[China Security Exhibition Network Market Analysis] Big data is entering people's vision in the form of a revolutionary storm. Its technology and market are developing rapidly, and the call for mastering big data is getting louder and louder. So some people say that China's big data industry is suspected of being overhyped, while others believe that it is the right time to invest in big data. With the continuous acceleration of the national industrial informatization process in recent years and the continuous evolution of the international community in industrial modernization, Industry 4.0 and other aspects, big data technology has also undergone relatively deep technological and application integration in the industrial sector and manufacturing. Let's talk about the application of big data in the above-mentioned fields.

In recent years, problems such as labor shortages, rising wages, short product delivery periods and significant fluctuations in market demand have emerged, presenting the manufacturing industry with a new wave of transformation challenges. The main purpose of transformation is to control production costs while also enhancing productivity and efficiency. Against this backdrop, manufacturing developed countries such as Germany and the United States are all actively promoting "Industry 4.0".

"Industry 4.0" essentially integrates the data from the equipment sensing and control layers of factories with enterprise information systems through cyber-physical systems, enabling production big data to be transmitted to cloud computing data centers for storage and analysis, forming decisions that in turn guide production. The role of big data is not limited to this. It can permeate every link of manufacturing to play a role, such as product design, raw material procurement, product manufacturing, warehousing and transportation, order processing, wholesale operation and terminal retail.

Big data improves the way orders are processed

As we all know, no matter in which industry big data technology is applied, its most fundamental advantage lies in its predictive ability. Users can accurately understand various data such as market development trends, user demands, and industry directions by leveraging the predictive power of big data, thereby formulating more suitable strategies and plans for the development of their own enterprises. Through the prediction results of big data, enterprises can obtain the quantity of potential orders and then directly proceed to the design and manufacturing of products as well as subsequent stages.

That is to say, enterprises can process orders before customers place them through big data technology. Traditional enterprises, through market research and analysis, obtain a rough estimate of customer demand, and then start to produce and process products. Only after customers place orders do they begin to handle the orders. This greatly extends the production cycle of the product. Nowadays, many enterprise users in the manufacturing industry have begun to utilize big data technology to conduct big data analysis on sales data, which is highly beneficial for enhancing corporate profits.

Big data beats traditional warehousing and transportation

As big data can accurately predict the demands of individual consumers and their expectations for product prices, enterprises can directly deliver the products to consumers after they are designed and manufactured. Although consumers have not placed orders yet at this point, it is highly likely that they will eventually accept the product. This makes the enterprise does not have the problem of excess inventory, there is no need for warehousing transportation and wholesale operation.

Industrial procurement has become more precise

Big data technology can acquire knowledge and predict trends from data analysis, and can merge and match the supply and demand information of raw material procurement for enterprises on a larger scale, with higher efficiency. Big data, through a highly integrated approach, gathers information from relatively independent departments of an enterprise, breaking down the original information barriers and achieving intensive management.

Users can more scientifically arrange the enterprise's expense expenditure based on the priority of each link in the process. Meanwhile, by leveraging the massive storage of big data, the attached attributes of the purchased raw materials can be described and certified in a more refined manner. Through classification labels and correlation analysis, the expenditure effect of the enterprise's procurement funds can be better evaluated.

Big data makes product design more optimized

With the help of big data technology, people can monitor the quality of raw materials, detect potential problems and issue early warnings immediately, so as to solve problems early and maintain product quality. Big data technology can also monitor and predict the future failure probability of processing equipment, so that engineers can make the most appropriate decisions in a timely manner. Big data technology can also be applied to accurately predict the life cycle of parts and offer suggestions at the best time for replacement, helping manufacturers achieve a win-win situation in terms of quality and cost.

For instance, Honda, a Japanese motor company, has applied big data analysis technology to the batteries of its electric vehicles. Since electric vehicles do not use gasoline as their power source like cars or hybrid vehicles, and their sole power source is the battery, Honda hopes to further understand under what circumstances the battery performs best and has the longest service life. Honda can collect and analyze some information about vehicles during driving through big data technology, such as: Road conditions, the driving behavior of car owners, and the environmental conditions during driving, etc., on the one hand, can help car manufacturing companies predict how long the battery life is left, so as to remind car owners to replace it in time. On the other hand, they can also be provided to the R&D department as a reference for future battery design.