コグニティブサプライチェーンの世界市場：市場規模・シェア分析 - 動向、促進要因、競合情勢、予測 (2024年～2030年)

市場概要

世界のコグニティブサプライチェーン産業は、2023年には87億9,820万米ドルと評価され、2030年には249億8,270万米ドルに急増すると予測され、予測期間中のCAGRは16.2%です。

コグニティブSCMソリューションは、ロスの最小化、有利な流通チャネルの選択、世界なビジネスコミュニティで一層浸透しているグリーンな実践の促進に貢献する強力なツールです。この意味で、ビジネスの持続可能性の目標と、その企業が属する世界・サプライチェーンの残りの部分の両方が、より持続可能な慣行へと同時に移行することができます。

おそらく最も顕著なケースは、貿易拡大の時代に経験した、グリーンで効率的なサプライチェーン・ソリューションに対す高い需要でしょう。複雑な世界ネットワークにおけるクローズドループ制御と完全なオペレーション監督は、コグニティブサプライチェーン・ソリューションの助けを借りて行われます。そのため、複雑なサプライチェーンに関連する意思決定の処理をスムーズに行うことができます。現在、廃棄物サイクルに組み込まれている資源の有効活用や、環境に優しい処理方法のためのスラップ技術として追求されているのは、持続可能性です。

サプライチェーン・オペレーションは、インテリジェントな洞察とプロセスの自動化をもたらすAI・ML技術の領域へと突入しつつあります。AIが支援する需要予測、在庫最適化、ダイナミックなルート計画は、予測分析を通じてデータ内のパターンを分析することで実現しました。

主な洞察

大企業は、コグニティブサプライチェーン・ソリューションのような最新技術に投資する能力があるため、市場シェアが大きいです。

中小企業は、より手頃な価格で適切なサプライチェーン・ソリューションのコグニティブを事業全体に適用することで、より迅速な成長を遂げることができると思われます。

機械学習 (ML) カテゴリーは、2024年から2030年にかけて16.5%のCAGRで成長し、最大の市場シェアを占めると予想されます。

2023年の市場シェアは、オンプレミスが約65%と大きいです。この導入形態では、特定のビジネスニーズに合わせたコグニティブサプライチェーン・ソリューションのカスタマイズ・オプションがより多く提供されます。

北米は最大の市場地域であり、2030年までに世界売上高の約50%を占めると予想されています。北米の優位性を高める要因には、効率性、コスト削減、生産性向上への強い注目があります。

北米と並んで、欧州もかなり大きなパイを占めており、ドイツ、英国、フランスなどの国々がサプライチェーン・マネジメントのためのコグニティブ・ソリューションを迅速に導入しています。

当レポートでは、世界のコグニティブサプライチェーンの市場について分析し、市場の基本構造・最新情勢や主な促進・抑制要因、世界全体および地域別・主要国の市場規模の動向見通し (金額ベース、2017～2030年)、企業規模別・技術別・展開方式別・エンドユーザー別の詳細動向、現在の市場競争の状況、主要企業のプロファイルなどを調査しております。

目次

第1章 調査範囲

第2章 調査手法

第3章 エグゼクティブサマリー

第4章 市場指標

第5章 業界の展望

• 市場力学
  • 動向
  • 促進要因
  • 抑制要因/課題
  • 促進要因/抑制要因の影響分析
• 新型コロナウイルス感染症 (COVID-19) の影響
• ポーターのファイブフォース分析

第6章 世界市場

• 概要
• 市場収益：企業規模別 (2017～2030年)
• 市場収益：技術別 (2017～2030年)
• 市場収益：展開方式別 (2017～2030年)
• 市場収益：エンドユーザー別 (2017～2030年)
• 市場収益：地域別 (2017～2030年)

第7章 北米市場

• 概要
• 市場収益：企業規模別 (2017～2030年)
• 市場収益：技術別 (2017～2030年)
• 市場収益：展開方式別 (2017～2030年)
• 市場収益：エンドユーザー別 (2017～2030年)
• 市場収益：国別 (2017～2030年)

第8章 欧州市場

第9章 アジア太平洋市場

第10章 ラテンアメリカ市場

第11章 中東・アフリカ市場

第12章 米国市場

• 概要
• 市場収益：企業規模別 (2017～2030年)
• 市場収益：技術別 (2017～2030年)
• 市場収益：展開方式別 (2017～2030年)
• 市場収益：エンドユーザー別 (2017～2030年)

第13章 カナダ市場

第14章 ドイツ市場

第15章 フランス市場

第16章 英国市場

第17章 イタリア市場

第18章 スペイン市場

第19章 日本市場

第20章 中国市場

第21章 インド市場

第22章 オーストラリア市場

第23章 韓国市場

第24章 ブラジル市場

第25章 メキシコ市場

第26章 サウジアラビア市場

第27章 南アフリカ市場

第28章 アラブ首長国連邦 (UAE) 市場

第29章 競合情勢

• 市場参入企業とその提供品目の一覧
• 主要企業の競合ベンチマーク
• 主要企業の製品ベンチマーク
• 最近の戦略展開状況

第30章 企業プロファイル

• IBM Corporation
• Accenture plc
• Oracle Corporation
• Amazon.com
• Intel Corporation
• NVIDIA Corporation
• Honeywell International Inc.
• Panasonic Holdings Corporation
• SAP SE
• Siemens AG
• Microsoft Corporation

第31章 付録

Market 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