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市場調查報告書

未來有希望部門的投資:製造業的巨量資料

Investing in the Currency of the Future: Big Data for the Manufacturing Domain

出版商 Frost & Sullivan 商品編碼 336868
出版日期 內容資訊 英文 52 Pages
商品交期: 最快1-2個工作天內
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未來有希望部門的投資:製造業的巨量資料 Investing in the Currency of the Future: Big Data for the Manufacturing Domain
出版日期: 2015年07月29日 內容資訊: 英文 52 Pages
簡介

本報告提供製造業上巨量資料及分析的市場機會相關調查,提供您巨量資料解決方案的引進案例,有前途的引進處與活用方法,必要基礎設施,地區的展望,並彙整提供附加價值產品·解決方案·服務的創新企業之簡介等資料。

第1章 摘要整理

  • 主要調查結果

第2章 IOIT (INTERNET OF INDUSTRIAL THINGS):調查的展望

  • IOIT (INTERNET OF INDUSTRIAL THINGS):4個功能
  • Frost & Sullivan所提供的內容

第3章 調查範圍·調查目的

第4章 巨量資料解決方案的功能性零組件與其潛在性檢驗

  • 巨量資料解決方案的實行商務案例
  • 製造業 vs 其他產業:概要
  • 製造業巨量資料解決方案的Building Blocks
  • 資料的儲存與整合:基礎layer分析
  • 內部部署 vs. 雲端基礎:選擇恰當的引進模式
  • 雲端基礎模式:資料敏感性的焦點
  • Hadoop·NoSQL資料庫:現有基礎設施的擴張
  • 製造業的分析:大市場機會
  • 危險報酬矩陣:生產線·廠房層級
  • 維護相關的活動和資料分析的演進
  • 面向擴張價值鏈分析:供應鏈的最佳化
  • 資料的視覺化
  • 基於資料的員工管理等

第5章 把握巨量資料的莫大市場機會:「ATM組成架構」·首發者的優勢

  • ATM組成架構的市場機會製圖
  • 維修分析:設備的運作時間和性能的提高
  • 先進的機器學習時代:專業活性方法
  • 排除製造業的7個浪費
  • 能源的最佳化
  • 非結構化資料和NoSQL資料庫
  • 巨量資料計劃:預測大半將建立Hadoop
  • M2M應用開發平台
  • 地區的展望:全球據點的動態
  • 各部門市場分析:分離式製造 vs 製造過程
  • 新興市場:製造業的污染管理
  • 彙整:主要市場機會等

第6章 創新的企業

  • Mtell
  • ThingWorx (PTC Company)
  • Sisense Inc.
  • MongoDB Inc.
  • Hortonworks Inc.

第7章 關於Frost&Sullivan

目錄
Product Code: MB1A-01-00-00-00

Investing in the Currency of the Future: Big Data for the Manufacturing Domain

Transition Towards Data-driven Real-time Visibility and Decision Making Compels Manufacturers to Adopt Big Data Solutions

As part of the Internet of Industrial Things (IOIT) research portfolio offering from the Industrial Automation and Process Control practice, this strategic research service provides a detailed assessment of the key opportunities for Big Data and Analytics in the manufacturing domain from an application, technology, and market standpoint. While the deliverable encompasses a combination of both qualitative trends and quantitative data points, some of the key focal areas here include new storage requirements for high volume multiple data types , the role of analytics in manufacturing, emerging applications within the facility , new markets for sustainable growth, and innovative company initiatives that are gaining wide-scale acceptance.

Key Questions This Study Will Answer

  • How does the Big Data and Analytics market compare across different sectors? What impact does it have on the manufacturing sector? What are the four functional blocks of a holistic Big Data and Analytics solution offering?
  • What additional infrastructure is required for manufacturing end users to leverage vital Big Data? What are the key parameters they need to take into account to select the right deployment model? What are the benefits of adoption?
  • How can analytics be applied across business segments in a manufacturing facility? In which area are manufacturing end users expected to witness the greatest value? How do individual personnel prefer to visualize their data? What are some of the new roles and responsibilities required?
  • What is the market size for maintenance analytics? Why are companies transitioning from reactive to proactive analytics? How does Big Data aid in facilitating lean manufacturing? What are some of the emerging applications for Big Data that are still underpenetrated?
  • Why are NoSQL and Hadoop critical for any future Big Data project? What is the size of the opportunity? Why do distributed assets require a machine-to-machine application development platform? What is the size of the opportunity?
  • What are some of the key trends across geographical regions? Which among process or discrete manufacturing is likely to witness greater acceptance for Big Data and Analytics? What are some of the key vertical markets expected to experience higher growth? What are some of the emerging markets?

Table of Contents

1. EXECUTIVE SUMMARY

  • 1. Key Findings

2. INTERNET OF INDUSTRIAL THINGS-A RESEARCH PERSPECTIVE

  • 1. Internet of Industrial Things-The Four Functional Facets
  • 2. Frost & Sullivan's Offering

3. RESEARCH SCOPE AND OBJECTIVES

  • 1. Research Scope and Objective
  • 2. Key Questions This Study Will Answer

4. EXAMINING THE FUNCTIONAL COMPONENTS OF A BIG DATA SOLUTION AND UNEARTHING ITS POTENTIAL FOR

  • 1. Business Case for the Implementation of a Big Data Solution
  • 2. Snapshot of Manufacturing vs. Other Sectors
  • 3. Building Blocks of a Big Data Solution for Manufacturing
  • 4. Data Storage and Integration-Analyzing the Foundation Layers
  • 5. On-Premise vs. Cloud Based-Choosing the Right Deployment Model
  • 6. Cloud-based Models-Acute Focus on Data Sensitivity
  • 7. Hadoop and NoSQL Databases-Extension to Existing Infrastructure
  • 8. Manufacturing Analytics-A Gold Mine of Opportunities
  • 9. Risk Reward Matrices-Production Line and Plant Level
  • 10. Evolution of Data Analytics for Maintenance-related Activities
  • 11. Analytics for the Extended Value Chain-Supply Chain Optimization
  • 12. Data Visualization-Customized Dashboards for Individual Personnel
  • 13. Data Driven Workforce-Dissolving Barriers Across the Enterprise

5. SEIZING LUCRATIVE BIG DATA OPPORTUNITIES TO GAIN A FIRST-MOVER ADVANTAGE-"ATM" FRAMEWORK

  • 1. Opportunity Mapping Using the ATM Framework
  • 2. Maintenance Analytics-Increased Equipment Uptime and Performance
  • 3. Era of Advanced Machine Learning-A Proactive Approach
  • 4. Curtailing the 7 Wastes in Manufacturing to Form a Lean Enterprise
  • 5. Push Towards Energy Optimization
  • 6. Influx of Unstructured Data to Stir Demand for NoSQL Databases
  • 7. Majority of Big Data Projects are Expected to be Built on Hadoop
  • 8. M2M Application Development Platforms
  • 9. Regional Outlook-Key Dynamics Across Global Hotspots
  • 10. Vertical Market Analysis-Discrete vs. Process Manufacturing
  • 11. New Emerging Markets-Manufacturing Pollution Control
  • 12. Size of the Pie-Summary of Key Market Opportunities

6. MARKET DIRECTION-INNOVATIVE COMPANIES DEVELOPING VALUE-ADDED PRODUCTS, SOLUTIONS, AND SERVICES

  • 1. Company 1-Mtell
  • 2. Company 2-ThingWorx (a PTC Company)
  • 3. Company 3-Sisense Inc.
  • 4. Company 4-MongoDB Inc.
  • 5. Company 5-Hortonworks Inc.
  • 6. Legal Disclaimer

7. THE FROST & SULLIVAN STORY

  • 1. The Frost & Sullivan Story
  • 2. Value Proposition: Future of Your Company & Career
  • 3. Industry Convergence
  • 4. Global Perspective
  • 5. 360° Research Perspective
  • 6. Implementation Excellence
  • 7. Our Blue Ocean Strategy
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