자율적 수요 감지 및 인지적 예측 : 시장 점유율 분석, 업계 동향 및 통계, 성장 예측(2026-2031년)

According to Mordor Intelligence, the autonomous demand sensing and cognitive forecasting market size is expected to increase from USD 1.47 billion in 2025 to USD 1.63 billion in 2026 and reach USD 2.56 billion by 2031, growing at a CAGR of 9.46% over 2026-2031.

This report is Segmented by Component (Software and Services), Deployment Mode (Cloud, On-Premise, and Hybrid), End-User Industry (Consumer Packaged Goods, Retail and E-Commerce, Automotive and Transportation, and More), Forecasting Technique (Machine Learning Based Forecasting, and More), and Geography. The Market Forecasts are Provided in Terms of Value (USD).

Global Autonomous Demand Sensing And Cognitive Forecasting Market Trends and Insights

AI-Based Real-Time Demand Signal Capture from POS and E-Commerce Channels

Retailers and consumer packaged goods brands have upgraded from weekly batch forecasts to streaming pipelines that parse checkout, clickstream, and social media sentiment at sub-second intervals. Walmart's roll-out of 90 million IoT sensors across its network funnels temperature, humidity, and position data into edge devices that cleanse and compress signals before dispatching them to cloud models, cutting latency and bandwidth costs. Enterprises that combine these leading indicators with weather data report 15%-25% lower forecast error and 5%-8% additional accuracy when sentiment spikes or regional heatwaves influence demand. Continuous recalculation narrows planning cycles from monthly to hourly, letting planners adjust safety-stock settings the moment anomalies surface.

Rising Adoption of Cloud-Native Supply-Chain Platforms

Cloud-native suites such as SAP Integrated Business Planning and Kinaxis RapidResponse attracted more than 1,200 new logos in 2025 as supply chain chiefs de-risked legacy upgrades by moving to subscription pricing and elastic compute. Public-cloud scalability underpins Monte Carlo simulations that test thousands of scenarios per hour, while out-of-the-box connectors pull data from sales, finance, and logistics systems without custom code. Hybrid topologies further accelerate adoption by keeping personally identifiable information on-premise while bursting model-training workloads to public regions during peak cycles, satisfying European and Chinese data-residency rules.

Data Silos and Poor Master-Data Quality

Many enterprises still house demand, product, and customer records in isolated enterprise resource planning, warehouse management, and customer relationship management systems. Duplicate SKUs, inconsistent units of measure, and missing hierarchy fields undermine model accuracy and extend implementation timelines. Mid-sized organizations often face 12- to 18-month master-data harmonization projects that add USD 0.5 million to USD 2 million in up-front cost, delaying autonomous demand sensing and cognitive forecasting market deployments. Mergers and acquisitions compound the challenge as acquirers must reconcile disparate schemas before model training can begin.

Other drivers and restraints analyzed in the detailed report include:

1. Rapid Proliferation of IoT Sensors across Logistics Nodes
2. Integration of Generative AI for Scenario-Driven Forecasting
3. High Total Cost of Ownership for SMEs

For complete list of drivers and restraints, kindly check the Table Of Contents.

Segment Analysis

The services segment of the autonomous demand sensing and cognitive forecasting market is projected to grow at a 9.86% CAGR through 2031 as enterprises lean on consultants to clean data, retrain models, and manage agentic AI workflows. The software segment retained a 48.31% revenue share, reflecting license commitments to platforms that bundle data ingestion, feature engineering, and probabilistic forecasting engines. Demand for managed services has intensified as organizations realize that forecast accuracy hinges on continuous feature updates, prompt engineering, and guardrail monitoring that internal teams often lack the bandwidth to perform.

Implementation partners embed industry know-how, whether it is seasonality curves for fashion retailers or serialization workflows for pharmaceutical manufacturers. They also orchestrate hybrid deployments that synchronize on-premise master data with public-cloud training clusters, a prerequisite for regulated sectors. This services momentum is widening partner ecosystems around core platforms and is likely to reshape vendor revenue mixes by 2031.

Cloud configurations accounted for 56.43% of the autonomous demand sensing and cognitive forecasting market share in 2025, as elastic compute simplifies Monte Carlo runs and external data ingestion. Hybrid setups are on track for a 10.06% CAGR, the fastest among deployment modes, as European and Chinese data-residency statutes require sensitive data to remain on local servers while permitting anonymized aggregates to flow to cloud training nodes. Kubernetes-centric orchestration abstracts workload placement, enabling data scientists to prototype locally and deploy models to production clusters without code rewrites.

Hybrid adoption also supports a gradual and systematic migration process. Organizations typically begin by transitioning demand-sensing workloads to the new system, ensuring that initial changes are manageable and low-risk. Once this phase is successfully implemented, they proceed with migrating supply planning, network design, and integrated business-planning modules. This step-by-step approach minimizes the risks associated with large-scale transformations and allows companies to realize incremental value at each stage. Furthermore, it ensures that mission-critical on-premises systems remain operational and unaffected during the transition, providing a seamless, efficient migration experience.

Geography Analysis

North America accounted for 34.74% of global revenue in 2025, buoyed by Fortune 500 retailers, automotive OEMs, and consumer packaged goods giants that embedded demand-sensing engines in enterprise suites during pandemic recovery. The region benefits from mature cloud stacks and an abundant data-science talent pool. Federal food-safety and pharmaceutical traceability laws encourage continuous monitoring, while nearshoring trends drive cross-border synchronization with Mexican facilities.

Asia-Pacific is forecast to post a 10.67% CAGR between 2026 and 2031, the highest worldwide. China's cross-border e-commerce surge, India's tier-2 city digitization, and Japan's aging-workforce automation imperatives underpin spending. Updated 2026 Chinese data-transfer guidelines clarify that anonymized aggregates can be sent out of the country for analysis, catalyzing hybrid adoption. In India, public cloud price drops and government AI roadmaps have driven adoption across retail and manufacturing. South Korea, Australia, and ASEAN countries mirror this trajectory, albeit from smaller bases.

Europe, the Middle East and Africa, and South America share the remaining revenue. Europe's General Data Protection Regulation lengthens project lead times, yet the bloc's advanced industrial base drives sustainability and waste-minimization use cases. The Middle East, led by the United Arab Emirates and Saudi Arabia, funds smart-city pilots that integrate demand sensing with urban logistics. South America's e-commerce accelerationis drivings marketplaces to optimize fulfillment locations, though macroeconomic volatilityis temperings spending outside Brazil and Argentina.

1. o9 Solutions Inc.
2. Blue Yonder Group Inc.
3. Kinaxis Inc.
4. E2open Parent Holdings Inc.
5. ToolsGroup B.V.
6. Anaplan Inc.
7. Aera Technology Inc.
8. Antuit.ai LLC
9. Relex Solutions Oy
10. Logility Inc.
11. John Galt Solutions Inc.
12. Llamasoft Inc.
13. Demand Driven Technologies LLC
14. Business Forecast Systems Inc. (Forecast Pro)
15. Lokad SAS
16. GMDH LLC
17. Prevedere Inc.
18. DataRobot Inc.
19. Inform Software
20. Solvoyo Cozum Yazilim A.S.

Additional Benefits:

• The market estimate (ME) sheet in Excel format
• 3 months of analyst support

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 Study Assumptions and Market Definition
• 1.2 Scope of the Study

2 RESEARCH METHODOLOGY

3 EXECUTIVE SUMMARY

4 MARKET LANDSCAPE

• 4.1 Market Overview
• 4.2 Market Drivers
  • 4.2.1 AI-Based Real-Time Demand Signal Capture from POS and E-Commerce Channels
  • 4.2.2 Rising Adoption of Cloud-Native Supply-Chain Platforms
  • 4.2.3 Rapid Proliferation of IoT Sensors across Logistics Nodes
  • 4.2.4 Integration of Generative AI for Scenario-Driven Forecasting
  • 4.2.5 Increasing Use of External Data Lakes (Weather Social Mobility)
  • 4.2.6 Vendor-Managed Inventory Programs Expanding to Tier-2 Cities
• 4.3 Market Restraints
  • 4.3.1 Data Silos and Poor Master-Data Quality
  • 4.3.2 High Total Cost of Ownership for SMEs
  • 4.3.3 Regulatory Barriers on Cross-Border Data Flow
  • 4.3.4 Shortage of Domain-Specific AI Talent
• 4.4 Impact of Macroeconomic Factors on the Market
• 4.5 Industry Value Chain Analysis
• 4.6 Regulatory Landscape
• 4.7 Technological Outlook
• 4.8 Porter's Five Forces Analysis
  • 4.8.1 Bargaining Power of Suppliers
  • 4.8.2 Bargaining Power of Buyers
  • 4.8.3 Threat of New Entrants
  • 4.8.4 Threat of Substitutes
  • 4.8.5 Intensity of Competitive Rivalry

5 MARKET SIZE AND GROWTH FORECASTS (VALUE)

• 5.1 By Component
  • 5.1.1 Software
  • 5.1.2 Services
• 5.2 By Deployment Mode
  • 5.2.1 Cloud
  • 5.2.2 On-Premise
  • 5.2.3 Hybrid
• 5.3 By End-User Industry
  • 5.3.1 Consumer Packaged Goods
  • 5.3.2 Retail and E-Commerce
  • 5.3.3 Automotive and Transportation
  • 5.3.4 Industrial Manufacturing
  • 5.3.5 Healthcare and Life Sciences
  • 5.3.6 Food and Beverage
  • 5.3.7 Logistics and Supply Chain
  • 5.3.8 Energy and Utilities
  • 5.3.9 Other End-User Industries
• 5.4 By Forecasting Technique
  • 5.4.1 Machine Learning Based Forecasting
  • 5.4.2 Deep Learning Based Forecasting
  • 5.4.3 Traditional Statistical Models Enhanced with AI
  • 5.4.4 Reinforcement Learning Approaches
  • 5.4.5 Hybrid Models
• 5.5 By Geography
  • 5.5.1 North America
    • 5.5.1.1 United States
    • 5.5.1.2 Canada
    • 5.5.1.3 Mexico
  • 5.5.2 South America
    • 5.5.2.1 Brazil
    • 5.5.2.2 Argentina
    • 5.5.2.3 Rest of South America
  • 5.5.3 Europe
    • 5.5.3.1 United Kingdom
    • 5.5.3.2 Germany
    • 5.5.3.3 France
    • 5.5.3.4 Italy
    • 5.5.3.5 Spain
    • 5.5.3.6 Rest of Europe
  • 5.5.4 Asia-Pacific
    • 5.5.4.1 China
    • 5.5.4.2 Japan
    • 5.5.4.3 India
    • 5.5.4.4 South Korea
    • 5.5.4.5 Rest of Asia-Pacific
  • 5.5.5 Middle East and Africa
    • 5.5.5.1 Middle East
      • 5.5.5.1.1 United Arab Emirates
      • 5.5.5.1.2 Saudi Arabia
      • 5.5.5.1.3 Rest of Middle East
    • 5.5.5.2 Africa
      • 5.5.5.2.1 South Africa
      • 5.5.5.2.2 Egypt
      • 5.5.5.2.3 Rest of Africa

6 COMPETITIVE LANDSCAPE

• 6.1 Market Concentration
• 6.2 Strategic Moves
• 6.3 Market Share Analysis
• 6.4 Company Profiles (includes Global Level Overview, Market Level Overview, Core Segments, Financials as available, Strategic Information, Market Rank/Share, Products and Services, Recent Developments)
  • 6.4.1 o9 Solutions Inc.
  • 6.4.2 Blue Yonder Group Inc.
  • 6.4.3 Kinaxis Inc.
  • 6.4.4 E2open Parent Holdings Inc.
  • 6.4.5 ToolsGroup B.V.
  • 6.4.6 Anaplan Inc.
  • 6.4.7 Aera Technology Inc.
  • 6.4.8 Antuit.ai LLC
  • 6.4.9 Relex Solutions Oy
  • 6.4.10 Logility Inc.
  • 6.4.11 John Galt Solutions Inc.
  • 6.4.12 Llamasoft Inc.
  • 6.4.13 Demand Driven Technologies LLC
  • 6.4.14 Business Forecast Systems Inc. (Forecast Pro)
  • 6.4.15 Lokad SAS
  • 6.4.16 GMDH LLC
  • 6.4.17 Prevedere Inc.
  • 6.4.18 DataRobot Inc.
  • 6.4.19 Inform Software
  • 6.4.20 Solvoyo Cozum Yazilim A.S.

7 MARKET OPPORTUNITIES AND FUTURE OUTLOOK

• 7.1 White-Space and Unmet-Need Assessment