Perform a Six Sigma Statistical Analysis

At the end of a Lean Game, the players can download data about their performance in Excel format.

Data available for export

Four sheets are available in the export:

Average cycle times

Average of all the products time spent on each activity (processing, transport, changeover or waiting) on each station. This is the same data used in the balance chart.

Detailed product cycle times

Time spent for each product for each activity on each station. You can use this level of detail to extract the variability (standard deviation) of each station processing time. The goal can be to standardize so that the station processing time consistently gets closer to the takt time.

Detailed operations status

During the game, each player individual click is recorded, together with the distance vs. the center of the shape the student was trying to paint. In the export, the status/type of the operation is also shown, whether the student properly painted the shape ("Successful"), created a defect ("Failed") or clicked to remove a defect ("Rework").

WIP Evolution

In this sheet, you can find for each second elapsed in the game, a photo of the WIP inventory that was present at each station, either in the WIP-in, WIP-out or Workdesk zone, at this specific time.

Statistical analysis using Excel

To showcase the six-sigma principles and methods, you can ask students to extract their data at the end of a game and perform statistical analysis on it.

Distribution of click performance

It is possible to create a chart on Excel, showcasing the distribution of click distance-to-targets.

You can download an Excel example here.

Note that in our example all the clicks below the limit are valid (if the distance to target is 0 it is a perfect click).

Students can draw the chart above by sorting the distance to target (here in column G) and using the Excel formula below:

=NORM.DIST(G2, AVERAGE(G:G), STDEV.S(G:G), FALSE)

Sigma-level (Z-score)

Students can calculate the Z-score for this process:

= (Limit - Average) / StDev

In the example above, the process is operating at a 0.74 Sigma level (Z = 0.74).

Success (or Defect) rate

Students can compute the percentage of successful operations by using the formula below:

=NORM.DIST(8, AVERAGE(G:G), STDEV.S(G:G), TRUE)

For example, if the tolerance limit is 8px, and the average distance is 6.3px with a standard deviation of 2.3, then 77% of operations should be successful (statistically passing)

To increase the percentage of success, we want to reduce the deviation of clicks. To do so, students can implement Poka-yoke or Standardization techniques.