Every day, the United States Army depends on hundreds of processes to help manage more than 1 million military personnel. The efficiency of these processes is key for sustaining the well-being of the Army and keeping its units ready. When it became evident that the process for deciding if a soldier can no longer perform assigned duties due to medical reasons had an extensive average lead time, the Army assigned a Lean Six Sigma team to improve it. To aid them with their data analysis, the team relied on Minitab Statistical Software.
Every soldier’s job description, or Military Occupational Specialty (MOS), lays out that solder’s duties. When a soldier faces medical concerns, the Army must determine if the soldier should continue in that MOS, move to a new MOS, or leave the service. This decision is made through the MOS/Medical Retention Board process (MMRB). The MMRB process affects nearly 8,000 soldiers and their families per year, as well as numerous military commanders in charge of staffing.
The Army was taking an average of 61 days to reach decisions, and some outlier cases took as long as 400 days. The long lead time for this assessment kept soldiers’ careers in limbo, because they cannot deploy, make a permanent change of station, or go to school until a determination is made. "It’s scary for the soldier and their family," says Shane Wentz, an Army Master Black Belt working with the project team. "Some soldiers were waiting in excess of a year for this process to go through."
Wentz, along with team members from several Army groups, set out to reduce the wait these soldiers, their families, and their leaders face. They sought to decrease the average process cycle time from 61 to 45 days, while also standardizing and centralizing the process to remove variability.
The Army Lean Six Sigma team used the DMAIC approach to frame their project. This method divides improvement projects into five phases: define, measure, analyze, improve, and control. The team developed process maps to fully understand the current state of the MMRB process and identified possible root causes for the long lead time. After the team defined process bottlenecks and collected baseline data, Wentz turned to Minitab for further analysis to investigate the factors that were impacting the process.
"Minitab helped right away," he says. "The ability to easily manipulate thousands of lines of data in Minitab saved numerous hours."
Wentz used Minitab’s Analysis of Variance (ANOVA) tools to both uncover new factors that were negatively affecting the process and to statistically verify that the factors the team had identified were indeed causing increases in cycle time. With Minitab, he and the project team could clearly tell that defects in MMRB documentation, as well as variations in process guidelines across different units within the Army, were contributing to the long cycle times.
"Minitab graphs helped us to see where variation was occurring within the process," says Wentz.
Wentz used Minitab to create a Pareto Chart of documentation errors for the MMRB process, which helped the team quickly identify and prioritize critical documentation errors. They were able to pinpoint missing documentation as a key concern, and then made improvements to specifically address this process weakness.
Minitab’s Multi-Vari Charts also aided the team by presenting ANOVA data graphically, revealing relationships between various factors and how they affected cycle time. For example, a Multi-Vari chart of cycle time showed that cycle times differed across MMRB processing locations, verifying the importance of standardizing the process.
Using Minitab for analysis helped Wentz and the team extract the knowledge they needed from their data in order to improve the MMRB process. They brainstormed and identified possible solutions for process problems, then mapped out a new centralized process and performed pilot testing to ensure the new process reduced cycle time. After a few more tweaks to their new process, which the team renamed MOS Administrative Retention Review, or MAR2, they collected additional pilot-test data.
The team employed Minitab Control Charts to compare process stability before and after the new process was implemented, which helped the team confirm their solutions were meeting project goals.
The comparison of before-and-after Control Charts revealed a dramatic change in the MMRB process cycle time—a decrease from an average of 61 days to 29 days from start to finish. This greatly surpassed the team’s goal of an average 45-day cycle time. The new MAR2 process also reduced error rates in process documentation, from 30% to less than 1%. Another impressive operational improvement from this project included a decrease in Defects per Million Opportunities (DPMO), which dropped from 516,129 to 75,758. With the new process now standardized and centralized through one location, staff hours spent per MMRB case decreased from 5.5 hours to 3.5 hours, or by more than 74,800 hours annually. The improved process will save an estimated $15.3 million each year, for a total financial benefit of $99,805,881 between 2011 and 2017.
Perhaps most important, Army readiness improved as a direct result of the new process. Since each soldier awaiting the results of an MAR2 decision is in a non-deployable status, the reduced cycle time of the process returns soldiers to duty more efficiently, thus increasing the Army’s readiness posture.
Wentz’s contributions in helping to reduce the MMRB processing time earned him an award from the Army Lean/Six Sigma Excellence Awards Program. While he doesn’t take credit for developing the MAR2 process, Wentz is proud of his role in validating the results of the team’s effort, and he acknowledges Minitab for helping him along the way.
"Because we were dealing with data from across the Army—including the Regular Army, the Army National Guard, and the Army Reserve—everything that goes with a project like this is extremely time-consuming," he says. "Without Minitab, I am not sure we would have been able to analyze and share project data as quickly as we did."
The United States Army
Reduce the MMRB process cycle time