Artificial Intelligence In Supply Chain Market Size, Type Analysis, Application Analysis, End-Use, Industry Analysis, Regional Outlook, Competitive Strategies And Forecasts, 2023-2032
- Report ID: ME_00129088
- Format: Electronic (PDF)
- Publish Type:
- Number of Pages: 250
Market Snapshot
Source: Market Expertz
| Study Period | 2019-2032 |
| Base Year | 2023 |
| Forcast Year | 2023-2032 |
| CAGR | 5.86 |
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The Artificial Intelligence (AI) in Supply Chain Market is projected to experience a compound annual growth rate (CAGR) of 7.82% between 2022 and 2032. The market size is expected to expand by USD 25,678.89 million during this period. The growth of the market hinges on several factors, including the increasing adoption of AI technologies in supply chain management, the demand for improved operational efficiency, and the need to handle complex logistics and inventory challenges. Artificial Intelligence in the supply chain involves the integration of AI technologies like machine learning, predictive analytics, and automation to enhance decision-making, optimize processes, and drive innovation.
Artificial Intelligence in Supply Chain Market Overview:
Drivers:
A prominent driver of the artificial intelligence in the supply chain market is the quest for enhanced operational efficiency. Supply chain management involves intricate processes, including demand forecasting, inventory management, and logistics coordination. AI-powered solutions enable organizations to analyze vast amounts of data, predict demand fluctuations, optimize inventory levels, and streamline distribution, leading to cost savings and improved customer satisfaction.
Furthermore, the complexity of modern supply chains, spanning global networks, necessitates advanced technologies to manage risks and uncertainties. AI algorithms can assess market trends, anticipate disruptions, and recommend alternative strategies, enabling companies to adapt quickly and maintain continuity.
Trends:
The integration of AI in supply chain management is driving the market trend towards predictive analytics and prescriptive solutions. Predictive analytics harnesses historical and real-time data to forecast future trends and potential disruptions. These insights empower organizations to proactively adjust their strategies and respond to challenges before they escalate.
Prescriptive solutions take predictive analytics a step further by not only predicting outcomes but also suggesting optimal courses of action. This trend is transforming supply chains from reactive to proactive, enabling businesses to make informed decisions and implement effective solutions.
Restraints:
One of the key challenges restraining the growth of the AI in the supply chain market is data quality and integration issues. Successful implementation of AI technologies relies on access to accurate and comprehensive data from various sources. Poor data quality, inconsistencies, and siloed information can lead to flawed insights and suboptimal decision-making.
Additionally, the upfront investment and complexity associated with integrating AI solutions into existing supply chain processes can be a barrier for some organizations. The transition requires technological expertise, change management, and a clear understanding of the potential benefits.
Artificial Intelligence in Supply Chain Market Segmentation By Application:
The inventory management segment is anticipated to witness substantial growth during the forecast period. AI technologies play a critical role in optimizing inventory levels by analyzing historical data, demand patterns, and external factors. Through machine learning algorithms, supply chains can dynamically adjust inventory quantities, reducing carrying costs while ensuring products are available when needed.
Another significant application is demand forecasting, where AI-powered models analyze historical sales data, market trends, and seasonal variations to predict future demand accurately. These forecasts enable businesses to align production and distribution processes with customer needs, reducing overstocking or stockouts.
Artificial Intelligence in Supply Chain Market Segmentation By Type:
The machine learning segment is driving the growth of AI adoption in the supply chain. Machine learning algorithms learn from data patterns and make predictions or decisions without being explicitly programmed. In the supply chain context, machine learning is used for demand forecasting, route optimization, anomaly detection, and quality control.
Additionally, robotic process automation (RPA) is gaining traction. RPA involves using software robots to automate repetitive tasks, such as data entry, order processing, and invoice validation. This technology streamlines operations and reduces human errors.
Regional Overview:
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North America is poised to contribute significantly to the global AI in supply chain market growth during the forecast period. The region is characterized by the presence of leading technology companies and early adopters of innovative solutions. The United States, in particular, houses major players in AI development and application across various industries, including supply chain management.
Moreover, the European market is witnessing increased AI integration in supply chains. Countries like Germany, with its strong manufacturing sector, are focusing on leveraging AI to optimize production processes and logistics.
Artificial Intelligence in Supply Chain Market Customer Landscape:
The customer landscape of the AI in supply chain market ranges from early innovators, who are pioneering the adoption of AI technologies, to laggards, who are slower in adopting these solutions. Factors influencing adoption rates include industry-specific challenges, technological readiness, and awareness of AI benefits. The report provides insights into different stages of adoption and customer preferences.
Major Artificial Intelligence in Supply Chain Market Companies:
Companies in the AI in supply chain market are employing diverse strategies, such as partnerships, mergers, geographical expansion, and product launches, to strengthen their market presence.
- IBM Corporation:
Global technology leader with deep expertise in enterprise software, cloud computing, and artificial intelligence.
- Amazon Web Services, Inc.:
(AWS) Leading cloud computing platform that offers a wide range of services, including computing, storage, networking, database, analytics, machine learning, and artificial intelligence.
- SAP SE:
German multinational software corporation that provides enterprise software to businesses of all sizes.
The research report also includes a detailed analysis of the competitive landscape, featuring information about key players in the market, including:
- IBM Corporation
- Amazon Web Services, Inc.
- SAP SE
- Microsoft Corporation
- Oracle Corporation
- Google LLC
- Nvidia Corporation
- Blue Yonder Group, Inc.
- Llamasoft, Inc.
- Element AI Inc.
These companies are actively developing AI-driven solutions to address supply chain challenges, enhance operational efficiency, and drive innovation.
Segment Overview:
The artificial intelligence in the supply chain market report provides comprehensive forecasts of market growth by revenue globally, regionally, and by country from 2019 to 2032.
- Application Outlook (USD Million, 2019 - 2032)
- Inventory Management
- Demand Forecasting
- Logistics Optimization
- Supplier Management
- Others
- Type Outlook (USD Million, 2019 - 2032)
- Machine Learning
- Robotic Process Automation (RPA)
- Natural Language Processing (NLP)
- Others
- Geography Outlook (USD Million, 2019 - 2032)
- North America
- The U.S.
- Canada
- Europe
- U.K.
- Germany
- France
- Rest of Europe
- APAC
- China
- India
- South America
- Brazil
- Argentina
- Rest of South America
- Middle East & Africa
- UAE
- Saudi Arabia
- Rest of Middle East & Africa
Key Benefits for Stakeholders
- Comprehensive Market Analysis- The study meticulously segments the market based on both economic and non-economic criteria, conducting both qualitative and quantitative analyses. This rigorous approach offers a deep understanding of various market segments and sub-segments.
- Accurate Market Valuation- Precise market values in USD Billion are furnished for each segment and sub-segment, facilitating stakeholders in making precise assessments of market opportunities.
- Identification of Growth Prospects- The report pinpoints regions and market segments poised for rapid expansion, as well as those with substantial market share potential. This assists stakeholders in making well-informed strategic decisions.
- Insightful Regional Analysis- The research explores regional factors influencing the market and scrutinizes product or service utilization in diverse geographical areas, enhancing comprehension of regional dynamics.
- Comprehensive Competitive Insight- The report encompasses critical data, including market share of leading players, recent service/product launches, collaborations, expansions, and acquisitions made by profiled companies in the last five years. This provides a holistic view of the competitive landscape.
- Detailed Company Profiles- In-depth profiles of key market participants encompass company overviews, business insights, product benchmarking, and SWOT analyses. This equips stakeholders with the means to evaluate the strengths and weaknesses of these entities.
- Future Market Outlook- The research furnishes an industry perspective on the present and future based on recent developments, enabling stakeholders to anticipate market trends and forthcoming changes.
- Thorough Market Examination- Porter’s five forces analysis is employed in the study to provide a comprehensive examination of the market from multiple angles, including competitive forces and market dynamics.
- Value Chain Insights- The research leverages the Value Chain concept to elucidate the market, offering insights into how products or services traverse the market ecosystem.
- Market Dynamics and Growth Prospects- The study presents current market dynamics and growth prospects for the foreseeable future, empowering stakeholders with invaluable information for strategic decision-making.
TABLE OF CONTENTS: GLOBAL Artificial Intelligence in Supply Chain MARKET
Chapter 1. MARKET SYNOPSIS
1.1. Market Definition
1.2. Research Scope & Premise
1.3. Methodology
1.4. Market Estimation Technique
Chapter 2. EXECUTIVE SUMMARY
2.1. Summary Snapshot, 2016 – 2027
Chapter 3. INDICATIVE METRICS
3.1. Macro Indicators
Chapter 4. Artificial Intelligence in Supply Chain MARKET SEGMENTATION & IMPACT ANALYSIS
4.1. Artificial Intelligence in Supply Chain Segmentation Analysis
4.2. Industrial Outlook
4.3. Price Trend Analysis
4.4. Regulatory Framework
4.5. Porter’s Five Forces Analysis
4.5.1. Artificial Intelligence in Supply Chain Of Suppliers
4.5.2. Artificial Intelligence in Supply Chain Of Buyers
4.5.3. Threat Of Substitutes
4.5.4. Threat Of New Entrants
4.5.5. Competitive Rivalry
Chapter 5. Artificial Intelligence in Supply Chain MARKET BY Component landscape
SIGHTS & TRENDS
5.1. Segment 1 Dynamics & Market Share, 2019 & 2027
5.2 Platforms
5.2.1. Market Estimates And Forecast, 2016 – 2027 (USD Million)
5.2.2. Market Estimates And Forecast, By Region, 2016 – 2027 (USD Million)
5.3 Solutions
5.3.1. Market Estimates And Forecast, 2016 – 2027 (USD Million)
5.3.2. Market Estimates And Forecast, By Region, 2016 – 2027 (USD Million)
Chapter 6. Artificial Intelligence in Supply Chain MARKET BY Application INSIGHTS & TRENDS
6.1. Segment 2 Dynamics & Market Share, 2019 & 2027
6.2 Warehouse
6.2.1. Market Estimates And Forecast, 2016 – 2027 (USD Million)
6.2.2. Market Estimates And Forecast, By Region, 2016 – 2027 (USD Million)
6.3 Fleet
6.3.1. Market Estimates And Forecast, 2016 – 2027 (USD Million)
6.3.2. Market Estimates And Forecast, By Region, 2016 – 2027 (USD Million)
6.4 Inventory Management
6.4.1. Market Estimates And Forecast, 2016 – 2027 (USD Million)
6.4.2. Market Estimates And Forecast, By Region, 2016 – 2027 (USD Million)
Chapter 7. Artificial Intelligence in Supply Chain MARKET REGIONAL OUTLOOK
7.1. Artificial Intelligence in Supply Chain Market Share By Region, 2019 & 2027
7.2. NORTH AMERICA
7.2.1. North America Artificial Intelligence in Supply Chain Market Estimates And Forecast, 2016 – 2027 (USD Million)
7.2.2. North America Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 1, 2016 –2027, (USD Million)
7.2.3. North America Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 2, 2016 –2027, (USD Million)
7.2.4. North America Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 3, 2016 –2027, (USD Million)
7.2.5. U.S.
7.2.5.1. U.S. Artificial Intelligence in Supply Chain Market Estimates And Forecast, 2016 – 2027, (USD Million)
7.2.5.2. U.S. Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 1, 2016 –2027, (USD Million)
7.2.5.3. U.S. Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 2, 2016 –2027, (USD Million)
7.2.5.4. U.S. Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 3, 2016 –2027, (USD Million)
7.2.6. CANADA
7.2.6.1. Canada Artificial Intelligence in Supply Chain Market Estimates And Forecast, 2016 – 2027, (USD Million)
7.2.6.2. Canada Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 1, 2016 –2027, (USD Million)
7.2.6.3. Canada Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 2, 2016 –2027, (USD Million)
7.2.6.4. Canada Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 3, 2016 –2027, (USD Million)
7.3. EUROPE
7.3.1. Europe Artificial Intelligence in Supply Chain Market Estimates And Forecast, 2016 – 2027, (USD Million)
7.3.2. Europe Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 1, 2016 –2027, (USD Million)
7.3.3. Europe Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 2, 2016 –2027, (USD Million)
7.3.4. Europe Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 3, 2016 –2027, (USD Million)
7.3.5. GERMANY
7.3.5.1. Germany Artificial Intelligence in Supply Chain Market Estimates And Forecast, 2016 – 2027, (USD Million)
7.3.5.2. Germany Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 1, 2016 –2027, (USD Million)
7.3.5.3. Germany Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 2, 2016 –2027, (USD Million)
7.3.5.4. Germany Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 3, 2016 –2027, (USD Million)
7.3.6. FRANCE
7.3.6.1. France Artificial Intelligence in Supply Chain Market Estimates And Forecast, 2016 – 2027, (USD Million)
7.3.6.2. France Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 1, 2016 –2027, (USD Million)
7.3.6.3. France Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 2, 2016 –2027, (USD Million)
7.3.6.4. France Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 3, 2016 –2027, (USD Million)
7.3.7. U.K.
7.3.7.1. U.K. Artificial Intelligence in Supply Chain Market Estimates And Forecast, 2016 – 2027, (USD Million)
7.3.7.2. U.K. Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 1, 2016 –2027, (USD Million)
7.3.7.3. U.K. Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 2, 2016 –2027, (USD Million)
7.3.7.4. U.K. Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 3, 2016 –2027, (USD Million)
7.4. ASIA-PACIFIC
7.4.1. Asia Pacific Artificial Intelligence in Supply Chain Market Estimates And Forecast, 2016 – 2027, (USD Million)
7.4.2. Asia Pacific Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 1, 2016 –2027, (USD Million)
7.4.3. Asia Pacific Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 2, 2016 –2027, (USD Million)
7.4.4. Asia Pacific Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 3, 2016 –2027, (USD Million)
7.4.5. CHINA
7.4.5.1. China Artificial Intelligence in Supply Chain Market Estimates And Forecast, 2016 – 2027, (USD Million)
7.4.5.2. China Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 1, 2016 –2027, (USD Million)
7.4.5.3. China Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 2, 2016 –2027, (USD Million)
7.4.5.4. China Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 3, 2016 –2027, (USD Million)
7.4.6. INDIA
7.4.6.1. India Artificial Intelligence in Supply Chain Market Estimates And Forecast, 2016 – 2027, (USD Million)
7.4.6.2. India Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 1, 2016 –2027, (USD Million)
7.4.6.3. India Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 2, 2016 –2027, (USD Million)
7.4.6.4. India Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 3, 2016 –2027, (USD Million)
7.4.7. JAPAN
7.4.7.1. Japan Artificial Intelligence in Supply Chain Market Estimates And Forecast, 2016 – 2027, (USD Million)
7.4.7.2. Japan Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 1, 2016 –2027, (USD Million)
7.4.7.3. Japan Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 2, 2016 –2027, (USD Million)
7.4.7.4. Japan Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 3, 2016 –2027, (USD Million)
7.4.8. AUSTRALIA
7.4.8.1. Australia Artificial Intelligence in Supply Chain Market Estimates And Forecast, 2016 – 2027, (USD Million)
7.4.8.2. Australia Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 1, 2016 –2027, (USD Million)
7.4.8.3. Australia Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 2, 2016 –2027, (USD Million)
7.4.8.4. Australia Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 3, 2016 –2027, (USD Million)
7.5. MIDDLE EAST AND AFRICA (MEA)
7.5.1. Mea Artificial Intelligence in Supply Chain Market Estimates And Forecast, 2016 – 2027, (USD Million)
7.5.2. Mea Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 1, 2016 –2027, (USD Million)
7.5.3. Mea Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 2, 2016 –2027, (USD Million)
7.5.4. Mea Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 3, 2016 –2027, (USD Million)
7.6. LATIN AMERICA
7.6.1. Latin America Artificial Intelligence in Supply Chain Market Estimates And Forecast, 2016 – 2027, (USD Million)
7.6.2. Latin America Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 1, 2016 –2027, (USD Million)
7.6.3. Latin America Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 2, 2016 –2027, (USD Million)
7.6.4. Latin America Artificial Intelligence in Supply Chain Market Estimates And Forecast By Production Process, 2016 –2027, (USD Million)
7.6.5. Latin America Artificial Intelligence in Supply Chain Market Estimates And Forecast By Segment 3, 2016 –2027, (USD Million)
Chapter 8. COMPETITIVE LANDSCAPE
8.1. Market Share By Manufacturers
8.2. Strategic Benchmarking
8.2.1. New Product Launches
8.2.2. Investment & Expansion
8.2.3. Acquisitions
8.2.4. Partnerships, Agreement, Mergers, Joint-Ventures
8.3. Vendor Landscape
8.3.1. North American Suppliers
8.3.2. European Suppliers
8.3.3. Asia-Pacific Suppliers
8.3.4. Rest Of The World Suppliers
Chapter 9. COMPANY PROFILES
9.1 Amazon.com
9.1.1. Company Overview
9.1.2. Financial Performance
9.1.3. Product Insights
9.1.4. Strategic Initiatives
9.2 ClearMetal
9.2.1. Company Overview
9.2.2. Financial Performance
9.2.3. Product Insights
9.2.4. Strategic Initiatives
9.3 Deutsche Post AG DHL
9.3.1. Company Overview
9.3.2. Financial Performance
9.3.3. Product Insights
9.3.4. Strategic Initiatives
9.4 FedEx
9.4.1. Company Overview
9.4.2. Financial Performance
9.4.3. Product Insights
9.4.4. Strategic Initiatives
9.5 General Electric
9.5.1. Company Overview
9.5.2. Financial Performance
9.5.3. Product Insights
9.5.4. Strategic Initiatives
9.6 Google LLC
9.6.1. Company Overview
9.6.2. Financial Performance
9.6.3. Product Insights
9.6.4. Strategic Initiatives
9.7 IBM Corporation
9.7.1. Company Overview
9.7.2. Financial Performance
9.7.3. Product Insights
9.7.4. Strategic Initiatives
9.8 Intel Corporation
9.8.1. Company Overview
9.8.2. Financial Performance
9.8.3. Product Insights
9.8.4. Strategic Initiatives
9.9 LLamasoft
9.9.1. Company Overview
9.9.2. Financial Performance
9.9.3. Product Insights
9.9.4. Strategic Initiatives
9.10 Micron Technology
9.10.1. Company Overview
9.10.2. Financial Performance
9.10.3. Product Insights
9.10.4. Strategic Initiatives
RESEARCH METHODOLOGY
A research methodology is a systematic approach for assessing or conducting a market study. Researchers tend to draw on a variety of both qualitative and quantitative study methods, inclusive of investigations, survey, secondary data and market observation.
Such plans can focus on classifying the products offered by leading market players or simply use statistical models to interpret observations or test hypotheses. While some methods aim for a detailed description of the factors behind an observation, others present the context of the current market scenario.
Now let’s take a closer look at the research methods here.
Secondary Research Model
Extensive data is obtained and cumulated on a substantial basis during the inception phase of the research process. The data accumulated is consistently filtered through validation from the in-house database, paid sources as well reputable industry magazines. A robust research study requires an understanding of the overall value chain. Annual reports and financials of industry players are studied thoroughly to have a comprehensive idea of the market taxonomy.
Primary Insights
Post conglomeration of the data obtained through secondary research; a validation process is initiated to verify the numbers or figures. This process is usually performed by having a detailed discussion with the industry experts.
However, we do not restrict our primary interviews only to the industry leaders. Our team covers the entire value chain while verifying the data. A significant number of raw material suppliers, local manufacturers, distributors, and stakeholders are interviewed to make our findings authentic. The current trends which include the drivers, restraints, and opportunities are also derived through the primary research process.
Market Estimation
The market estimation is conducted by analyzing the data collected through both secondary and primary research. This process involves market breakdown, bottom-up and top- down approach.
Moreover, while forecasting the market a comprehensive statistical time series model is designed for each market. Macroeconomic indicators are considered to understand the current trends of the market. Each data point is verified by the process of data triangulation method to arrive at the final market estimates.
Final Presentation
The penultimate process results in a holistic research report. The study equips key industry players to undertake significant strategic decisions through the findings. The report encompasses detailed market information. Graphical representations of the current market trends are also made available in order to make the study highly comprehensible for the reader.
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