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Big Data Analytics in Manufacturing Industry Market Research 2019 | Growth Analysis by Technological Advancement, Product Size, Share, Latest Trends Forecast to 2024


The “Big Data Analytics in Manufacturing Industry Market” 2019-2024 focuses on highlighting technological developments, innovations, and growth prospects. The study of the Big Data Analytics in Manufacturing Industry industry is very important to enhance industry productivity and for the study of the market forecast. A detailed analysis of key trends, drivers, restraints, and opportunities manipulating revenue growth is presented in this Big Data Analytics in Manufacturing Industry market research report. Overall, the study offers an in-depth overview of the market covering all major parameters.

Big Data Analytics in Manufacturing Industry Market report aims to provide useful and comprehensive insights into current market trends and future growth scenarios. Big Data Analytics in Manufacturing Industry market report contains information like SWOT analysis, business highlights, strengths, weaknesses, threats, and opportunities of the industry.

Key Players Mentioned in the Big Data Analytics in Manufacturing Industry Market Are:

  • Fair Isaac Corporation
  • Angoss Software Corporation
  • Alteryx Inc.
  • IBM Corporation
  • Microsoft Corporation
  • Tibco Software Inc. (Alpine Data)
  • SAS Institute Inc.
  • SAP SE
  • Oracle Corporation
  • RapidMiner Inc.
  • MicroStrategy Incorporated
  • Knime AG

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    Market Overview:

  • The Big Data Analytics In Manufacturing Industry Market is expected to register a CAGR of over 30.9% during the forecast period 2019 – 2024. With the high rate of adoption of sensors and connected devices and the enabling of M2M communication, there has been a massive increase in the data points that are generated in the manufacturing industry. These data points could be of various types, ranging from a metric detailing the time taken for a material to pass through one process cycle or a more complex one, such as the calculation of the material stress capability in the automotive industry.
  • Manufacturing is a crucial component of a company’s end-to-end supply chain. The value chain participants, like Raw material suppliers, Inventory managers, and Manufacturers have moved from manual product, tracking to the use of barcode scanners and investing in technologies, like RFID and sensors to monitor the stock, production processes, and ascertain when maintenance is required and to take action before the production quality is affected. Such technology adoption was made to monitor aging manufacturing equipment, in order to avoid production downtime, which could be as high as EUR 180 billion (for Britain’s manufacturers, according to Oneserve, the field service management company).
  • In addition to monitoring the asserts, these technologies are being used to gain information about the location of raw materials and finished products within the production facility, to know the status of the availability status (both volume and location data). In addition, advancement in UHF technology has made creating RFID systems more efficient and evolved in such a way that they are able to identify raw material/product specifications like SKU, color, and type thus improving the traceability throughout the supply chain.
  • Manufacturing is one of the most targeted industries by cyber attackers owing to the presence of vital data related to company and government. According to EEF (formerly the Engineering Employers’ Federation), over 45% of the manufacturers have been subjected to a cybersecurity incident.
  • With the increasing integration of technological advancements in the manufacturing industry, the security concerns are also ascending at a significant pace.
  • Scope of the Report:

  • The manufacturing industry has evolved since the last industrial revolution. Technology has played a critical role in shaping the modern manufacturing industry. With the introduction of Industry 4.0, the production establishments took a step forward and implemented many IoT and IIoT solutions to get live feedback from factories and working environments. With the implementation of Machine to Machine services and telematics solutions in production establishments, the industry has moved from the traditional value chain to technology, asset, and engineering-oriented value chain

    Big Data Analytics in Manufacturing Industry Market Report Answers the Following Questions:

    • What will the market size and the growth rate be in 2024?
    • What are the key factors driving the global Big Data Analytics in Manufacturing Industry market?
    • What are the key market trends impacting the growth of the global Big Data Analytics in Manufacturing Industry market?
    • What are the challenges to market growth?
    • Who are the key vendors in the global Big Data Analytics in Manufacturing Industry market?
    • What are the market opportunities and threats faced by the vendors in the global Big Data Analytics in Manufacturing Industry market?
    • Trending factors influencing the market shares of the relevant regions.
    • What are the key outcomes of the five forces analysis of the global Big Data Analytics in Manufacturing Industry market?

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    Key Market Trends:

    Condition Monitoring is expected to register a Significant Growth

    Condition monitoring or the act of monitoring the condition of an asset, especially through real-time data points, forms the foundation of what has become known as Industry 4.0, in its basic form. An integral part of condition monitoring, within the IIoT ecosystem, is providing data that can then be used for Predictive Maintenance (PdM) and many more smart factory applications, such as Digital Twin.

    Big data analytics, especially with predictive analytics, is a growing trend and often prompts discussions around centralizing data across multiple sites, so that the consistency of data is achieved. However, a significant roadblock remains the inability of many customers to convert the flood of new data into actionable information. Big Data systems need to monitor machine failures repeatedly before they can analyze adequately and predict effectively for the future.

    For instance, overhead conveyor systems are used in assembly production lines in the automotive and other manufacturing industries. The failure of single support frames can lead to the disruption of entire production lines. A condition monitoring system based on big data analytics detects the problem at an early stage and, thus, prevents unplanned downtime.

    North America is Expected to Hold Major Share

    North America is among the lead innovators and pioneers, in terms of adoption, for big data analytics in the manufacturing industry, and is expected to hold a significant share over the forecast period. Manufacturing sector adds a lot of value to the US economy. According to Trading Economics, GDP from manufacturing in the United States increased to USD 2125.80 billion in the second quarter of 2018, from USD 2113.80 billion in the first quarter of 2018.

    The manufacturing sector is also forecast to increase faster than the general economy. According to the MAPI (Manufacturers Alliance for Productivity and Innovation) foundation, production will grow by 2.8% from 2018 to 2021. According to the Digital Change Survey done by IFS in 2017, to assess the maturity of digital transformation in a range of sectors, such as manufacturing, oil and gas, aviation, construction and contracting, 46% of the companies in all industries are looking to invest in the big data and analytics.

    American multinational corporation, Intel is finding significant value in big data. The company uses big data to develop chips faster, identify manufacturing glitches, and warn about security threats.

    Report Objectives:

    • Analyzing the size of the Big Data Analytics in Manufacturing Industry market on the basis of value and volume.
    • Accurately calculating the market segments, consumption, and other dynamic factors of different sections of the global Big Data Analytics in Manufacturing Industry market.
    • Determining the key dynamics of the Big Data Analytics in Manufacturing Industry market.
    • Highlighting significant trends of the Big Data Analytics in Manufacturing Industry market in terms of manufacture, revenue, and sales.
    • Deeply summarizing top players of the Big Data Analytics in Manufacturing Industry market and showing how they compete in the industry.
    • Studying industry processes and costs, product pricing, and various trends related to them.
    • Displaying the performance of different regions and countries in the Big Data Analytics in Manufacturing Industry market.

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    Detailed TOC of Big Data Analytics in Manufacturing Industry Market Report 2019-2024:

    1.1 Study Deliverables
    1.2 Study Assumptions
    1.3 Scope of the Study



    4.1 Market Overview
    4.2 Introduction to Market Drivers and Restraints
    4.3 Market Drivers
    4.3.1 Evolving Value Chains
    4.3.2 Rapid Industrial Automation Led by Industry 4.0
    4.4 Market Restraints
    4.4.1 Lack of Awareness and Security Concerns
    4.5 Value Chain / Supply Chain Analysis
    4.6 Industry Attractiveness Porters Five Force Analysis
    4.6.1 Threat of New Entrants
    4.6.2 Bargaining Power of Buyers/Consumers
    4.6.3 Bargaining Power of Suppliers
    4.6.4 Threat of Substitute Products
    4.6.5 Intensity of Competitive Rivalry

    5.1 By End User
    5.1.1 Semiconductor
    5.1.2 Aerospace
    5.1.3 Automotive
    5.1.4 Other End Users
    5.2 By Application
    5.2.1 Condition Monitoring
    5.2.2 Quality Management
    5.2.3 Inventory Management
    5.2.4 Other Applications
    5.3 Geography
    5.3.1 North America
    5.3.2 Europe
    5.3.3 Asia Pacific
    5.3.4 Latin America
    5.3.5 Middle East and Africa

    6.1 Company Profiles
    6.1.1 Fair Isaac Corporation
    6.1.2 Angoss Software Corporation
    6.1.3 Alteryx Inc.
    6.1.4 IBM Corporation
    6.1.5 Microsoft Corporation
    6.1.6 Tibco Software Inc. (Alpine Data)
    6.1.7 SAS Institute Inc.
    6.1.8 SAP SE
    6.1.9 Oracle Corporation
    6.1.10 RapidMiner Inc.
    6.1.11 MicroStrategy Incorporated
    6.1.12 Knime AG




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