Smart factory is based on digital factory, which uses Internet of Things technology and equipment monitoring technology to strengthen information management and service, clearly grasp the production and marketing process, improve the controllability of the production process, reduce manual intervention of the production line, collect production line data immediately and correctly, and make reasonable production plan and production schedule. Smart factory includes five aspects of factory operation management, including manufacturing resource control, on-site operation supervision, logistics process control, production execution tracking and quality work supervision. By integrating MES, QMS, ERP, SCM and other systems, the intelligent factory management platform of enterprises is built by docking the sensor data of automation equipment, and the unification and digitalization of manufacturing management are realized.
Manufacturing resource control mainly refers to the management of people, machines, materials and other related production resources in the manufacturing process.
It involves the automatic generation of BOM, the acquisition of raw materials and accessories, the management of semi-finished products and side warehouses, and the input and output of finished products. It is necessary to monitor and analyze the material matching rate, material loss rate, semi-finished product turnover rate, input-output ratio, recovery rate and other indicators to ensure that manufacturing resources are in place in time, efficient circulation, and consumption reduction and reconstruction; Equipment efficiency has a great influence on manufacturing resources. It should be controlled from four aspects: equipment inspection, fault management, spare parts management and technical files. The management mode of one person, one thing and one yard should be realized by using electronic code scanning technology, and the accurate management system of enterprise equipment whole process should be constructed.
The development of automation technology has promoted the birth of unmanned factories, but unmanned factories have great limitations and are not suitable for many enterprises. Therefore, at present, personnel is still one of the cores of manufacturing resources. Through the combination of process improvement, production planning and scheduling, and personnel scheduling management, the purpose of optimizing production efficiency and maintaining production rhythm can be achieved.
On-site operation supervision is the digital transformation of 7S management. On the one hand, the sensor-based real-time data acquisition system is used to complete the collection of environmental data, equipment operation parameters and status data, and productivity data of key positions in assembly line operation, which solves the problems such as the lag of the original 7S management data collection and the errors caused by manual collection.
On the other hand, the use of video surveillance and image recognition technology to realize the early warning and push of abnormal situations such as equipment downtime, conveyor belt blockage, product backlog and employee turnover is a powerful basis for 7S management scoring;
Finally, through the data analysis software FineBI, the data of the production system and the above-mentioned collected data are compared and analyzed in multiple dimensions to assist the production managers to make effective decisions. Logistics process control includes supplier delivery, factory internal turnover and customer delivery.
Using the Internet of Vehicles technology and big data processing technology, the real-time geographic location and trajectory data of logistics vehicles are collected in real time, so as to complete the logistics process control of suppliers and customers; Using AGV car to realize automatic receiving, semi-finished product turnover and finished product warehousing, thus creating unmanned sorting,
The intelligent warehouse operation system with intelligent handling greatly improves the turnover efficiency of factory logistics.
Production execution tracking is the real-time monitoring of the execution process of production plan and the management decision of execution results. Combining MES system and data analysis tool FineBI, managers at all levels can keep abreast of production trends, including attendance, planned production progress, planned completion rate and efficiency. , and realize the online analysis and closed-loop follow-up of production anomalies, optimize the data extraction and analysis mode, reduce the burden and empower, manage in advance, and establish a problem discovery and hierarchical management mechanism.
Quality supervision includes three links: incoming quality control, process quality control and delivery quality control. The key points are quality planning, quality inspection, quality assurance, quality supervision, quality improvement, quality service, system and process. Using coding technology to realize batch control of products and materials and reduce the cost loss caused by batch quality problems. At the same time, SPC method is used to analyze the process capability and quality control level to ensure the product quality fluctuates within a reasonable range.
In the process of building smart factories, different business activities derive different information functional requirements, and different functional requirements promote the development of different new technologies. The combination of business, function and technology forms the application scenario of smart factory. Based on the contents of the five modules of the above-mentioned intelligent factory management platform, Pan Soft extracts four application scenarios of intelligent factory.
The management of smart factories puts forward higher requirements for the comprehensive management of industrial parks. Due to many management tasks, many modules in traditional industrial parks are managed separately, which makes it impossible to achieve unified coordination of resources, and many data are not online in real time, which greatly increases the management difficulty.
Smart factories require real-time online unified management of video surveillance, security alarm, personnel inspection, access control attendance, visitor management, one-card management, parking spaces, conference rooms, information release, energy use, environmental changes and equipment parameters. Enterprises can use sensor technology to realize dynamic snapshot, thermal imaging alarm, face recognition, temperature and humidity sensing and so on. Then use OA or report system to realize online patrol inspection, information release, online reservation of conference room, online registration of visitors, etc. And call system data and sensor data through micro-service interface to form the overall management index of the park. Finally, the global management model of the smart park is developed by using 3D modeling technology or the comprehensive management cockpit of the park is made by using data analysis tools to realize the unified and efficient management of the park resources and create a green, efficient and safe smart park.
Logistics has always been a weak link in factory management. Most manufacturing enterprises rely on third-party logistics companies to transport products and raw materials, but they lack powerful and effective means to manage third-party logistics institutions, which leads to inaccurate control of customer delivery time and inability to trace the real reasons for abnormal logistics.
Enterprises can save the real-time geographical location information of logistics vehicles based on the Internet of Vehicles technology, and then use big data processing technology to monitor the running status of all logistics vehicles in real time, and give real-time alarms for parking overtime, not driving according to the prescribed route, abnormal speed and so on. For sending and receiving abnormal orders, we can trace the historical trajectory and parking records of logistics vehicles, realize refined, dynamic and visual management of all aspects of logistics, improve the intelligent analysis and decision-making ability of logistics system and the execution ability of automated operations, and improve the efficiency of logistics operations.
Application Scenario 3: Trinity Manufacturing Supervision Platform
With the improvement of internal production process management ability of manufacturing enterprises, there is a demand for upstream and downstream manufacturing supervision and management. On the one hand, it is a further extension of supplier's raw material quality control, on the other hand, it pays more attention to the embodiment of customer satisfaction. The trinity manufacturing supervision platform from supplier to factory to final customer is the core application scenario of smart factory.
In order to meet the access needs of large customers, enterprises can use micro-service technology to extract production process data and job videos through interfaces, and at the same time, use data analysis platform to provide customers with product factory data with analysis results, and open them to corresponding customers through rights management, so as to quickly respond to customers' demand for data docking and remote factory inspection on manufacturing supervision platform, and enhance customers' trust in products.
The supervision and management of suppliers need to start from four aspects. First, by docking the sensor data of its production line equipment, we can master the equipment parameters of the supplier in the production process, which is convenient for the abnormal tracing in the later period; Second, access to production monitoring video to realize real-time monitoring of suppliers' production operation and improve management; Thirdly, getting through the supplier's production information system and mastering the execution progress and quality of the supplier's order can effectively predict the order risk; Fourthly, develop data reporting interface, collect the temporarily scattered data of suppliers in a timely and standardized manner, and improve the collaborative ability.
The traditional quality management method is only limited to monitoring the data of the product production process at that time. When the batch quality is abnormal, the bad batch cannot be effectively locked, and the abnormal material cannot be traced back to which finished product, which increases the quality processing cost and control difficulty.
Quality traceability can help enterprises to realize production process and quality management in real time, efficiently, accurately and reliably. By combining bar code automatic identification technology, serial number management idea and bar code equipment, the relevant information and data of products or materials in production and logistics operations are effectively collected, and the inspection results, existing problems, names, time, place and situation analysis of operators and inspectors after each working procedure or work is completed are recorded. In order to track the whole process of circulation movement in the product life cycle, the enterprise can realize the goals of tracking and monitoring materials in mining, sales and production, traceability of product quality and tracking of goods in sales.
Finally, a closed-loop management platform for quality planning, process control, problem discovery, exception handling, management decision-making and problem closure is established by using data analysis tools, and an experience base and analysis report are formed to support enterprises to build a set of quality closed-loop traceability system with traceable sources, traceable destinations and traceable responsibilities.
The application of smart factories goes far beyond this. With the birth of new technologies and new ideas, smart factories will have new forms of expression in the new era. Managers of manufacturing industry should grasp the new situation and accelerate the pace of building smart factories by integrating automation and management informatization.