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The Big Data Analytics in Manufacturing Industry Market is segmented by End User (Semiconductor, Aerospace, and Automotive), Application (Condition Monitoring, Quality Management, and Inventory Management), and Region.
Fastest Growing Market:
The Big Data Analytics in Manufacturing Industry Market was valued at USD 904.65 million in 2019 and is expected to reach USD 4.55 billion by 2025, at a CAGR of 30.9% over the forecast period 2020 - 2025. 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.
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.
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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 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 is 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.
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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. The 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 2,125.80 billion in the second quarter of 2018, from USD 2,113.80 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 were looking to invest in 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.
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The Big Data analytics in manufacturing industry market is highly competitive and consists of several major players. In terms of market share, few of the major players currently dominate the market. These major players with prominent shares in the market are focusing on expanding their customer base across foreign countries. These companies are leveraging on strategic collaborative initiatives to increase their market shares and profitability. The companies operating in the market are also acquiring start-ups working on Big Data analytics in manufacturing technologies to strengthen their product capabilities. In January 2018, Datawatch completed the acquisition of Angoss Software. This acquisition is expected to help the company to expand its data science capabilities, which will enable the data scientists to perform predictive and prescriptive analytics in a wide variety of enterprise applications.
1.1 Study Deliverables
1.2 Study Assumptions
1.3 Scope of the Study
2. RESEARCH METHODOLOGY
3. EXECUTIVE SUMMARY
4. MARKET DYNAMICS
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 - Porter's Five Forces 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. MARKET SEGMENTATION
5.1 By End User
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
5.3.1 North America
5.3.4 Latin America
5.3.5 Middle East & Africa
6. COMPETITIVE LANDSCAPE
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
7. INVESTMENT ANALYSIS
8. MARKET OPPORTUNITIES AND FUTURE TRENDS
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