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Mobile WebSiteA scene is a small closed loop, including data collection, processing and application. However, we can often hear problems such as "don't know what data to collect" or "do not know how to use the data" and so on. This means that at the level of production, operation and management, there are still obstacles in how to achieve data-driven.
Industrial Internet of Things is not only a methodology for solving congestion points, but also a tool or solution with all controllable costs, technologies, and applications. To unearth the "secrets" of industrial IoT data, this needs to be combined with business scenarios. As the number of small closed loops becomes more and more, it will drive the deepening of the enterprise industrial Internet from a single point to a local or global level.
Industrial Internet of Things and Data Driven
At present, many companies generally have basic information applications, such as digital R&D, process control, etc., and the operating status of the company can be observed through the dimensions of weekly and monthly reports. However, after an enterprise reaches a certain scale and develops to a certain stage, it needs more and more detailed data behind the business process. At the same time, the enterprise's requirements for inventory and equipment utilization will also be significantly improved to achieve refined management , Improve management level.
A recycled material processing company whose production is mainly to smelt scrap aluminum materials such as cans to form aluminum ingots. The biggest problem for this company is that it has a large amount of scrap backlog and aluminum ingot inventory. The company must pull through sales and production data to reduce inventory occupation; second, in the aluminum smelting process, it is necessary to increase rare metals to form alloys. The traditional method is repeated modulation. Laboratory tests lead to higher energy costs. The company realizes data collection through the Industrial Internet of Things and realizes one-time modulation, thereby improving efficiency and reducing costs.
In this case, what data is needed and where the data comes from. This is the primary problem to be solved by data-driven. For industrial manufacturing companies, the organization of equipment, raw materials and other elements = arrangement of production. Therefore, production data and equipment data are the primary objects of collection.
Production data is generally data related to the production line. One is material and output data, such as material picking, workpiece processing, product processing quantity, etc.; the second is process data or process quality data; the third is energy consumption data, including water, electricity, Resource consumption such as wind and gas; fourth is equipment operation records, such as start-up, suspension, alarm, maintenance, etc.; fifth is personnel, shift, attendance and other data.
Most of the production data has been uploaded to the MES system to realize automated production scheduling, which is an important base of the digital factory. Furthermore, "based on the industrial Internet of Things, it is particularly important to realize the acquisition and utilization of equipment data." said Li Wanxiang, an expert on the Internet of Things of UFIDA. One is that all production activities are related to equipment, which is a heavy asset of an enterprise; the other is that refined management requires data to be sinking to the equipment layer.
Collect all kinds of online data and equipment through the Industrial Internet of Things to grasp the changes in production and ensure production. Of course, data collection is often dry, and how to drive business must be integrated with business. Li Wanxiang believes that in the Industrial Internet of Things, data-driven businesses can be refined into the following categories.
Rapid synergy of production. For example, in the coordination of the supply chain of car companies, during the assembly process, every time a component is used, there will be a clear record on the material rack, and the material information will be synchronized to the supplier to ensure that the supplier can make ingredients according to a certain time node to ensure the production line Continuous production.
Process optimization, this is an important application of the Industrial Internet of Things. For example, in the process of processing metal products, companies will collect data such as machine tool speed according to technological requirements. If the surface finish of the product needs to be improved, it is often necessary to adjust the equipment data. After the process data is determined, it is issued as a standard process to improve the processing quality.
Realize energy saving and consumption reduction. Driven by the "dual-carbon" goal, many companies currently use technological means to achieve business transformation. For example, companies collect energy consumption data of high-energy-consuming equipment, combine energy-saving technologies to reduce energy consumption, and calculate the reduction of corporate carbon emissions. In addition, after data collection, the enterprise associates with the business to drive business adjustments.
AIoT: Innovative business scenarios activate data assets
Logically, the Industrial Internet of Things opens up data islands through the interconnection of underlying systems and devices, realizes the digital integration of enterprises, and serves as a platform for continuous iteration to help enterprises achieve the integration and integration of underlying data. At the same time, it is also a middleware system that connects lower-level industrial equipment and upper-level business systems, helping to achieve loose coupling of enterprise application services, making equipment smarter and more precise in production.
Li Wanxiang pointed out that for small and medium-sized enterprises, if the progress of the production line cannot be known in time, or the process is not stable, and the one-time pass rate of the product is very low, it is necessary to implement the industrial Internet of Things. And if the company produces intelligent products, or needs to do remote operation and maintenance after the product is sold, or needs to make a production plan for consumables based on equipment operating data, these are more necessary to do industrial Internet of things.
In the discrete industry, cutting tools are a heavy consumable, which is an important value point of the Industrial Internet of Things. By collecting tool processing data for analysis, it helps companies and tool suppliers to jointly confirm the material, shape, and size of the tool, so as to achieve the most products with the least amount of money. Another example is AGV collaboration, which reduces on-site manual operations by connecting with equipment and robots.
For manufacturing companies that already have a high degree of on-site automation, there are two directions in which industrial IoT can be practiced. One is equipment diagnosis and maintenance based on equipment data. Because the production site has been unmanned, the comprehensive and stable operation of the equipment is a prerequisite for production stability. At the same time, the company must train a team based on the equipment data to understand the equipment data and diagnose the equipment. The second is the optimization of the process. A high degree of automation means that there will be no problems with the production cycle. Enterprises need to collect process data for process optimization, reduce energy consumption, improve quality, etc.; and then develop next-generation production lines based on these data.
An auto parts company has more than 500 production lines, and has built the Industrial Internet of Things through a batch connection strategy. The company's first concern is the accuracy of production beats. The second is the utilization rate of the equipment, because the equipment value is high, and the insufficient utilization rate is a huge waste. The third focus is on tools. Based on data collection and analysis, you can know the use and capacity of a tool on the equipment, and use this as a basis to settle with the tool supplier instead of buying it directly.
A large manufacturing company found that its equipment utilization rate was not high. After the Industrial Internet of Things project was launched, the data showed that the equipment efficiency was the lowest between 8 am and 9 am every day, and the equipment was basically not started. Through analysis, the company found the problem: After the night shift processing is completed, the workpiece is often left to the day shift the next day for processing. After working during the day shift, the workpiece needs to be hoisted from the machine tool to load a new workpiece. Therefore, at this point in time, the workers are all looking for a crane and unloading the workpiece. Based on this, the company optimized management and improved equipment utilization.
Of course, mining the business secrets in the big data of the Industrial Internet of Things. From a functional perspective, an industrial Internet of Things platform carries three major contents: data storage and release, data visualization display, and data processing and circulation. When artificial intelligence and the Internet of Things are integrated in more and more scenarios, AIoT has become an inevitable trend. Artificial intelligence + Internet of Things is indispensable for innovative manufacturing scenarios and the value of industrial data.
Li Wanxiang introduced that UFIDA’s YonBIP|AIoT intelligent IoT platform has been deeply applied in many scenarios such as production management, safety production, industrial chain coordination, and scrap determination, allowing industrial data to speak.
Shanghai Xinpeng Lianzhong uses the UFIDA Smart Industrial Internet platform to open up the factory's equipment data and management data, and realize the full connection of people, machines, materials, methods, and the environment. The per capita output value has increased by 79.8%, and the defect rate has decreased by 2‰. Sichuan Atlantic Welding Materials Co., Ltd., through the transformation of intelligent production lines, can count the OEE of production equipment and production energy consumption in real time. Production personnel are reduced by 50%, and comprehensive energy consumption is reduced by 30%. Jiyuan Iron & Steel launched machine vision to identify scrap steel, not only the judgment result is objective, fair and traceable. It can also be seamlessly connected with the measurement system and financial system, so that the data does not fall, and the annual cost is estimated to be 20 million yuan. Jiangtong Guiye Metallurgical Co., Ltd. is based on the platform to accumulate industrial big data, and through UFIDA's smart industrial brain, it provides intelligent algorithm construction and optimization services, and successfully builds an intelligent production dispatching command center.
Over the years, data as an asset has become a consensus. However, the value measurement, conversion and even transaction of data assets are not smooth. As the central document incorporates data into the scope of factors of production, data is an asset and a factor of production is gradually being accepted by enterprises. Based on AIoT, companies can not only quickly collect data that has never been obtained, but also activate data assets to realize the value-added of all factors of production.
