What are the applications of industrial big data?

The application of industrial big data will bring a new era of innovation and change of industrial enterprises. Through the low-cost perception, high-speed mobile connection, distributed computing and advanced analysis brought by the Internet and the mobile Internet of Things, information technology and the global industrial system are deeply integrated, bringing profound changes to the global industry and innovating the R&D, production, operation, marketing and management methods of enterprises. These innovative industrial enterprises in different industries have brought faster speed, higher efficiency and higher insight. Typical applications of industrial big data include product innovation, product fault diagnosis and prediction, Internet of Things analysis of industrial production lines, supply chain optimization of industrial enterprises, and product precision marketing. This paper will sort out the application scenarios of industrial big data in manufacturing enterprises one by one.

1. Accelerate product innovation

The interaction and transaction between customers and industrial enterprises will produce a lot of data. Mining and analyzing these customer dynamic data can help customers participate in innovative activities such as product demand analysis and product design, and make contributions to product innovation.

2. Product fault diagnosis and prediction

This can be used for after-sales service and product improvement. The introduction of ubiquitous sensors and Internet technology makes the real-time diagnosis of product faults a reality, while the application of big data, modeling and simulation technology makes dynamic prediction possible.

3. The application of big data in production line

Modern industrial production lines are equipped with thousands of small sensors for detecting temperature, pressure, heat energy, vibration and noise. Because data are collected every few seconds, various forms of analysis can be realized by using these data, including equipment diagnosis, electricity consumption analysis, energy consumption analysis, quality accident analysis (including violation of production regulations, parts failure) and so on. First of all, in terms of production process improvement, using these big data in the production process can analyze the whole production process and understand how each link is implemented.

4. Analysis and optimization of industrial supply chain

At present, big data analysis has become an important means for many e-commerce companies to enhance the competitiveness of their supply chains. For example, JD.COM Mall, an e-commerce company, uses big data to analyze and predict the demand of goods in various places in advance, thus improving the efficiency of distribution and warehousing and ensuring the customer experience of the next day's arrival. Electronic identification technology of products such as RFID, Internet of Things technology and mobile Internet technology can help industrial enterprises to obtain complete product supply chain big data. Using these data for analysis will greatly improve the efficiency of warehousing, distribution and sales and greatly reduce the cost.

5. Product sales forecast and demand management

Analyze the current demand changes and combination forms through big data. Big data is a good sales analysis tool. Through the multi-dimensional combination of historical data, we can see the proportion and change of regional demand, the market popularity of product categories, the most common combination forms and the level of consumers, so as to adjust product strategies and distribution strategies.

6. Production planning and scheduling

The manufacturing industry is facing a multi-variety and small-batch production mode. Meticulous automatic and timely data collection (MES/DCS) and variability lead to a sharp increase in data. Coupled with the historical data of informatization for more than ten years, it is a huge challenge for APS that needs rapid response.

Big data can give us more detailed data information, discover the deviation probability between historical forecast and actual situation, consider the constraints of production capacity, personnel skills, material availability and tooling, formulate pre-planned production scheduling through intelligent optimization algorithm, monitor the deviation between planned and actual situation, and dynamically adjust planned production scheduling.

Help us avoid the defects of "portrait" and directly impose the group characteristics on individuals (the data of the work center is directly changed into the data of a specific equipment, personnel, mold, etc.). By analyzing and monitoring data, we can plan the future. Although big data is slightly flawed, as long as it is used properly, big data will become our powerful weapon. At that time, Ford asked what the customer needs of big data were? The answer is "faster horses", not cars that have become popular now. Therefore, in the world of big data, creativity, intuition, adventurous spirit and intellectual ambition are particularly important.

7. Product quality management and analysis

Traditional manufacturing industry is facing the impact of big data. In product research and development, process design, quality management, production operation and other aspects, innovative methods are urgently expected to be born to meet the challenges of big data under the industrial background. For example, in the semiconductor industry, chips will go through many complicated processes, such as doping, adding layers, lithography, heat treatment, etc., and each step must meet extremely demanding physical characteristics. While processing products, highly automated equipment will also produce a large number of test results.

8. Industrial pollution and environmental protection detection

The value potential of industrial big data applications is huge. However, there is still much work to be done to realize these values. One is the establishment of big data awareness. In the past, there were also these big data, but due to the lack of understanding of big data and data analysis methods, many real-time data were discarded or shelved, and the potential value of a large number of data was buried. Another important issue is the problem of data islands. The data of many industrial enterprises are distributed on isolated islands, especially large multinational companies. It is quite difficult to extract these data from the whole enterprise. Therefore, an important topic of industrial big data application is integrated application.

The application of industrial big data will promote industrial enterprises to collect, store and analyze data related to internal and external environment, realize the perception and interconnection between enterprises and internal and external related environment, and use industrial big data analysis technology to carry out mining analysis, support industrial enterprises to make decision-making control based on data, and improve the pertinence and effectiveness of enterprise decision-making control.