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市場調查報告書
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2102417

倉儲自動化分析市場：預測至 2034 年 - 按組件、分析類型、部署模式、應用、最終用戶和地區分類的全球分析

Warehouse Automation Analytics Market Forecasts to 2034 - Global Analysis By Component (Software, Services and Hardware), Analytics Type, Deployment Mode, Application, End User and By Geography
出版日期: | 出版商: Stratistics Market Research Consulting | 英文 | 商品交期: 2-3個工作天內

價格

根據 Statista 的數據，預計到 2026 年，全球倉庫自動化分析市場規模將達到 30 億美元，並在預測期內以 22.9% 的複合年成長率成長，到 2034 年將達到 107 億美元。

倉庫自動化分析是指利用數據驅動的軟體平台和分析工具，處理來自自動化倉庫系統的營運訊息，從而最佳化庫存管理、訂單處理、員工生產力和設備性能。這些分析解決方案整合了來自倉庫管理系統、自動化倉庫系統 (AS/RS)、輸送機網路、機器人揀選系統和物聯網感測器的數據，以產生可執行的洞察。該技術包括用於營運視覺性的說明分析、用於根本原因識別的診斷性分析、用於需求預測的預測性分析以及用於自動決策建議的指示性分析。倉庫自動化分析能夠實現複雜履約流程的資料驅動最佳化。

電子商務日益複雜

電子商務訂單量的指數級成長以及當日達和次日達服務的廣泛普及，帶來了前所未有的營運複雜性，需要先進的倉庫自動化分析技術才能實現高效管理。現代化的履約中心處理數千個SKU、數百萬件商品，而需求模式又瞬息萬變。分析平台能夠即時最佳化貨位佈局策略、揀貨路線排序和人員配置，以滿足服務等級協定 (SLA)。終端用戶回饋，實施分析系統後，訂單準確率和處理能力提升了15%至25%。隨著消費者對配送速度的期望不斷提高，這種商業性需求變得日益重要。

數據品質問題

倉庫自動化分析的有效性從根本上取決於從各種倉庫系統和感測器獲取的基礎營運資料的品質、完整性和及時性。傳統的倉庫管理系統通常會產生不完整或不一致的資料格式，因此在分析之前需要進行大規模的資料清洗和轉換。 RFID 和條碼掃描錯誤會導致庫存準確度誤差，進而影響整個分析模型。自動化設備和分析平台之間的即時資料同步存在延遲，限制了預測能力。應對這些資料品質挑戰需要持續投資於資料管治，而許多倉庫卻低估了資料治理的重要性。

人工智慧驅動的需求預測

將先進的機器學習演算法應用於倉庫需求預測，為庫存最佳化和提高營運資金效率帶來了變革性的機會。人工智慧模型分析歷史銷售模式、促銷日曆、天氣資料和外部市場訊號，產生SKU（庫存​​單位）和庫位層級的精細化需求預測。這些預測能夠實現動態安全庫存調整、自動補貨觸發和主動產能規劃。季節性零售和快速消費品（FMCG）行業的終端用戶可以顯著降低庫存持有成本。此外，共用上游需求訊號的供應商協作平台也能從中受益。

勞動力短缺

能夠解讀複雜自動化數據並將這些洞見轉化為營運改善的數據科學家、分析工程師和倉儲營運分析師嚴重短缺，正威脅市場成長。科技公司、金融服務公司和物流供應商之間對分析人才的競爭日益激烈，導致薪資水準飆升至中小倉儲業者難以承受的地步。由於需要同時掌握倉儲營運和統計建模方面的專業知識，候選人才庫十分有限。這些人才限制可能會影響終端用戶從其分析投資中獲得最大價值的能力，並降低他們未來採用分析技術的意願。

新型冠狀病毒（COVID-19）的影響：

新冠疫情初期，由於人員短缺和安全規程的變更，倉庫營運和資料收集的連續性受到干擾。疫情期間，電子商務交易量的激增暴露了傳統倉庫管理方法的局限性，促使人們更加關注以數據分析主導的最佳化方案，以應對需求波動。遠端管理的需求也增加了對可隨時隨地存取的雲端分析儀錶板的需求。疫情後，隨著全通路履約和當日達服務的普及，倉庫自動化分析的投資已成為維持競爭力的必要條件。

在預測期內，軟體領域預計將佔據最大的市場佔有率。

預計在預測期內，軟體領域將佔據最大的市場佔有率，因為它充當分析引擎，處理倉庫營運數據，並針對庫存、人員和設備生成最佳化方案。軟體平台包括資料整合層、分析處理引擎、視覺化儀表板和機器學習模型管理系統。 SAP、Oracle 和 Manhattan Associates 等領先供應商在更廣泛的供應鏈管理套件中提供全面的倉庫分析模組。最終用戶優先考慮能夠與現有倉庫管理系統和企業資源計劃 (ERP) 系統無縫整合的軟體解決方案。

預計在預測期內，預測分析領域將呈現最高的複合年成長率。

在預測期內，預測分析領域預計將呈現最高的成長率，這主要得益於企業日益成長的需求，即在需求波動、設備故障和勞動力需求影響營運績效之前提前預測這些情況。預測模型利用歷史模式、季節性趨勢和即時訊號，以越來越高的精度預測未來狀況。設備的預測性維護可以減少自動化倉庫系統和輸送機網路的意外停機時間。需求預測能夠實現主動庫存配置和人員排班。主動回應而非被動因應所帶來的商業性價值，正促使企業投資於預測能力。

市佔率最大的地區：

在預測期內，北美預計將佔據最大的市場佔有率，這主要得益於該地區集中了眾多領先的電商履約網路、先進的第三方物流供應商以及專注於倉庫最佳化軟體的技術廠商。美國憑藉亞馬遜龐大的、以數據分析主導的履約基礎設施以及競爭對手零售商的積極擴張，在市場中佔據領先地位。加拿大受益於跨境電商的成長和對低溫運輸物流的投資。在墨西哥，近岸外包趨勢正在催生現代化倉庫分析的需求。該地區成熟的雲端基礎設施為先進分析平台的應用提供了支援。

複合年成長率最高的地區：

在預測期內，亞太地區預計將呈現最高的複合年成長率，這主要得益於中國、印度、日本以及東南亞地區電子商務的爆炸式成長、大規模倉儲建設計畫以及政府主導的物流現代化舉措。中國國內消費的蓬勃發展推動了對分析最佳化型履約中心的需求，這些中心為阿里巴巴和京東等平台提供支援。印度物流業的自由化吸引了外資進入自動化倉庫領域。在日本，勞動力老化正在加速自動化和分析技術的應用。韓國和新加坡正透過政府主導的智慧物流項目獲得財政和監管支持。

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目錄

第1章執行摘要

  • 市場概覽及主要亮點
  • 促進因素、挑戰與機遇
  • 競爭格局概述
  • 戰略洞察與建議

第2章：研究框架

  • 研究目標和範圍
  • 相關人員分析
  • 研究假設和限制
  • 調查方法

第3章 市場動態與趨勢分析

  • 市場定義與結構
  • 主要市場促進因素
  • 市場限制與挑戰
  • 投資成長機會和重點領域
  • 產業威脅與風險評估
  • 技術與創新展望
  • 新興市場/高成長市場
  • 監管和政策環境
  • 新冠疫情的影響及復甦前景

第4章：競爭環境與策略評估

  • 波特五力分析
    • 供應商的議價能力
    • 買方的議價能力
    • 替代品的威脅
    • 新進入者的威脅
    • 競爭公司之間的競爭
  • 主要公司市佔率分析
  • 產品基準評效和效能比較

第5章 全球倉儲自動化分析市場：依組件分類

  • 軟體
  • 服務
  • 硬體

第6章：全球倉儲自動化分析市場：依分析類型分類

  • 說明分析
  • 診斷分析
  • 預測分析
  • 指示性分析
  • 即時分析
  • 績效分析

第7章：全球倉儲自動化市場分析：依部署模式分類

  • 現場
  • 基於雲端的
  • 混合部署
  • 邊緣開發

第8章：全球倉儲自動化分析市場：按應用領域分類

  • 庫存最佳化
  • 訂單處理分析
  • 員工績效分析
  • 空間利用率分析
  • 設備效能監控
  • 需求預測
  • 路線和工作流程最佳化

第9章：全球倉儲自動化分析市場：依最終用戶分類

  • 倉庫營運商
  • 物流服務供應商
  • 配銷中心
  • 製造工廠
  • 零售履約中心

第10章 全球倉儲自動化分析市場：按地區分類

  • 北美洲
    • 美國
    • 加拿大
    • 墨西哥
  • 歐洲
    • 英國
    • 德國
    • 法國
    • 義大利
    • 西班牙
    • 荷蘭
    • 比利時
    • 瑞典
    • 瑞士
    • 波蘭
    • 其他歐洲國家
  • 亞太地區
    • 中國
    • 日本
    • 印度
    • 韓國
    • 澳洲
    • 印尼
    • 泰國
    • 馬來西亞
    • 新加坡
    • 越南
    • 其他亞太國家
  • 南美洲
    • 巴西
    • 阿根廷
    • 哥倫比亞
    • 智利
    • 秘魯
    • 其他南美國家
  • 世界其他地區（RoW）
    • 中東
      • 沙烏地阿拉伯
      • 阿拉伯聯合大公國
      • 卡達
      • 以色列
      • 其他中東國家
    • 非洲
      • 南非
      • 埃及
      • 摩洛哥
      • 其他非洲國家

第11章 策略市場資訊

  • 工業價值網路和供應鏈評估
  • 空白區域和機會地圖
  • 產品演進與市場生命週期分析
  • 通路、經銷商和打入市場策略的評估

第12章 產業趨勢與策略舉措

  • 併購
  • 夥伴關係、聯盟和合資企業
  • 新產品發布和認證
  • 擴大生產能力和投資
  • 其他策略舉措

第13章：公司簡介

  • SAP SE
  • Oracle Corporation
  • Manhattan Associates, Inc.
  • Blue Yonder Group, Inc.
  • Korber AG
  • Honeywell International Inc.
  • Zebra Technologies Corporation
  • IBM Corporation
  • Microsoft Corporation
  • Siemens AG
  • SSI SCHAEFER Group
  • Daifuku Co., Ltd.
  • Dematic Corporation
  • Swisslog Holding AG
  • Infor Inc.
  • Descartes Systems Group Inc.
  • Rockwell Automation, Inc.
Product Code: SMRC38304

According to Statista, the Global Warehouse Automation Analytics Market is accounted for $3.0 billion in 2026 and is expected to reach $10.7 billion by 2034 growing at a CAGR of 22.9% during the forecast period. Warehouse automation analytics refers to data-driven software platforms and analytical tools that process operational information from automated warehouse systems to optimize inventory management, order fulfillment, workforce productivity, and equipment performance. These analytics solutions integrate data from warehouse management systems, automated storage and retrieval systems, conveyor networks, robotic picking systems, and IoT sensors to generate actionable insights. The technology encompasses descriptive analytics for operational visibility, diagnostic analytics for root cause identification, predictive analytics for demand forecasting, and prescriptive analytics for automated decision recommendations. Warehouse automation analytics enables data-driven optimization of complex fulfillment operations.

Market Dynamics:

Driver:

E-commerce complexity

The exponential growth in e-commerce order volumes and the proliferation of same-day and next-day delivery promises are creating unprecedented operational complexity that demands sophisticated warehouse automation analytics for effective management. Modern fulfillment centers process millions of individual items across thousands of stock keeping units with highly variable demand patterns. Analytics platforms optimize slotting strategies, pick path sequences, and labor allocation in real time to meet service level agreements. End users report fifteen to twenty-five percent improvements in order accuracy and throughput following analytics implementation. The commercial imperative intensifies as consumer expectations for delivery speed continue escalating.

Restraint:

Data quality issues

The effectiveness of warehouse automation analytics is fundamentally constrained by the quality, completeness, and timeliness of underlying operational data from diverse warehouse systems and sensors. Legacy warehouse management systems often produce incomplete or inconsistent data formats that require extensive cleansing and transformation before analysis. RFID and barcode scanning errors create inventory accuracy gaps that propagate through analytical models. Real-time data synchronization between automated equipment and analytics platforms introduces latency that limits predictive capabilities. These data quality challenges require ongoing data governance investments that many warehouses underestimate.

Opportunity:

AI demand forecasting

The application of advanced machine learning algorithms to warehouse demand forecasting presents transformative opportunities for inventory optimization and working capital efficiency. AI models analyze historical sales patterns, promotional calendars, weather data, and external market signals to generate granular demand predictions at the stock keeping unit and location level. These predictions enable dynamic safety stock adjustments, automated replenishment triggers, and proactive capacity planning. End users in seasonal retail and fast-moving consumer goods sectors achieve significant inventory carrying cost reductions. The commercial opportunity extends to supplier collaboration platforms that share demand signals upstream.

Threat:

Talent scarcity

The acute shortage of data scientists, analytics engineers, and warehouse operations analysts capable of interpreting complex automation data and translating insights into operational improvements threatens market growth. Competition for analytics talent between technology companies, financial services, and logistics providers drives salary inflation that smaller warehouse operators cannot match. The specialized knowledge required to understand both warehouse operations and statistical modeling creates a narrow candidate pool. These talent constraints limit the ability of end users to extract full value from analytics investments, potentially reducing future procurement enthusiasm.

Covid-19 Impact:

The COVID-19 pandemic initially disrupted warehouse operations and data collection continuity as facilities faced staffing shortages and safety protocol changes. Mid-pandemic, e-commerce volume surges exposed the limitations of traditional warehouse management approaches, accelerating interest in analytics-driven optimization for demand volatility management. Remote management requirements increased demand for cloud-based analytics dashboards accessible from anywhere. Post-pandemic, the normalization of omnichannel fulfillment and same-day delivery sustains investment in warehouse automation analytics as a competitive necessity.

The software segment is expected to be the largest during the forecast period

The software segment is expected to account for the largest market share during the forecast period, due to its role as the analytical engine processing warehouse operational data and generating optimization insights across inventory, labor, and equipment dimensions. Software platforms encompass data integration layers, analytical processing engines, visualization dashboards, and machine learning model management systems. Major vendors, including SAP, Oracle, and Manhattan Associates, offer comprehensive warehouse analytics modules within broader supply chain management suites. End users prioritize software solutions that integrate seamlessly with existing warehouse management and enterprise resource planning investments.

The predictive analytics segment is expected to have the highest CAGR during the forecast period

Over the forecast period, the predictive analytics segment is predicted to witness the highest growth rate, driven by the escalating need to anticipate demand fluctuations, equipment failures, and labor requirements before they impact operational performance. Predictive models leverage historical patterns, seasonal trends, and real-time signals to forecast future states with increasing accuracy. Equipment predictive maintenance reduces unplanned downtime in automated storage and retrieval systems and conveyor networks. Demand prediction enables proactive inventory positioning and labor scheduling. The commercial value of anticipation over reaction drives enterprise investment in predictive capabilities.

Region with largest share:

During the forecast period, the North America region is expected to hold the largest market share, due to the concentration of major e-commerce fulfillment networks, advanced third-party logistics providers, and technology vendors specializing in warehouse optimization software. The United States dominates with Amazon's extensive analytics-driven fulfillment infrastructure and aggressive expansion by competing retailers. Canada benefits from cross-border e-commerce growth and cold chain logistics investments. Mexico's nearshoring trends create demand for modern warehouse analytics. The region's mature cloud infrastructure supports advanced analytics platform deployment.

Region with highest CAGR:

Over the forecast period, the Asia Pacific region is anticipated to exhibit the highest CAGR, due to explosive e-commerce growth, massive warehouse construction programs, and government logistics modernization initiatives across China, India, Japan, and Southeast Asia. China's domestic consumption boom drives demand for analytics-optimized fulfillment centers supporting platforms such as Alibaba and JD.com. India's logistics sector liberalization attracts foreign investment in automated warehousing. Japan's aging workforce accelerates automation and analytics adoption. Government smart logistics programs in South Korea and Singapore provide funding and regulatory support.

Key players in the market

Some of the key players in Warehouse Automation Analytics include SAP SE, Oracle Corporation, Manhattan Associates, Inc., Blue Yonder Group, Inc., Korber AG, Honeywell International Inc., Zebra Technologies Corporation, IBM Corporation, Microsoft Corporation, Siemens AG, SSI SCHAEFER Group, Daifuku Co., Ltd., Dematic Corporation, Swisslog Holding AG, Infor Inc., Descartes Systems Group Inc. and Rockwell Automation, Inc.

Key Developments:

In June 2026, SAP SE launched an integrated warehouse intelligence platform combining real-time operational analytics with AI-driven demand forecasting, enabling predictive inventory positioning across multi-echelon distribution networks.

In May 2026, Oracle Corporation introduced advanced machine learning modules for its warehouse management cloud platform, automating labor scheduling optimization and slotting recommendations based on real-time throughput and demand signals.

In April 2026, Manhattan Associates, Inc. expanded its warehouse management analytics suite with prescriptive optimization capabilities that automatically generate actionable recommendations for order batching and pick path efficiency improvements.

Components Covered:

  • Software
  • Services
  • Hardware

Analytics Types Covered:

  • Descriptive Analytics
  • Diagnostic Analytics
  • Predictive Analytics
  • Prescriptive Analytics
  • Real-Time Analytics
  • Performance Analytics

Deployment Modes Covered:

  • On-Premise
  • Cloud-Based
  • Hybrid Deployment
  • Edge Deployment

Applications Covered:

  • Inventory Optimization
  • Order Fulfillment Analytics
  • Workforce Performance Analytics
  • Space Utilization Analytics
  • Equipment Performance Monitoring
  • Demand Forecasting
  • Route and Workflow Optimization

End Users Covered:

  • Warehouse Operators
  • Logistics Service Providers
  • Distribution Centers
  • Manufacturing Facilities
  • Retail Fulfillment Centers

Regions Covered:

  • North America
    • United States
    • Canada
    • Mexico
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Spain
    • Netherlands
    • Belgium
    • Sweden
    • Switzerland
    • Poland
    • Rest of Europe
  • Asia Pacific
    • China
    • Japan
    • India
    • South Korea
    • Australia
    • Indonesia
    • Thailand
    • Malaysia
    • Singapore
    • Vietnam
    • Rest of Asia Pacific
  • South America
    • Brazil
    • Argentina
    • Colombia
    • Chile
    • Peru
    • Rest of South America
  • Rest of the World (RoW)
    • Middle East
  • Saudi Arabia
  • United Arab Emirates
  • Qatar
  • Israel
  • Rest of Middle East
    • Africa
  • South Africa
  • Egypt
  • Morocco
  • Rest of Africa

What our report offers:

  • Market share assessments for the regional and country-level segments
  • Strategic recommendations for the new entrants
  • Covers Market data for the years 2023, 2024, 2025, 2026, 2027, 2028, 2030, 2032 and 2034
  • Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
  • Strategic recommendations in key business segments based on the market estimations
  • Competitive landscaping mapping the key common trends
  • Company profiling with detailed strategies, financials, and recent developments
  • Supply chain trends mapping the latest technological advancements

Free Customization Offerings:

All the customers of this report will be entitled to receive one of the following free customization options:

  • Company Profiling
    • Comprehensive profiling of additional market players (up to 3)
    • SWOT Analysis of key players (up to 3)
  • Regional Segmentation
    • Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
  • Competitive Benchmarking
    • Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances

Table of Contents

1 Executive Summary

  • 1.1 Market Snapshot and Key Highlights
  • 1.2 Growth Drivers, Challenges, and Opportunities
  • 1.3 Competitive Landscape Overview
  • 1.4 Strategic Insights and Recommendations

2 Research Framework

  • 2.1 Study Objectives and Scope
  • 2.2 Stakeholder Analysis
  • 2.3 Research Assumptions and Limitations
  • 2.4 Research Methodology
    • 2.4.1 Data Collection (Primary and Secondary)
    • 2.4.2 Data Modeling and Estimation Techniques
    • 2.4.3 Data Validation and Triangulation
    • 2.4.4 Analytical and Forecasting Approach

3 Market Dynamics and Trend Analysis

  • 3.1 Market Definition and Structure
  • 3.2 Key Market Drivers
  • 3.3 Market Restraints and Challenges
  • 3.4 Growth Opportunities and Investment Hotspots
  • 3.5 Industry Threats and Risk Assessment
  • 3.6 Technology and Innovation Landscape
  • 3.7 Emerging and High-Growth Markets
  • 3.8 Regulatory and Policy Environment
  • 3.9 Impact of COVID-19 and Recovery Outlook

4 Competitive and Strategic Assessment

  • 4.1 Porter's Five Forces Analysis
    • 4.1.1 Supplier Bargaining Power
    • 4.1.2 Buyer Bargaining Power
    • 4.1.3 Threat of Substitutes
    • 4.1.4 Threat of New Entrants
    • 4.1.5 Competitive Rivalry
  • 4.2 Market Share Analysis of Key Players
  • 4.3 Product Benchmarking and Performance Comparison

5 Global Warehouse Automation Analytics Market, By Component

  • 5.1 Software
  • 5.2 Services
  • 5.3 Hardware

6 Global Warehouse Automation Analytics Market, By Analytics Type

  • 6.1 Descriptive Analytics
  • 6.2 Diagnostic Analytics
  • 6.3 Predictive Analytics
  • 6.4 Prescriptive Analytics
  • 6.5 Real-Time Analytics
  • 6.6 Performance Analytics

7 Global Warehouse Automation Analytics Market, By Deployment Mode

  • 7.1 On-Premise
  • 7.2 Cloud-Based
  • 7.3 Hybrid Deployment
  • 7.4 Edge Deployment

8 Global Warehouse Automation Analytics Market, By Application

  • 8.1 Inventory Optimization
  • 8.2 Order Fulfillment Analytics
  • 8.3 Workforce Performance Analytics
  • 8.4 Space Utilization Analytics
  • 8.5 Equipment Performance Monitoring
  • 8.6 Demand Forecasting
  • 8.7 Route and Workflow Optimization

9 Global Warehouse Automation Analytics Market, By End User

  • 9.1 Warehouse Operators
  • 9.2 Logistics Service Providers
  • 9.3 Distribution Centers
  • 9.4 Manufacturing Facilities
  • 9.5 Retail Fulfillment Centers

10 Global Warehouse Automation Analytics Market, By Geography

  • 10.1 North America
    • 10.1.1 United States
    • 10.1.2 Canada
    • 10.1.3 Mexico
  • 10.2 Europe
    • 10.2.1 United Kingdom
    • 10.2.2 Germany
    • 10.2.3 France
    • 10.2.4 Italy
    • 10.2.5 Spain
    • 10.2.6 Netherlands
    • 10.2.7 Belgium
    • 10.2.8 Sweden
    • 10.2.9 Switzerland
    • 10.2.10 Poland
    • 10.2.11 Rest of Europe
  • 10.3 Asia Pacific
    • 10.3.1 China
    • 10.3.2 Japan
    • 10.3.3 India
    • 10.3.4 South Korea
    • 10.3.5 Australia
    • 10.3.6 Indonesia
    • 10.3.7 Thailand
    • 10.3.8 Malaysia
    • 10.3.9 Singapore
    • 10.3.10 Vietnam
    • 10.3.11 Rest of Asia Pacific
  • 10.4 South America
    • 10.4.1 Brazil
    • 10.4.2 Argentina
    • 10.4.3 Colombia
    • 10.4.4 Chile
    • 10.4.5 Peru
    • 10.4.6 Rest of South America
  • 10.5 Rest of the World (RoW)
    • 10.5.1 Middle East
      • 10.5.1.1 Saudi Arabia
      • 10.5.1.2 United Arab Emirates
      • 10.5.1.3 Qatar
      • 10.5.1.4 Israel
      • 10.5.1.5 Rest of Middle East
    • 10.5.2 Africa
      • 10.5.2.1 South Africa
      • 10.5.2.2 Egypt
      • 10.5.2.3 Morocco
      • 10.5.2.4 Rest of Africa

11 Strategic Market Intelligence

  • 11.1 Industry Value Network and Supply Chain Assessment
  • 11.2 White-Space and Opportunity Mapping
  • 11.3 Product Evolution and Market Life Cycle Analysis
  • 11.4 Channel, Distributor, and Go-to-Market Assessment

12 Industry Developments and Strategic Initiatives

  • 12.1 Mergers and Acquisitions
  • 12.2 Partnerships, Alliances, and Joint Ventures
  • 12.3 New Product Launches and Certifications
  • 12.4 Capacity Expansion and Investments
  • 12.5 Other Strategic Initiatives

13 Company Profiles

  • 13.1 SAP SE
  • 13.2 Oracle Corporation
  • 13.3 Manhattan Associates, Inc.
  • 13.4 Blue Yonder Group, Inc.
  • 13.5 Korber AG
  • 13.6 Honeywell International Inc.
  • 13.7 Zebra Technologies Corporation
  • 13.8 IBM Corporation
  • 13.9 Microsoft Corporation
  • 13.10 Siemens AG
  • 13.11 SSI SCHAEFER Group
  • 13.12 Daifuku Co., Ltd.
  • 13.13 Dematic Corporation
  • 13.14 Swisslog Holding AG
  • 13.15 Infor Inc.
  • 13.16 Descartes Systems Group Inc.
  • 13.17 Rockwell Automation, Inc.

List of Tables

  • Table 1 Global Warehouse Automation Analytics Market Outlook, By Region (2023-2034) ($MN)
  • Table 2 Global Warehouse Automation Analytics Market Outlook, By Component (2023-2034) ($MN)
  • Table 3 Global Warehouse Automation Analytics Market Outlook, By Software (2023-2034) ($MN)
  • Table 4 Global Warehouse Automation Analytics Market Outlook, By Services (2023-2034) ($MN)
  • Table 5 Global Warehouse Automation Analytics Market Outlook, By Hardware (2023-2034) ($MN)
  • Table 6 Global Warehouse Automation Analytics Market Outlook, By Analytics Type (2023-2034) ($MN)
  • Table 7 Global Warehouse Automation Analytics Market Outlook, By Descriptive Analytics (2023-2034) ($MN)
  • Table 8 Global Warehouse Automation Analytics Market Outlook, By Diagnostic Analytics (2023-2034) ($MN)
  • Table 9 Global Warehouse Automation Analytics Market Outlook, By Predictive Analytics (2023-2034) ($MN)
  • Table 10 Global Warehouse Automation Analytics Market Outlook, By Prescriptive Analytics (2023-2034) ($MN)
  • Table 11 Global Warehouse Automation Analytics Market Outlook, By Real-Time Analytics (2023-2034) ($MN)
  • Table 12 Global Warehouse Automation Analytics Market Outlook, By Performance Analytics (2023-2034) ($MN)
  • Table 13 Global Warehouse Automation Analytics Market Outlook, By Deployment Mode (2023-2034) ($MN)
  • Table 14 Global Warehouse Automation Analytics Market Outlook, By On-Premise (2023-2034) ($MN)
  • Table 15 Global Warehouse Automation Analytics Market Outlook, By Cloud-Based (2023-2034) ($MN)
  • Table 16 Global Warehouse Automation Analytics Market Outlook, By Hybrid Deployment (2023-2034) ($MN)
  • Table 17 Global Warehouse Automation Analytics Market Outlook, By Edge Deployment (2023-2034) ($MN)
  • Table 18 Global Warehouse Automation Analytics Market Outlook, By Application (2023-2034) ($MN)
  • Table 19 Global Warehouse Automation Analytics Market Outlook, By Inventory Optimization (2023-2034) ($MN)
  • Table 20 Global Warehouse Automation Analytics Market Outlook, By Order Fulfillment Analytics (2023-2034) ($MN)
  • Table 21 Global Warehouse Automation Analytics Market Outlook, By Workforce Performance Analytics (2023-2034) ($MN)
  • Table 22 Global Warehouse Automation Analytics Market Outlook, By Space Utilization Analytics (2023-2034) ($MN)
  • Table 23 Global Warehouse Automation Analytics Market Outlook, By Equipment Performance Monitoring (2023-2034) ($MN)
  • Table 24 Global Warehouse Automation Analytics Market Outlook, By Demand Forecasting (2023-2034) ($MN)
  • Table 25 Global Warehouse Automation Analytics Market Outlook, By Route and Workflow Optimization (2023-2034) ($MN)
  • Table 26 Global Warehouse Automation Analytics Market Outlook, By End User (2023-2034) ($MN)
  • Table 27 Global Warehouse Automation Analytics Market Outlook, By Warehouse Operators (2023-2034) ($MN)
  • Table 28 Global Warehouse Automation Analytics Market Outlook, By Logistics Service Providers (2023-2034) ($MN)
  • Table 29 Global Warehouse Automation Analytics Market Outlook, By Distribution Centers (2023-2034) ($MN)
  • Table 30 Global Warehouse Automation Analytics Market Outlook, By Manufacturing Facilities (2023-2034) ($MN)
  • Table 31 Global Warehouse Automation Analytics Market Outlook, By Retail Fulfillment Centers (2023-2034) ($MN)

Note: Tables for North America, Europe, APAC, South America, and Rest of the World (RoW) Regions are also represented in the same manner as above.