I have had the opportunity to deploy in support of two Operation Iraqi Freedom (OIF) rotations as a guardsman while being a full-time employee of General Electric Company. During both deployments, I found that I used many of the data and quality skills that I had learned at General Electric as much as I did my military training.
Based on my experience, I believe that we could be much more effective as an Army if we could incorporate some of the Six Sigma, quality, Lean manufacturing, continuous improvement, and data training processes and principles from the corporate world into the military's DNA. Everything is a process, and these business tools can be used with everything that happens in the Army.
What follows are some of the lessons my advisory team learned using these tools while working with the Iraqi Army maintenance system during OIF. I also will offer some recommendations for improving our Army's ability to find, manipulate, and analyze data.
|This sample chart shows the fill rate for class IX (repair parts) by Iraqi Army medium workshop location. It was developed from raw data in the Iraqi Asset Management Program (IAMP).
Iraqi Asset Management Program
My team spent 9 months in Iraq working at all levels of Iraqi Army (IA) maintenance and advising IA personnel, from the battalion maintenance officer all the way up to the commander of the national parts warehouse. The automated class IX (repair parts) system used by the IA is the Iraqi Asset Management Program (IAMP). The IA was using IAMP for daily transactions but not as an effective maintenance management tool. IAMP was still a new system, and U.S. Forces had not developed a strong enough base knowledge to effectively advise the IA in the management capabilities of IAMP.
One of our initial missions was to advise an IA medium workshop (MWS), which provided the equivalent of direct support maintenance on an area basis. The MWS's primary customer was the 8th IA Division. The initial feedback from the stability and transition team advisers of the division was that the class IX system was broken. No evidence indicated that any IA units were tracking the status of open class IX requisitions on a document control register. We thought that this information would be readily available and tracked at multiple levels throughout the IA and shadow-tracked through the adviser network, but that was not the case.
We initially pulled preformatted reports from IAMP to gain situational awareness, but these reports were not easy to manipulate or summarize and did not give a good common operating picture of class IX. With extensive coordination and requests to the contracting company that developed IAMP, we were able to obtain a report that detailed every single class IX request since the inception of the program. This report provided one easy-to-read and customizable file for reporting class IX status for the entire 8th IA Division.
Using a variety of Microsoft Excel tools, we developed a report that showed fill rates by unit and tracked trends on a weekly basis. (See chart at right.) The 8th IA Division's personnel were now able to "see themselves" from a class IX perspective for the first time. None of this would have been possible without a good base knowledge of Microsoft Excel.
80/20 Parts Pareto Chart
Situational awareness of status is great in seeing your supply system, but now the challenge was helping our IA counterparts to capitalize on this newly found common operating picture. The 8th IA Division's fill rate on class IX was 18 percent, and we needed to figure out why it was so low. A Pareto chart was created to determine the top parts requested.
A Pareto chart is a simple bar graph that displays the frequency of events in descending order. The Pareto chart showed that the majority of the fill rate misses were due to the nonavailability of just a small portion of parts. In most processes, 80 percent of the faults stem from 20 percent of the defects; this is commonly referred to as the 80/20 rule. After analyzing our data, we found that the top 15 parts requested made up 27 percent of the total quantity requested and the top 30 parts requested made up 41 percent of the total quantity requested.
Over 1,000 different parts were on order at the time of our analysis, so this was a clear example of the 80/20 rule commonly seen in Pareto charts. The data provided an easy solution to solving the lack of repair parts: Letting contracts for 30 parts would take care of 41 percent of the parts on order for the entire IA.
Budgetary and process issues prevented the IA from acting on the data, but at least the data now were available. The preformatted reports in IAMP would display the top 100 most-requested parts, but they did not provide an analysis of the effect of those parts on the entire class IX system. The Pareto chart provides a prioritized action list that helps units avoid spending time and effort working on defects that do not truly affect the overall performance of the process.
Drive to 65 Campaign
The same data were also used to analyze the potential for repairing parts that were on order and not in stock. Using a variety of Excel tools, we determined that the An Numaniyah MWS had the ability to repair 65 percent of all parts that were on order and not in stock for the 8th IA Division. These data were essential to the development of the An Numaniyah MWS "Drive to 65 Campaign," which had the goal of getting to a 65-percent fill rate through a combination of regular fills, repairing parts at the MWS, and local purchases.
The IA has yet to develop a formal reparable exchange program, but by using the data, we were able to identify which parts had the potential to be repaired to increase the fill rate. At the time of our departure, we had not gotten as far as we would have liked with the development of this program, but we had over 100 lines turned in and repaired that otherwise would have not been replaced.
The data were also used to develop a quick reaction force package to specifically buy equipment to build repair capability at the MWS. This specific repair capability represented 36 percent of all orders placed by the 8th IA Division in calendar year 2010. This never would have been possible without first analyzing the data.
The data also provided maintenance management tools, such as volume of requests by unit as an indicator of preventive maintenance checks and services, fault analysis that indicates shortcomings in driver's training or maintenance training, and the cycle time of requests. All of this analysis came from the same base report.
Looking at the information in its tabulated form provided no useful data. The base knowledge of Microsoft Excel provided a tool to turn this information into a plethora of data-filled reports. The more we worked with 8th IA Division soldiers and provided them the data, the greater their desire became to see how they compared to the other IA divisions. This desire spurred a push to start looking at data for the entire theater. We subsequently created the same type of report for all class IX requests throughout the theater.
Joint Repair Parts Command Scorecard
When our advisory team began its work, the Iraq Training and Advisory Mission–Army had just recently received advising responsibilities for the Joint Repair Parts Command (JRPC), which runs the IA national parts warehouse. We were newly assigned to this mission, but we had only a short time remaining in theater. To make this time worthwhile, we determined that we needed an assessment tool to set a baseline that showed where JRPC's warehouse stood as measured against normal industry standards. For the assessment to be effective, we needed skills in manipulating data as well as the ability to analyze the data.
One of the derivatives of the assessment tool was a scorecard. One of the purposes of the scorecard was to develop metrics to measure effectiveness and track progress over time. Metrics drive behavior, and developing the right metrics and having the data to show performance helped JRPC immensely.
A good portion of the scorecard addressed the cycle time of orders processed at JRPC. The presentation of the data and the metrics allowed JRPC to concentrate on two key areas of order cycle time, cutting the backlog of one by 50 percent and another by 90 percent in just 3 weeks.
One of the performance functions addressed in the assessment tool was inventory effectiveness. A number of preformatted reports are available in IAMP, but none of these reports provide the ability to find the needed data. We were able to request a data run from IAMP showing total stock on hand at JRPC. A VLookup was done to match each part on hand to its demand. From this, we were able to show that only 3,500 of the 17,000 lines in stock had ever had any demand at all.
Inventory information is crucial to effectively advising an Army on its class IX replenishment system. Its availability would not have been possible without these simple Excel skills and access to the raw data. An automated system will never be able to provide a preformatted report to answer all questions, but access to raw data and sound Excel skills will provide the ability to address most questions asked.
We used these tools to build our own situational awareness and common operating picture so we could advise effectively. We did have mixed results sharing these tools with our IA counterparts as well as fellow advisers. A common understanding of these tools across the Army will not only help the Army to continuously improve, but it will also aid in the advising of our allies.
Building and Sustaining the Tools
Just as successful infantry squad battle drills start with a foundation of the individual skills of moving, shooting, and communicating and the proper equipment to do so, so data analysis requires a foundation of individual task mastery and the proper tools and equipment as well. A way to start building that mastery of individual data skills and the required toolbox is to—
- Incorporate basic and advanced Microsoft Excel courses and basic Microsoft Access courses, through a combination of correspondence and classroom training, into officer basic courses and advanced noncommissioned officer courses, and add a refresher at captains career courses.
- Add basic Six Sigma training, quality training, and Lean manufacturing training to the curriculum once basic Excel and Access skill sets are developed. The result of this training will be Soldiers and officers who are familiar with the DMAIC (define, measure, analyze, improve, control) process, value stream mapping, advanced process mapping, defect tracking, quality improvement tools, and data collection plans.
- Institute a fairly robust distance-learning course for individuals already developed in their careers who require these skill sets.
- Incorporate these skills into a capstone project that addresses improvement of a process at the school or post. This will give students practical experience as well as improve school or post processes.
For nonautomated processes, data are obtained by using a simple, coherent, and effective data collection plan. Automated processes must be able to access raw data at all levels. A number of preformatted reports are available in automated systems right now, but having access to the raw data is essential to any user's ability to manipulate the data specifically to meet his needs.
How can a junior officer incorporate these skills into his daily operations? Think of your battalion or state Soldier Readiness Program, where you spend 2 days standing in various lines for a total of about 1 hour of value-added time. Using a value stream map to identify areas of waste will cut out hours of non-value-added time.
Another example is late evaluations. By using a defect tracker and a data collection plan to identify your top reasons for lateness, you can put in place a process and controls to prevent the defects from happening in the future and cut down on the total number of late evaluations. This article has discussed numerous ways to use these tools in maintenance.
Everything is a process, and by focusing on data analysis and continuous improvement, then everything the Army does will become better.