Intelligent Big Data Analysis System
We have developed a Big Data Analysis DAS system based on years of project experience, incorporating the needs and prior knowledge from different partners. This system is customized for our MES, utilizing standard production line data. The system modules quickly analyze and correct factors such as production line capacity, quality defect causes, equipment performance, and potential accident risks.
Analyze finished product data with quality issues based on time, material batch, process, equipment, and environmental parameters to quickly identify root causes, reducing the workload for process personnel.
Perform overall OEE performance analysis of the production line based on time, equipment, and batch dimensions, and break down OEE details to quickly identify bottlenecks and their causes, providing guidance for on-site improvements to the manufacturing department.
Conduct a comprehensive analysis of on-site equipment based on time, personnel, equipment, capacity, and abnormal alarms, quickly identifying equipment defects and risks. This helps MK rapidly improve on-site production capacity and mitigate risks in the equipment manufacturing process.
Analyze the utilization across multiple devices within the same process unit, identifying devices with abnormal or insufficient utilization. Quickly locate the devices deviating from normal utilization based on factors such as the equipment itself, material reasons, and environmental factors, enabling prompt corrective actions.
Perform a closed-loop analysis of all workstations on the production line, including equipment, personnel, logistics, storage, material input, and other processes. Identify the bottleneck in each segment and further analyze whether it is caused by equipment issues, raw material problems, personnel factors, or environmental conditions. Quickly locate the issue and address the bottleneck efficiently.
Uniting by process, this feature analyzes the defective batteries produced at each process stage, providing a detailed breakdown of which stage generates the most defects and which type of defect holds the highest absolute percentage. This allows for the rapid identification and localization of problems within specific processes or production cycles, enabling targeted process optimization.
We provide dynamic, real-time SPC control tools for production lines, helping on-site process engineers and quality personnel efficiently assess and control product quality. Our system covers all the necessary charts used in SPC, including X-Bar R, X-Bar Sigma, Individual Range, Median Range, EWMA, MA, MAMR, MAMS, CuSum charts, as well as measurement-based p-, np-, c-, u-, and DPMO charts.
A tool for process correlation analysis that uses various dimensions of artificial intelligence analysis methods and data dimensionality enhancement and reduction techniques. This tool quickly identifies the root causes of quality issues in production, such as whether product defects are caused by personnel, raw materials, or the production process itself. It outputs attributions rapidly, saving a significant amount of work for process and production staff who would otherwise have to check each factor individually. This reduces the difficulty of troubleshooting and improves the efficiency of issue identification.
By analyzing the three dimensions of OEE—availability, performance, and quality—production issues and defects can be quickly pinpointed. It helps identify whether the cause is related to equipment, materials, the manufacturing process, human error, or other factors. This analysis assists in rapidly improving production capacity and efficiency.
By analyzing the production activity of individual workstations over time, this feature provides a visual comparison to identify which specific equipment within a workstation is causing production delays. It helps to investigate potential issues related to equipment, material input, scheduling, maintenance, and other abnormalities within specific time intervals.
By analyzing equipment on an individual basis, this feature provides insights into the abnormal situations and their progression over a specific time period. It breaks down the data by cumulative occurrences and cumulative duration, enabling the identification of which specific abnormalities impacted production efficiency. This allows for targeted optimization of the equipment.
At the workstation level, using cumulative duration and cumulative occurrences as dimensions, this feature performs comparative analysis of various abnormalities within a specific time period. It helps identify devices within the workstation whose abnormality rates differ from the others, enabling in-depth optimization of the specific device’s abnormal causes.
Using SPC XBar-S and XBar-R charts, this tool evaluates the quality of production process data, while also obtaining real-time process capability ratios on the production line, including process capability ratios (Cp, Cpl, Cpu, Cpk, and Cpm) and process performance indices (Pp, Ppl, Ppu, and Ppk).
These charts are used for count-type data analysis of on-site production data, where p-, np-, c-, u- and other count charts are employed to perform accurate and real-time SPC control on product defects.
