The synergy of data analytics and quality

How BI and Six Sigma are transforming businesses

The basics of Six Sigma

Business intelligence involves analyzing and interpreting data to make informed decisions. You can find a detailed explanation of what business intelligence is in this blog post.
Six Sigma, on the other hand, is a systematic method for process improvement and quality management that aims to reduce the error rate in business processes and improve the quality of products or services. The name Six Sigma comes from statistical quality control and refers to the maturity level of a process, the Sigma level, which indicates the percentage of error-free products. In a Six Sigma process, statistically speaking, 99.99966 percent of one million results should be error-free – or in other words: for every million possible errors, a maximum of 3.4 errors may occur. The basic idea behind Six Sigma is that customers expect suppliers to ensure that their products work flawlessly and that their service is fast and reliable. Suppliers should therefore actually ensure 100 percent quality. However, as errors can always occur, 100 percent is the ideal that Six Sigma methods should strive for.

The core process of Six Sigma

Six Sigma is not about finding and sorting out faulty products, but rather about finding the causes of errors and eliminating them permanently. To achieve this, the method starts with the processes, procedures, or activities of a company to understand and continuously improve them. Regardless of how good the processes already are, Six Sigma assumes that processes can always be improved to increase efficiency, reduce costs, or improve quality.
The basis of Six Sigma is the DMAIC methodology with the five central process steps that are run through in every Six Sigma project and which provide a systematic framework for defining problems, collecting data, analyzing causes, implementing solutions, and monitoring the results. The term therefore stands for:

1. Define: In this phase, goals are set and customer requirements are defined. Based on this, measurement parameters are determined and a clear understanding of the current process is developed.
2. Measure: In the measure phase, data is collected and the current process performance is analyzed. Statistical techniques are usually used for this.
3. Analyze: In the analysis phase, the causes of process errors or inefficiencies are identified. Cause-effect diagrams or fault tree analyses are used for this purpose, for example. Based on the results, priorities are then set for potential improvements.
4. Improve: The defined optimizations are introduced in the subsequent improvement phase. This means that processes are redesigned, workflows are adapted and quality control measures are introduced in this phase.
5. Control: The control phase is intended to ensure that the improvements are maintained over time and deliver the desired results. Therefore, the optimizations are monitored to ensure continuous process quality.

Combination of Six Sigma and Business Intelligence

Since the Six Sigma process is based on measuring, analyzing, and monitoring defined indicators, it stands to reason that BI software can be used seamlessly for this purpose. Business intelligence can be used not only to monitor key performance indicators in real time to track the progress of Six Sigma projects, identify difficulties, and intervene in time. BI software can also be used to analyze company data to identify patterns or trends that indicate inefficient processes or quality issues. Six Sigma methods can then be applied in the next step to understand, quantify, and solve these problems. BI can also be used to identify risks in business processes and take preventative measures. By analyzing historical data, predictive models can be developed to predict potential errors or quality problems before they occur and plan appropriate countermeasures. BI can also be used to monitor supply chains, identify bottlenecks, and plan the optimal use of resources. Six Sigma methods such as process optimization and variation reduction can then help to improve the efficiency of the supply chain and thus ensure the quality of products or services.

However, not only can BI be used for Six Sigma processes, but Six Sigma can also help to improve business intelligence solutions, for example by using Six Sigma processes such as DMAIC to improve data quality for BI systems. Such a process could look like this:

• Define: Six Sigma methods are used to define clear and measurable standards for data quality. This includes criteria such as accuracy, completeness, consistency, timeliness, and reliability of the data.
• Measure: By applying statistical methods and metrics, companies can then quantify the quality of their data. This can include how often data is incorrect, how quickly errors are corrected, and how well data meets user requirements.
• Analysis: Using Six Sigma tools such as cause-and-effect diagrams and fault tree analysis, companies can identify and understand the causes of data errors. This can help uncover systemic issues that lead to data quality problems.
• Improve: Based on the results of the root cause analysis, companies can take action to correct data errors and prevent future errors. This can include revising data entry processes, improving validation rules, or training employees.
• Control: Six Sigma provides methods to monitor and control data quality over time. This can include implementing control processes, regular audits, and setting up alert systems to respond to potential data quality issues at an early stage.

Conclusion

The combination of Six Sigma methods and business intelligence software therefore not only helps to make more precise decisions in Six Sigma processes and thus optimize processes. BI tools also promote the monitoring and control of processes and continuous improvement. At the same time, the application of Six Sigma methods can increase the quality of the data that is evaluated in the BI software.

However, the most important synergy effects arise because the use of business intelligence software in Six Sigma projects also allows predictions to be made about future process errors or deviations. In this way, processes can also be adapted preventively, whereas, without BI tools, processes can only be analyzed retrospectively.
The myPARM BI^act business intelligence software integrates seamlessly into your Six Sigma processes, making it easier for you to analyze relevant data or key performance indicators and make decisions. In addition, myPARM BI^act allows you to immediately translate your findings into measures and manage and monitor them in the same system. This is how your Six Sigma projects are implemented efficiently and successfully.

Would you like to get to know myPARM BI^act in a demo presentation? Then make an appointment with us right away!