Recently, Finesoft held the 4th FineBI Data Analysis Competition, allowing business personnel from various fields to use Finefine's BI products for self-service data analysis, resulting in a large number of excellent analysis cases.
Based on this, Data Ape has planned a series of case analysis topics. In the previous article, we analyzed the user operation strategy of Luckin Coffee. As the second article in a series of articles, we will share the data analysis case of Nanjing Donghua Intelligent Steering System***hereinafter referred to as "Donghua Steering") auto parts quality management.
As one of the key pillars of the global economy, the automotive manufacturing industry is undergoing unprecedented changes in recent years. With the advancement of technology and the evolution of consumer demand, the industry is constantly moving towards intelligence, electrification, and connectivity. Especially in China, the automobile manufacturing industry is ushering in a new wave of growth, driving the development of the global automotive market.
However, the industry is also facing fierce domestic and international competition, increasingly stringent environmental standards, and changing technological innovations. Together, these factors are driving automakers to constantly seek new management and production methods to improve efficiency and reduce costs.
Donghua Steering, as one of the important enterprises in China's auto parts manufacturing, its products cover automotive steering systems and related parts. As a member of SAIC, Donghua Steering enjoys a high position in the market, but also faces pressure from domestic and foreign competitors.
With the diversification of customer needs and the rapid changes in the market, Donghua Steering needs to continuously optimize its product line and production process to maintain a competitive edge. In addition, with the rise of smart cars and new energy vehicles, the quality and performance requirements for auto parts are also increasing, which puts forward higher requirements for the process quality management of Donghua Turning. In this case, some of the previous quality management methods can no longer meet the business requirements.
For example, under the pressure of increased orders and shortened customer lead times, the company's main business needs are focused on improving the efficiency and effectiveness of process quality management. Since the production line is at full capacity for long periods of time, it is particularly critical to monitor the quality of the product and to respond quickly. The current single method of process quality control, coupled with the insufficient accuracy and visualization of production data, leads to the inefficiency of engineers in dealing with problems, which affects the effective output of the production line. In addition, due to the variability of business analysis requirements, the original report development method can no longer meet the needs of rapid and multi-dimensional analysis, resulting in waste of time and cost, and the lag of data analysis.
As a result, there is an urgent need for companies to conduct more in-depth data analysis to improve efficiency. This includes not only accurate analysis and real-time monitoring of existing data, but also the establishment of more flexible and efficient data analysis models. The key to achieving this goal is the adoption of advanced Kanban tools and self-service data analysis methods that reflect the quality level of the production line in real time, enabling engineers to quickly extract data, build analytical models, and handle anomalies in a timely manner. In this way, the company can control the quality of products more effectively, improve production efficiency, reduce costs, and ultimately enhance market competitiveness.
Next, let's take a practical case in the Finesoft Data Analysis Competition to see how business personnel can realize the data analysis of auto parts quality management.
In Donghua Turn, a team called "Donghua Steering Data Analysis Team" undertakes the important task of improving the quality of the process. The team is led by the company's Director of Digitalization, Tang Jingyou, and includes Chen Congshuang, Cao Hang, and He Qi. Their professional backgrounds span big data, artificial intelligence, and other fields, which gives the team a unique advantage when dealing with complex data analysis tasks.
The main task faced by the team is to improve the process quality management through Finesoft BI tools to adapt to the rapid changes in market demand and improve product quality. To this end, the team set a clear goal: through in-depth analysis of data in the production process, quality problems can be quickly identified and resolved, so as to improve the first-pass rate and process pass rate, and ultimately achieve the dual purpose of reducing costs and improving product quality.
Before the formal data exploration and analysis, data processing and data analysis model construction are necessary preparations. The team used Finesoft BI tools to play a key role in the entire process from raw data to data cleaning, to analysis and visualization.
First, they collected a lot of production-related data from the company's Manufacturing Execution System (MES). These data include product processing inspection parameters, the number of offline reports, and the number of qualified products. Since the data was originally scattered in different database tables and the format was not uniform, the team first cleaned and integrated the data on the FDL platform. They set up a data scheduling task to summarize the processing parameters of all production lines into a detailed list of data warehouses, laying a solid foundation for subsequent analysis.
After the data cleansing and integration is complete, the data analysis methodology and model need to be built.
In terms of data analysis, the team has adopted a variety of advanced analytical models and methods, the core of which is SPC (Statistical Process Control) process capability analysis and 5M1E analysis. The team utilizes SPC analysis tools to monitor and control the production process, and they monitor key production parameters such as processing size, temperature, etc. through SPC charts to ensure that these parameters are always under control. By analyzing fluctuations in these parameters, the team was able to identify possible problems in the production process in a timely manner and quickly take action to make adjustments. For example, if a parameter fluctuates beyond a preset control limit, the team immediately investigates to find out the cause of the fluctuation and take action to correct it.
5M1E analysis is a comprehensive approach to problem analysis involving man, machine, material, method, measurement, and environment, through which the team provides an in-depth analysis of the various factors that affect product quality. For example, they check the skills and experience of the operators, the performance and maintenance of the machines, the quality of the raw materials used, the methods and environmental conditions used in the production process, and the accuracy of quality measurements. Through this comprehensive analysis, the team was able to accurately identify the root causes affecting product quality and develop effective countermeasures.
Let's take a look at some of the interesting conclusions that the analytics team has drawn from this data.
In this case, the team demonstrated the great potential of data analysis to improve production efficiency and quality control through the application of Finesoft BI tools. Specifically, they explore the problems and patterns hidden in the data through the analysis of the following multiple indicators and business dimensions:
Index analysis: production line first-pass rate
The first thing the team focused on was the line first-pass rate, which is a macro reflection of the quality level of the line at the current date. Through the visualization function of Finesoft BI, the team was able to clearly show the first-pass rate of different production lines, and set up 85% of the warning lines. They found that the first-pass rate of EPS2 and EPS3 lines was below the warning value, indicating that the quality level of these lines was poor and needed immediate attention and improvement.
Process pass rate analysis
The team further analysed the process yield rate, which is a key indicator of process quality at a more granular level. They distinguish between the first pass rate and the final pass rate, focusing on those processes where the first pass rate and the final pass rate are very different. For example, they found that the E3OP510 process had a first-pass rate of 87% and a final pass rate of 94%, indicating that there was a quality problem in the process.
Defect type distribution analysis
Using the pie chart, the team analyzed the distribution of defect types in the label printing records and found that the static test had the highest percentage of failures, which further confirmed the issue of the pass rate of the E3510 process. In addition, they analyzed the number of process hours pass rate, the number of hours of output, and the number of product types through line charts, and concluded that there was no significant correlation between the pass rate and the number of outputs.
SPC Process Capability Analysis
Once the problem has been located to a specific process, further analysis of the process capability of that process is required. To this end, through Finesoft BI, the team conducted an SPC process capability analysis. They monitor key parameters in the production process and use control charts to reflect parameter trends and distributions. For example, when analyzing the E2OP100 process, they found that the distribution of run-in no-load fluctuation values was skewed, indicating possible quality problems.
5m1e analysis
Next, the team used the 5m1e analysis method to delve into the root cause of the problem, and they conducted a detailed investigation of man, machine, material, method, environment, measurement, etc., and identified problems in technical skills, equipment maintenance plans, and material quality.
These data analysis results have far-reaching business implications for Nanjing Donghua, as they enable the company to quickly identify and respond to quality issues in the production process, thereby improving product quality and customer satisfaction. Second, these analyses help companies optimize production processes, increase efficiency, and reduce resource waste. In addition, through in-depth analysis and improvement, the company was able to reduce costs due to quality issues, such as labor costs for containment personnel and losses due to production delays.
It can be seen that in this case, the application of Finesoft BI tools has significantly lowered the threshold for data analysis and greatly promoted the exploration and practicability of data analysis.
Donghua turned to the data analysis team and told the data ape, "When editing data, the design of adding columns, merging data, grouping and summarizing, and field settings is obviously helpful to reduce the data analysis threshold of business personnel. For example, if a business person needs to change the field type during analysis, click the field header to modify it. After the administrator sets up the association of the data set, the business personnel can directly conduct joint analysis of the related data when using the self-service data set for self-service data retrieval, without the need to sort out and configure the association relationship. ”
Finesoft BI's powerful filtering and dataset association capabilities enable engineers to explore data more flexibly and quickly locate and analyze key factors that affect production quality. For example, during the SPC analysis process, the team was able to filter out outliers that affected the analysis on their own, and intuitively trace quality issues through data correlation. Such a feature not only improves the efficiency of the analysis, but also enhances the accuracy and depth of the analysis.
In short, in this case, Finesoft BI tool is not only used as a tool for data processing and analysis, but also a platform to promote the data analysis thinking and ability improvement of business personnel. Its application not only improves the ability of the quality management team to process complex data, but also helps to achieve significant improvements in product quality and production efficiency, thereby strengthening the overall competitiveness of the company.
The case of Donghua Turn's component quality management analysis through Finesoft BI not only shows the practical application of data analysis in improving process quality, but also reveals the importance of self-service data analysis and data inclusiveness in modern enterprise management and its impact on the future development of the industry.
Donghua turned to the data analysis team believes that the self-service analysis tools greatly save the time of business personnel to obtain information, they can grasp the business dynamics they are most concerned about faster, and the work efficiency is greatly improved. "For example, our quality process personnel, it takes two days to manually calculate IPTV reports in the traditional way, while BI tools only need to drag and drop to generate reports that meet their different analysis dimensions in a few seconds, and business personnel have more time to continuously disassemble the impact factors behind the indicators to form a complete experience base. ”
Furthermore, they believe that the realization of self-service data analysis by business people can bring about a transformation of the entire business process and even the related organizational structure. Self-service data analysis makes it easier for business personnel to access and analyze data, which helps to reduce misunderstandings and information biases, improve collaboration and teamwork within the organization, and promote the optimization of business processes and the improvement of efficiency.
Second, self-service data analysis will affect our existing organizational structure, in which data analysis is usually done by professional data analysts or teams. The self-service data analysis enables more business personnel to directly participate in the data analysis process, further enhance the decision-making ability of business personnel, and promote the reduction of decision-making levels and the flat development of organizational structure.
Data inclusion is about bringing data analysis capabilities to every business unit, so that every employee can participate in the data-driven decision-making process. This inclusiveness not only improves data literacy across the organization, but also fosters a more democratic decision-making process. As Donghua turned to data analysis team, "Self-service analytics can encourage employees to participate in the decision-making and problem-solving process, giving employees more opportunities to express their opinions, make suggestions and contribute innovative ideas, and this open culture and engagement can stimulate employees' creativity and innovation." "In the case of Donghua Turn, through the Finesoft BI tool, data analysis is no longer the exclusive domain of a few experts, but has become a process in which all employees participate. Not only does this change foster broader innovation and improvement, but it also increases team members' buy-in and engagement with business goals.
With the continuous development of BI and data analysis technologies, it is expected that their application in the automotive manufacturing industry will be more extensive and deeper. In the future, we can foresee that data analysis will not only be limited to production quality management, but will also be extended to chain optimization, customer demand, product design innovation and other fields. With the convergence of artificial intelligence and big data technology, BI tools will provide more accurate and intelligent analysis to help enterprises maintain a competitive advantage in a rapidly changing market. In addition, with the spread of cloud computing and IoT technology, real-time data analysis and remote monitoring will be possible, further improving productivity and flexibility.