The 336th Transportation Group (Forward) conducted a study to determine the point in a vehicle’s life when the value of a refurbishment at or above the –20 level is maximized. [Maintenance at or below the –20 level is performed by unit mechanics.] Our expectation as we began the study was that a refurbishment would curb the rapid escalation in cost and frequency of repairs and extend the usable life of the vehicle. To explore this hypothesis, I collected data from the CALIBRE Integrated Logistics Analysis Program (ILAP) support office at Camp Arifjan, Kuwait, and data from the 336th Transportation Group’s Unit Level Logistics System-Ground (ULLS–G) computer. [CALIBRE is a management and technology services company serving the Army and the other services.] I aggregated the data from both data sets, matching dates, mileages, repair costs, and days down for the M1114 up-armored high-mobility multipurpose wheeled vehicle, M915 truck, and M1070 heavy equipment transporter truck.

To test the hypothesis, I built a simple time/value microeconomic model, where mileage represented time on the x-axis and both days down and repair cost represented value on the y-axis. I created a scatter plot for each vehicle type and added a trend line to clarify patterns in the data. I also conducted a regression analysis for the parts cost only to determine the relationship between percent change in repair cost with respect to mileage.

I expected repair costs to increase gradually early in the vehicle’s life. I also hypothesized that a readily identifiable point would exist in the mileage of a vehicle, after which the cost and frequency of repairs would increase rapidly. Performing a refurbishment at that point would flatten the cost curve and extend the life of the vehicle, maximizing the value of the refurbishment. What follows is a summary of my results for each vehicle type.

M1114

A weak relationship exists between higher mileage and greater number of days down per repair for the M1114 vehicle type. Over a 70,000-mile life of M1114s in the Army’s fleet, the average number of days down per repair increased only 3 days, from 38 to 41 days. This represented only an 8-percent increase over 70,000 miles, which was far less than I initially expected.

For parts cost, the trend line appeared almost flat, showing no increase in parts cost over the lifetime of the M1114. Analyzing the greatest concentration of vehicles, which had between 45,000 and 65,000 miles of use, supported this trend. No significant increase in parts cost over the lifetime of an M1114 was observed. This conclusion also contradicted my hypothesis that parts cost would increase over the life-time of a vehicle.

The regression analysis also contradicted my hypothesis. The slope of the regression equation was -0.121, meaning that an increase of 1 unit of mileage is related to a 12-percent decrease in 1 unit of parts cost. Essentially, increased mileage is related to decreased parts cost. [Regression analysis measures the relationship between one or more independent variables and a dependent variable. In this case, mileage is the independent variable and parts cost is the dependent variable. A regression equation is generated from the dataset, and the coefficient of correlation is derived from the regression equation. The coefficient of correlation measures the strength of the relationship between the two variables. A coefficient of correlation of 1 is the perfect linear relationship: an increase of 1 unit of mileage is correlated to an increase in 1 unit of parts cost. A coefficient of correlation of 0 indicates no relationship. To say that a 1-unit increase in mileage “causes” a 1-unit increase in parts cost is a misnomer. We can only state that a strong relationship exists; we cannot say that there is causation.]

M915

A weak relationship also exists between higher mileage and greater days down per repair for the M915 trucks, mirroring the results from the M1114 series. The trend line increased from 38 to 42 days down per repair over the lifetime of the M915 series. This represented an 11-percent increase over 100,000 miles, from 20,000 to 120,000 miles. I initially expected the relationship between higher mileage and greater days down per repair to be stronger, especially for vehicles with over 100,000 miles of use.

Counterintuitively, an inverse relationship exists between mileage and parts cost for the M915. While I expected that higher mileage would positively correlate with higher parts cost, I found that, as mileage increased, parts cost decreased over the lifetime of the M915. While the trend line demonstrated this strong relationship, the result may be caused by problems in the data set, which I will discuss later in this article.

The regression analysis returned a different result from the trendline analysis. The slope of the regression equation was 0.012, meaning that mileage accounted for approximately 1.2 percent of the change in parts cost.

M1070

A weak relationship also exists between higher mileage and greater number of days down per repair for the M1070 vehicle type. Over the lifetime of an M1070, the number of days down per repair increased from 35 to 38. This represented only a 9-percent increase over 130,000 miles, which was far less than I originally expected, especially for such a long lifetime.

A positive relationship exists between higher mileage and greater parts cost for the total data set of the M1070. A $20,000 increase in cost occurred between 0 and 200,000 miles. Breaking the data set down to observe the majority of the fleet’s concentration, which had accrued between 25,000 and 50,000 miles, revealed an increase from $1,900 to $2,200 per repair. The $300 increase represented a 16-percent increase over the parts cost for repairs at 25,000 miles—a statistically significant result.

The regression analysis returned an interesting result. The slope of the regression equation was 0.149, which meant that mileage accounted for approximately 15 percent of the change in repair cost. This represented the strongest relationship returned by a regression analysis.

Discussion

For all vehicle types, the fundamental assumption of this analysis was that all vehicles of a specific type are fungible. In other words, one M1114 with 50,000 miles of use is exactly the same as another M1114 with 50,000 miles of use. On-the-ground experience undermines this assumption. Company-level leaders know that different vehicles have different “personalities,” which may result in one vehicle needing more repairs than another. Perhaps these “personalities” are due to manufacturing irregularities and will be eliminated as the Army increases demand for Six Sigma quality during manufacturing. This assumption of vehicle fungibility was one of the weaknesses of the study.

Another weakness was that data collected from ULLS and ILAP are snapshots in time. Battle damage and battle loss costs and days down are not reported to either the unit’s ULLS box or ILAP, so they are not reflected in this study. Vehicles used by surge units may keep reported mileages and repair costs artificially low. When surge units complete their short tours, they usually turn their vehicles over to year-long tour units, and year-long tour units subsequently turn their vehicles in for refurbishment. This further complicates tracking repair cost over the lifetime of an individual vehicle. For all vehicles, the data collected may be inaccurate. The imperatives of conducting combat logistics patrols trump accurate data collection, and this may cause irregular reporting of repair costs at specific mileages. For the M915 vehicle type, too few data points existed at the time the data were collected. This problem may have caused the counterintuitive result for decreasing parts cost over increasing mileage. For the M1070 vehicle type, the data indicate a positive relationship between higher mileage and higher parts cost, especially for vehicles in the 25,000- to 50,000-mile range in which the majority of the fleet is concentrated. Unfortunately, neither the M1114 nor the M915 vehicle types indicate such a strong positive relationship for either parts cost or number of days down per repair. I observed less than a 10-percent increase in parts cost over the lifetime of the M1114 and M915. These results are insignificant considering that the increases occur over 100,000 miles of use.

My initial hypothesis was incorrect. The presence of a readily identifiable point along the trend line representing the mileage of a vehicle, after which costs would rise rapidly, did not exist for any vehicle type. A gradual increase early in the lifetime of a vehicle was also not present in the data. Overall, a very slight increase in a linear relationship over the lifetime of a vehicle was observed. The regression analysis also returned a weak correlation between increased mileage and increased parts cost, with the exception of the 16-percent relationship on the M1070.

The conclusion for Army leaders? This study could find no significant statistical relationship for higher mileage accounting for higher repair cost, except for the M1070. Perhaps a follow-up study would find different conclusions, but operators and mechanics are still our best way of determining which vehicles need to be refurbished. As a result, refurbishment programs should be conducted on a vehicle-by-vehicle basis, as determined by the unit’s maintenance officer, with input from the line mechanics and operators.
ALOG

First Lieutenant James B. (Brad) Hamlett is the Supply and Services Officer of the 336th Transpor-tation Group at Camp Arifjan, Kuwait. He holds a bachelor’s degree from Furman University and is a graduate of the Transportation Officer Basic Course.