Box-and-whisker plots, often known as box plots, are graphical representations of numerical data sets. It is employed to depict the distribution of the data and highlight any outliers or skewness in the data. The plot consists of a box encompassing the interquartile range (IQR) of the data and a line drawn at the data’s median. Extending from the box, whiskers show the lower and upper limits of the data, omitting outliers.
In the context of Lean Six Sigma process improvement projects, box-and-whisker plots can be utilised to identify process contributors to variability and faults. By viewing the distribution of data, process owners can decide whether the process is functioning within the specified parameters and the permitted range of variation. Box plots are very helpful for identifying outliers, which may suggest sections of a process that need improvement.
For instance, a product management organisation may use box-and-whisker plots to monitor the delivery timing of their items to customers. The organisation may collect data on delivery timings for each order and generate a box-and-whisker plot to illustrate the data’s dispersion. If the scatterplot reveals that there are a considerable number of outliers (orders with delivery timeframes outside the whiskers), this may indicate that the delivery process needs attention. The organisation might then utilise Lean Six Sigma tools and methodologies to determine the core cause of the delivery delays and adopt changes to reduce delivery time variability.
Certainly, here’s one more:
Assume a healthcare firm desires to decrease the turnaround time for laboratory test results. As part of their Lean Six Sigma initiative, they collect data on how long it takes to process and submit each lab test to the physician. To depict the distribution of the data, a box-and-whisker plot is produced.
The scatterplot demonstrates that the median turnaround time for lab tests is within the required range, although there are a substantial number of outliers (tests with much longer turnaround times). This information can be used by the healthcare organisation to concentrate their efforts on determining the core cause of the longer turnaround times and implementing changes to reduce variability.
For instance, they may discover that the longer turnaround times are due to a bottleneck in the process caused by an insufficient number of technicians available to do the tests. To address this issue, the company may develop a cross-training programme for employees to increase the number of technicians available to do tests, or it may invest in extra equipment to decrease the processing time for each test.
In this instance, the box-and-whisker plot gave useful information about the distribution of the data, allowing the healthcare organisation to concentrate its improvement efforts on areas that had the most impact on the desired outcome.
Overall, box-and-whisker plots are a valuable tool for Lean Six Sigma process improvement initiatives because they provide a visual depiction of data distribution and help identify parts of the process that need improvement. It is crucial to highlight, however, that box plots should be used in conjunction with other data analysis methods to create a comprehensive picture of the process.
