Big Data Analytics in Manufacturing Industry Market - Growth, Trends, COVID-19 Impact, and Forecasts (2021 - 2026)
The Big Data Analytics in Manufacturing Industry Market is segmented by End-user Industry (Semiconductor, Aerospace, Automotive), Application (Condition Monitoring, Quality Management, Inventory Management), and Geography.
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Scope of the Report
Key Market Trends
TABLE OF CONTENTS
2018 - 2026
Fastest Growing Market:
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The Big Data Analytics in Manufacturing Industry Market was valued at USD 904.65 million in 2020 and is expected to reach USD 4.55 billion by 2026, at a CAGR of 30.9% over the forecast period (2021 - 2026). 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 can 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 calculating the material stress capability in the automotive industry.
Industries are aggressively trying to adopt the concept of “smart industry,” where the data generation and visualization become real-time. The evolution of analytics from descriptive to predictive analytics has made the industry aware of the benefits it can reap from this data volume. The motto of the manufacturing industry is moving toward a metrics-based sector, which can improve the decision based on the data-driven use of statistics.
Although Big Data analytics results are encouraging, the manufacturing industry has not yet realized the full potential of the technology. This provides good scope to the Big Data analytics in the manufacturing industry to expand in the future. It is estimated that the manufacturing industry has variable efficiency among similar verticals, which has multiple variables to work. For instance, an automotive manufacturer may deal with numerous vendors and suppliers for the different parts required to assemble a vehicle.
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. This shift forced manufacturers to monitor multiple variables to gain a competitive edge over rivals. This led to the data being generated from modern manufacturing environments to double in the past four years.
Among all the end-user industries, the semiconductor is expected to gain the most significant market share due to the rising volume of data at each new process node, where there are more things to keep track of and advanced packages, where there are multiple dies.
The COVID-19 pandemic has affected almost all the countries and industries across the world, with industries, such as manufacturing facing the blunt of the effects. Due to supply chain disruption during the initial months of the spread of March, April, and May, many countries forced lockdown and restricted movement, which influenced various industries, such as automotive and aerospace. For instance, due to factory shutdowns, EU-wide production losses amounted to a decline in production of at least 2,446,344 motor vehicles (as in June 2020; ACEA estimates).
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.
By End-user Industry
Other End-user Industries
Middle East and Africa
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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, primarily 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.
The introduction of various approaches in diagnosing machinery and bearings' health is revolutionizing the machine maintenance activities. With the challenge of low-cost mass production, the current maintenance professionals turn to condition monitoring to succeed in this intensely competitive environment.
Stimulated by its successes in a wide range of applications right from turbine protection to the monitoring of bearings in paper-making machines, monitoring techniques, which enable conveying of current machine status to a centralized server, are enhancing the characteristics of products with a clear eye toward the future and to compete in the global arena.
Online machine monitoring is gaining traction these days since it provides frequent measurements all the time. It is always in place and ready to collect data, especially during transitional states, such as start-ups, commissioning, and coast-downs. This further increases the need for big data analytics
The condition monitoring arena vendors are actively deploying sensors, which is resulting in the growing demand for big data analytics solutions. For instance, in September 2019, Sulzer introduced the Sulzer Sense wireless IoT condition monitoring system. The solution includes wireless sensors attached to a pump, agitator, motor, or any rotating equipment. The sensors measure temperature and vibration and send the data to the cloud.
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Asia-Pacific to Witness Highest Growth
Asia-Pacific's manufacturing sector is going through a transformational phase with smart manufacturing, gaining increasing ground in regional business operations. This is especially true for countries, such as China, India, South Korea, and Japan, which are the key manufacturing hubs of the region and hence, are major adopters of big data analytics.
The technological advancement level among production facilities in developing the Asia- Pacific region remains uneven, as government policies, infrastructure, technical skills, and research and development (R & D) investments vary significantly.
According to new figures released by Microsoft, if the region's manufacturing sector embraces digital transformation opportunities and unlocks the potential of digital transformation, the whole region's GDP can increase by USD 387 billion by 2021.
The pace of automation in China is faster than any other countries globally, as China seeks to modernize its processes and retain its cost advantage in the global market, as its middle-class population soars, and wages continue to rise.
China, traditionally seen as the world's manufacturing factory, has spent significant effort to transform from (cheap) labor-intensive manufacturing to high-end manufacturing through digitalization and industrialization. According to GSMA, China will account for one-third of the global IIoT market by 2025, increasing the need for data analytics to analyze the huge amount of data.
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The Big Data analytics in the manufacturing industry market is moderately competitive and consists of several major players. The major players with prominent shares in the market are focusing on expanding their customer base across foreign countries. They are leveraging strategic collaborative initiatives to increase their market shares and profitability. Some of the recent developments in the market are:
June 2020 - Microsoft Corporation partnered with Hitachi to meet the growing demand for predictive maintenance and process automation in the manufacturing and logistics industries across Southeast Asia, Japan, and North America. Both the companies plan to expand the scope of the collaboration to additional industries.
October 2019 - Erbessd Instruments launched Phantom, a wireless vibration monitoring system that integrates other parameters, such as temperature, current, RPM, and speed into a single diagnosis system. It can send data to a local database or to a cloud-based system. At any moment, the user can keep track of their machinery using any device, such as a smartphone, computer, or tablet.