The "Artificial Intelligence (AI) in Supply Chain Management (SCM) Market: AI in SCM by Technology, Solution, Management Function (Automation, Planning and Logistics, Inventory, Fleet, Freight, Risk), and Region 2020 - 2025" report has been added to ResearchAndMarkets.com's offering.
This research evaluates how AI is revamping the operational process and facilitating cost-effective supply chain solutions. It provides analysis of leading companies and solutions that are leveraging AI in their supply chains and those they manage on behalf of others, with evaluation of key strengths and weaknesses of these solutions. The report also provides a view into the future of AI in Supply Chain Management (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.
Select Report Findings:
- Cloud-based AI-as-a-Service for SCM will reach $1.9B by 2025 globally
- AI in SCM involving context-aware computing will reach $1.3B by 2025 globally
- AI SCM in edge computing for IoT enabled solutions will reach $3.2B by 2025 globally
- Artificial Intelligence of Things (AIoT) is emerging as a major enabler of SCM optimization
- Material movement and tracking is the largest sub-segment within AI SCM in manufacturing
- AI-enabled supply chains are up to 60% more effective with reduced risk and lower overall costs
Key Topics Covered:
1.0 Executive Summary
2.1 Supply Chain Management
2.2 AI in SCM
3.0 AI in SCM Challenges and Opportunities
3.1 Market Dynamics
3.2 Technology and Solution Opportunities
3.3 Implementation Challenges
4.0 Supply Chain Ecosystem Company Analysis
4.1 Vendor Market Share
4.5 Arvato SCM Solutions
4.9 C. H.Robinson
4.10 Cainiao Network (Alibaba)
4.11 Cisco Systems
4.13 Coca-Cola Co.
4.15 Coupa Software
4.16 Descartes Systems Group
4.19 Epicor Software Corporation
4.21 Fraight AI
4.24 Home Depot
4.25 HP Inc.
4.28 Infor Global Solutions
4.31 Johnson & Johnson
4.34 LLamasoft Inc.
4.36 Manhattan Associates
4.37 Micron Technology
4.41 Novo Nordisk
4.46 Relex Solution
4.48 Samsung Electronics
4.50 Schneider Electric
4.51 SCM Solutions Corp.
4.52 Splice Machine
5.0 AI in SCM Market Analysis and Forecasts 2020 - 2025
5.1 AI in SCM Market 2020 - 2025
5.2 AI in SCM by Solution 2020 - 2025
5.3 AI in SCM by Solution Components 2020 - 2025
5.4 AI in SCM by Management Function 2020 - 2025
5.5 AI in SCM by Technology 2020 - 2025
5.6 AI in SCM by Industry Vertical 2020 - 2025
5.7 AI in SCM by Deployment 2020 - 2025
5.8 AI in SCM by AI System 2020 - 2025
5.9 AI in SCM by AI Type
5.10 AI in SCM by Connectivity 2020 - 2025
5.11 AI in SCM Market by IoT Edge Network 2020 - 2025
5.12 AI in SCM Analytics Market 2020 - 2025
5.13 AI in SCM Market by Intent-Based Networking 2020 - 2025
5.14 AI in SCM Market by Virtualization 2020 - 2025
5.15 AI in SCM Market by 5G Network 2020 - 2025
5.16 AI in SCM Market by Blockchain Network 2020 - 2025
5.17 AI in SCM by Region 2020 - 2025
5.18 AI in SCM by Country 2020 - 2025
6.0 Summary and Recommendations
6.1 Artificial Intelligence Providers
6.2 Automation System Providers
6.3 Communication Service Providers
6.4 Computing Companies
6.5 Data Analytics Providers
6.6 Enterprise and Government
6.7 Immersive Technology (AR, VR, and MR) Providers
6.8 IoT Suppliers and Service Providers
6.9 Logistics Management Companies
6.10 Semiconductor Companies
For more information about this report visit https://www.researchandmarkets.com/r/xgojns
Laura Wood, Senior Press Manager
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