First, 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. Ford is an example in this respect. They applied big data technology to the product innovation and optimization of Ford Focus electric vehicle, and became a veritable "big data electric vehicle". The first generation Ford Focus electric vehicle will generate a lot of data when driving and parking. During driving, the driver constantly updates the acceleration, braking, battery charging and position information of the vehicle. This is very useful for drivers, but the data will also be sent back to Ford engineers to understand the driving habits of customers, including how to charge, when to charge and where to charge. Even if the vehicle is stationary, it will continue to transmit the data of vehicle tire pressure and battery system to the nearest smartphone.
This customer-centric big data application scenario has many benefits, because big data realizes valuable new product innovation and collaboration methods. Drivers get useful and up-to-date information, while engineers in Detroit collect information about driving behavior to understand customers, make product improvement plans and implement new product innovations. Moreover, power companies and other third-party suppliers can also analyze millions of miles of driving data to decide where to build new charging stations and how to prevent the fragile power grid from being overloaded.
Second, the equipment fault analysis and prediction
In the manufacturing production line, industrial production equipment will be subjected to continuous vibration and impact, which will lead to the wear and aging of equipment materials and parts, thus leading to industrial equipment failure. When people realize the fault, they may have produced many defective products, and even the whole industrial equipment has collapsed and stopped, causing huge losses.
If the failure can be predicted before the failure occurs, and the parts that are about to have problems can be repaired and replaced in advance, the service life of industrial equipment can be improved, and the sudden failure of a certain equipment can be avoided, which will have a serious impact on the whole industrial production. With the arrival of Industry 4.0, the industrial equipment in smart factories are equipped with various sensors, and it is easy to collect data such as vibration, temperature, current and voltage. By analyzing these real-time sensor data, it will be an effective means to predict the failure of industrial equipment.
Therefore, the equipment fault prediction scheme has become a favored solution in manufacturing industry, and its core function is:
1, fault early warning, reducing equipment downtime;
2. The analysis results are pushed in real time to reduce the labor cost;
3. It is suitable for various types of equipment in enterprises and has strong versatility.
Third, the big data application of industrial Internet of Things 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. Once a process deviates from the standard process, it will generate an alarm signal, which can find errors or bottlenecks faster and solve problems more easily. Using big data technology, we can also establish a virtual model of the production process of industrial products to simulate and optimize the production process. When all process and performance data can be reconstructed in the system, this transparency will help manufacturers improve their production processes. For another example, in terms of energy consumption analysis, using sensors to monitor all production processes in the production process of equipment can find the abnormal or peak energy consumption, thus optimizing the energy consumption in the production process, and analyzing all processes will greatly reduce energy consumption.
Fourth, product sales forecast and demand management
In recent years, the insurance industry has accelerated the digitalization process, and the deep integration of big data and insurance marketing has become an important weapon of modern insurance marketing. Huidu Big Data helps the insurance industry to accurately market, and successfully helps Sino-Italian Life Insurance Co., Ltd. to better serve customers and give play to loyal customers, and improve sales efficiency and customer repurchase rate.
Verb (abbreviation of verb) 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.
Production planning and scheduling of intransitive verbs
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.
Seven, production quality analysis and prediction
In industrial production, equipment failure, personnel negligence, abnormal parameters, raw material differences, environmental fluctuations and other factors lead to quality deviation, resulting in huge defects and losses in quality grades. In large-scale manufacturing industries with complex processes, such as steel, automobile, electronics, clothing and other industries, information and data islands are prominent, leading to frequent quality problems. In particular, it is necessary to "discover and predict anomalies in time, quickly control and analyze the causes of quality anomalies, improve production processes, stabilize production processes, and reduce product quality fluctuations".
Production quality analysis, from factory order-order production-flow into the market, to conduct a comprehensive quality analysis of the whole production chain. Among them, the data of quality, people, machines, materials, methods and environment are opened, and all production data are interlocking, focusing on the total data analysis of quality management to help enterprises quickly explore the root causes of defects.
1. Get through the relationship between quality and people, machines, materials, methods and environment, conduct interactive analysis on all data that affect quality, explore the mutual relationship, dig out the real reasons behind the data, get the result "what" and answer "why".
2. Change the traditional static reporting mode into interactive dynamic meetings, and organize special meetings related to production and quality anytime and anywhere. By showing the production and quality KPI in the dimension, we can make an early warning and grasp the running status of the production line in real time.
3. Easy-to-use quality analysis tools, employees only need to select and drag data, and they can flexibly achieve the desired data results.
4. Abandon the static data reports in the past, integrate multiple business system data, large-screen multi-scene data and adaptive multi-screen, and conduct comprehensive display and analysis to make the decision clearer.
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