サプライチェーン管理 (SCM) 向けAIの世界市場 (2023～2028年)：技術・プロセス・ソリューション・管理機能 (自動化・計画&物流・在庫・リスク)・展開モデル・ビジネスタイプ・産業別
Artificial Intelligence in Supply Chain Management Market by Technology, Processes, Solutions, Management Function (Automation, Planning and Logistics, Inventory, Risk), Deployment Model, Business Type and Industry Verticals 2023 - 2028
|サプライチェーン管理 (SCM) 向けAIの世界市場 (2023～2028年)：技術・プロセス・ソリューション・管理機能 (自動化・計画&物流・在庫・リスク)・展開モデル・ビジネスタイプ・産業別
発行: Mind Commerce
ページ情報: 英文 276 Pages
世界のサプライチェーン管理 (SCM) 向けAIの市場規模は、2028年には175億米ドルの規模に成長すると予測されています。
地域別では、アジア太平洋地域が最大かつ最速の成長を示しています。また、クラウドベースのSCM向けAIaaS (AI as a Service) の部門は2028年に37億米ドルを超える規模に、IoT対応ソリューションのエッジコンピューティングにおけるSCM向けAIの部門は、2028年に61億2,000万米ドルの規模に成長すると予測されています。
当レポートでは、サプライチェーン管理 (SCM) 向けAIの市場を調査し、市場概要、市場成長への各種影響因子の分析、ケーススタディ、市場規模の推移・予測、各種区分・地域/主要国別の内訳、競合環境、市場シェア、主要企業のプロファイルなどをまとめています。
This report provides detailed analysis and forecasts for AI in SCM by solution (Platforms, Software, and AI as a Service), solution components (Hardware, Software, Services), management function (Automation, Planning and Logistics, Inventory Management, Fleet Management, Freight Brokerage, Risk Management, and Dispute Resolution), AI technologies (Cognitive Computing, Computer Vision, Context-aware Computing, Natural Language Processing, and Machine Learning), and industry verticals (Aerospace, Automotive, Consumer Goods, Healthcare, Manufacturing, and others).
This is the broadest and most detailed report of its type, providing analysis across a wide range of go-to-operational process considerations, such as the need for identity management and real-time location tracking, and market deployment considerations, such as AI type, technologies, platforms, connectivity, IoT integration, and deployment model including AI-as-a-Service (AIaaS).
Each aspect evaluated includes forecasts from 2023 to 2028 such as AIaaS by revenue in China. It provides an analysis of AI in SCM globally, regionally, and by country including the top ten countries per region by market share. The report also provides an analysis of leading companies and solutions that are leveraging AI in their supply chains and those they manage on behalf of others, with an evaluation of key strengths and weaknesses of these solutions.
It assesses AI in SCM by industry vertical and application such as material movement tracking and drug supply management in manufacturing and healthcare respectively. The report also provides a view into the future of AI in SCM including analysis of performance improvements such as optimization of revenues, supply chain satisfaction, and cost reduction.
Modern supply chains represent complex systems of organizations, people, activities, information, and resources involved in moving a product or service from supplier to customer. Supply Chain Management (SCM) solutions are typically manifest in software architecture and systems that facilitate the flow of information among different functions within and between enterprise organizations.
Leading SCM solutions catalyze information sharing across organizational units and geographical locations, enabling decision-makers to have an enterprise-wide view of the information needed in a timely, reliable, and consistent fashion. Various forms of Artificial Intelligence (AI) are being integrated into SCM solutions to improve everything from process automation to overall decision-making. This includes greater data visibility (static and real-time data) as well as related management information system effectiveness.
In addition to fully automated decision-making, AI systems are also leveraging various forms of cognitive computing to optimize the combined efforts of artificial and human intelligence. For example, AI in SCM is enabling improved supply chain automation through the use of virtual assistants, which are used both internally (within a given enterprise) as well as between supply chain members (e.g. customer-supplier chains). It is anticipated that virtual assistants in SCM will leverage an industry-specific knowledge database as well as company, department, and production-specific learning.
AI-enabled improvements in supply chain member satisfaction causes a positive feedback loop, leading to better overall SCM performance. One of the primary goals is to leverage AI to make supply chain improvements from production to consumption within product-related industries as well as create opportunities for supporting "servitization" of products in a cloud-based "as a service" model. AI will identify opportunities for supply chain members to have greater ownership of "outcomes as a service" and control of overall product/service experience and profitability.
With Internet of Things (IoT) technologies and solutions taking an ever-increasing role in SCM, the inclusion of AI algorithms and software-driven processes with IoT represents a very important opportunity to leverage the Artificial Intelligence of Things (AIoT) in supply chains. More specifically, AIoT solutions leverage the connectivity and communications power of IoT, along with the machine learning and decision-making capabilities of AI, as a means of optimizing SCM by way of data-driven managed services.