The White Sands Missile Range has long been one of the world’s preeminent military test facilities. As a major range and test facility base, White Sands is a U.S. Department of Defense asset that is managed by the Army. It is also the home to an array of organizations with a broad range of missions that extend well beyond developmental testing. One service offered at the facility, which is located just outside of Las Cruces, New Mexico, involves providing clients with classified video surveillance of missions. To streamline surveillance processes and to reduce the work hours involved, an improvement team from White Sands Missile Range conducted a Lean Six Sigma project and used Minitab Statistical Software to analyze project data.
A quality improvement team at White Sands Missile Range used Minitab to reduce the man hours involved in providing their clients classified video surveillance. As part of the improved process, the van pictured above is outfitted with new surveillance cameras that greatly reduce the amount of manpower needed to carry out surveillance missions.
When clients request classified video surveillance missions, the White Sands Missile Range contracts with independent optics technicians to complete hands-on surveillance at various mission sites. The labor costs for providing these grounds checks became very costly for clients and the process itself was arduous. "The goal of the project was to reduce the work hours incurred by 30 percent," says Sue Schlegel, Lean Six Sigma black belt and quality improvement mentor to Robert S. Carter, the black belt leading this project at White Sands Missile Range. "By eliminating non-value-added activity from the process, we knew we could cut costs and make more efficient use of people’s time—allowing them to focus on other important projects."
How Minitab Helped
The White Sands Lean Six Sigma team followed the DMAIC methodology and organized their project into five phases: define, measure, analyze, improve, and control. As part of the define and measure phases, Minitab helped the team analyze data to learn more about the video surveillance process. From start to finish, the process involved fifteen steps, which included time for technicians to travel to and from the mission site, set up and tear down equipment, and monitor video surveillance streams.
Minitab Bar Charts helped the team analyze the time technicians spent at each step of the surveillance process across mission sites. They were able to easily pinpoint process steps where major bottlenecks occurred.
To assess the capability of their current process, the White Sands Quality Improvement team performed process capability analysis with Minitab Statistical Software. They were able to verify that the process was not meeting the upper specification limit for cycle time.
With Minitab charts, the team was able to view the distribution of process cycle time data and identify the current mean process cycle time. To assess the baseline process performance and capability of the current process, they performed process capability analysis in Minitab. The capability histogram they created verified that the process was not meeting the upper specification limit for cycle time. For further insight into each process step, the team used stacked bar charts to analyze the time technicians spent at each step across mission sites. This made it easy to see where process bottlenecks were
occurring at each site. “Early in a project, Minitab helps us to stratify the data, quantify our wastes, and pinpoint where problems lie within a process,” says Schlegel. “In this case, we found that 68 percent of the total process cycle time was expended on the non-value added task of monitoring video streams.”
Armed with this understanding, the team set out to understand root causes and prioritize possible solutions. Because the surveillance missions required manpower to monitor video streams for long periods, they looked for alternative ways to monitor streams that did not require human attention. After researching and talking through various solutions to address major bottlenecks, the team decided to implement and pilot-test automated video surveillance cameras equipped with powerful motion-detection software. The new cameras were cost-effective, easy to install and maintain, and drastically reduced the
manpower needed to carry out the monitoring phase of video surveillance missions. Process improvements were evident following the first pilot test, and new standard operating procedures were created soon after to outline the new process.
The original goal of the project was to reduce the work hours involved in the classified video surveillance process by 30 percent. After implementation of the new surveillance system, the hours were reduced by 47 percent—greatly surpassing the original project goal. What formerly took four optics technicians to complete can now be done with two technicians, freeing the remaining technicians to work on other missions to support White Sands Missile Range customers. The team anticipates the new process will also save customers $1.6 million through 2018.
"We improved the process by replacing almost 50 percent of the man power with surveillance cameras," says Schlegel. "The new process not only saved our customers money, but it also redirected the work of two technicians and created a more efficient process."
By removing non-value-added time from the process and reducing the number of process steps, the project also increased the process cycle efficiency from 10 percent to 19 percent. The team verified that the new process was in control and the post-project capability analysis revealed they met their goals for reducing the process cycle time.
Perhaps the greatest benefit of this project is its value for replication. "This application is being replicated in other processes and programs across White Sands Missile Range," says Schlegel, "and can be applied to similar agencies within the Army."
Schlegel says she gets the most satisfaction out of working with quality improvement teams and teaching them how data can be used to make decisions. "For us, Lean Six Sigma projects are data-driven," she says. "It’s about using data analysis to focus on the waste and understand where we need to go to get the most bang for our buck."