by Griffith A. Watkins
Equipment used by U.S. Army soldiers must be ready at all times. Army mechanics and U.S. Department of Defense (DoD) contractors must maintain the Army’s automotive equipment to ensure it is “mission capable” when needed. The Army has a standard of maintenance for every vehicle in its inventory. The standard left just enough wiggle room for each individual inspector to interpret the requirements in a manner that fits their concept of maintenance. This caused great diversity in actual maintenance standards, leading to cases of two or more inspectors looking at a single piece of equipment, resulting in excessive parts and labor costs.
In this case study, one quality management team at Fort Campbell, a U.S. Army base in Kentucky near the Tennessee border, sought to measure the cost of these varying interpretations and bring clarity to the application of the Army’s standard. Using customer comments from the soldiers, real-time inspection and repair data, measurement system analysis (MSA), and design of experiment (DoE), the team identified the extent of inspector variance. Once the current state was found, they used the DMAIC methodology of Lean Six Sigma to reduce inspector variance, increase accuracy, and save money.
-Increased demand on U.S. soldiers means increased demand on their equipment. Maintenance management holds the key to keeping costs as low as possible.
-A U.S. Army contractor was tasked with identifying the variance existing in an equipment inspection program, the cost created, and clear up the inspection standard.
-Using DMAIC and other quality tools, the quality management team reduced inspector variance by 97 percent, while increasing accuracy by nearly 20 percent.
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Griffith “Griff” A. Watkins was the quality manager for a DoD contractor for five years augmenting Fort Campbell’s automotive maintenance program by placing skilled technicians in unit motor pools across the post. The primary mission for the contractor’s maintenance teams was the Army’s Left Behind Equipment (LBE) program. At the time, from 2007 to 2011, this program was designed to service and repair all equipment that the Army left behind when deploying, ensuring that soldiers would have mission-capable equipment to train with upon their return. The expected standard of maintenance was Technical Manual (TM) 10 and TM 20 series standards.
In 2010, the Fort Campbell maintenance facility experienced a change in government oversight. The new leadership was highly focused on production numbers and budget, so Watkins and Benson needed to find a way to continue providing Fort Campbell’s soldiers with the same high-quality service, but faster and with less cost. To do this, they needed reliable data. For the most reliable data, they had to go right to the source: maintenance records for the equipment repaired at the facility.
From a population of more than 5,000 High Mobility Multipurpose Wheeled Vehicles (HMMWVs) the QC inspection team took a sample of 436 service and repair documents and entered the maintenance information into a detailed database. Items recorded were parts required, labor hours, parts cost, and labor cost per repair task.
The data collected gave Watkins the answer to the customer’s most frequent comment, “We love what you guys do, but it costs so much.” In fact, the data revealed the following:
Improvement of the inspection program began in June 2010 and ran until June 2011. The primary approach used throughout this project was the DMAIC methodology, consistent with the company’s procedure for Lean Six Sigma implementation.
Project selection used a combination of the Pareto chart, database tools, multivoting, and DoE to define and validate the problem. The current state measurement, analysis, and future state development required the use of the tools, as seen in Table 1.
After identifying the problem, Watkins and Benson conducted small-scale measurement system analysis (MSA) to validate selection of the project. The MSA was completed by having each of the three QC inspectors conduct a final QC inspection on the same HMMWV. The vehicle deficiencies recorded by each inspector were identified as within standard or above standard.
The current state was measured in three areas:
Thirty-three inspectors were assigned to survey three HMMWVs (11 inspectors per vehicle). The scatterplot in Figure 1 showing one set of results is fairly representative of all three vehicles, and the level of variation present in the inspection team.
After the data was collected and processed, the team analyzed for root causes. They started by completing a cause and effect diagram by answering the five whys. This brought forward a total of 39 possible root causes (12=interpretation of standards, 15=personnel, and 12=management).
The final root causes were determined to be:
Then the team analyzed every process to ensure all possible solutions met business, contractual, and regulatory requirements. The team was left with 10 possible solutions.
Stakeholder buy-in for solution implementation was not hard to find. Communication with the stakeholders was ongoing throughout the project, and all internal stakeholders were actively engaged from project selection to project completion. Perhaps the most significant buy-in came from the project management when they directly implemented the No. 1 solution. They gave a well-defined VoC statement on interpretation of inspection standards, which clearly described a bottom-up approach to interpretation—inspect only what the PMCS checklist directs and no more.
The impact of the improvements was measured by analyzing the work order documentation and conducting another inspector MSA six months after the improvement. Similar to the initial MSA, Watkins and Benson measured inspector variance and its cost based on “within standard” and “above standard” faults. They measured inspector accuracy using “within standard” faults only. Finally, the project impact on parts and labor costs were calculated from the analysis of work order documentation as it was completed.
The team’s efforts decreased inspector variance by 97 percent, and increased inspector accuracy by almost 20 percent. At the same time, they reduced the overall cost of service by 15 percent. The savings came from the following:
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