The Six Sigma software JMP, from SAS, the world's largest statistics software group, is a standout among similar softwares in terms of real-time monitoring and trend prediction. By linking computers to data acquisition devices via serial structures using the JSL language, real-time calculations and monitoring of production processes can be performed. When process anomalies are detected, corresponding staff members are notified via email. Its time series tools also help users predict trends in changes of the production process state.
Although Lean and Six Sigma have different origins and focus on different core issues, they share an essential characteristic: speaking with data.
Recently, a specialized lecture on "Lean Six Sigma Year," hosted by SBTI, a well-known domestic Six Sigma consulting agency, was successfully held in Guangzhou, sparking widespread discussions on the integration of Six Sigma statistical analysis software (informatics) with Lean Six Sigma.
Lean Six Sigma is the product of combining Lean Production with Six Sigma and is gaining popularity among more and more enterprises. Lean originated from the "TPS (Toyota Production System)" of Toyota Motor Corporation in Japan, with its core idea being to discover, reduce, and even eliminate "waste" in various stages of production, thereby improving the utilization rate of production materials (including time) and reducing inventory. The core idea of Six Sigma is to minimize variability in process and product quality. In the 21st century, more and more experts and companies have begun to realize that combining both approaches organically is necessary to maximize business process improvements. Currently, not only manufacturing companies but also many service industry companies are beginning to introduce Lean Six Sigma into their quality management and process control to enhance efficiency in serving customers.
Enterprise informatization is not merely about data collection and analysis, and the integration of Six Sigma with IT technology is no exception. True informatization should not only analyze and understand the past but also grasp the present and predict the future. By extending the wings of the Six Sigma methodology and information technology, enterprises can fly further, higher, and more steadily.
Analyzing production data through statistical analysis software is just the first step. Statistics, once considered the "commoner" of academia, is difficult for ordinary employees without much knowledge of statistics to understand and apply. Statistical analysis software helps users analyze data, but if Six Sigma teams want to move from statistical analysis results to actual quality improvement decisions, a necessary step is to have a comprehensive and thorough understanding of the analysis results. Thus, the demand for visualizing statistical analysis results becomes apparent. Therefore, statistical analysis software (such as JMP) uses vivid graphics and even real-time animations to intuitively explain statistical analysis results to users, presenting the relationships between data using different types of graphs, colors, and shapes, making statistical analysis results easier to understand.
Taking a manufacturing company's Six Sigma project as an example, under the leadership of a Black Belt, the Six Sigma team needs to collect large amounts of data. These data cover all aspects of the production process, such as raw material suppliers, physical and chemical characteristics of raw materials, processing times for raw materials, processing accuracy, temperature and humidity information of the production environment, etc., which are everywhere. Without sufficiently powerful statistical analysis tools, these data will remain mere data, and the critical information hidden behind them will never automatically reveal itself. Only by leveraging IT technology and statistical analysis software can the Six Sigma team discover relationships within the data through data analysis and establish distribution models between data points.
Nowadays, the integration of Six Sigma statistical analysis software with Lean Six Sigma is becoming increasingly tight. Although Lean and Six Sigma have different origins and focus on different core issues, they share an essential characteristic: speaking with data. Lean requires monitoring and analyzing each stage of the production process, while Six Sigma relies on regression, hypothesis testing, and variance analysis to identify key performance indicators (KPIs), find influencing factors of KPIs, and analyze impact patterns.
While analyzing past data and making decisions based on it is certainly important, to truly achieve informatization in Lean or Six Sigma, quality improvement personnel and teams also need to perform real-time monitoring of the current situation and make predictions about future trends, preventing potential problems before they occur.
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