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시장보고서
상품코드
1468705
세계의 인지 공급망 시장 : 규모, 점유율 분석 - 동향, 촉진요인, 경쟁 구도, 예측(2024-2030년)Cognitive Supply Chain Market Size & Share Analysis - Trends, Drivers, Competitive Landscape, and Forecasts (2024 - 2030) |
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시장 개요
세계의 인지 공급망(Cognitive Supply Chain) 산업은 2023년에 87억 9,820만 달러로 평가되었으며, 2030년에는 249억 8,270만 달러로 급증할 것으로 예상되며, 예측 기간 동안 CAGR은 16.2%입니다.
인지적 SCM 솔루션은 손실을 최소화하고, 유리한 유통 채널을 선택하고, 글로벌 비즈니스 커뮤니티에서 점점 더 많이 수용되고 있는 친환경 관행을 강화하는 데 기여하는 강력한 도구입니다. 이러한 의미에서 비즈니스 지속 가능성 목표와 기업이 속한 글로벌 공급망의 나머지 부분 모두 동시에 보다 지속 가능한 관행으로 전환할 수 있습니다.
가장 눈에 띄는 사례는 교역이 증가하는 시대에 친환경적이고 효율적인 공급망 솔루션에 대한 높은 수요일 것입니다. 복잡한 글로벌 네트워크에서 폐쇄 루프 제어와 완벽한 운영 감독은 인지 공급망 솔루션의 도움으로 이루어집니다. 따라서 복잡한 공급망과 연결된 의사 결정을 원활하게 처리할 수 있습니다. 현재 폐기물 주기에 내재된 자원의 더 나은 활용과 환경 친화적 처리 관행을 위한 슬랩 기술은 지속 가능성을 지향하고 있습니다.
공급망 운영은 지능형 인사이트와 프로세스 자동화를 제공하는 AI 및 ML 기술 영역으로 발전하고 있습니다. 예측 분석을 통해 데이터 내 패턴을 분석함으로써 AI를 활용한 수요 예측, 재고 최적화, 동적 경로 계획이 가능해졌습니다.
주요 인사이트
대기업은 인지 공급망 솔루션과 같은 최신 기술에 투자할 수 있기 때문에 시장 점유율이 큽니다.
중소기업은 보다 저렴한 가격으로 적절한 공급망 솔루션의 인지를 사업 전체에 적용함으로써 보다 신속한 성장을 이룰 수 있을 것으로 보입니다.
머신러닝(ML) 카테고리는 2024년부터 2030년까지 16.5%의 CAGR로 성장하여 최대 시장 점유율을 차지할 것으로 예상됩니다.
2023년 시장 점유율은 온프레미스가 약 65%로 최대 점유율을 기록했습니다. 이 배포에서는 특정 비즈니스 요구에 맞는 인지 공급망 솔루션에 대한 사용자 지정 옵션을 더 많이 제공합니다.
북미는 최대 시장지역으로 2030년까지 세계 매출의 약 50%를 차지할 것으로 예상됩니다. 북미의 우위성을 높이는 요인은 효율성, 비용 절감, 생산성 향상에 대한 강한 주목이 있습니다.
북미와 함께 유럽도 상당히 큰 파이를 차지하고 있으며 독일, 영국, 프랑스 등 국가들이 공급망 관리를 위한 인지 솔루션을 신속하게 도입하고 있습니다.
세계의 인지 공급망(Cognitive Supply Chain) 시장에 대해 분석했으며, 시장 기본 구조/최신 정세/주요 촉진·억제요인/세계 전체 및 지역별·주요 국가 시장 규모 동향 전망(금액 기준, 2017-2030년)/기업 규모별·기술별·배포별·최종 사용자별 상세 동향/현재 시장 경쟁 구도/주요 기업 프로파일 등 정보를 전해드립니다.
목차
제1장 조사 범위
제2장 조사 방법
제3장 주요 요약
제4장 시장 지표
제5장 업계 전망
- 시장 역학
- 동향
- 성장 촉진요인
- 억제요인/과제
- 성장 촉진요인/억제요인 영향 분석
- COVID-19 영향
- Porter's Five Forces 분석
제6장 시장
- 개요
- 시장 매출 : 기업 규모별(2017-2030년)
- 시장 매출 : 기술별(2017-2030년)
- 시장 매출 : 배포별(2017-2030년)
- 시장 매출 : 최종 사용자별(2017-2030년)
- 시장 매출 : 지역별(2017-2030년)
제7장 북미 시장
- 개요
- 시장 매출 : 기업 규모별(2017-2030년)
- 시장 매출 : 기술별(2017-2030년)
- 시장 매출 : 배포별(2017-2030년)
- 시장 매출 : 최종 사용자별(2017-2030년)
- 시장 매출 : 국가별(2017-2030년)
제8장 유럽 시장
제9장 아시아 태평양 시장
제10장 남미 시장
제11장 중동 및 아프리카 시장
제12장 미국 시장
- 개요
- 시장 매출 : 기업 규모별(2017-2030년)
- 시장 매출 : 기술별(2017-2030년)
- 시장 매출 : 배포별(2017-2030년)
- 시장 매출 : 최종 사용자별(2017-2030년)
제13장 캐나다 시장
제14장 독일 시장
제15장 프랑스 시장
제16장 영국 시장
제17장 이탈리아 시장
제18장 스페인 시장
제19장 일본 시장
제20장 중국 시장
제21장 인도 시장
제22장 호주 시장
제23장 한국 시장
제24장 브라질 시장
제25장 멕시코 시장
제26장 사우디아라비아 시장
제27장 남아프리카공화국 시장
제28장 아랍에미리트(UAE) 시장
제29장 경쟁 구도
- 시장 진출기업 및 제공 품목 목록
- 주요 기업의 경쟁 벤치마킹
- 주요 기업의 제품 벤치마킹
- 최근 전략 전개 상황
제30장 기업 프로파일
- IBM Corporation
- Accenture plc
- Oracle Corporation
- Amazon.com
- Intel Corporation
- NVIDIA Corporation
- Honeywell International Inc.
- Panasonic Holdings Corporation
- SAP SE
- Siemens AG
- Microsoft Corporation
제31장 부록
LYJMarket Overview
The cognitive supply chain industry was valued at USD 8,798.2 million in 2023, which is projected to surge to USD 24,982.7 million in 2030, experiencing a 16.2% CAGR during the forecast period.
Cognitive SCM solutions represent the potent tools that contribute to minimizing loss, selecting favorable distribution channels, and empowering green practices increasingly accepted by the global business community. In this sense, both business sustainability goals and the rest of the global supply chain in which the company is part are able to simultaneously shift to more sustainable practices.
Perhaps the most prominent case is the high demand experienced in the era of increasing trade for green and efficient supply chain solutions. Closed loop control and complete operation supervising on complex global networks are done with the help of cognitive supply chain solutions. Therefore, it makes the processing of decisions that are connected with intricate supply chains smooth. Now what is being pursued as slap technology for better utilization of the resources that are embedded in the current waste cycles and environment-friendly processing practices is guided by sustainability.
Supply chain operations are forging into the AI and ML technologies sphere as these bring intelligent insights and process automation. AI-assisted in-demand forecasting, inventory optimization, and dynamic route planning were achieved by analyzing the patterns within data through predictive analytics.
Key Insights
Large enterprises held a larger market share due to their ability to invest in modern technologies like cognitive supply chain solutions.
These enterprises can afford complete cognitive systems with autonomous decision-making, real-time visibility, and predictive analytics.
Large organizations most often integrate into the global supply chain, involving several regions and companies within the network, therefore it is technology-oriented and intended to simplify operations, help managers make better decisions, and mitigate risks.
SMEs will be able to see quicker growth when they apply cognitive more affordable and suitable supply chain solutions across their businesses.
The machine learning category is expected to grow at a CAGR of 16.5% during 2024-2030 and hold the largest market share.
ML enables data-driven decision-making, cost reduction, productivity increase, and optimization of supply chain processes.
ML-driven solutions automate tasks, analyze large data volumes, and identify patterns and insights for a competitive edge.
The on-premises category held a larger market share, approximately 65%, in 2023.
This deployment mode offers more customization options for cognitive supply chain solutions tailored to specific business needs.
Integrating these solutions into existing workflows is easier with on-premises deployment.
Older technologies can often work more efficiently when combined with on-premises solutions.
North America is the largest market region, expected to contribute around 50% of global revenue by 2030.
Factors driving North America's dominance include a strong focus on efficiency, cost savings, and productivity improvement.
Cognitive supply chain technologies enable businesses in North America to detect patterns, forecast demand, and optimize logistics so that the number of resources involved is reduced with a subsequent drop in waste.
The emerging AI and big data are the fundamental enablers of the transition to cognitive supply chain solutions across the region.
Along with North America, Europe represents a rather big piece of the pie, as countries like Germany, the UK, and France quickly implement cognitive solutions for supply chain management.
A partnership between technology firms, institutions of learning, and business leaders makes it possible for Europe to shift forward with innovation and quickly find solutions for implementation.
Table of Contents
Chapter 1. Research Scope
- 1.1. Research Objectives
- 1.2. Market Definition
- 1.3. Analysis Period
- 1.4. Market Size Breakdown by Segments
- 1.4.1. Market size breakdown, by enterprise size
- 1.4.2. Market size breakdown, by technology
- 1.4.3. Market size breakdown, by deployment mode
- 1.4.4. Market size breakdown, by end user
- 1.4.5. Market size breakdown, by region
- 1.4.6. Market size breakdown, by country
- 1.5. Market Data Reporting Unit
- 1.5.1. Value
- 1.6. Key Stakeholders
Chapter 2. Research Methodology
- 2.1. Secondary Research
- 2.1.1. Paid
- 2.1.2. Unpaid
- 2.1.3. P&S Intelligence database
- 2.2. Primary Research
- 2.3. Market Size Estimation
- 2.4. Data Triangulation
- 2.5. Currency Conversion Rates
- 2.6. Assumptions for the Study
- 2.7. Notes and Caveats
Chapter 3. Executive Summary
Chapter 4. Market Indicators
Chapter 5. Industry Outlook
- 5.1. Market Dynamics
- 5.1.1. Trends
- 5.1.2. Drivers
- 5.1.3. Restraints/challenges
- 5.1.4. Impact analysis of drivers/restraints
- 5.2. Impact of COVID-19
- 5.3. Porter's Five Forces Analysis
- 5.3.1. Bargaining power of buyers
- 5.3.2. Bargaining power of suppliers
- 5.3.3. Threat of new entrants
- 5.3.4. Intensity of rivalry
- 5.3.5. Threat of substitutes
Chapter 6. Global Market
- 6.1. Overview
- 6.2. Market Revenue, by Enterprise Size (2017-2030)
- 6.3. Market Revenue, by Technology (2017-2030)
- 6.4. Market Revenue, by Deployment Mode (2017-2030)
- 6.5. Market Revenue, by End User (2017-2030)
- 6.6. Market Revenue, by Region (2017-2030)
Chapter 7. North America Market
- 7.1. Overview
- 7.2. Market Revenue, by Enterprise Size (2017-2030)
- 7.3. Market Revenue, by Technology (2017-2030)
- 7.4. Market Revenue, by Deployment Mode (2017-2030)
- 7.5. Market Revenue, by End User (2017-2030)
- 7.6. Market Revenue, by Country (2017-2030)
Chapter 8. Europe Market
- 8.1. Overview
- 8.2. Market Revenue, by Enterprise Size (2017-2030)
- 8.3. Market Revenue, by Technology (2017-2030)
- 8.4. Market Revenue, by Deployment Mode (2017-2030)
- 8.5. Market Revenue, by End User (2017-2030)
- 8.6. Market Revenue, by Country (2017-2030)
Chapter 9. APAC Market
- 9.1. Overview
- 9.2. Market Revenue, by Enterprise Size (2017-2030)
- 9.3. Market Revenue, by Technology (2017-2030)
- 9.4. Market Revenue, by Deployment Mode (2017-2030)
- 9.5. Market Revenue, by End User (2017-2030)
- 9.6. Market Revenue, by Country (2017-2030)
Chapter 10. LATAM Market
- 10.1. Overview
- 10.2. Market Revenue, by Enterprise Size (2017-2030)
- 10.3. Market Revenue, by Technology (2017-2030)
- 10.4. Market Revenue, by Deployment Mode (2017-2030)
- 10.5. Market Revenue, by End User (2017-2030)
- 10.6. Market Revenue, by Country (2017-2030)
Chapter 11. MEA Market
- 11.1. Overview
- 11.2. Market Revenue, by Enterprise Size (2017-2030)
- 11.3. Market Revenue, by Technology (2017-2030)
- 11.4. Market Revenue, by Deployment Mode (2017-2030)
- 11.5. Market Revenue, by End User (2017-2030)
- 11.6. Market Revenue, by Country (2017-2030)
Chapter 12. U.S. Market
- 12.1. Overview
- 12.2. Market Revenue, by Enterprise Size (2017-2030)
- 12.3. Market Revenue, by Technology (2017-2030)
- 12.4. Market Revenue, by Deployment Mode (2017-2030)
- 12.5. Market Revenue, by End User (2017-2030)
Chapter 13. Canada Market
- 13.1. Overview
- 13.2. Market Revenue, by Enterprise Size (2017-2030)
- 13.3. Market Revenue, by Technology (2017-2030)
- 13.4. Market Revenue, by Deployment Mode (2017-2030)
- 13.5. Market Revenue, by End User (2017-2030)
Chapter 14. Germany Market
- 14.1. Overview
- 14.2. Market Revenue, by Enterprise Size (2017-2030)
- 14.3. Market Revenue, by Technology (2017-2030)
- 14.4. Market Revenue, by Deployment Mode (2017-2030)
- 14.5. Market Revenue, by End User (2017-2030)
Chapter 15. France Market
- 15.1. Overview
- 15.2. Market Revenue, by Enterprise Size (2017-2030)
- 15.3. Market Revenue, by Technology (2017-2030)
- 15.4. Market Revenue, by Deployment Mode (2017-2030)
- 15.5. Market Revenue, by End User (2017-2030)
Chapter 16. U.K. Market
- 16.1. Overview
- 16.2. Market Revenue, by Enterprise Size (2017-2030)
- 16.3. Market Revenue, by Technology (2017-2030)
- 16.4. Market Revenue, by Deployment Mode (2017-2030)
- 16.5. Market Revenue, by End User (2017-2030)
Chapter 17. Italy Market
- 17.1. Overview
- 17.2. Market Revenue, by Enterprise Size (2017-2030)
- 17.3. Market Revenue, by Technology (2017-2030)
- 17.4. Market Revenue, by Deployment Mode (2017-2030)
- 17.5. Market Revenue, by End User (2017-2030)
Chapter 18. Spain Market
- 18.1. Overview
- 18.2. Market Revenue, by Enterprise Size (2017-2030)
- 18.3. Market Revenue, by Technology (2017-2030)
- 18.4. Market Revenue, by Deployment Mode (2017-2030)
- 18.5. Market Revenue, by End User (2017-2030)
Chapter 19. Japan Market
- 19.1. Overview
- 19.2. Market Revenue, by Enterprise Size (2017-2030)
- 19.3. Market Revenue, by Technology (2017-2030)
- 19.4. Market Revenue, by Deployment Mode (2017-2030)
- 19.5. Market Revenue, by End User (2017-2030)
Chapter 20. China Market
- 20.1. Overview
- 20.2. Market Revenue, by Enterprise Size (2017-2030)
- 20.3. Market Revenue, by Technology (2017-2030)
- 20.4. Market Revenue, by Deployment Mode (2017-2030)
- 20.5. Market Revenue, by End User (2017-2030)
Chapter 21. India Market
- 21.1. Overview
- 21.2. Market Revenue, by Enterprise Size (2017-2030)
- 21.3. Market Revenue, by Technology (2017-2030)
- 21.4. Market Revenue, by Deployment Mode (2017-2030)
- 21.5. Market Revenue, by End User (2017-2030)
Chapter 22. Australia Market
- 22.1. Overview
- 22.2. Market Revenue, by Enterprise Size (2017-2030)
- 22.3. Market Revenue, by Technology (2017-2030)
- 22.4. Market Revenue, by Deployment Mode (2017-2030)
- 22.5. Market Revenue, by End User (2017-2030)
Chapter 23. South Korea Market
- 23.1. Overview
- 23.2. Market Revenue, by Enterprise Size (2017-2030)
- 23.3. Market Revenue, by Technology (2017-2030)
- 23.4. Market Revenue, by Deployment Mode (2017-2030)
- 23.5. Market Revenue, by End User (2017-2030)
Chapter 24. Brazil Market
- 24.1. Overview
- 24.2. Market Revenue, by Enterprise Size (2017-2030)
- 24.3. Market Revenue, by Technology (2017-2030)
- 24.4. Market Revenue, by Deployment Mode (2017-2030)
- 24.5. Market Revenue, by End User (2017-2030)
Chapter 25. Mexico Market
- 25.1. Overview
- 25.2. Market Revenue, by Enterprise Size (2017-2030)
- 25.3. Market Revenue, by Technology (2017-2030)
- 25.4. Market Revenue, by Deployment Mode (2017-2030)
- 25.5. Market Revenue, by End User (2017-2030)
Chapter 26. Saudi Arabia Market
- 26.1. Overview
- 26.2. Market Revenue, by Enterprise Size (2017-2030)
- 26.3. Market Revenue, by Technology (2017-2030)
- 26.4. Market Revenue, by Deployment Mode (2017-2030)
- 26.5. Market Revenue, by End User (2017-2030)
Chapter 27. South Africa Market
- 27.1. Overview
- 27.2. Market Revenue, by Enterprise Size (2017-2030)
- 27.3. Market Revenue, by Technology (2017-2030)
- 27.4. Market Revenue, by Deployment Mode (2017-2030)
- 27.5. Market Revenue, by End User (2017-2030)
Chapter 28. U.A.E. Market
- 28.1. Overview
- 28.2. Market Revenue, by Enterprise Size (2017-2030)
- 28.3. Market Revenue, by Technology (2017-2030)
- 28.4. Market Revenue, by Deployment Mode (2017-2030)
- 28.5. Market Revenue, by End User (2017-2030)
Chapter 29. Competitive Landscape
- 29.1. List of Market Players and their Offerings
- 29.2. Competitive Benchmarking of Key Players
- 29.3. Product Benchmarking of Key Players
- 29.4. Recent Strategic Developments
Chapter 30. Company Profiles
- 30.1. IBM Corporation
- 30.1.1. Business overview
- 30.1.2. Product and service offerings
- 30.1.3. Key financial summary
- 30.2. Accenture plc
- 30.2.1. Business overview
- 30.2.2. Product and service offerings
- 30.2.3. Key financial summary
- 30.3. Oracle Corporation
- 30.3.1. Business overview
- 30.3.2. Product and service offerings
- 30.3.3. Key financial summary
- 30.4. Amazon.com
- 30.4.1. Business overview
- 30.4.2. Product and service offerings
- 30.4.3. Key financial summary
- 30.5. Intel Corporation
- 30.5.1. Business overview
- 30.5.2. Product and service offerings
- 30.5.3. Key financial summary
- 30.6. NVIDIA Corporation
- 30.6.1. Business overview
- 30.6.2. Product and service offerings
- 30.6.3. Key financial summary
- 30.7. Honeywell International Inc.
- 30.7.1. Business overview
- 30.7.2. Product and service offerings
- 30.7.3. Key financial summary
- 30.8. Panasonic Holdings Corporation
- 30.8.1. Business overview
- 30.8.2. Product and service offerings
- 30.8.3. Key financial summary
- 30.9. SAP SE
- 30.9.1. Business overview
- 30.9.2. Product and service offerings
- 30.9.3. Key financial summary
- 30.10. Siemens AG
- 30.10.1. Business overview
- 30.10.2. Product and service offerings
- 30.10.3. Key financial summary
- 30.11. Microsoft Corporation
- 30.11.1. Business overview
- 30.11.2. Product and service offerings
- 30.11.3. Key financial summary
Chapter 31. Appendix
- 31.1. Abbreviations
- 31.2. Sources and References
- 31.3. Related Reports
