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市場調查報告書

全球條件監測的巨量資料分析的預測:2023年

Big Data Analytics in Global Condition Monitoring, Forecast to 2023

出版商 Frost & Sullivan 商品編碼 509450
出版日期 內容資訊 英文 64 Pages
商品交期: 最快1-2個工作天內
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全球條件監測的巨量資料分析的預測:2023年 Big Data Analytics in Global Condition Monitoring, Forecast to 2023
出版日期: 2017年05月11日 內容資訊: 英文 64 Pages
簡介

本報告提供全球條件監測的巨量資料分析市場相關調查,提供主要市場成長促進·阻礙因素,各地區·垂直市場的收益預測,市場佔有率·案例研究,成長機會,及經營模式等系統性資訊

第1章 摘要整理

第2章 調查範圍·區分

第3章 促進要素·抑制因素:條件監測用途市場上巨量資料分析

  • 市場成長的促進要素
  • 促進要素的說明
  • 條件監測用途市場上先進分析的附加性主要啟用
  • 市場阻礙成長要素
  • 抑制因素的說明

第4章 預測·趨勢:條件監測用途市場上巨量資料分析

  • 市場工程檢測
  • 預測的前提條件
  • 收益預測
  • 收益預測的議論
  • 收益預測的議論:服務的明細
  • 條件監測的巨量資料分析的目前用途
  • 條件監測用途的巨量資料的發展
  • 下一個發展:處方的分析
  • 製造 vs. 其他部門概要
  • 收益比預測:各地區
  • 收益預測:各地區
  • 收益預測:各地區的議論
  • 收益預測:垂直市場
  • 收益預測:垂直市場議論

第5章 市場佔有率·競爭分析:條件監測用途市場上巨量資料分析

  • 競爭情形
  • 案例研究:BP使用GE的Predix平台
  • 案例研究:Mtell的處方分析平台
  • 案例研究:Siemens的遠程維護解決方案
  • 案例研究:National Instruments和IBM合作夥伴關係
  • 競爭要素·評估

第6章 成長機會·企業趨勢

  • 成長機會:生產效率的改善
  • 成長機會:技術進步
  • 成功·成長的策略性必需條件
  • TIES計劃:對條件監測而言的5大成長機會

第7章 經營模式的發展

  • 經營模式的分類
  • B2B經營模式的分類
  • 服務型模式:PaaS,平台即服務,及DaaS
  • 自由型模式:peipayuzu (計量收費) 、出租/租賃,及訂閱模式 (SaaS)
  • 發展的經營模式
  • 案例研究:Rolls-Royce

第8章 結論

  • 結論:3大預測
  • 免責聲明

第9章 附錄

目錄
Product Code: K09D-30

Rise of New Business Models Through Focus on Software

The application of Big Data analytics in the condition monitoring market is at a nascent stage. Current solutions offered by vendors are only able to analyze condition data such as vibration. The true value of big data will be realized when analytics service providers are able to offer solutions by combining condition and process data (SCADA and PLC data).

The condition monitoring market is gradually changing. In the past, this market was highly hardware driven. The needs of customers are evolving as they look for a more holistic solution that combines hardware, software, and services.

Hardware is becoming increasingly commoditized and product differentiation is diminishing. The main areas of innovation are in software and data analytics, which will represent future opportunities in which companies can invest.

Traditional condition monitoring hardware companies are struggling to develop the right market approach and business model. The transition from a hardware company to a subscription-based services company has been a challenge for most condition monitoring vendors. In the process of growth in condition monitoring, predictive maintenance techniques are designed to help determine the condition of in-service equipment in order to predict when maintenance should be performed. The main goal is to allow convenient scheduling of corrective maintenance and to prevent unexpected equipment failures.

By installing sensors on key assets and analyzing the data, maintenance teams know that equipment needs maintenance, maintenance work can be better planned (spare parts, people, and so on), and what would have been an unscheduled breakdown is transformed to shorter and fewer planned maintenance, thus, increasing plant availability.

Other potential advantages include increased equipment lifetime, increased plant safety, fewer accidents with a negative impact on the environment, and optimized spare parts handling.

While predictive maintenance is still in its infancy, there is already talk about moving to prescriptive maintenance, where experts can recommend actions based on desired outcomes, taking into account specific scenarios, resources, and knowledge of past and current events.

All this has been possible through the introduction of Big Data analytics to the world of condition monitoring.

Additionally, because of an aging workforce and the lack of skilled personnel, customers are turning to their hardware providers for additional support. Opportunities in design, installation, maintenance, data collection, and diagnostic services have created alternate revenue streams for condition monitoring equipment companies.

Data analytics has the potential to save billions of dollars in annual operating expenses for businesses by analyzing historical and real-time data to predict faults with greater statistical accuracy.

Condition monitoring equipment companies are expected to be more than hardware solution providers, with software and data analytics services being critical requirements for customers.

Although condition monitoring companies will continue to invest in software development and improve their condition data analysis capability, it is likely that they will partner or acquire a big data analytics company to provide their customers with a holistic solution rather than develop this capability in house.

Big data is expected to play a more comprehensive role to improve predictive and prescriptive maintenance, manufacturing, supply chain, sales , design and R&D.

Big Data will help create new growth opportunities and entirely new categories of companies. Traditional condition monitoring companies will be incapable of handling such large volumes of data and may look to partner with Big Data experts such as IBM, HP, and Oracle among others.

Big Data revenue is expected to exponentially rise to a billion-dollar market to $2.9 billion by 2023.

Table of Contents

1. EXECUTIVE SUMMARY

  • Key Findings
  • Key Conclusions and Future Outlook
  • Market Engineering Measurements
  • CEO's Perspective

2. RESEARCH SCOPE AND SEGMENTATION

  • Research Scope
  • Segment Definitions
  • End-user Industries Covered
  • Research Methodology
  • Key Questions this Study will Answer

3. DRIVERS AND RESTRAINTS-BIG DATA ANALYTICS IN CONDITION MONITORING APPLICATIONS MARKET

  • Market Drivers
  • Drivers Explained
  • Drivers Explained (continued)
  • Drivers Explained (continued)
  • Additional Key Enablers for Advanced Analytics in Condition Monitoring Applications Market
  • Market Restraints
  • Restraints Explained
  • Restraints Explained (continued)
  • Restraints Explained (continued)
  • Restraints Explained (continued)

4. FORECASTS AND TRENDS-BIG DATA ANALYTICS IN CONDITION MONITORING APPLICATIONS MARKET

  • Market Engineering Measurements
  • Forecast Assumptions
  • Revenue Forecast
  • Revenue Forecast Discussion
  • Revenue Forecast Discussion-Breakdown of Services
  • Current Application of Big Data Analytics in Condition Monitoring
  • Evolution of Big Data in Condition Monitoring Applications
  • The Next Evolution-Prescriptive Analytics
  • Snapshot of Manufacturing Versus Other Sectors
  • Percent Revenue Forecast by Region
  • Revenue Forecast by Region
  • Revenue Forecast by Region Discussion
  • Revenue Forecast by Vertical Market
  • Revenue Forecast by Vertical Market Discussion
  • Revenue Forecast by Vertical Market Discussion (continued)

5. MARKET SHARE AND COMPETITIVE ANALYSIS-BIG DATA ANALYTICS IN CONDITION MONITORING APPLICATIONS MARKET

  • Competitive Landscape
  • Case Study-BP Using GE's Predix Platform
  • Case Study-Mtell's Prescriptive Analytics Platform
  • Case Study-Siemens' Remote Maintenance Solution
  • Case Study-National Instruments and IBM Partnership
  • Competitive Factors and Assessment

6. GROWTH OPPORTUNITIES AND COMPANIES TO ACTION

  • Growth Opportunity-Improving Production Efficiency
  • Growth Opportunity-Technology Advancement
  • Strategic Imperatives for Success and Growth
  • TIES Project-5 Major Growth Opportunities for Condition Monitoring

7. EVOLVING BUSINESS MODELS

  • Taxonomy of Business Models
  • Taxonomy of B2B Business Models
  • Service-based Model-PaaS, Platform as a Service, and DaaS
  • Fee-based Model-Pay Per Use, Renting/Leasing, and Subscription Model (SaaS)
  • Evolving Business Models
  • Case Study-Rolls-Royce

8. THE LAST WORD

  • The Last Word-3 Big Predictions
  • Legal Disclaimer

9. APPENDIX

  • Market Engineering Methodology
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