A Uniform Approach to Inspection Standards
Reducing Automotive Maintenance Costs at Fort Campbell, KY
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.
Case Study At a Glance . . .
-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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Case Study Highlights
About Inspection by Design
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.
A Contractor's Quality Journey
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:
- An inspection program that relied on quantity of inspections rather than quality
- An alarming rate of defective or redundant work
- Extreme variance between mechanics for individual maintenance task times
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.
- Within standard: Those deficiencies identified by explicit reference in the inspection standard. These deficiencies can be clearly linked to the inspection criteria regardless of the inspector. This is a bottom-up interpretation of the standard (e.g., the mirror is missing).
- Above standard: Those deficiencies identified by inferring what the inspection criteria should include. These deficiencies cannot be clearly linked to the inspection criteria. They rely on the interpretation of the individual inspector. This is a top-down interpretation of the standard (e.g., the paint on the mirror is faded). It must be noted that these may be actual deficiencies, just not within the scope of maintenance requested by the customer.
The current state was measured in three areas:
- Inspector variance and adherence to the inspection standard
- Inspector accuracy regarding “within standard” repairs
- The cost associated with “above standard” repairs
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:
- No clear voice of the customer (VoC): The customer quality assurance representatives (QARs) conducting acceptance inspections were subject to the same standard interpretation errors as the contractor, which increased the confusion as to what the customer wanted. This was validated by reviewing customer acceptance inspections on completed work orders where an average of 40 percent of customer-identified faults were “above standard.”
- Lack of inspector training and accountability: This was validated by internal audit of the inspection process.
- Poor management communication and work loading: This nwas validated by interviews, internal audit documentation, and review of historical records of management meetings.
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.
Results and Control
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 &lldquo;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:
- "Above standard” related labor hours—reduced from 9.1 percent to 1.8 percent
- “Above standard” related parts cost—reduced from 21.5 percent to 1.9 percent
- Inspection labor hours—reduced inspection time by 45 percent