製造業巨量資料分析的全球市場 - 成長性，趨勢，及未來預測
Big Data Analytics in Manufacturing Industry Market - Growth, Trends, and Forecast (2019 - 2024)
|出版商||Mordor Intelligence LLP||商品編碼||546582|
|出版日期||內容資訊||英文 100 Pages
|製造業巨量資料分析的全球市場 - 成長性，趨勢，及未來預測 Big Data Analytics in Manufacturing Industry Market - Growth, Trends, and Forecast (2019 - 2024)|
|出版日期: 2019年04月01日||內容資訊: 英文 100 Pages||
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.
The 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
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.
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 a prominent share 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 share and increase their 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 has completed the acquisition of Angoss Software. This acquisition is expected to help the company to expand data science capabilities, which will enable the data scientists to perform predictive and prescriptive analytics in a wide variety of enterprise applications.