCTION OF GLOBAL ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MANAGEMENT MARKET

1.1 Overview of the Market

1.2 Scope of Report

1.3 Assumptions

2 EXECUTIVE SUMMARY

3 RESEARCH METHODOLOGY

3.1 Data Mining

3.2 Validation

3.3 Primary Interviews

3.4 List of Data Sources

4 GLOBAL ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MANAGEMENT MARKET OUTLOOK

4.1 Overview

4.2 Market Dynamics

4.2.1 Drivers

4.2.2 Restraints

4.2.3 Opportunities

4.3 Porters Five Force Model

4.3.1. Bargaining Power of Suppliers

4.3.2. Threat of New Entrants

4.3.3. Threat of Substitutes

4.3.4. Competitive Rivalry

4.3.5. Bargaining Power among Buyers

4.4 Value Chain Analysis

5 GLOBAL ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MANAGEMENT MARKET, BY COMPONENT

5.1 Overview

5.2 Hardware

5.3 Software

5.4 Services

6 GLOBAL ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MANAGEMENT MARKET, BY TECHNOLOGY

6.1 Overview

6.2 Natural Language Processing

6.3 Machine Learning

6.4 Computer Vision

6.5 Context Aware Computing

7 GLOBAL ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MANAGEMENT MARKET, BY APPLICATION

7.1 Overview

7.2 Risk Management

7.3 Freight Brokerage

7.4 Supply Chain Planning

7.5 Ware house Management

7.6 Fleet Management

7.7 Virtual Assistant

7.8 Others

8 GLOBAL ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MANAGEMENT MARKET, BY END USE INDUSTRIES

8.1 Overview

8.2 Healthcare

8.3 Retail

8.4 Automotive

8.5 Aerospace

8.6 Manufacturing

8.7 Food and Beverages

8.8 Consumer-packaged Goods

8.9 Others

9 GLOBAL ARTIFICIAL INTELLIGENCE IN SUPPLY CHAIN MANAGEMENT MARKET, By REGION

9.1 North America

9.1.1 U.S.

9.1.2 Canada

9.2 Europe

9.2.1 Germany

9.2.3 U.K.

9.2.4 France

9.2.5 Rest of Europe

9.3 Asia Pacific

9.3.1 China

9.3.2 Japan

9.3.3 India

9.3.4 South Korea

9.3.5 Singapore

9.3.6 Malaysia

9.3.7 Australia

9.3.8 Thailand

9.3.9 Indonesia

9.3.10 Philippines

9.3.11 Rest of Asia Pacific

9.4 Others

9.4.1 Saudi Arabia

9.4.2 U.A.E.

9.4.3 South Africa

9.4.4 Egypt

9.4.5 Israel

9.4.6 Rest of Middle East and Africa (MEA)

9.4.7 Brazil

9.4.8 Argentina

9.4.9 Mexico

9.4.10 Rest of South America

10 COMPANY PROFILES

10.1 Teknowlogis

10.1.1. Company Overview

10.1.2. Key Executives

10.1.3. Operating Business Segments

10.1.4. Product Portfolio

10.1.5. Financial Performance (As per availability)

10.1.6 Key News

10.2 Fraight AI

10.2.1. Company Overview

10.2.2. Key Executives

10.2.3. Operating Business Segments

10.2.4. Product Portfolio

10.2.5. Financial Performance (As per availability)

10.2.6. Key News

10.3 LLamasoft, Inc

10.3.1. Company Overview

10.3.2. Key Executives

10.3.3. Operating Business Segments

10.3.4. Product Portfolio

10.3.5. Financial Performance (As per availability)

10.3.6. Key News

10.4  Cainiao Network

10.4.1. Company Overview

10.4.2. Key Executives

10.4.3. Operating Business Segments

10.4.4. Product Portfolio

10.4.5. Financial Performance (As per availability)

10.4.6. Key News

10.5 Deutsche Post AG DHL

10.5.1. Company Overview

10.5.2. Key Executives

10.5.3. Operating Business Segments

10.5.4. Product Portfolio

10.5.5. Financial Performance (As per availability)

10.5.6. Key News

10.6 Relex Solution

10.6.1. Company Overview

10.6.2. Key Executives

10.6.3. Operating Business Segments

10.6.4. Product Portfolio

10.6.5. Financial Performance (As per availability)

10.6.6. Key News

10.7 E2Open

10.7.1. Company Overview

10.7.2. Key Executives

10.7.3. Operating Business Segments

10.7.4. Product Portfolio

10.7.5. Financial Performance (As per availability)

10.7.6. Key News

10.8 ClearMetal

10.8.1. Company Overview

10.8.2. Key Executives

10.8.3. Operating Business Segments

10.8.4. Product Portfolio

10.8.5. Financial Performance (As per availability)

10.8.6. Key News

10.9 Splice Machine

10.9.1. Company Overview

10.9.2. Key Executives

10.9.3. Operating Business Segments

10.9.4. Product Portfolio

10.9.5. Financial Performance (As per availability)

10.9.6. Key News

10.10 Logility

10.10.1. Company Overview

10.10.2. Key Executives

10.10.3. Operating Business Segments

1010.4. Product Portfolio

10.10.5. Financial Performance (As per availability)

10.10.6. Key News

10.11 FedEx

10.11.1. Company Overview

10.11.2. Key Executives

10.11.3. Operating Business Segments

10.11.4. Product Portfolio

10.11.5. Financial Performance (As per availability)

10.11.6. Key News

10.12 Micron Technology

10.12.1. Company Overview

10.12.2. Key Executives

10.12.3. Operating Business Segments

10.12.4. Product Portfolio

10.12.5. Financial Performance (As per availability)

10.12.6. Key News

10.13 Samsung Electronics

10.13.1. Company Overview

10.13.2. Key Executives

10.13.3. Operating Business Segments

10.13.4. Product Portfolio

10.13.5. Financial Performance (As per availability)

10.13.6. Key News

10.14  Honeywell

10.14.1. Company Overview

10.14.2. Key Executives

10.14.3. Operating Business Segments

10.14.4. Product Portfolio

10.14.5. Financial Performance (As per availability)

10.14.6. Key News

10.15 Bosch

10.15.1. Company Overview

10.15.2. Key Executives

10.15.3. Operating Business Segments

10.15.4. Product Portfolio

10.15.5. Financial Performance (As per availability)

10.15.6. Key News

10.16 GE Digital

10.16.1. Company Overview

10.16.2. Key Executives

10.16.3. Operating Business Segments

10.16.4. Product Portfolio

10.16.5. Financial Performance (As per availability)

10.16.6. Key News

10.17 ABB

10.17.1. Company Overview

10.17.2. Key Executives

10.17.3. Operating Business Segments

10.17.4. Product Portfolio

10.17.5. Financial Performance (As per availability)

10.17.6. Key News

10.18 Siemens

10.18.1. Company Overview

10.18.2. Key Executives

10.18.3. Operating Business Segments

10.18.4. Product Portfolio

10.18.5. Financial Performance (As per availability)

10.18.6. Key News

10.19 Salesforce

10.19.1. Company Overview

10.19.2. Key Executives

10.19.3. Operating Business Segments

10.19.4. Product Portfolio

10.19.5. Financial Performance (As per availability)

10.19.6. Key News

10.20 Google

10.20.1. Company Overview

10.20.2. Key Executives

10.20.3. Operating Business Segments

10.20.4. Product Portfolio

10.20.5. Financial Performance (As per availability)

10.20.6. Key News

10.21 Microsoft Corporation

10.21.1. Company Overview

10.21.2. Key Executives

10.21.3. Operating Business Segments

10.21.4. Product Portfolio

10.21.5. Financial Performance (As per availability)

10.21.6. Key News

10.22 Micron Technology, Inc

10.22.1. Company Overview

10.22.2. Key Executives.

10.22.3. Operating Business Segments

10.22.4. Product Portfolio

10.22.5. Financial Performance (As per availability)

10.22.6. Key News

10.23 Intel Corporation

10.23.1. Company Overview

10.23.2. Key Executives

10.23.3. Operating Business Segments

10.23.4. Product Portfolio

10.23.5. Financial Performance (As per availability)

10.23.6. Key News

10.24 Amazon Web Services, Inc

10.24.1. Company Overview

10.24.2. Key Executives

10.24.3. Operating Business Segments

10.24.4. Product Portfolio

10.24.5. Financial Performance (As per availability)

10.24.6. Key News

10.25 Logility, Inc.

10.25.1. Company Overview

10.25.2. Key Executives

10.25.3. Operating Business Segments

10.25.4. Product Portfolio

10.25.5. Financial Performance (As per availability)

10.20.6. Key News

10.26 IBM Corporation

10.26.1. Company Overview

10.26.2. Key Executives

10.26.3. Operating Business Segments

10.26.4. Product Portfolio

10.26.5. Financial Performance (As per availability)

10.26.6. Key News

10.27 SAP SE

10.27.1. Company Overview

10.27.2. Key Executives

10.27.3. Operating Business Segments

10.27.4. Product Portfolio

10.27.5. Financial Performance (As per availability)

10.27.6. Key News

10.28 NVIDIA Corporation

10.28.1. Company Overview

10.28.2. Key Executives

10.28.3. Operating Business Segments

10.28.4. Product Portfolio

10.28.5. Financial Performance (As per availability)

10.28.6. Key News

10.29 Oracle Corporation

10.29.1. Company Overview

10.29.2. Key Executives

10.29.3. Operating Business Segments

10.29.4. Product Portfolio

10.29.5. Financial Performance (As per availability)

10.29.6. Key News

10.30  Xilinx, Inc

10.30.1. Company Overview

10.30.2. Key Executives

10.30.3. Operating Business Segments

10.30.4. Product Portfolio

10.30.5. Financial Performance (As per availability)

10.30.6. Key News

Global Artificial Intelligence in Supply Chain Management Market Segmentation

Artificial Intelligence in Supply Chain Management by Component: Market Size & Forecast 2023-2032

• Software
• Hardware
• Services

Artificial Intelligence in Supply Chain Management by Technology: Market Size & Forecast 2023-2032

• Natural Language Processing
• Machine Learning
• Computer Vision
• Context Aware Computing

Artificial Intelligence in Supply Chain Management by Application: Market Size & Forecast 2023-2032

• Risk Management
• Freight Brokerage
• Supply Chain Planning
• Ware house Management
• Fleet Management
• Virtual Assistant
• Others

Artificial Intelligence in Supply Chain Management by Industry Vertical: Market Size & Forecast 2023-2032

• Healthcare
• Retail
• Automotive
• Aerospace
• Manufacturing
• Food and Beverages
• Consumer-packaged Goods
• Others

Artificial Intelligence in Supply Chain Management by Geography: Market Size & Forecast 2023-2032

• North America (USA, Canada, Mexico)
• Europe (Germany, UK, France, Russia, Italy, Rest of Europe)
• Asia-Pacific (China, Japan, South Korea, India, Southeast Asia, Rest of Asia-Pacific)
• South America (Brazil, Argentina, Columbia, Rest of South America)
• Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria, South Africa, Rest of MEA)

Major Players:

• Teknowlogis
• Fraight AI
• LLamasoft, Inc
• Cainiao Network
• Deutsche Post AG DHL
• Google
• Salesforce
• Bosch
• Samsung Electronics
• GE Digital
• Siemens
• ABB
• Honeywell
• Micron Technology
• FedEx
• Logility
• Splice Machine
• ClearMetal
• E2Open
• Relex Solution
• Xilinx, Inc.
• Oracle Corporation
• NVIDIA Corporation
• SAP SE
• IBM Corporation
• Logility, Inc.
• Amazon Web Services, Inc.
• Intel Corporation
• Micron Technology, Inc.
• Microsoft Corporation