Big Data Analytics in Manufacturing Industry Market 2018 Observed at 38.62% CAGR Growth | Global Key Players Review - SAP, TIBCO SOFTWARE, MICROSOFT CORPORATION, SAS INSTITUTE, IBM, ORACLE CORPORATION, RAPIDMINER, ANGOSS SOFTWARE CORPORATION

The global Big Data analytics in manufacturing industry market is expected to register a CAGR of 38.62 %, over the forecast period (2018 - 2023).
It is estimated that the data generated in a day in current global scenario is equivalent to the data generated in last decade. To handle such huge amounts of data, Big Data has often proved to be a useful tool. With the concept of Industry 4.0 shaping the production establishments in the modern manufacturing industry, the amount of data produced from the manufacturing sector grew rapidly.

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Many application in the field of semiconductor, pharmaceutical, and automotive industry where manufacturers have to monitor several variables to ensure the quality of end products, Big Data analytics proved to be more fruitful than the traditional methods. Although the results of Big Data analytics are encouraging, the manufacturing industry has not yet realized the complete potential of the technology. This provides good scope to the Big Data analytics in the manufacturing industry to expand in the future.

Key Developments in the Market
• February 2018 - RapidMiner announced the immediate availability of their products RapidMiner 8.1 and RapidMiner Auto Model. These models were released as a new addition to RapidMiner Studio, a prominent machine learning tool.
• December 2017 - Knime AG announced the release of KNIME Analytics Platform 3.4. The model is set to support machine learning integrations and cloud connectivity. These additional capabilities are expected to give a competitive edge to the company over the rivals.

The major players include –
SAP, TIBCO SOFTWARE, INC. (ALPINE DATA), MICROSOFT CORPORATION, SAS INSTITUTE, INC., IBM CORP., ORACLE CORPORATION, RAPIDMINER, INC., ANGOSS SOFTWARE CORPORATION, and KNIME AG, amongst others.

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Evolving Value Chains to Drive Demand in Manufacturing
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 traditional value chain to technology, asset, and engineering-oriented value chain.
This shift forced manufacturers to monitor multiple variables to gain a competitive edge over rivals. This led to the data being generated from the modern manufacturing environments to double in the past four years. Since the traditional applications or software are proving to be obsolete to handle or analyze such large amounts of data, Big Data analytics are becoming more popular in the manufacturing industry. According to a study conducted by Forbes, 89% of business leaders in the world believe that Big Data will revolutionize business operations in the same way the internet did and are pursuing Big Data projects in order to seize a competitive edge.

Condition Monitoring Stands to be Top Priority
Among all the different applications that Big Data analytics is used for in the manufacturing industry, condition monitoring proves to be growing at a faster pace. Before the era of Industry 4.0, the Big Data analytics were more popular with the product quality management applications in the manufacturing industry. However, with pre proven machine efficiencies, industrial standards, and government regulation the modern industrial machinery are estimated to perform to their maximum capacity if they are in good condition.
This is encouraging manufacturing companies, especially in the automotive, energy and metal processing sectors, to invest more on machine condition monitoring applications of Big Data analytics to detect the risk of any potential failure of the machines. According to a recent study conducted by Enterprise Irregulars, a diverse group of practitioners, consultants, investors, and analysts who are specialized in enterprise technology and its application to business, more than 65% of manufacturing enterprises are focused on monitoring assets to identify operating issues. It is estimated that already 58% of the respondents have capabilities to collect operating data and analyzing the data to produce insights, which create huge opportunities for the Big Data analytics in the manufacturing industry market.

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This release was published on openPR.