Supply chain management has significantly transformed in the past few decades. The integration of artificial intelligence in these supply chain systems was a watershed moment for this sector. Various supply chain management (SCM) software have flooded the marketplace. As a result, the valuation of the cognitive supply chain market is estimated staggering US$ 10.40 billion as of 2024.
Cognitive supply chain platforms and services also allow dynamic inventory optimization by considering factors such as demand variability, lead times, and service level requirements. Their demand in the SCM sector is proof of their efficient, reliable, and cost-effective capabilities. The market is slated to grow at a CAGR of 15.60% through 2034.
With companies leveraging cognitive technologies to optimize their supply chains, the market valuation is poised to surpass US$ 44.50 billion by the end of 2034.
| Attributes | Details |
|---|---|
| Market Value for 2024 | US$ 10.40 billion |
| Projected Market Value for 2034 | US$ 44.50 billion |
| Value-based CAGR of the Market for 2024 to 2034 | 15.60% |
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With the incorporation of AI and ML in supply chain management platforms, companies involved in shipping and transportation have saved a substantial amount of their financial capital. Below are the growth rates of the supply chain management market and IoT in the supply chain management market along with their ongoing challenges and trends.
Drawing from these analytics, it can be confidently projected that the cognitive supply chain solutions industry is likely to have a brighter future than the other two.
Cognitive Supply Chain Market:
| Attributes | Cognitive Supply Chain Market |
|---|---|
| Value-based CAGR (2024 to 2034) | 15.60% |
| Projected Market Value (2024) | US$ 10.40 billion |
| Growth Factor | Companies in logistics and transportation businesses actively seeking ways to monitor and make their shipments faster, efficient, and more advanced to better suit consumers |
| Restraint | Lack of awareness about benefits of these technologies coupled with technical unavailability and shortage of skilled workforce to handle such complex systems |
| Trend | Integration of artificial intelligence and machine learning algorithms in supply chain management applications for better outputs |
Supply Chain Management Market:
| Attributes | Supply Chain Management Market |
|---|---|
| Value-based CAGR (2024 to 2034) | 11.8% |
| Growth Factor | Highly competitive environment for end-user companies pushes demand for SCM to maintain its relevance by adopting technologically advanced solutions |
| Restraint | High costs associated with replacing traditional supply management tools with these new and advanced ones remain largest challenge for budget-sensitive businesses |
| Trend | Procurement processes that support design and management of sustainable supply chains are becoming critical for providers of goods and services |
IoT in Supply Chain Market:
| Attributes | IoT in Supply Chain Market |
|---|---|
| Value-based CAGR (2024 to 2034) | 15.5% |
| Growth Factor | Increasing demand for Internet of Things (IoT) in supply chain sector can be attributed to improved operational efficiency and cost reduction in industrial processes |
| Restraint | Concerns regarding privacy and security of users’ data |
| Trend | By leveraging IoT sensors and data, companies can optimize their logistics and transportation processes, ensuring timely delivery and improving customer satisfaction |
Companies in the market provide their offerings through solutions, services, and other means. Among these, the solutions segment is anticipated to hold the largest market share of 62% as of 2024.
| Attributes | Details |
|---|---|
| Offerings | Solutions |
| Market Share (2024) | 62.00% |
The demand for cognitive supply chain solutions has touched the skies. This is because they offer sophisticated and advanced predictive analytics, demand forecasting, and risk management recommendations to businesses. Using this, organizations proactively address challenges and capitalize on opportunities in their supply chain operations.
These solutions can also leverage data from Internet of Things (IoT) devices and big data analytics, to gain deeper insights into supply chain operations which improves the overall decision-making across the supply chain.
Cognitive supply chain solutions are mainly deployed in two main ways, cloud-based and on-premise. Among these, the on-premise deployment takes the majority share, 66.0% of the overall market as estimated for 2024.
| Attributes | Details |
|---|---|
| Deployment | On-premise |
| Market Share (2024) | 66.0% |
The main reason why companies involved in highly regulated industries like healthcare and finance prefer on-premise deployment over cloud-based is their concern for security and privacy. On-premise deployment provides greater security and compliance as compared to the alternative.
On-premise deployment also provides organizations with greater customization options and control over their SCM software. This has also increased their adoption rates in the past few years.
Principal Consultant
| Countries | CAGR (2024 to 2034) |
|---|---|
| South Korea | 18.00% |
| Japan | 17.40% |
| The United Kingdom | 17.00% |
| China | 16.50% |
| The United States | 16.00% |
South Korea is one of the leading markets in the world when it comes to cognitive supply chains. The South Korean market is estimated to grow at a CAGR of 18.00% through 2034.
South Korea is well known for its technological innovation and advanced manufacturing capabilities. The country, in the last few years, has been increasingly investing in Industry 4.0 technologies, including AI and machine learning to optimize their supply chain operations and gain a competitive edge.
This has surged the prominence of cognitive supply chain solutions in the country. Besides this, South Korean reliance on these solutions for the international trade of electronics and automotive parts has also positively affected the market.
The Japanese market is also a lucrative one. It is anticipated to expand at a CAGR of 17.40% through 2034.
Japan, as of 2024, is facing the problem of a growing aging population. One in every three Japanese adult persons is above the age of sixty-five. This has resulted in acute shortages of labor in the country. The manufacturing sector has to thus rely on cognitive supply chain solutions for their day-to-day operations.
These applications and platforms efficiently streamline processes by automating tasks such as inventory management, production scheduling, and logistics optimization. Apart from this, the pandemic also brought before the world, the importance of resilient supply chain networks. Japanese companies are thus investing heavily in these solutions to strengthen their supply chain capabilities.
The market in the United Kingdom is also predicted to flourish in the coming future. It is in line to progress at a CAGR of 17.00% for the forecast period in the United Kingdom.
Over the past few years, thanks to the rising disposable incomes and the preference for comfort and convenience among the middle class, the United Kingdom’s e-commerce sector has experienced significant growth. This has put tremendous pressure on companies involved in supply chain management to provide efficient and reliable solutions.
Logistics companies in the United Kingdom are also generating huge demand for cognitive supply chain solutions. They help optimize inventory management, order fulfillment, and last-mile delivery processes to meet the demands of online shoppers effectively and compete in the fast-paced e-commerce market.
China is one of the prominent countries in this market. The market is slated to progress at an outstanding CAGR of 16.50% through 2034.
China is considered a notable manufacturing economy. The country also hosts a vast network of suppliers, manufacturers, and logistics providers. This has created a conducive environment for cognitive supply chain solutions. They enable Chinese companies to optimize production processes, improve supply chain visibility, and enhance operational efficiency to maintain their competitive edge in markets.
Besides this, the country is also going through a phase of digital transformation across industries and these solutions play a crucial role in this transformation.
The United States market is also a promising one. It is anticipated to progress at a CAGR of 16.00% through 2034.
The United States is blessed with a multitude of tech companies and start-ups that are well-versed in artificial intelligence and machine learning algorithms. This environment has benefitted the companies present in the logistics sector as they leverage cognitive supply chain solutions to enhance their operations and gain a competitive edge. Also, the market is gaining traction in the United States due to the country’s tendency to harness the potential of advanced and sophisticated technologies in its industrial operations.
The market is still in its nascent stages as there is still a lot of room for improvement. The market is still dominated by a few tech giants, making entry of new start-ups seem very difficult. These companies already have a well-established consumer base due to their prolonged presence in the industry. These players are tapping the markets in emerging economies to effectively expand their consumer base.
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The cognitive supply chain market is expected to be worth US$ 10.40 billion by 2024.
The cognitive supply chain market is expected to reach US$ 44.50 billion by 2034.
The cognitive supply chain market is set to display a CAGR of 15.60% from 2024 to 2034.
IBM Corporation, Oracle, Amazon.com, Accenture plc, and Intel Corporation, are some of the major players in the the cognitive supply chain market.
The valuation for the cognitive supply chain market in 2023 was US$ 8.70 billion.
1. Executive Summary
1.1. Global Market Outlook
1.2. Demand-side Trends
1.3. Supply-side Trends
1.4. Technology Roadmap Analysis
1.5. Analysis and Recommendations
2. Market Overview
2.1. Market Coverage / Taxonomy
2.2. Market Definition / Scope / Limitations
3. Market Background
3.1. Market Dynamics
3.1.1. Drivers
3.1.2. Restraints
3.1.3. Opportunity
3.1.4. Trends
3.2. Scenario Forecast
3.2.1. Demand in Optimistic Scenario
3.2.2. Demand in Likely Scenario
3.2.3. Demand in Conservative Scenario
3.3. Opportunity Map Analysis
3.4. Investment Feasibility Matrix
3.5. PESTLE and Porter’s Analysis
3.6. Regulatory Landscape
3.6.1. By Key Regions
3.6.2. By Key Countries
3.7. Regional Parent Market Outlook
4. Global Market Analysis 2019 to 2023 and Forecast, 2024 to 2034
4.1. Historical Market Size Value (US$ Million) Analysis, 2019 to 2023
4.2. Current and Future Market Size Value (US$ Million) Projections, 2024 to 2034
4.2.1. Y-o-Y Growth Trend Analysis
4.2.2. Absolute $ Opportunity Analysis
5. Global Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Offering
5.1. Introduction / Key Findings
5.2. Historical Market Size Value (US$ Million) Analysis By Offering, 2019 to 2023
5.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Offering, 2024 to 2034
5.3.1. Solutions
5.3.2. Forecasting
5.3.3. Analytics
5.3.4. Inventory Management
5.3.5. Risk Management
5.3.6. Services
5.3.7. Others
5.4. Y-o-Y Growth Trend Analysis By Offering, 2019 to 2023
5.5. Absolute $ Opportunity Analysis By Offering, 2024 to 2034
6. Global Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Deployment
6.1. Introduction / Key Findings
6.2. Historical Market Size Value (US$ Million) Analysis By Deployment, 2019 to 2023
6.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Deployment, 2024 to 2034
6.3.1. Cloud- Based
6.3.2. On-Premises
6.4. Y-o-Y Growth Trend Analysis By Deployment, 2019 to 2023
6.5. Absolute $ Opportunity Analysis By Deployment, 2024 to 2034
7. Global Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Enterprise size
7.1. Introduction / Key Findings
7.2. Historical Market Size Value (US$ Million) Analysis By Enterprise size, 2019 to 2023
7.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By Enterprise size, 2024 to 2034
7.3.1. SMEs
7.3.2. Large Enterprise
7.4. Y-o-Y Growth Trend Analysis By Enterprise size, 2019 to 2023
7.5. Absolute $ Opportunity Analysis By Enterprise size, 2024 to 2034
8. Global Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By End User
8.1. Introduction / Key Findings
8.2. Historical Market Size Value (US$ Million) Analysis By End User, 2019 to 2023
8.3. Current and Future Market Size Value (US$ Million) Analysis and Forecast By End User, 2024 to 2034
8.3.1. Manufacturing
8.3.2. Automotive
8.3.3. Retail & E-Commerce
8.3.4. Logistics & Transportation
8.3.5. Healthcare
8.3.6. Food & Beverages
8.3.7. Others
8.4. Y-o-Y Growth Trend Analysis By End User, 2019 to 2023
8.5. Absolute $ Opportunity Analysis By End User, 2024 to 2034
9. Global Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Region
9.1. Introduction
9.2. Historical Market Size Value (US$ Million) Analysis By Region, 2019 to 2023
9.3. Current Market Size Value (US$ Million) Analysis and Forecast By Region, 2024 to 2034
9.3.1. North America
9.3.2. Latin America
9.3.3. Western Europe
9.3.4. Eastern Europe
9.3.5. South Asia and Pacific
9.3.6. East Asia
9.3.7. Middle East and Africa
9.4. Market Attractiveness Analysis By Region
10. North America Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Country
10.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2019 to 2023
10.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2024 to 2034
10.2.1. By Country
10.2.1.1. USA
10.2.1.2. Canada
10.2.2. By Offering
10.2.3. By Deployment
10.2.4. By Enterprise size
10.2.5. By End User
10.3. Market Attractiveness Analysis
10.3.1. By Country
10.3.2. By Offering
10.3.3. By Deployment
10.3.4. By Enterprise size
10.3.5. By End User
10.4. Key Takeaways
11. Latin America Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Country
11.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2019 to 2023
11.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2024 to 2034
11.2.1. By Country
11.2.1.1. Brazil
11.2.1.2. Mexico
11.2.1.3. Rest of Latin America
11.2.2. By Offering
11.2.3. By Deployment
11.2.4. By Enterprise size
11.2.5. By End User
11.3. Market Attractiveness Analysis
11.3.1. By Country
11.3.2. By Offering
11.3.3. By Deployment
11.3.4. By Enterprise size
11.3.5. By End User
11.4. Key Takeaways
12. Western Europe Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Country
12.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2019 to 2023
12.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2024 to 2034
12.2.1. By Country
12.2.1.1. Germany
12.2.1.2. UK
12.2.1.3. France
12.2.1.4. Spain
12.2.1.5. Italy
12.2.1.6. Rest of Western Europe
12.2.2. By Offering
12.2.3. By Deployment
12.2.4. By Enterprise size
12.2.5. By End User
12.3. Market Attractiveness Analysis
12.3.1. By Country
12.3.2. By Offering
12.3.3. By Deployment
12.3.4. By Enterprise size
12.3.5. By End User
12.4. Key Takeaways
13. Eastern Europe Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Country
13.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2019 to 2023
13.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2024 to 2034
13.2.1. By Country
13.2.1.1. Poland
13.2.1.2. Russia
13.2.1.3. Czech Republic
13.2.1.4. Romania
13.2.1.5. Rest of Eastern Europe
13.2.2. By Offering
13.2.3. By Deployment
13.2.4. By Enterprise size
13.2.5. By End User
13.3. Market Attractiveness Analysis
13.3.1. By Country
13.3.2. By Offering
13.3.3. By Deployment
13.3.4. By Enterprise size
13.3.5. By End User
13.4. Key Takeaways
14. South Asia and Pacific Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Country
14.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2019 to 2023
14.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2024 to 2034
14.2.1. By Country
14.2.1.1. India
14.2.1.2. Bangladesh
14.2.1.3. Australia
14.2.1.4. New Zealand
14.2.1.5. Rest of South Asia and Pacific
14.2.2. By Offering
14.2.3. By Deployment
14.2.4. By Enterprise size
14.2.5. By End User
14.3. Market Attractiveness Analysis
14.3.1. By Country
14.3.2. By Offering
14.3.3. By Deployment
14.3.4. By Enterprise size
14.3.5. By End User
14.4. Key Takeaways
15. East Asia Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Country
15.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2019 to 2023
15.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2024 to 2034
15.2.1. By Country
15.2.1.1. China
15.2.1.2. Japan
15.2.1.3. South Korea
15.2.2. By Offering
15.2.3. By Deployment
15.2.4. By Enterprise size
15.2.5. By End User
15.3. Market Attractiveness Analysis
15.3.1. By Country
15.3.2. By Offering
15.3.3. By Deployment
15.3.4. By Enterprise size
15.3.5. By End User
15.4. Key Takeaways
16. Middle East and Africa Market Analysis 2019 to 2023 and Forecast 2024 to 2034, By Country
16.1. Historical Market Size Value (US$ Million) Trend Analysis By Market Taxonomy, 2019 to 2023
16.2. Market Size Value (US$ Million) Forecast By Market Taxonomy, 2024 to 2034
16.2.1. By Country
16.2.1.1. GCC Countries
16.2.1.2. South Africa
16.2.1.3. Israel
16.2.1.4. Rest of MEA
16.2.2. By Offering
16.2.3. By Deployment
16.2.4. By Enterprise size
16.2.5. By End User
16.3. Market Attractiveness Analysis
16.3.1. By Country
16.3.2. By Offering
16.3.3. By Deployment
16.3.4. By Enterprise size
16.3.5. By End User
16.4. Key Takeaways
17. Key Countries Market Analysis
17.1. USA
17.1.1. Pricing Analysis
17.1.2. Market Share Analysis, 2023
17.1.2.1. By Offering
17.1.2.2. By Deployment
17.1.2.3. By Enterprise size
17.1.2.4. By End User
17.2. Canada
17.2.1. Pricing Analysis
17.2.2. Market Share Analysis, 2023
17.2.2.1. By Offering
17.2.2.2. By Deployment
17.2.2.3. By Enterprise size
17.2.2.4. By End User
17.3. Brazil
17.3.1. Pricing Analysis
17.3.2. Market Share Analysis, 2023
17.3.2.1. By Offering
17.3.2.2. By Deployment
17.3.2.3. By Enterprise size
17.3.2.4. By End User
17.4. Mexico
17.4.1. Pricing Analysis
17.4.2. Market Share Analysis, 2023
17.4.2.1. By Offering
17.4.2.2. By Deployment
17.4.2.3. By Enterprise size
17.4.2.4. By End User
17.5. Germany
17.5.1. Pricing Analysis
17.5.2. Market Share Analysis, 2023
17.5.2.1. By Offering
17.5.2.2. By Deployment
17.5.2.3. By Enterprise size
17.5.2.4. By End User
17.6. UK
17.6.1. Pricing Analysis
17.6.2. Market Share Analysis, 2023
17.6.2.1. By Offering
17.6.2.2. By Deployment
17.6.2.3. By Enterprise size
17.6.2.4. By End User
17.7. France
17.7.1. Pricing Analysis
17.7.2. Market Share Analysis, 2023
17.7.2.1. By Offering
17.7.2.2. By Deployment
17.7.2.3. By Enterprise size
17.7.2.4. By End User
17.8. Spain
17.8.1. Pricing Analysis
17.8.2. Market Share Analysis, 2023
17.8.2.1. By Offering
17.8.2.2. By Deployment
17.8.2.3. By Enterprise size
17.8.2.4. By End User
17.9. Italy
17.9.1. Pricing Analysis
17.9.2. Market Share Analysis, 2023
17.9.2.1. By Offering
17.9.2.2. By Deployment
17.9.2.3. By Enterprise size
17.9.2.4. By End User
17.10. Poland
17.10.1. Pricing Analysis
17.10.2. Market Share Analysis, 2023
17.10.2.1. By Offering
17.10.2.2. By Deployment
17.10.2.3. By Enterprise size
17.10.2.4. By End User
17.11. Russia
17.11.1. Pricing Analysis
17.11.2. Market Share Analysis, 2023
17.11.2.1. By Offering
17.11.2.2. By Deployment
17.11.2.3. By Enterprise size
17.11.2.4. By End User
17.12. Czech Republic
17.12.1. Pricing Analysis
17.12.2. Market Share Analysis, 2023
17.12.2.1. By Offering
17.12.2.2. By Deployment
17.12.2.3. By Enterprise size
17.12.2.4. By End User
17.13. Romania
17.13.1. Pricing Analysis
17.13.2. Market Share Analysis, 2023
17.13.2.1. By Offering
17.13.2.2. By Deployment
17.13.2.3. By Enterprise size
17.13.2.4. By End User
17.14. India
17.14.1. Pricing Analysis
17.14.2. Market Share Analysis, 2023
17.14.2.1. By Offering
17.14.2.2. By Deployment
17.14.2.3. By Enterprise size
17.14.2.4. By End User
17.15. Bangladesh
17.15.1. Pricing Analysis
17.15.2. Market Share Analysis, 2023
17.15.2.1. By Offering
17.15.2.2. By Deployment
17.15.2.3. By Enterprise size
17.15.2.4. By End User
17.16. Australia
17.16.1. Pricing Analysis
17.16.2. Market Share Analysis, 2023
17.16.2.1. By Offering
17.16.2.2. By Deployment
17.16.2.3. By Enterprise size
17.16.2.4. By End User
17.17. New Zealand
17.17.1. Pricing Analysis
17.17.2. Market Share Analysis, 2023
17.17.2.1. By Offering
17.17.2.2. By Deployment
17.17.2.3. By Enterprise size
17.17.2.4. By End User
17.18. China
17.18.1. Pricing Analysis
17.18.2. Market Share Analysis, 2023
17.18.2.1. By Offering
17.18.2.2. By Deployment
17.18.2.3. By Enterprise size
17.18.2.4. By End User
17.19. Japan
17.19.1. Pricing Analysis
17.19.2. Market Share Analysis, 2023
17.19.2.1. By Offering
17.19.2.2. By Deployment
17.19.2.3. By Enterprise size
17.19.2.4. By End User
17.20. South Korea
17.20.1. Pricing Analysis
17.20.2. Market Share Analysis, 2023
17.20.2.1. By Offering
17.20.2.2. By Deployment
17.20.2.3. By Enterprise size
17.20.2.4. By End User
17.21. GCC Countries
17.21.1. Pricing Analysis
17.21.2. Market Share Analysis, 2023
17.21.2.1. By Offering
17.21.2.2. By Deployment
17.21.2.3. By Enterprise size
17.21.2.4. By End User
17.22. South Africa
17.22.1. Pricing Analysis
17.22.2. Market Share Analysis, 2023
17.22.2.1. By Offering
17.22.2.2. By Deployment
17.22.2.3. By Enterprise size
17.22.2.4. By End User
17.23. Israel
17.23.1. Pricing Analysis
17.23.2. Market Share Analysis, 2023
17.23.2.1. By Offering
17.23.2.2. By Deployment
17.23.2.3. By Enterprise size
17.23.2.4. By End User
18. Market Structure Analysis
18.1. Competition Dashboard
18.2. Competition Benchmarking
18.3. Market Share Analysis of Top Players
18.3.1. By Regional
18.3.2. By Offering
18.3.3. By Deployment
18.3.4. By Enterprise size
18.3.5. By End User
19. Competition Analysis
19.1. Competition Deep Dive
19.1.1. IBM Corporation
19.1.1.1. Overview
19.1.1.2. Product Portfolio
19.1.1.3. Profitability by Market Segments
19.1.1.4. Sales Footprint
19.1.1.5. Strategy Overview
19.1.1.5.1. Marketing Strategy
19.1.2. Oracle
19.1.2.1. Overview
19.1.2.2. Product Portfolio
19.1.2.3. Profitability by Market Segments
19.1.2.4. Sales Footprint
19.1.2.5. Strategy Overview
19.1.2.5.1. Marketing Strategy
19.1.3. Amazon.com
19.1.3.1. Overview
19.1.3.2. Product Portfolio
19.1.3.3. Profitability by Market Segments
19.1.3.4. Sales Footprint
19.1.3.5. Strategy Overview
19.1.3.5.1. Marketing Strategy
19.1.4. Accenture plc
19.1.4.1. Overview
19.1.4.2. Product Portfolio
19.1.4.3. Profitability by Market Segments
19.1.4.4. Sales Footprint
19.1.4.5. Strategy Overview
19.1.4.5.1. Marketing Strategy
19.1.5. Intel Corporation
19.1.5.1. Overview
19.1.5.2. Product Portfolio
19.1.5.3. Profitability by Market Segments
19.1.5.4. Sales Footprint
19.1.5.5. Strategy Overview
19.1.5.5.1. Marketing Strategy
19.1.6. NVIDIA Corporation
19.1.6.1. Overview
19.1.6.2. Product Portfolio
19.1.6.3. Profitability by Market Segments
19.1.6.4. Sales Footprint
19.1.6.5. Strategy Overview
19.1.6.5.1. Marketing Strategy
19.1.7. Honeywell International Inc.
19.1.7.1. Overview
19.1.7.2. Product Portfolio
19.1.7.3. Profitability by Market Segments
19.1.7.4. Sales Footprint
19.1.7.5. Strategy Overview
19.1.7.5.1. Marketing Strategy
19.1.8. Panasonic
19.1.8.1. Overview
19.1.8.2. Product Portfolio
19.1.8.3. Profitability by Market Segments
19.1.8.4. Sales Footprint
19.1.8.5. Strategy Overview
19.1.8.5.1. Marketing Strategy
19.1.9. SAP SE
19.1.9.1. Overview
19.1.9.2. Product Portfolio
19.1.9.3. Profitability by Market Segments
19.1.9.4. Sales Footprint
19.1.9.5. Strategy Overview
19.1.9.5.1. Marketing Strategy
20. Assumptions & Acronyms Used
21. Research Methodology
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Cognitive Supply Chain Market
