Global AI in Supply Chain Management Industry, Forecast to 2027 Featuring Leading Vendors SAP, Oracle, JDA, Epicor Software, Infor Global, and More

DUBLIN, Feb. 3, 2022 /PRNewswire/ -- The "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 2022 - 2027" report has been added to ResearchAndMarkets.com's offering.

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 2022 to 2027 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.

Select Report Findings

• AI in SCM solutions as a whole will reach $16.7B globally by 2027
• The Asia Pac region is the largest and fastest-growing for AI in SCM
• Cloud-based AI-as-a-Service for SCM will exceed $3B globally by 2027
• AI SCM in edge computing for IoT enabled solutions will reach $5.35B by 2027
• Artificial Intelligence of Things is emerging as a major enabler of SCM optimization
• Material movement and tracking is the largest sub-segment within AI SCM in the manufacturing
• Leading vendors covered include SAP, Oracle, JDA, Epicor Software, Infor Global, and others
• AI-enabled supply chains are over 65% more effective with reduced risk and lower overall costs

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.

Key Topics Covered

1. Executive Summary

2. Introduction
2.1 Supply Chain Management
2.1.1 Challenges
2.1.2 Opportunities
2.2 AI in SCM
2.2.1 Key AI Technologies for SCM
2.2.2 AI and Technology Integration

3. AI in SCM Challenges and Opportunities
3.1 Market Dynamics
3.1.1 Companies with Complex Supply Chains
3.1.2 Logistics Management Companies
3.1.3 SCM Software Solution Companies
3.2 Technology and Solution Opportunities
3.2.1 Leverage Artificial Intelligence (AI)
3.2.1.1 Integrate AI with Existing Processes
3.2.1.2 Integrate AI with Existing Systems
3.2.2 Integrate AI with Internet of Things (IoT)
3.2.2.1 Leverage AIoT Platforms, Software, and Services
3.2.2.2 Leverage Data as a Service Providers
3.3 Implementation Challenges
3.3.1 Management Friction
3.3.2 Legacy Processes and Procedures
3.3.3 Outsource AI SCM Solution vs. Legacy Integration

4. Supply Chain Ecosystem Company Analysis
4.1 Vendor Market Share
4.2 Top Vendor Recent Developments
4.3 3M
4.4 Adidas
4.5 Amazon
4.6 Arvato SCM Solutions
4.7 BASF
4.8 Basware
4.9 BMW
4.10 C. H.Robinson
4.11 Cainiao Network (Alibaba)
4.12 Cisco Systems
4.13 ClearMetal
4.14 Coca-Cola Co.
4.15 Colgate-Palmolive
4.16 Coupa Software
4.17 Descartes Systems Group
4.18 Diageo
4.19 E2open
4.20 Epicor Software Corporation
4.21 FedEx
4.22 Fraight AI
4.23 H&M
4.24 HighJump
4.25 Home Depot
4.26 HP Inc.
4.27 IBM
4.28 Inditex
4.29 Infor Global Solutions
4.30 Intel
4.31 JDA
4.32 Johnson & Johnson
4.33 Kimberly-Clark
4.34 L'Oreal
4.35 LLamasoft Inc.
4.36 Logility
4.37 Manhattan Associates
4.38 Micron Technology
4.39 Microsoft
4.40 Nestle
4.41 Nike
4.42 Novo Nordisk
4.43 NVidia
4.44 Oracle
4.45 PepsiCo
4.46 Presenso
4.47 Relex Solution
4.48 Sage
4.49 Samsung Electronics
4.50 SAP
4.51 Schneider Electric
4.52 SCM Solutions Corp.
4.53 Splice Machine
4.54 Starbucks
4.55 Teknowlogi
4.56 Unilever
4.57 Walmart
4.58 Xilinx

5. AI in SCM Market Case Studies
5.1 IBM Case Study with the Master Lock Company
5.2 BASF: Supporting smarter supply chain operations with cognitive cloud technology
5.3 Amazon Customer Retention Case Study
5.4 BMW Employs AI for Logistics Processes
5.5 Intelligent Revenue and Supply Chain Management
5.6 AI-Powered Customer Experience
5.7 Rolls Royce uses AI to safely transport its Cargo
5.8 Robots deliver medicine, groceries and packages with AI
5.9 Lineage Logistics Company Case Study

6. AI in SCM Market Analysis and Forecasts 2022-2027
6.1 AI in SCM Market 2022-2027
6.2 AI in SCM by Solution 2022-2027
6.2.1 Platforms
6.2.2 Software
6.2.3 AI as a Service
6.3 AI in SCM by Solution Components 2022-2027
6.3.1 Hardware
6.3.1.1 Non-IoT Device
6.3.1.2 IoT Embedded Device
6.3.1.2.1 Security Devices
6.3.1.2.2 Surveillance Robots and Drone
6.3.1.2.3 Networking Devices
6.3.1.2.4 Smart Appliances
6.3.1.2.5 Healthcare Device
6.3.1.2.6 Smart Grid Devices
6.3.1.2.7 In-Vehicle Devices
6.3.1.2.8 Energy Management Device
6.3.1.3 Components
6.3.1.3.1 Wearable and Embedded Components
6.3.1.3.1.1 Real-Time Location System (RTLS)
6.3.1.3.1.2 Barcode
6.3.1.3.1.3 Barcode Scanner
6.3.1.3.1.4 Barcode Stickers
6.3.1.3.1.5 RFID
6.3.1.3.1.6 RFID Tags
6.3.1.3.1.7 Sensor
6.3.1.3.2 Processors
6.3.2 Software
6.3.3 Services
6.3.3.1 Professional Services
6.4 AI in SCM by Management Function 2022-2027
6.4.1 Automation
6.4.2 Planning and Logistics
6.4.3 Inventory Management
6.4.4 Fleet Management
6.4.5 Virtual Assistance
6.4.6 Freight Brokerage
6.4.7 Risk Management and Dispute Resolution
6.5 AI in SCM by Technology 2022-2027
6.5.1 Cognitive Computing
6.5.2 Computer Vision
6.5.3 Context-aware Computing
6.5.4 Natural Language Processing
6.5.5 Predictive Analytics
6.5.6 Machine Learning
6.5.6.1 Reinforcement Learning
6.5.6.2 Supervised Learning
6.5.6.3 Unsupervised Learning
6.5.6.4 Deep Learning
6.6 AI in SCM by Industry Vertical 2022-2027
6.6.1 Aerospace and Government
6.6.2 Automotive and Transportation
6.6.3 Retail and Consumer Electronics
6.6.4 Consumer Goods
6.6.5 Healthcare
6.6.6 Manufacturing
6.6.7 Building and Construction
6.6.8 Others
6.7 AI in SCM by Deployment 2022-2027
6.7.1 Cloud Deployment
6.8 AI in SCM by AI System 2022-2027
6.9 AI in SCM by AI Type 2022-2027
6.10 AI in SCM by Connectivity
6.10.1 Non-Telecom Connectivity
6.10.2 Telecom Connectivity
6.10.3 Connectivity Standard
6.10.4 Enterprise
6.11 AI in SCM Market by IoT Edge Network 2022-2027
6.12 AI in SCM Analytics Market 2022-2027
6.13 AI in SCM Market by Intent Based Networking 2022-2027
6.14 AI in SCM Market by Virtualization 2022-2027
6.15 AI in SCM Market by 5G Network 2022-2027
6.16 AI in SCM Market by Blockchain Network 2022-2027
6.17 AI in SCM by Region 2022-2027
6.17.1 North America
6.17.2 Asia Pacific
6.17.3 Europe
6.17.4 Middle East and Africa
6.17.5 Latin America
6.18 AI in SCM by Country
6.18.1 Top Ten Country Market Share
6.18.2 USA
6.18.3 China
6.18.4 Canada
6.18.5 Mexico
6.18.6 Japan
6.18.7 UK
6.18.8 Germany
6.18.9 South Korea
6.18.10 France
6.18.11 Russia

7. Summary and Recommendations

List of Tables & Figures

For more information about this report visit https://www.researchandmarkets.com/r/u4dy0w

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