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Optimal Replacement Cycles of Highway Operations Equipment (2018)

Chapter: Chapter 3 - Project Results

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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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Suggested Citation:"Chapter 3 - Project Results." National Academies of Sciences, Engineering, and Medicine. 2018. Optimal Replacement Cycles of Highway Operations Equipment. Washington, DC: The National Academies Press. doi: 10.17226/25036.
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14 Project Results The three primary products of the project are: • A guide for optimal replacement cycles of highway opera- tions equipment, • An Excel-based optimization tool for performing LCCA and supporting replacement processes, and • A user manual for the replacement optimization analysis tool. The guide and tool user manual are included as Parts II and III in this report, respectively. The optimization tool is available for download from TRB’s website at www.trb.org by searching on NCHRP Research Report 879. The project results are described in the following sections. 3.1 Equipment Classification Scheme For the optimization tool to be universally applicable, it is necessary to adopt a standardized equipment classification and numbering scheme within the tool. Table 2 lists the 40 major equipment classes that are preloaded in the optimi- zation tool. The numbering scheme closely follows the equip- ment classification codes adopted by the National Association of Fleet Administrators (6). The tool class codes are used solely by the optimization tool and are not meant to replace the user agency’s class codes. In the initial tool setup, the agency’s internal class codes are cross-referenced to the preloaded tool class codes. Most of the class code descriptions in Table 2 are self- explanatory; however, two class codes may need further clarification: • Class 8786, Bottom Dump, is used to describe the full bottom dump truck unit, including the tractor and bot- tom dump trailer. Although few agencies have these units in their fleet, this class was included to allow those C H A P T E R 3 agencies that do have them to analyze life cycle costs of the units. • Class 0110, Salt Spreader, uses the same number as the gen- eral NAFA class for “Snow Removal” units. For the purposes of analyzing life cycle cost in the optimization tool, this class code was adopted from NAFA and represents a generic type class description for salt spreaders, regardless of the con- figuration or type of salt spreader used by the agency. The 0110 class code may be used for tailgate spreaders, drop-in spreaders, or any other configuration that is most prevalent within the agency. Because these units typically do not have hour meters, their utilization is measured by hours of usage reported daily by maintenance crews. Most agencies have unique classification and numbering schemes. Some schemes are very detailed and have many class codes, while other schemes are more generalized. The optimi- zation tool adopts a more generalized scheme. Using the ½-Ton Pickup Class as an example, the optimi- zation tool performs LCCA for all pickup types under one universal code listed in Table 3. In the example, six different pickup configurations are included under Class 1521, ½-Ton Pickup. A 2WD extended cab gas pickup, for instance, is ana- lyzed in the same way as a 4WD regular cab diesel pickup. The replacement factors considered by the LCCA are similar for all pickup types, leading to similar optimal life cycles. Each of the six pickup types may be analyzed individually or all six may be analyzed under one pickup class. The tool also allows analysis of various equipment groupings such as manufac- turer. It is important to note that the equipment class or sub- group must have a sufficient number of units in the fleet to render a statistically valid sample for analysis. With the cross-reference table, the agency’s cost history for all pickups is combined under the 1521 class code so that the optimal life cycle determined by the tool applies to all six of the agency’s classes. The tool allows the user to analyze each of the six pickup classes individually. However, breaking

15 equipment classes down too finely can affect the quality of the LCCA results if there are not enough equipment units in the class to render a realistic cost analysis. 3.2 Equipment Replacement Factors The following 11 factors were initially identified from the literature search as being potentially relevant to highway operations: Each factor was evaluated to determine if and how it applies to highway operations equipment. • Age, • Utilization, • Depreciation, • Maintenance and repair cost, • Fuel cost, • Downtime, • Obsolescence, • Replacement cost, • Purchase price, • Cost of money, and • Soft cost. In addition to these factors, the guide suggests three addi- tional factors as significant to performing LCCA and making equipment replacement decisions. These are • Physical condition, • Mission criticality, and • Overhead costs. Not all of the factors are monetary. For instance, physi- cal condition and mission criticality do not impact LCCA, but they are important for prioritizing equipment replace- ment in the decision-making process. The following sections describe the factors and how they are used by the tool and in the replacement processes. 3.2.1 Age Most highway agencies have established equipment replace- ment targets or criteria based on age and utilization. Accord- ing to NCHRP Synthesis 452 (1) a large percentage of agencies use age and utilization as the primary criteria for equipment replacement. Example replacement targets are shown in Table 4. Tool Code Description 1300 Sedan 1600 SUV 1348 Police Cruiser 1521 ½-Ton Pickup 1531 ¾-Ton Pickup 1523 ½-Ton Crew Cab 1533 ¾-Ton Crew Cab 1424 Vans 3514 Mechanic Shop Truck 2513 1-Ton Crew Cab 3711 Flat Bed Truck 3744 Scissor-Bed Truck 6712 Single-Axle Dump 8712 Tandem Dump 8785 Tri-axle Dump 8786 Bottom Dump 8810 Tractor/Trailer 8743 Bucket Truck 8744 Bridge Snooper Truck 8771 Roadway Sweeper Truck 8741 Mobile Crane 8773 Culvert Cleaner/Vacuum Truck 8778 Asphalt Distributor 8731 Wrecker Truck 9452 Striping/Paint Truck 9132 End Loader, Wheeled 9220 End Loader, Track 9110 Loader, Skid Steer 9160 Motor Grader 9440 Roller, Pneumatic 9441 Roller, Steel Wheel 9150 Excavator, Wheeled 9250 Excavator, Track 9230 Dozer 9290 Dragline 9142 Backhoe 9431 Asphalt Paver 9426 Pavement Profiler 9623 Tractor, Mower 0110 Salt Spreader Maintenance Equipment Equipment Class Passenger Vehicles Light Trucks Medium Duty Trucks Equipment Group Large Trucks Specialty Trucks Heavy/Off-Road Equipment Table 2. Optimization tool equipment classes, codes, and descriptions. Equipment Types Included in Tool Class 1521 ½-Ton Pickup Optimization Tool Class Equipment Types Included ½‐Ton Pickup _ 2WD _ 4WD _ Regular Cab _ Extended Cab _ Diesel _ Gas Table 3. Equipment types in tool class 1521.

16 The guide does not propose making replacement decisions based solely on age. However, age is an important factor to be considered in the replacement process and is used in three basic ways: • To express optimal replacement cycles in years, • As a planning value to identify equipment units as potential replacement candidates, and • To project the year that an equipment unit will reach its planned replacement life. The optimization tool contains preloaded default planning values for target replacement age for each of the 40 equip- ment classes. These are only target planning values and are not hard-and-fast replacement standards. In the initial tool setup, the user can review the preloaded default values and opt to use them or override them with values specific to the agency. 3.2.2 Utilization Utilization normally goes hand in hand with age, although utilization can vary significantly between equipment classes and from year to year for individual units. Figure 3 shows the average annual utilization for all pickup trucks in the fleets of three DOTs. The trend for lower utilization in later years was found to be consistent for nearly all equipment classes, including Dump Truck and Heavy Equipment. Older units tend to be used less because operators prefer to oper- ate new equipment, and older units normally incur higher downtime. Repair costs are tied more directly to utilization than to age; the more a unit is operated, the more it requires main- tenance. Likewise, utilization, not age, generally dictates the true depreciated value of a unit. Utilization is used in equip- ment replacement in three ways: • In LCCA to calculate LTD cost per mile/hour; • As a planning value to identify potential replacement can- didates; and • To express optimal replacement cycles by miles/hours. As with age, the optimization tool contains preloaded default planning values for replacement utilization targets based on miles or hours for each equipment class. The user may opt to use the default values or override them with values specific to the agency. 3.2.3 Depreciation Depreciation is a major factor to be considered in LCCA and is generally made up of between 30% and 40% of the total cost to own and operate equipment over its lifetime. Most LCCA methodologies define depreciation in account- ing terms that determine the book value of an asset over its life. Within the project, various data sources were analyzed to evaluate how depreciation by accounting methods can be applied to highway operations equipment. Equipment Class Replacement Target Years Miles Hours Sedan ½-Ton Pickup ¾-Ton Crew Cab 120,000 Mobile Crane Loader, Wheel Motor Grader 90,0006 6 110,000 8 110,000 Single-Axle Dump 12 15 120,000 12 5,000 15 5,000 Table 4. Example of replacement targets. Figure 3. Utilization for pickup trucks, by age.

17 Analysis of DOT Salvage Values State DOTs do not keep records that show salvage values during each year of a unit’s life, and thus no data exist with which to analyze year-over-year depreciation. However, for this project, DOT data were obtained and analyzed for end- of-life salvage values for three equipment classes: Pickup Truck, Dump Truck, and End Loader. Pickup Trucks. Figures 4 and 5 were developed from records provided by two DOTs and show the salvage values of the pickup class for 319 and 2,084 units designated DOT 1 and DOT 2. The vehicles were grouped by age and utilization, and the average salvage value of all units in the group was computed. Figure 4 shows that the trend of pickups class salvage values in the sample is downward with age, as expected for both DOTs. Figure 5 shows that the salvage values trend is downward with miles for DOT 2 but upward for DOT 1. For example, vehicles in DOT 1 with an average of 50,000 miles when sold had a salvage value of approximately 6.5%, while vehicles with 200,000 miles had a salvage value of about 11%. Looking at the details of the data, the same variability of salvage values is observed within mileage and age groups. For example, 16 vehicles with utilization between 150,000 and 160,000 miles when sold ranged from a low of 3% to a high of 13% salvage value. 8-Yard Dump Trucks. The same type of analysis was performed for the 8-Yard Dump Truck Class from the DOT data. The results are shown in Figures 6 and 7. Salvage values trend for the 8-Yard Dump Truck sample from DOT 1 is upward, with both age and miles but down- ward with age and upward with miles for DOT 2. As with the pickup class, there was considerable variability in salvage values between units with the same age and miles. End Loaders. The same analysis was performed for the End Loader Class using the DOT data, representing 99 and 176 units for DOT 1 and DOT 2. The results are shown in Figures 8 and 9. 0% 5% 10% 15% 20% 25% 0 5 10 15 20 25 30 Sa lv ag e Va lu e - % Age at Sale, Yrs. DOT 1 DOT 2 Linear (DOT 1) Linear (DOT 2) Figure 4. Salvage value for pickups, by age. Figure 5. Average salvage value for pickups, by miles.

18 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 5 55 105 155 205 255 305 355 Sa lv ag e V al ue - % LTD Miles at Sale, 1,000 DOT 1 DOT 2 Linear (DOT 1) Linear (DOT 2) Figure 7. Salvage value for dump trucks, by miles. 0% 5% 10% 15% 20% 25% 30% 35% 40% 5 10 15 20 25 30 Sa lv ag e V al ue - % Age at Sale, Yrs. DOT 1 DOT 2 Linear (DOT 1) Linear (DOT 2) Figure 8. Salvage value for end loaders, by age. 0% 2% 4% 6% 8% 10% 12% 14% 16% 18% 5 10 15 20 25 Sa lv ag e V al ue - % Age at Sale, Yrs. DOT 1 DOT 2 Linear (DOT 1) Linear (DOT 2) Figure 6. Salvage value for dump trucks, by age.

19 Figures 8 and 9 show that the salvage values trend with age and hours for both DOTs is upward, and the values are con- siderably higher than would be expected. For example, the average salvage value for 20-year-old end loaders was about 24% for both agencies but approximately 20% for DOT 1 and 32% for DOT 2 for end loaders with 12,000 hours. Two important conclusions can be derived from the analy- sis of DOT salvage values: • Salvage values tended to be higher than expected and exceeded the 10% value normally projected in accounting depreciation methods. • There is considerable variability in salvage values among units of similar age and utilization, most likely due to the individual conditions of the units and market conditions at the time of resale. PennDOT Data A recent report by Vance & Renz, LLC, Pennoni Associates, Inc., and SF & Company further illustrates evidence of sal- vage value variability (8). Table 5, extracted from the report, shows actual resale values in relation to PennDOT’s conven- tional accounting depreciation values. Salvage value percent- ages in the last column were added for this report. Two key observations were derived from the data: • Actual depreciated values do not match accounting depreciation. • Salvage values are generally higher than what might intui- tively be expected; loader salvage values are consistent with the results found in the two-state analysis described in the previous section. Interpretation of the DOT Data The salvage value analyses performed in the project and presented in the PennDOT report (8) show no direct correla- tion between a unit’s salvage value and its age or utilization. Salvage values varied quite significantly for units with similar age and miles and tended to be higher than expected. While no data exist to explain the variation in salvage values or the high resale values, consideration of the operating environ- ment of highway operations may provide some insight. First, 0% 10% 20% 30% 40% 50% 60% 2.0 4.0 6.0 8.0 10.0 12.0 14.0 Sa lv ag e V al ue - % LTD Hours at Sale, 1,000 DOT 1 DOT 2 Linear (DOT 1) Linear (DOT 2) Figure 9. Salvage value for end loaders, by hours. Equipment Type No. of Units Avg. Acquisition Price Avg. Months Owned Avg. Sale Price Avg. PennDOT Depreciation at Sale Avg. Difference, Sale-Depreciated Value Salvage Value Backhoes 54 $52,897 199 $10,765 $8,960 $1,805 20.4% Crew Cabs 36 $28,208 122 $1,943 $9,208 -$7,265 6.9% Gradall Excavators 32 $144,897 204 $8,526 $23,306 -$14,780 5.9% Loaders 243 $69,203 180 $20,218 $14,477 $5,741 29.2% Single-Axle Trucks 177 $66,634 153 $8,089 $17,516 -$9,427 12.1% Tandem-Axle Trucks 546 $75,046 152 $17,241 $18,552 -$1,311 23.0% Tri-Axle Trucks 19 $90,719 133 $22,188 $26,022 -$3,834 24.5% Table 5. PennDOT salvage values.

20 DOTs are often able to purchase equipment units at a sig- nificantly lower cost than the private sector. However, DOT vehicles are sold at auction at prices equal to or higher than private sector vehicles. Because the DOT paid significantly less to purchase the vehicle, the amount of depreciation will be lower. Second, many DOTs have gained a reputation for maintaining their equipment units in good condition and are able to realize better resale values than might otherwise be the case for private vehicles. These results of the salvage value analysis were an impor- tant input into developing realistic depreciation tables for use in performing LCCA. Salvage Values from Industry Data Salvage value data were evaluated from industry sources to supplement the DOT salvage value analysis. The industry data sources are not entirely applicable to highway opera- tions equipment in terms of absolute dollar values, because DOTs typically purchase equipment considerably below retail cost. For transportation vehicles such as cars and trucks, the data sources were the Kelley Blue Book Car Values (9) and the National Appraisal’s NADA Guides (10). Hypothetical units for sedans, pickups, and vans were constructed to see how they would depreciate based on miles and average condition at sale. Although the dollar values cannot be compared, the annual percentage depreciation of privately owned vehicles would be expected to follow a similar trend as that for state DOT vehicles. An example of the results for ½-Ton Pickups is shown in Figure 10. With an average annual usage of 15,000 miles, the results show that a pickup would normally depreciate about 18%, which is in line with the first-year deprecia- tion rate found for state DOT vehicles. The rate of annual depreciation declines with the vehicle’s age and flattens in later years. Depreciation Calculations in LCCA In LCCA, depreciation costs must be calculated for each year of a vehicle’s life and predicted into the future. For LCCA to provide accurate results, depreciation must be defined as the true cost of ownership. The analysis presented in this section demonstrates that the true cost of depreciation is not necessarily the same as the depreciation computed by accounting methods. To model true depreciation costs as closely as is practical, default depreciation tables were developed for the project based on the results of the salvage value analysis. The optimi- zation tool contains default depreciation schedules for each of the 40 equipment classes. The full depreciation table is shown in Appendix A and excerpted in Table 6. Because uti- lization is a better predictor of depreciation than age is, the tool computes depreciation based on a unit’s LTD utilization. The user may choose to use the default depreciation values preloaded in the tool or override them with values specific to the agency. 3.2.4 Maintenance and Repair Cost M&R cost is a required factor for LCCA. The costs used by the optimization tool to perform LCCA are obtained from the user agency’s internal equipment data source. M&R costs can fluctuate significantly from year to year, as illustrated in Figures 11 and 12. Figure 11 shows the annual M&R costs for an individual dump truck. Figure 12 shows the average annual M&R costs for all units in a sample of motor Figure 10. Annual depreciation for ½-ton pickups.

21 Table 6. Excerpt of optimization tool depreciation schedule. Figure 11. Example of annual M&R cost for a dump truck. Figure 12. Example of average annual M&R cost for motor graders.

22 The first-year anomaly in M&R costs warrants careful con- sideration when performing LCCA. Units that have a substan- tially higher first-year cost per mile/hour should be evaluated to determine if they should be included in the LCCA for determining optimal life cycles. For instance, a unit with only 50 miles and $1,500 in prep cost would not represent a typical first-year cost in the life cycle. M&R Calculations in LCCA The LCCA in the optimization tool models M&R costs by averaging the historical costs of all units in a specific equip- ment class. Past-year costs are normalized to current-year constant dollars by using the inflation factor input in the configuration file of the optimization tool. M&R Cost Data Consistency Review of the M&R cost data provided by the DOTs revealed some issues with respect to data consistency and detail. To ensure that consistent and complete maintenance data Figure 13. Example of accumulated M&R cost for dump trucks. Figure 14. Example of accumulated M&R cost, graders. graders. The similar annual fluctuations were consistently observed for other equipment samples. The annual fluctuations of M&R costs occur over a unit’s life. Units may go a year or two with only preventive main- tenance and minor repairs, followed by a year with a sharp rise in cost due to a major repair or service, such as tire replacement, and then experience low M&R costs in the following years. Although these annual cost fluctuations occur, the graphs show that M&R costs tend to increase over a unit’s life. M&R costs are included in LCCA as part of the operat- ing cost component and are analyzed on a per-mile or per- hour basis. Using the same cost data for the dump truck and motor graders and the units’ LTD miles/hours, the accumu- lated cost per mile/hour for the data samples is shown in Figures 13 and 14. In both cases, the cost per mile/hour is relatively higher in the first year. This high first-year cost is commonly attributed to expenditures for preparing and out- fitting the equipment prior to field deployment, coupled with the typically low utilization in the first year because units are received at varying times throughout the year.

23 are used to perform LCCA, the guide outlines recommended guidelines for tracking and allocating M&R costs. Generally, the guide recommends the following: • All services and repairs performed on a vehicle should be captured on a shop work order so that M&R costs are fully tracked, including – Preventive maintenance, – Scheduled and unscheduled repairs, – Accident repairs and body work, and – Rebuilds and overhauls. The literature varies on how to treat accident repairs. From a practical consideration, it can be difficult for an agency to separate accident repairs from other work, and the cost of accident repairs generally tends to be very small when averaging all units in a class. • Vehicle prepping and outfitting prior to field deployment for items such as decals, safety striping, light bars, and radios should be considered in the vehicle’s capital cost and not counted as M&R costs; • Mechanic and technician time charged to work orders should only include actual services and repairs. Activi- ties such as training, shop cleanup, safety meetings, and other nonmaintenance activities should be included in overhead and not charged to a repair work order; and • All parts and fluids used for services and repairs should be charged on a repair work order. 3.2.5 Fuel Cost Fuel costs make up 20% to 40% of the cost to own equip- ment over its lifetime. In a unit’s early years, fuel costs can often exceed M&R costs. Figure 15 shows the proportion of operating cost attributable to fuel from a sample of 300 single-axle dump trucks. Fuel costs in the early years make up more than half of the operating cost, declining to about 39% in the later years. Fuel cost is an important replacement factor but must be considered somewhat differently from the other fac- tors because of wide fluctuations in fuel prices. Figure 16 shows average fuel prices for 10 years (from 2006 to 2016) from Bureau of Labor Statistics data (11). A report on analysis Figure 15. Annual fuel cost as percent of operating cost for single-axle dump trucks. $1.00 $1.50 $2.00 $2.50 $3.00 $3.50 $4.00 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Av g. $ /g al Year Figure 16. Historical average fuel prices in the United States.

24 of fleet replacement life cycle for the South Florida Water Management District (7) proposes that fuel costs can be esti- mated based on averages over time and price trends. Fuel Cost Calculations in LCCA The LCCA methodology used in the optimization tool assumes that the historical price trends for fuel can be used to predict future trends, which allows the LCCA to model future operating costs. The approach uses average per-mile/hour fuel costs for all equipment units in the class based on cost history downloaded from the agency’s internal data source. Past-year costs are normalized to current-year constant dollars. Fuel cost is also used as the basis for determining obsolescence costs, as discussed in Section 3.2.9. 3.2.6 Downtime Equipment downtime can be a significant operating cost fac- tor, especially in a unit’s later years when downtime increases substantially. It is often overlooked because it does not result in a direct outlay of money by the agency. However, down- time has a direct and significant impact on highway agency costs to perform maintenance and provide services. Quantifying Downtime Costs There are two generally accepted methods for computing downtime cost: • Lost production time. In this method, the actual cost of lost production is determined for each downtime occurrence. Lost production costs can be very high in instances when a large maintenance crew is interrupted because a critical equipment unit goes down. While this is the most accurate method for determining downtime cost, it requires both rigorous reporting and an accurate accounting mechanism for determining the hourly crew cost. This method is not practical in a highway operations environment. • Hourly rental rates. This method assumes that the cost of equipment downtime is the cost of either having addi- tional units in the fleet to cover downtime occurrences or the cost to rent equipment. Downtime cost is computed by multiplying downtime hours by an hourly rental rate. For agencies with an established rental rate system, the hourly rental rates can be used. Recognizing that not all DOTs have established equipment rental rates, the opti- mization tool is preloaded with default hourly downtime rates for each equipment class. The default hourly down- time rates represent averages derived from information provided by DOTs. The user agency may use the default rates or override them with hourly rates specific to their agency. Figure 17 shows how downtime cost can affect life cycle cost. In this example, downtime hours were extracted from the work order history of an individual dump truck. The down- time cost was calculated using a rental rate of $38 per hour. The analysis shows that adding downtime to the operating cost can have a significant impact on the accumulated cost. In this case, a 9-year-old dump truck had accumulated 879 hours of downtime over its life. The accumulated operating cost per mile at the end of nine years was $0.70 without downtime and $1.20 when downtime is added, an increase of 70%. Guidelines for Tracking and Recording Downtime Tracking downtime hours can be challenging, and prac- tices for reporting downtime vary among state DOTs. The project found that many agencies do not maintain down- time history. Figure 17. Operating cost comparison using downtime.

25 To ensure that downtime is effectively included as a replace- ment factor, the guide contains guidelines for tracking and reporting downtime. As a general rule, downtime is the time that a unit is out of service during normal working hours. Agencies are encouraged to review their downtime reporting procedures and initiate a reporting process consistent with the guidelines so that downtime can be appropriately considered in LCCA. Downtime Cost Calculation in LCCA The optimization tool uses the hourly rental rate method for calculating downtime cost. Including downtime is optional for the user agency, although it is highly recommended because of its relevance in the operating cost component. If an agency has reliable downtime data, the data can be uploaded to the tool as part of the data input routine. If no downtime is reported, the tool will ignore downtime cost when it per- forms LCCA. The optimization tool contains default hourly downtime rates for the 40 equipment classes. The user agency should review and replace the default hourly rates with its own rates, if available. If an agency routinely rents or leases equipment, the hourly rate should be the equivalent hourly rent/lease value. 3.2.7 Physical Condition and Mission Criticality Physical condition and mission criticality are not monetary factors used to perform LCCA. However, they are important factors to be considered in fleet replacement decisions. The guide outlines an annual equipment replacement pro- cess, in which units are targeted for replacement based on their age, miles, and LTD cost. All targeted units should be assessed for their physical condition and be assigned a mis- sion criticality level. The optimization tool provides an Excel spreadsheet for conducting the condition assessments, shown in Figure 18. After the condition scores are logged onto the spread- sheet, the optimization tool calculates an overall condition score that factors in mission criticality. The optimization tool uses the overall condition score and LTD cost to determine replacement priorities. 3.2.8 Overhead Cost To perform LCCA accurately, maintenance and repair costs must include both direct and indirect expenses. Reviewing and discussing cost data with the DOTs suggests that labor costs are understated in most cases because mechanic hourly rates are not fully loaded to account for overhead (although most agencies do include some amount of overhead). To provide a true measure of M&R costs, shop overhead costs must be factored into the mechanic hourly labor rate. Over- head costs occur as direct and indirect expenses and include the components shown in Table 7. Direct Overhead Direct overhead costs are associated with mechanic labor, including payroll additives and employee benefits such as FICA, worker’s compensation, health insurance, and retirement benefits. They may also include tool and uniform allowances. Another major component of direct overhead is noncharge- able time, that is, when a mechanic is not charging directly to a repair work order. This time is necessary and unavoidable and will always be present in a fleet maintenance environ- ment. Leave time—annual, sick, and vacation—is the larg- est component of nonchargeable time. Other nonchargeable time includes mechanic time for training, shop cleanup, safety meetings, and other productive, but nonchargeable, hours. Table 8 shows the analysis of nonchargeable hours for a selected DOT fleet organization. The analysis shows that 69% of a mechanic’s total paid annual hours were charged to repair work orders. However, because the remaining 31% is an oper- ational cost, it should be included when analyzing the cost of operating and maintaining the fleet. This is accomplished by allocating the nonchargeable time, as well as other mechanic direct overhead costs, as a percentage to be added to the base hourly mechanic pay rate. Indirect Overhead Indirect overhead costs consist of general and administra- tive costs at the fleet enterprise level, including nonmechanic personnel such as warehouse operations, procurement, dis- posal, and warranty management. Other indirect costs include general overhead expenses, facilities operating costs, and shop tools/operations that are not directly chargeable on a repair work order. Similar to direct overhead, indirect over- head expenses should be prorated to M&R costs on the basis of mechanic hours charged. Example Overhead Cost Calculation Table 9 shows an example for calculating direct and indi- rect overhead rates using real data from a DOT agency’s fis- cal year 2017 budget. The analysis shows that the direct and indirect overhead rates should be 115% and 93%, respectively. The agency’s average mechanic hourly rate for all mechanic classifications is $17.62. The agency charges mechanic labor cost to work orders at the rate of $35 per hour. As shown in the example, the true cost for fully loaded labor should be $53.01 per hour. This means that the agency’s equipment

26 Equipment Number Description: Date: By: Equipment Criticality Weight 15 Good No rust, no body damage Fair Very little rust, minor body damage Poor Visible rust, some minor body repairs needed Very Poor Major rust or body damage. Requires major work within one year SCORE: 3.8 Weight 20 Good Good mechanical condition Fair Some minor services needed Poor Major repairs and more frequent maintenance needed Very Poor Major repairs needed within one year SCORE: 5.0 Weight 20 Good Good mechanical condition Fair Some minor services needed Poor Major repairs and more frequent maintenance needed Very Poor Major repairs needed within one year SCORE: 5.0 Weight 20 Good Good mechanical condition Fair Some minor services needed Poor Major repairs and more frequent maintenance needed Very Poor Major repairs needed within one year SCORE: 20.0 Weight 10 Good Good mechanical condition Fair Some minor services needed Poor Major repairs and more frequent maintenance needed Very Poor Major repairs needed within one year SCORE: 7.0 Weight 15 Good No rust or frame damage Fair Some minor rust and/or frame damage Poor Moderate rust and/or frame damage but safe to operate Very Poor Major rust and/or frame damage; safety concerns SCORE: 15.0 Overall Condition Score: 55.8 Notes: Steering/Suspension Electrical Frame Vehicle Condition Assessment Transmission Engine Very Low Body Figure 18. Vehicle condition assessment form.

27 Direct Overhead Indirect Overhead Payroll Additive/Benefits Training Uniforms Tool Allowance Nonchargeable Time Shop Management Warehouse Operations Expendable Parts and Supplies Facility Costs Table 7. Components of overhead costs. Mechanic Chargeable Versus Non-Chargeable Time Component Non-Chargeable Time Hours Nominal Hours per Year 2,080 Overtime Hours at 6% Average per Year 125 Total Paid Hours per Year 2,205 Holidays, 12 Days per Year 96 Annual Leave, 15 Days per Year, Average 120 Sick Leave, 10 Days per Year, Average 80 Administrative Time* 382 Total Non-Chargeable Time 678 31% 61% % Non-Chargeable Time % Chargeable Time *Includes safety meetings, training, shop cleanup, and other nonwork order time. Table 8. Example of mechanic non-chargeable time. M&R costs are being significantly understated and most likely will have a significant impact on determining optimal life cycles. Accounting for Overhead in LCCA The optimization tool is designed to allow agencies to account fully for shop overhead and to apply overhead factors to the mechanic labor cost. The templates shown in Tables 8 and 9 are included in the guide with instructions for deter- mining agency-specific overhead rates. The optimization tool contains default factors of 45% and 90%, respectively, for direct and indirect overhead rates, con- sidering that some overhead has already been accounted for in the downloaded labor cost. The tool multiplies the down- loaded labor cost by the overhead factors to arrive at a fully loaded cost for mechanic labor. 3.2.9 Obsolescence Equipment obsolescence is a factor that considers equip- ment efficiency and dependability. Fuel efficiency, safety fea- tures, operator convenience, and reliability make newer model equipment more productive and cheaper to operate than older models. This is particularly true for heavy equipment items that can have replacement cycles of 12 to 15 years, during which time major equipment advances can occur. Quantifying obsolescence related to productivity is diffi- cult and is not practical in a highway operations environment. However, it is possible to determine how newer vehicles com- pare with older vehicles for fuel economy. Using U.S. Depart- ment of Transportation Corporate Average Fuel Economy (CAFÉ) data (12), Figure 19 shows the performance of passen- ger cars and light trucks, measured in miles per gallon (mpg) from 2000 to 2014. The data include all makes and models. In the 15-year period, fuel economy for passenger cars rose from 24.8 mpg to 31.5 mpg, a 27% improvement, or an average of 1.8% per year. For light trucks, the performance rose from 21.3 to 26.3 mpg, a 23.4% increase or an average of 1.6% per year. Jeannin et al. (13) reported that fuel efficiency for over- the-road trucks improved by 11% over the 6-year period from 2005 to 2010, or approximately 1.8% annually. Accounting for Obsolescence in LCCA To account for obsolescence, the optimization tool quan- tifies obsolescence on the basis of fuel utilization. The tool contains a preloaded default obsolescence rate of 2%, which is applied to the vehicle’s fuel cost to calculate obso- lescence cost. Replacement Cost and Purchase Cost Replacement cost is used by the optimization tool and in equipment replacement to • Calculate depreciation when performing a class-level LCCA. • Project long-range replacement budget needs. Replacement cost is the original price paid for the unit plus the cost for outfitting the unit prior to field deployment. The optimization tool uses average replacement cost for all units in a class for determining depreciation in LCCA. Purchase cost is not used by the optimization tool; however, it is needed to analyze salvage history and to customize the depreciation schedules during initial tool setup. 3.2.10 Cost of Money The cost of money is used in economic analysis to develop discount factors when comparing the net present values of

28 competing investment options. Using the cost of money in equipment LCCA assumes that the fleet agency has viable investment options for using the available funds. In govern- ment agencies, and specifically highway operations, invest- ment alternatives do not exist in a practical sense. Additionally, the returns normally being garnered by agency investments are typically so small as to render alternative investments insignificant. Moreover, it is assumed that the agency needs each equipment unit in the fleet and that there is no viable alternative but to replace it (ignoring the lease-buy option). For these reasons, the optimization tool does not use the cost of money in the LCCA approach. Inflation rates, on the other hand, are used by the opti- mization tool to change historical equipment costs into cur- rent dollars and project the replacement cost of equipment in future years. The optimization tool contains a default infla- tion value of 2.5% for inflation, which the user agency can override in the initial tool setup. 3.2.11 Soft Costs Soft costs, such as image and operator morale, are non- monetary factors that are not considered in LCCA. However, these factors can come into play in the decision-making pro- Cost Item Factors Direct Salaries Direct OH Indirect OH Salaries Charged to Work Orders 562,815$ Mechanic Non-WO Time 249,799$ Shop Management Pro-rated 57,000$ FICA 6.20% 50,382$ 3,534$ Worker's Comp 7.24% 58,833$ 4,127$ Retirement 12.20% 96,706$ 6,954$ Health Insurance 19.00% 150,608$ 10,830$ Parts 23,142$ Mechanic General 70,800$ Paint Shop - General 24,000$ Welding Shop - General 24,000$ Shop Tools 35,150$ IT - Software License 13,000$ IT - Computers, every 5 years 1,500$ Building Maintenance 29,000$ Hazardous Material Disposal 15,000$ Utilities 25,600$ Uniforms 8,400$ 800$ Training 29,500$ -$ Tool Allowance $150 3,000$ 150$ Supplies, Postage and Freight 7,040$ Telephone 16,500$ Office Furniture 3,900$ Fuel/Oil 23,471$ Small Equipment 44,000$ Shop Vehicles 34,375$ Warehouse Operations 48,000$ Totals 562,815$ 647,228$ 521,873$ 115% 93% 1,731,916$ 32,674 53.01$ Total Budget for Maintenance and Repair Total Shop Hours Charged to WO Loaded Labor Rate for Mechanic Overhead as a % of Direct Salaries Note: WO designates work order; OH designates overhead. Table 9. Example of calculation of mechanic direct and overhead rates.

29 cess. Soft costs are discussed in the guide as part of the equip- ment replacement process. 3.3 Life Cycle Cost Analysis LCCA is used in many asset management applications such as pavements, bridges, culverts, and buildings. While concep- tually similar to other applications, LCCA for equipment uses a slightly different approach that depends on costs and other factors specific to equipment operations. The LCCA approach for equipment is depicted in the graph shown in Figure 20 and can be summarized by the following: • Equipment costs consist of two main components: owner- ship and operating. – Ownership cost is the capital investment in equipment, which is determined from depreciation. Depreciation is the original price paid for a unit less the amount that is realized from its resale. If a unit costs $20,000 new and is sold for $1,500, the ownership cost (depreciation) would be $18,500. Ownership costs per mile/hour decrease over the life of an equipment unit. – Operating cost is the cost to operate and maintain a unit. Operating cost consists of out-of-pocket expen- ditures for maintenance, repair, fuel, and overhead. It also includes downtime and obsolescence, which are not out-of-pocket expenditures but are nonetheless real costs that must be factored into LCCA. Operating costs per mile/hour increase over the life of an equip- ment unit. • LCCA is performed by calculating the LTD cost per unit of utilization in miles or hours for both the ownership and operating cost components. • The two cost components are plotted against age. The point at which the total cost—the sum of ownership and operating cost—is lowest is when a unit has reached its economic life. Passenger Cars Light Trucks Figure 19. Fuel economy history (CAFÉ performance) for passenger cars and light trucks. Co st p er M ile AGE Operating Cost Ownership Cost Total Cost Target Replacement Figure 20. LCCA approach for equipment.

30 Two levels of LCCA were developed for the project, each with a specific purpose: • A class-level LCCA to determine optimal life cycles and • A unit-level LCCA to analyze the cost history of individual pieces of equipment. 3.3.1 Class-Level LCCA The class-level LCCA determines optimal life cycles for the various equipment classes such as pickup, tandem dump truck, or end loader. It analyzes LTD cost by averaging the operating history of all units in the class. For example, if there are 657 units in the ½-Ton Pickup Class, the class-level LCCA looks at all 657 units to determine the optimal life cycle for the class. In essence, it attempts to model the life cycle costs of an average unit. The following data are needed for class-level LCCA: • Equipment number, • Agency class code, • Description, • In-service year, • LTD miles or hours, • LTD maintenance and operating costs, • LTD downtime hours, and • Replacement cost. Quality data are the keys to obtaining reliable LCCA results, and it is important that data downloaded from agency sources are reviewed and corrected for errors before performing the LCCA. The process for data cleanup is described in the guide. Once the equipment data are downloaded from the agency’s data sources, cleaned up, and input to the optimization tool, the tool produces a class-level LCCA report similar to that shown in Figure 21. The tool user manual provides step-by- step instructions for performing class-level LCCA. The optimization tool shows the LCCA results in tabular form by year and provides a graph to the right of the tabulated results. (Note the erratic nature of the operating and total cost curves in the graph.) This type of variation naturally occurs because of the practical way in which maintenance is performed on equipment. In some years, only preventive maintenance or minor repairs are performed and maintenance costs are low. In other years, major maintenance such as brakes or transmission services is performed and the maintenance costs are high. The erratic nature of the curves would make it difficult to determine when the LTD cost per mile is at its lowest point and therefore difficult to determine the optimal life cycle. To compensate for this, the tool develops a trend line to model the total cost curve. This trend line is used to project the opti- mal equipment life. The class-level LCCA output report in Figure 21 shows the number of units by age and the average LTD cost per mile. The optimal replacement year and optimal replace- ment mileage are shown in the highlighted boxes at the top of the report. In this case, which used actual DOT data, the LCCA determined that the optimal life cycle is 11 years and 105,198 miles. 3.3.2 Unit-Level LCCA Unit-level LCCA analyzes the cost performance of indi- vidual equipment units. It is similar in concept and approach to the class-level LCCA but uses annual instead of LTD costs. The following data are required for unit-level LCCA: • Equipment number, • Agency class code, • Description, • In-service year, • Annual miles or hours, • Annual maintenance and operating costs, • Annual downtime hours, and • Replacement cost. In unit-level LCCA, the costs must be broken down by year for each year of the unit’s life. If annual cost data are not available, the optimization tool will not produce reliable LCCA results. Historical annual costs are sometimes problematic for agen- cies because the data are lost or compiled into a lump sum amount when an agency switches to a new equipment infor- mation system. If annual data are available, once they are loaded into the optimization tool, the tool produces a unit-level LCCA report as shown in Figure 22. The unit-level LCCA report displays the unit’s cost his- tory. At the top of the report, the year with the lowest LTD cost per mile and the equivalent mileage are shown in high- lighted boxes. 3.3.3 Interpreting and Applying LCCA Results Class-level LCCA answers the question of optimal life cycles, but it does not tell the fleet manager if a specific unit should be replaced. Making replacement decisions is a pro- cess, and although optimal life cycles are key inputs to the process, there are a number of factors to be considered. Mak- ing sound replacement decisions can be art as much as it is science. In real life, equipment cost trends seldom follow the stylized graphic depicted in Figure 20. More often the cost curves are erratic, as illustrated in Figures 21 and 22. From a

31 Figure 21. Example of class-level LCCA. Figure 22. Example of unit-level LCCA.

32 practical viewpoint, optimal replacement times seldom occur at an exact point of age or utilization but rather over a win- dow of time. Unit-level LCCA is helpful in reviewing the cost history of individual units, but there are two practical limitations in interpreting and applying the results. First, the point at which the unit reaches its economic life cannot be known until after the fact. Second, if data are not available for annual costs in the unit’s early years, which can often be the case, the analysis cannot be performed. Determining optimal life cycles and making replacement decisions are two distinctly separate tasks. They can be time consuming and demand dedicated staff who can commit continuous effort. The guide outlines an annual equipment replacement process that incorporates LCCA, along with considerations of other realistic factors that affect replace- ment decisions. 3.4 Replacement Processes The guide presents a systematic and methodical annual process for analyzing equipment replacement needs and making replacement decisions. Appendix B contains a flow- chart of the process, which includes the following major activities: • Download data from the agency’s equipment informa- tion systems. Clean up the data by correcting errors and eliminating units that do not have sufficient cost history to represent a valid cost trend. • Make sure that there are enough units in the class to pro- vide a statistically valid sample of units for the class-level LCCA. Perform the class-level LCCA to determine optimal life cycles. If there are not enough units to perform a class- level LCCA, rely on the unit-level LCCA to analyze costs of individual units. • Identify replacement candidates based on a unit’s LTD miles/hours or cost. • Perform condition assessments on the candidate units and prioritize units for replacement based on mission critical- ity, condition, and cost analysis. • Make final replacement decisions based on available replacement funding and other agency-specific factors and considerations. • Develop a 5-year plan and analyze the cost consequences of various replacement scenarios to provide the fleet man- ager with information to make a business case for future funding levels. The replacement processes are further amplified in the next section describing how the optimization tool supports the processes. 3.5 Optimization Tool The optimization tool is built in Microsoft Excel and con- tains the 45 separate spreadsheets, or files, noted in Figure 23. The optimization tool has six main functions: • Determine optimal life cycles for each equipment class. • Perform LCCA on individual equipment units. • Identify candidate replacement units based on utilization or life cycle cost. • Prioritize equipment replacement units. • Develop a 5-year replacement needs budget. • Analyze the cost consequences of various replacement cycles. The following sections provide a general overview of the optimization tool. The tool user manual provides detailed instructions for using the tool and describes how the tool supports the equipment replacement process. 3.5.1 Tool Setup and Configuration Initial setup and configuration are required before using the optimization tool. This is done in the Configuration File, which allows the user agency to review the default values for the various replacement factors and customize the tool to fit the agency’s operating environment and needs. The configu- ration steps include • Cross reference agency class codes with the NAFA class codes for the 40 equipment classes preloaded in the tool. • Enter the agency’s average unit replacement cost for each equipment class. Condition Assessment File Configuration File Data Entry File 40 Analysis Files, Class-Level LCCA 2 Analysis Files, Unit-Level LCCA Figure 23. Optimization tool files.

33 • Review the default values for the preloaded replacement factors and either adopt or override them with agency- specific values. 3.5.2 Importing Agency Data into the Tool Because the optimization tool is built in Microsoft Excel, it cannot be interfaced with agency systems for automatic data loading. Equipment data must be downloaded from agency systems, formatted, and uploaded to the tool. Two types of data are required: • LTD data for all units in a specified class, used to perform class-level LCCA and • Annual cost data for each year of a unit’s life, used to per- form unit-level LCCA. The data elements and format for data uploading are shown in Figures 24 and 25. Once in the specified format, the data can be copied and pasted into the optimization tool. A thorough review of the data should be performed to eliminate errors or outlier data. The guide and tool user manual provide helpful tips for data cleanup. Figure 24. Data entry format for class-level LCCA. Figure 25. Data entry format for unit-level LCCA.

34 Figure 26. Example report of class-level LCCA. 3.5.3 Performing Class-Level LCCA Once setup and data entry are complete, the optimization tool performs LCCA automatically when one of the equip- ment analysis files is opened, using the replacement factors and values in the Configuration File and the data input to the system. Figure 26 shows an example report of the LCCA results for the dump truck class. 3.5.4 Performing Unit-Level LCCA Using the replacement factors in the Configuration File and the uploaded cost data, the optimization tool performs the unit-level LCCA and develops a report as shown in Figure 27. 3.5.5 Identifying Units as Potential Replacement Candidates The optimization tool identifies potential replacement candidates when one or both of two criteria are met: • When a unit reaches the replacement planning target val- ues in the Configuration File. • When a unit’s LTD cost per mile/hour exceeds the class average for similar-aged vehicles by 25%. The equipment units that are flagged as potential replace- ments are displayed on the Replacement Candidate List report shown in Figure 28. The units are listed in order of their LTD cost, with the highest cost units at the top. 3.5.6 Performing Condition Assessment The optimization tool provides an Excel spreadsheet for performing condition assessments (see Figure 18). The con- dition assessment spreadsheet is not linked to the other files in the tool. All units flagged as replacement candidates should have a condition assessment performed. The assessments can be done on the electronic spreadsheet or on a paper copy. Once the assessments have been completed, the scores must be manually entered in the Assessment column of the Replacement Candidate List file. 3.5.7 Prioritizing Equipment Replacements Only the equipment units that have condition scores will show up on the priority replacement list. Condition assess- ments are not required but are highly recommended. After the assessment scores have been entered, the tool will determine

35 Figure 27. Example report of unit-level LCCA. Equipment Number Description In- Service Year Exceeds Cost Exceeds Depreciation Assessment 008-03-0702 1/2 TON P-UP EXTEND CAB FULL 55 008-03-0678 1/2 TON P-UP EXTEND CAB FULL 64 008-03-0658 1/2 TON P-UP EXTEND CAB FULL 63 008-02-0834 1/2 TON P-UP STAND CAB FULL 77 008-01-0426 1/2 TON P-UP STAND CAB FULL 49 008-01-0562 1/2 TON P-UP EXTEND CAB FULL 97 008-02-0789 1/2 TON P-UP STAND CAB FULL 96 008-01-0596 1/2 TON P-UP EXTEND CAB FULL 57 008-03-0510 1/2 TON P-UP EXTEND CAB FULL 45 008-01-0603 1/2 TON P-UP EXTEND CAB FULL 89 008-03-0629 1/2 TON P-UP 4WD EXTEND FULL 69 008-03-0524 1/2 TON P-UP EXTEND CAB FULL 73 008-02-0839 1/2 TON P-UP STAND CAB FULL 68 008-03-0560 1/2 TON P-UP STAND CAB FULL 90% 90% 90% 90% 90% 90% 73 008-03-0546 1/2 TON P-UP EXTEND CAB FULL 57 008-03-0491 1/2 TON P-UP STAND CAB FULL 2010 2009 2009 1999 2003 2006 1999 2006 2007 2006 2008 2007 2001 2007 2007 2007 81 1.28 1.13 1.09 1.08 1.06 1.03 1.02 1.01 0.99 0.99 0.97 0.96 0.95 0.94 0.94 0.92 Figure 28. Example of the replacement candidate list.

36 the overall ranking and display the units in priority order, as illustrated in Figure 29. The optimization tool computes priority rankings using the weight factors from the Configu- ration File as follows: = × + ×Priority Rank Condition Rank 60% Cost Rank 40% where Condition Rank is computed by the tool based on the condi- tion assessment; 60% is the default value for the condition weight factor in the Configuration File; Cost Rank is computed by the tool based on the cost-per- mile analysis; and 40% is the default value for the cost-per-mile weight factor in the Configuration File. The 60% and 40% weight factors are default values that the user may change. 3.5.8 Developing a 5-Year Plan The optimization tool generates a 5-year plan based on the target replacement factors in the Configuration File and the LTD miles/hours from the downloaded data, as illustrated in Figure 30. The tool compares the unit’s current age with the replacement target planning values in the Configuration File. Units that are past their target age are shown as backlog needs. Units that will become due for replacement in the next 5 years are shown in the year in which they will be due, with replace- ment costs based on the inflation rate in the Configuration File. 3.5.9 Analyzing Cost Consequences Analyzing cost consequences is not needed to make replacement decisions; however, it does provide useful infor- mation for fleet managers to make a business case for fund- ing levels. The optimization tool calculates the annual cost of various replacement cycle scenarios based on the class-level LCCA and displays the results as shown in Figure 31. Figure 29. Example of the priority replacement list.

37 Figure 30. Example of a 5-year replacement plan. Figure 31. Example of cost consequences report.

Next: Chapter 4 - Summary and Suggested Research »
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TRB's National Cooperative Highway Research Program (NCHRP) Research Report 879: Optimal Replacement Cycles of Highway Operations Equipment acts as a handbook on equipment replacement concepts and an instruction manual for making cost-effective replacement decisions. The research report presents a process for determining replacement needs for highway operations equipment, identifying candidate equipment units for replacement, and preparing an annual equipment replacement program. The products include a guidance document and an Excel-based replacement optimization tool to support the equipment replacement process and facilitate its implementation.

Disclaimer - This software is offered as is, without warranty or promise of support of any kind either expressed or implied. Under no circumstance will the National Academy of Sciences, Engineering, and Medicine or the Transportation Research Board (collectively "TRB") be liable for any loss or damage caused by the installation or operation of this product. TRB makes no representation or warranty of any kind, expressed or implied, in fact or in law, including without limitation, the warranty of merchantability or the warranty of fitness for a particular purpose, and shall not in any case be liable for any consequential or special damages.

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