Role analysis of industrial big data cloud platform

Under the background of the continuous integration of informatization and industrialization, information technology has penetrated into all links of the industrial chain of industrial enterprises, and the amount of data owned by traditional industrial enterprises is increasingly rich. Big data analysis has brought a new dimension of manufacturing industry research and trend analysis. From the perspective of new multi-dimensional functions and expanded fields, data has become the compass leading the growth of manufacturing industry.

The fundamental strength of big data analysis lies in the quality of data, and the source of data naturally becomes the top priority. At present, the mass data faced by manufacturers is overwhelming. So, what kind of value is hidden behind these massive industrial data? Nowadays, industrial big data analysis is no longer limited to expressing the past situation, but more used to predict the future situation, so as to avoid risks, deepen the understanding of the gradually extending value chain, and improve the user experience.

▍ what is the data source?

Massive data is generated externally, internally or through the interaction between machines. Similarly, it is these data that provide manufacturers with all the information they need to understand customers, products, processes, employees and equipment.

External data source: build user data through user groups, social media, interest groups or survey reports; The neutral data collection platform provided by the third-party survey report, website and call center can also be used to build accurate user and demand documents, including subjective personalized attributes, such as color, design preference, common purchase motivation and evaluation criteria.

Machine to machine: intelligent sensors and the Internet of things can directly collect data from machines and devices and transfer it to other enterprise application platforms. The built-in low-cost sensor can detect a large amount of information, including position, weight, temperature, vibration, flow rate, humidity and balance. These data monitored from time to time can be used to confirm and predict the performance problems of the equipment and judge whether it needs service, repair and replacement. Through these, the manufacturer can find the possible problems early and take measures to prevent the accidents before they occur.

▍ what do you do with data?

For many years, predicting customer trends, preparing inventory, and maintaining sufficient supply have been the primary factors for manufacturers to consider. However, with the increasing importance of supply speed and timely delivery, the ability to accurately predict future demand is also enhanced. Therefore, it is increasingly critical to select which or which of the most suitable influencing factors. Obviously, in this case, a single data source is definitely not enough to meet the current situation.

The activity of forecasting and analysis has effectively transformed the data from a large number of sources into a blueprint for future action with practical guiding significance. At the same time, modern business intelligence solutions can also provide high-accuracy prediction trends. As in any data initiative, the input results cannot exceed the output. Therefore, for manufacturers, if they want to extract specific influencing factors from massive data as the best guide for future actions, they must carefully select reliable data sources.

Predictive analysis, making data valuable. The good prediction ability brings many benefits to manufacturers, such as ensuring that all employees are ready, better planning the real-time material inventory level, and accurately understanding the product life cycle. Similarly, forecasting customer demand has greatly strengthened the market competitiveness of manufacturers, enabling them to launch new products in the highly competitive market ahead of their competitors, and to take the lead in the market dominant competition.

A good start is half success. After taking the lead, successful products will play a more important role in the next competition. The innovation of successful products depends on the accurate interpretation of market preference and demand by manufacturers to a large extent. Design engineers need to understand the pain points of users, so as to measure the potential value of new products and help determine the direction of R & D investment. Big data is the key to achieve this.

▍ what can big data bring?

The answer is: provide a good return on investment and promote the growth of enterprise business.

How can big data provide a good return on investment and promote business growth? Manufacturers must answer this question if they want to make full use of the potential of big data. Big data is like a compass. It provides direction, but it cannot increase sales or win more customers out of thin air. Whether it is the data of machines collected through the Internet of things or the customer data from online websites, data collection is not the ultimate goal. Data must be translated into action to be valuable. It is this transformation process that requires careful study of details and in-depth understanding of relevant data. This is precisely what many manufacturers lack in their big data strategy.

Through careful analysis, data can be fully utilized to recognize, analyze and cultivate opportunities to help manufacturers determine new target geographical areas, expand suitable markets, mine customers, build good customer relations, innovate, optimize product life cycle, improve added value and improve profit space.

Cloud platform can help enterprises realize all this

Product marketing: the big data analysis results provide targeted marketing, targeted R & D, intelligent maintenance and other services for manufacturing enterprises.

Equipment remote fault diagnosis and analysis: big data predicts the time when equipment may fail in the future, provides solutions to avoid risks, and eliminates the losses caused by equipment failure and shutdown.

Customer experience: establish an enterprise publicity platform on the mobile end, let customers participate in product cognition in a scenario based manner, and increase the brand communication effect.

Technical innovation: improve the equipment management level in the field of operation and maintenance and reduce the industrial operation cost with the help of the sharing of expert experience and the establishment of intelligent decision base on the platform.

Saving efficiency: through data set segmentation and rule search, help to find the optimal data set, and realize energy saving and efficiency improvement of personnel input and control process.