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世界の工業・製造業向けビッグデータ市場の予測

Big Data in Global Industry and Manufacturing Market 2021

発行 NAVADHI Market Research Pvt Ltd 商品コード 637056
出版日 ページ情報 英文 74 Pages
納期: 即日から翌営業日
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世界の工業・製造業向けビッグデータ市場の予測 Big Data in Global Industry and Manufacturing Market 2021
出版日: 2018年05月04日 ページ情報: 英文 74 Pages
概要

当レポートでは、世界の工業・製造業向けビッグデータ市場について調査分析し、市場動向と予測、必要性、バリューチェーン情報、成長促進要因・阻害要因、関連政策、主要企業プロファイルなど、体系的な情報を提供しています。

第1章 エグゼクティブサマリー

第2章 工業・製造業向けビッグデータの必要性

第3章 工業・製造業向けビッグデータバリューチェーン

第4章 世界の工業・製造業向けビッグデータ市場区分と予測

  • 世界の工業・製造業向けビッグデータ市場全体の予測
  • 世界の工業・製造業向けビッグデータ市場の予測:地域別

第5章 世界の工業・製造業向けビッグデータの成長促進要因・阻害要因

  • 成長阻害要因
  • 成長促進要因

第6章 工業・製造業向けビッグデータ主要企業のプロファイル

  • SAS
  • VMware
  • Teradata
  • Mu Sigma Inc.

第7章 ケーススタディ

第8章 分析モデル

  • ファイブフォース分析
  • PESTLE分析
  • SWOT分析

第9章 成長機会

第10章 戦略的提言

目次
Product Code: NAV0518006

“Harnessing data is crucial” is what has been accepted globally and an example of this can be given by citing 2012 MIT Sloan Survey where two-thirds of companies said using analytics gave them a competitive edge. Most factories could use the boost.

In 2015, according to the Institute for Supply Management (ISM), U.S. manufacturing activity fell to 51.1 from 52.7 the month prior. The reading is the worst in two years. In Canada, the RBC Purchasing Managers Index (PMI) fell to 49.4 from 50.8 in July. A score below 50 signals industrywide contraction. In each market, factory managers are under renewed pressure to optimize processes and lower costs. Analytics tools could hold the key to finding areas of improvement.

Big Data tools are best for capturing machine-level information. Monitoring the time in which various lines in the industry manufactures certain products and then using them to derive meaningful insights, by backtracking, thus having a better understanding of the performance of the machines that are being used in the manufacturing unit.

Granular utilization data is one of the major aspects that a manufacturing industry can get by implementing Big Data Analytics. For example, when does your factory produce its greatest output? What days? What hours? And at what mix and with who on the floor? Again, the idea here is to study the conditions that lead to the very best outcomes and then seek to reproduce those outcomes on a regular basis.

Avoiding mistakes is every bit as important as optimizing the mix and hours on the floor. Using Big Data and analytics tools to study error rates and then correlating the results by product and employee. It can also be found out if some workers do particularly well with a certain brand of product and not so well with others, allowing the manufacturer to optimize his mix and make smarter decisions when it comes to training and employee incentives.

Of course, Big Data is only a tool and what matters is what you do with it. Track line and assembly speed, utilization data, error rates and prototyping costs to find out what drives efficiency and profit at your factory. Then, recognize and reward the behaviors and processes that produce them so that everyone wins including the workers who stand to lose the most during this most current downturn.

This report extensively studies the use and effects of using big data analytics in Industry and Manufacturing. Geographical estimation of the total market of big data in Industry and Manufacturing along with the expected growth rate till 2021 has been studied along with the technological areas in Data Analytics in which future growth prospects are higher is studied.

Scope of the Big Data Market in Industry and Manufacturing in 2021 Report:

  • This report provides a detailed view of global Big Data Market in Industry and Manufacturing with current demand and the forecasted demand for the market.
  • This report identifies the need for focusing on the usage of Big Data by various segments.
  • This report also provides a historical perspective of the usage and growth of Big Data as a growth promoter by farm house producers.
  • This report provides detailed information on global Big Data Market in Industry and Manufacturing with growth forecasts up to 2021.
  • This report also focuses on developing a better understanding of the current trends of the Big Data Market in Industry and Manufacturing.
  • This study also identifies various policies related to Big Data Market in Industry and Manufacturing and distribution across various countries in the world market.
  • The report identifies the growth drivers and inhibitors for the global Big Data Market in Industry and Manufacturing.
  • This report profiles ten manufacturers related to Big Data Market in Industry and Manufacturing.
  • This report provides detailed competitive landscape of the global Big Data Market in Industry and Manufacturing.
  • This report also provides information regarding the global industries associations related to this industry.
  • This report identifies major challenges faced by a new player in global Big Data Market in Industry and Manufacturing.
  • The report identifies the key risks associated with the Big Data Market in Industry and Manufacturing.
  • This report provides future trends for global Big Data Market in Industry and Manufacturing.
  • This report also provides recommendations for policy makers.

Companies covered:

  • 1. SAS
  • 2. VMware
  • 3. Teradata
  • 4. Mu Sigma Inc.

Table of Contents

1. Executive Summary

  • Scope of the Big Data Market in Industry and Manufacturing in 2021 Report
  • Research Methodology

2. Need for Big Data in Industry and Manufacturing Market

3. Big Data in Industry and Manufacturing Value Chain

4. Market Segments and Forecast of Global Big Data Market in Industry and Manufacturing till 2021

  • 4.1. Overall Forecast of Global Big Data Market in Industry and Manufacturing
  • 4.2. Global Big Data Market in Industry and Manufacturing Forecast by Region
    • 4.4.1. North America
    • 4.4.2. Latin and South America
    • 4.4.3. Middle East and Africa
    • 4.4.4. Europe
    • 4.4.5. Asia-Pacific

5. Growth Drivers and Growth Inhibitors in Big Data in Industry and Manufacturing

  • 5.1. Growth Inhibitors
    • 5.1.1. Data Challenges
    • 5.1.2. Process Challenges
    • 5.1.3. Management Challenges
  • 5.2. Growth Drivers
    • 5.2.1. Awareness
    • 5.2.2. Software
    • 5.2.3. Services
    • 5.2.4. Investment

6. Profile of Key Players in Big Data Market in Industry and Manufacturing

  • 6.1. SAS
    • 6.1.1.1. Company Profile
    • 6.1.2. SAS in Big Data in Industry and Manufacturing value chain
    • 6.1.3. Financial Performance of SAS
    • 6.1.4. Business Strategy
      • 6.1.4.1. Product Level Business Strategy
      • 6.1.4.2. Service Level Business Strategy
    • 6.1.5. SWOT Analysis for SAS
      • Strengths
      • Weaknesses
      • Opportunities
      • Threats
  • 6.2. VMware
    • 6.2.1. Company Profile
    • 6.2.2. VMware in Big Data Market in Industry & Manufacturing value chain
    • 6.2.3. Financial Performance of VMware
    • 6.2.4. Business Strategy
      • 6.2.4.1. Product Level Business Strategy
      • 6.2.4.2. Service Level Business Strategy
    • 6.2.5. SWOT Analysis for VMware
      • Strengths
      • Weaknesses
      • Opportunities
      • Threats
  • 6.3. Teradata
    • 6.3.1. Company Profile
    • 6.3.2. Teradata in Big Data Value Chain
    • 6.3.3. Financial Performance of Teradata
    • 6.3.4. Business Strategy
    • 6.3.5. SWOT Analysis for Teradata
      • Strengths
      • Weaknesses
      • Opportunities
      • Threats
  • 6.4. Mu Sigma Inc.
    • 6.4.1. Company Profile
    • 6.4.2. Mu Sigma in Global Big Data Value Chain
    • 6.4.3. Financial Performance of Mu Sigma Inc.
    • 6.4.4. Business Strategy
      • 6.4.4.1. Product Level Business Strategy
      • 6.4.4.2. Service Level Business Strategy
    • 6.4.5. SWOT Analysis for Mu Sigma
    • 6.4.6. Key Customers

7. Case Study

  • 7.1. Intel Case Study
  • 7.2. McLaren Case Study
  • 7.3. Self-Driving Cars Case Study

8. Analysis Models

  • 8.1. Porter Analysis Model
    • 8.1.1. Threat of New Entrants
  • 8.2. PESTLE Analysis
  • 8.3SWOT Analysis

9. Growth Opportunities

10.Strategic Recommendations

  • 10.1. Recommendations for Consulting Firms
  • 10.2. Recommendations for Infrastructure and Technology Providers
  • 10.3. Recommendations for Big Data Suppliers
    • List of Exhibits
    • Notes
    • Company Information

List of Exhibits

  • Exhibit 3.1: Global Big Data Market value chain
  • Exhibit 4.1: Market Share of Various Geographies in Big Data Market in Industry and Manufacturing(in %)
  • Exhibit 4.2: Geography wise CAGR Growth Forecast for Big Data Market in Industry and Manufacturing(in %)
  • Exhibit 4.3: Forecast for Global Big Data Market in Industry and Manufacturing(in US$ billion)
  • Exhibit 4.4: Geography wise Growth Forecast in Big Data Market in Industry and Manufacturing(in %)
  • Exhibit 4.5: Growth Forecast of North America in Big Data Market in Industry and Manufacturing(in %)
  • Exhibit 4.6: Growth Forecast of Latin and South America in Smart Fabric Market(in %)
  • Exhibit 4.7: Growth Forecast of Middle East & Africa in Big Data Market in Industry and Manufacturing(in %)
  • Exhibit 4.8: Growth Forecast of Europe in Big Data Market in Industry and Manufacturing(in %)
  • Exhibit 4.9: Growth Forecast of Asia-Pacific in Big Data Market in Industry and Manufacturing(in %)
  • Exhibit 5.1: Growth Drivers and Inhibitors of the industry
  • Exhibit 6.1: Company Profile - SAS
  • Exhibit 6.2: Contact Details - SAS
  • Exhibit 6.3: SAS in Big Data industry value chain
  • Exhibit 6.4: SAS Revenue from 2012-13 to 2016-17(in USD)
  • Exhibit 6.5: Year-wise SAS Revenue Growth from 2012-13 to 2016-17(in %)
  • Exhibit 6.6: Estimated SAS Revenue from 2017-18 to 2021-22(in Million USD)
  • Exhibit 6.7: Estimated Year-wise SAS Revenue from 2017-18 to 2021-22(in %)
  • Exhibit 6.8: Major Products and Services of SAS
  • Exhibit 6.9: SWOT Analysis of SAS
  • Exhibit 6.10: Company Profile - VMware
  • Exhibit 6.11: Contact Details - VMware
  • Exhibit 6.12: VMware in Big Data industry value chain
  • Exhibit 6.13: VMware Revenue from 2012-13 to 2016-17(in USD)
  • Exhibit 6.14: Year-wise VMware Revenue Growth from 2012-13 to 2016-17(in %)
  • Exhibit 6.15: Estimated Year-wise VMware Revenue from 2017-18 to 2021-22(in Million ¥)
  • Exhibit 6.16: Estimated Year-wise VMware Revenue from 2017-18 to 2021-22(in %)
  • Exhibit 6.17: Major Products and Services of VMware
  • Exhibit 6.18: SWOT Analysis of VMware
  • Exhibit 6.19: Company Profile - Teradata
  • Exhibit 6.20: Contact Details - Teradata
  • Exhibit 6.21: Teradata in Big Data value chain
  • Exhibit 6.22: Teradata revenue from 2011-12 to 2015-16(in million £)
  • Exhibit 6.23: Year-wise Teradata Revenue Growth from 2012-13 to 2016-17(in %)
  • Exhibit 6.24: Estimated Teradata Revenue from 2017-18 to 2021-22(in Thousand USD)
  • Exhibit 6.25: Estimated Year-wise Teradata Revenue from 2017-18 to 2021-22(in %)
  • Exhibit 6.26: Major Products and Services of Teradata
  • Exhibit 6.27: SWOT Analysis of Teradata
  • Exhibit 6.28: Company Profile - MuSigma
  • Exhibit 6.29: Contact Details - MuSigma
  • Exhibit 6.30: MuSigma in Big Data industry value chain
  • Exhibit 6.31: MuSigma revenue from 2011-12 to 2015-16(in USD)
  • Exhibit 6.32: Year-wise MuSigma Revenue Growth from 2011-12 to 2015-16(in %)
  • Exhibit 6.33: Estimated MuSigma Revenue from 2017-18 to 2021-22(in $)
  • Exhibit 6.34: Estimated Year-wise MuSigma Revenue from 2017-18 to 2021-22(in %)
  • Exhibit 6.35: Major Products and Services of MuSigma
  • Exhibit 6.36: SWOT Analysis of MuSigma
  • Exhibit 6.37: Key Customers of MuSigma
  • Exhibit 6.38: Porters Five Forces for Big Data Analytics in Industry and Manufacturing
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