Decision Making for Repair Versus Replacement of Highway Operations Equipment (2023)

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P A R T I I Guide

21   1.1 Purpose of the Guide This guide (Part II) provides readers with the concepts and intuition behind how to deal with a downed piece of equipment. The guide is intended to be used in conjunction with the Research Report (Part I), User Manual (Part III), and the Repair, Replace, Rebuild, Retire Tool (4R Tool). 1.2 Organization of the Guide This guide is designed to facilitate the use of the 4R Tool by providing the background and context for making repair, rebuild, replace, or retire decisions. It also includes information about how to calculate the cost rate for decisions to repair, replace or rebuild; this cost rate is needed when using the Economic Analysis Module of the 4R Tool. • Chapter 2 describes the fundamentals of managing a downed piece of equipment. • Chapter 3 describes approaches to decision making for downed equipment. • Chapter 4 describes a conceptual decision tree process for managing downed equipment. • Chapter 5 describes the impacts of repair, rebuild, replace, and retire decisions on fleet operations. • Chapter 6 provides the background for calculation of A and B coefficients. Purpose and Organization of the Guide C H A P T E R   1

22 This chapter provides fundamental concepts pertaining to fleet management and decisions around a downed piece of equipment. 2.1 Background Nearly all organizations use equipment to perform work or provide services, even if such tools are just routine office equipment. This equipment eventually breaks down as a result of wear and tear, accidents, or vandalism. The amount of time and effort devoted to deciding how to address the downed equipment varies by organization and situation. For private fleets, economic metrics— such as return on investment, minimizing life cycle costs, or money versus time—typically dominate the decision. Leading fleet practices entail a life cycle costing approach that incorporates a rela- tively straightforward set of criteria for making the decision. For public sector fleets, like those of state DOTs, these decisions are more complex. The ability of a state DOT to effectively deliver on its mission depends—to a large extent—on whether its highway maintenance equipment can perform required tasks. A downed piece of equipment can signifi- cantly impact agency operations and cause cascading delays in delivering required services. The core mission of state DOT fleets is public service provision rather than revenue production or profit maximization. Public organizations must balance multiple and often competing factors in these decisions—including procurement schedules, mission criticality of the equipment, avail- ability of replacement equipment and parts, promised highway levels of service (LOS), and budget constraints. Furthermore, unlike private fleets, public sector fleets are subject to both internal and external fiscal scrutiny of every decision. Decisions must be thoroughly documented, justified, and ready for audits. For DOT fleets, the urgency associated with certain decisions sometimes overrides the capacity for a careful quantitative analysis. For example, for decisions about equipment needed in emer- gency response service, DOTs must prioritize response time, equipment reliability, and return to service over other factors. In other cases, upcoming seasonal demands—such as the need for snowplows—may necessitate expedited decision making. Broadly, these situations complicate repair versus replace decisions by placing additional emphasis on making rapid decisions. DOT fleet managers need resources to evaluate the pros, cons, and situational merits when addressing these management decisions. Fundamentals of Managing a Downed Piece of Equipment C H A P T E R   2

Fundamentals of Managing a Downed Piece of Equipment 23 2.2 Drivers of Decision Making The desire to return equipment units to service as quickly as possible tends to drive the initial repair versus no repair decision-making process, with a bias toward the option that results in the shortest period of downtime (typically, this is the repair option). However, there are many factors that impact this decision, including the following: • Estimated cost to repair • Cumulative age or usage of the asset compared to the replacement criteria for its class • Current resale value of the equipment • Relative condition of the unit versus other units in its class of similar age • Availability of funds for equipment maintenance and repair • Estimated time to make repairs • Repair cost history • Repair frequency history • Environmental constraints to repairing • Criticality of unit need and the ability to temporarily or permanently reassign other units to meet operational needs 2.3 Possible Outcomes Most decisions about a downed piece of equipment fall into one of the following four outcomes: • Repair: Restoring the unit to its pre-repair condition. • Rebuild: Adding useful life to equipment (“rewinding the clock”) through the process of replacing potentially worn-out components prior to failure. • Replace: Substituting a damaged or malfunctioning piece of equipment with a similar piece of equipment (may be new or just a different unit). • Retire: Removing the unit from service without replacement. Which of the four outcomes is right for a given situation? Some situations have a relatively straightforward outcome, such as when minor repairs cost a small fraction of the existing value of the equipment (in which case the fleet manager should choose to repair). Similarly, old equip- ment that has reached the end of its target service or design life and has experienced a major component failure requires little discussion or analysis (the decision is to replace or retire it). Major damage to specialty equipment that is not available off the shelf may necessitate a rebuild. Other downed equipment decisions are less clear. For example, when a piece of equipment is near its midlife and needs a major repair, a fleet manager may use a variety of methods to determine the best course of action. These include expert judgment, historical precedent, orga- nizational guidelines, a repair limit equation, or a combination of methods. In general, the larger the replacement cost and complexity of the equipment, the stronger the justification for devoting time and effort to making the decision.

24 This chapter describes three approaches to deciding what to do about a downed piece of equipment: • Economic lifetime approach. Identifies the optimal age at which an asset should be retired. Economic models of equipment costs typically seek to minimize costs or maximize profit. The traditional model for equipment costs is the average total cost minimization model (Terborgh 1949), in which the objective is to minimize the total cost divided by the equipment item’s measured use. • Repair limit approach. Identifies a threshold (or limit) beyond which an asset should be replaced instead of repaired. The threshold is typically defined in terms of cost of repair, cumulative repair costs, cost rate, and number of repairs. • Hybrid approach. Uses a combination of quantitative and qualitative data, often relying on expert judgment and situational factors. This is the approach used in the accompanying 4R Tool. 3.1 Economic Lifetime Approach An economic lifetime approach to equipment decisions uses historic data on a given asset or asset category to determine the optimal target life or replacement cycle. As an asset ages, its operating costs rise and its ownership costs fall. Owning and operating costs are typically modeled separately and then combined to form a total cost model used by managers. Ownership costs typically include purchase price, resale price or disposal cost, licenses, insur- ance, taxes, and interest. Ownership costs are incurred irrespective of an equipment item’s use. The timing and magnitude of the cost transactions associated with ownership costs are known at the time of acquisition, other than the magnitude of the resale value. Operating costs include fuel, maintenance, repair, and similar costs. Operating costs are incurred for every hour (or other specified period of time) or mile the equipment item operates; these include the costs of repairs, fuel, preventive maintenance, tires or tracks, and wear of parts. Not included in operating costs are the labor wages of the operator, mobilization (in-service preparation) and demobilization (preparing equipment for fleet removal) costs. Most operating costs are incurred at a relatively constant rate and can be easily forecast. However, repair costs increase throughout an equipment item’s life because of increasing frequency and magnitude of repair actions. The target economic life of an equipment item is achieved by retiring the asset when the sum of the owning and operating costs is minimized. This age is determined through LCCA, a method that accounts for all costs associated with an asset—from purchase to disposal or resale—over time. The objective of LCCA is to minimize the total cost divided by the equipment item’s measured use. Decision-Making Approaches to Downed Equipment C H A P T E R   3

Decision-Making Approaches to Downed Equipment 25 Past research explores a variety of topics related to improving LCCA and the estimation of target economic lifetime. Mitchell (1998) developed a second order polynomial for estimating cumulative repair costs. The author suggests that the equation can be used to develop future cost plans and annual budgets for repair costs. Gransberg (2015) suggested that LCCA should include stochastic effects—variations in costs over time that are partly predictable and partly random. Specifically, the author suggests that LCCA should include expected variation in the costs of tires, fuel, and repairs (among others) rather than simply estimating a target economic life based on point estimates. Other literature describes various functional forms to use in forecasting or estimating the cost of repair parts, labor, and other owning and operating costs. 3.2 Repair Limits Approach A repair limits approach to a repair, rebuild, replace, or retire decision compares the magni- tude of the repair against some threshold or limit defined by individual, cumulative, or minimum number of repairs, as defined in the list included in this section. When a piece of equipment reaches the threshold, it should be replaced or retired instead of repaired. Individual Repair Limits Individual repair limits are defined as the maximum allowable costs for one-time repairs. The literature describes the following three types of individual repair limit approaches: • 50 percent rule. This rule of thumb holds that an asset should be replaced if the repair cost exceeds 50 percent of the replacement cost. The total repair cost should be considered—inclusive of parts, labor, and equipment. The advantages of this rule are that it is based on present-day costs and is not affected by external factors, such as inflation. Also, it is relatively easy to use and apply, serving as a convenient rule of thumb for making decisions. MacAllister (2017) notes that repair costs of approximately 50 percent of the replacement cost indicate that repair costs have already begun to increase rapidly; repair costs tend to increase gradually up to approximately 30 percent of the replacement cost and then make a jump to about 50 percent in the following year. • Practical limits reflecting age. This method is like the 50 percent rule but includes the nuance that the limit should vary with the age of the item. The U.S. Fish and Wildlife Service (2015) uses a table of repair limits as a percentage of replacement cost that starts at 50 percent at an age of one year and decreases gradually to 10 percent for equipment aged nine or more years. The U.S. Department of the Army (2020) sets the repair limit (referred to as mainte- nance expenditure limit) based on 50 percent of the current wholesale value of the equipment. A table of wholesale value for equipment of various types and ages is used to determine the repair limit. NAVFAC (2003) sets a repair limit in terms of percentage of original procurement cost, and the limit is dependent upon the current age and the expected life of the asset. The repair limit varies from 75 percent of procurement cost at an age of one year to 20 percent at an age equal to the expected life. • Theoretical limits. Methods for determining repair rate based on economic models and theories are also found in the literature. Drinkwater and Hastings (1967) present the seminal work around repair limits. Their method for determining the repair limit is based on future marginal cost, and the limit is found by confining the future cost rate to the rate charged for assets in the class. The advantage of individual repair limits is that they are easy to track and implement. How- ever, a disadvantage is that assets with frequent minor repairs can remain in the fleet for longer than is optimal due to the narrow focus on a single, individual repair rather than the entire history of repairs over an equipment asset’s life.

26 Decision Making for Repair Versus Replacement of Highway Operations Equipment Cumulative Repair Limits A cumulative repair limits approach considers the minimum repair cost rate, cumulative total cost of repairs, or maximum number of repairs in the repair versus replace decision. As with individual repair limits, cumulative repair limits are relatively easy to track and implement, although deci- sions can be complicated by having multiple criteria to consider. In practice, care must be taken to ensure that major investments in equipment (e.g., a replacement engine or transmission) do not trigger a decision to remove the units from the fleet, ignoring the decision to invest in the asset to extend its useful life. Minimal Cost Repair Limits The minimal repair cost limits are similar to the individual and cumulative repair limits, except that the estimated cost of repair is defined as the minimal cost needed to return the piece of equipment to a functional condition, rather than the cost to return the equipment to an average condition for its age. In other words, minimal repair will fix an asset so that it is in working condi- tion, rather than making it like new. 3.3 Hybrid Approach A hybrid approach encompasses any other approach that uses both quantitative data and qualita- tive data for a decision about whether to repair, rebuild, replace, or retire an asset. In the 4R Tool, the approach uses a decision tree along with a set of 19 criteria to guide the user to a recommended outcome.

27   This chapter describes a conceptual decision tree that fleet managers can follow when faced with a downed piece of equipment. The decision tree is based on data collected in the surveys and interviews in NCHRP Project 13-08 and aligns with the structure of the Decision Module in the 4R Tool described in the Research Report and User Manual. 4.1 Decision Tree Initially, a fleet manager considers two options when faced with a downed piece of equipment: to repair or not to repair. Primarily using the repair cost, equipment age, target age of replace- ment, equipment condition, and availability of operating funds, the fleet manager makes an initial decision to repair or not to repair. If the initial decision is to repair, a secondary decision is needed: to repair or to rebuild. A repair outcome will fix the piece of equipment; a rebuild (e.g., replacing potentially worn-out compo- nents prior to failure) will incur a higher cost but will also increase the longevity of the equip- ment. However, for the rebuild option to warrant additional evaluation, it would be because the associated repair decision is of high cost in terms of time and money. In other words, routine, minor repairs typically would not warrant evaluation of a need to rebuild. Conversely, a decision to replace one of more major components (e.g., engine, transmission, differential) or a major incident repair need might warrant conducting a rebuild analysis. If the initial decision is not to repair, the two outcomes to consider are to replace or to retire. This evaluation involves examining past and projected levels of unit utilization, the availability of other units for reassignment, and the availability of capital replacement funds, among other criteria. 4.2 Bias Toward Repair Criticality of a piece of equipment is defined as the agency’s desire to return the target equip- ment to service as quickly as possible. The need to make timely decisions about critical pieces of equipment can bias fleet managers toward making a decision to repair unless additional factors (e.g., cost of repair, length of downtime, remaining asset life as detailed in the 4R Tool input vari- ables) warrant examining the financial prudence of a simple repair decision. Although maximizing equipment availability is important, a DOT is also expected to serve as a good steward of public assets. This means making financially and operationally prudent decisions. Decision Tree for Fleet Managers C H A P T E R   4

28 This chapter discusses the future costs faced by fleet managers once they decide what to do about a downed piece of equipment. Importantly, these costs change over time depending on the decision made. To illustrate this point, Figure 6 was generated from the Economic Analysis Module of the 4R Tool and shows the projected operational costs of equipment following a repair, rebuild, or replacement in Year 0. In this hypothetical example, the repair option has the lowest cost of operations in the near term; the rebuild line is lower cost in years two to four. In year four and beyond, the replace option shows the lowest cost, reflecting the increasing cost of operating and maintaining equip- ment that has exceeded its design life. This indicates that although a decision may make financial sense in the near term, it may not make financial sense in the longer term. With respect to fleet readiness, the repair option offers a quicker return to service than the rebuild option. However, the rebuild option offers greater mid-term availability than is likely for units that only had minimal repairs. The replace option is typically the option with the longest near-term loss of availability, as many DOT equipment types have limited order windows and require the installation of additional ancillary items or equipment. An additional consideration when deciding whether to repair or replace is typical DOT usage patterns, which indicate that as an equipment item ages, its availability declines over time. This reflects a combination of (1) increasing needs for maintenance and repairs of equipment over time (and associated part supply chain issues with older equipment) and (2) a user preference for operating newer equipment when it is available. Future Cost and Availability Impacts on Equipment C H A P T E R   5

Future Cost and Availability Impacts on Equipment 29 Figure 6. Hypothetical impact on operating costs for repair, rebuild, and replace options.

30 This chapter describes how to calculate the A and B coefficients needed as inputs to forecast the cost rate of repair, rebuild, and replace decisions in the future. As the option to retire will not require assessment of future life cycle costs, it is not applicable for this analysis. It is known that operational costs for a piece of equipment grow in amount and in scale over time as the piece of equipment ages: repairs are required more frequently, and repair costs increase in multitude. Operational costs associated with consumables (fuel, tires, preventive maintenance, and part wear) are proportional to age (consistent cost per hour regardless of equipment age). As a result, it is necessary to use LTD costs and LTD age to forecast operational costs into the future for a specific piece of equipment. LTD costs refer to all the costs associated with the piece of equipment since the piece of equipment was brand new (age of zero hours) and the total number of hours operated (reading on the equipment hours meter). The form of the equation that describes LTD operating costs is a second order polynomial: LTDOperating Cost A Age B Age2= × + × The A and B coefficients describe the operating costs as the equipment ages and are the coeffi- cients of the equipment’s life cycle cost curve. The data necessary to obtain the A and B coefficients for calculation of cost rates are the LTD age and LTD total operating costs (e.g., labor, parts, fuel, tires) for a pool of equipment repre- sentative of the piece of downed equipment. Historic operational data should be obtained for equipment that is similar in type and operation to that of the piece of equipment being assessed. Data can be obtained from standard asset management and accounting reports for the fleet. It is important that accurate data be used for calculation of the cost rate; a small sample size of high- quality data is preferable to a large set of inaccurate data. A similar number of historic data points should be used for each piece of equipment in order to accurately develop average LTD cost data for the equipment type. The desired outcome is to have each piece of equipment equally represented in the dataset. Once the dataset is obtained, the data points are input into Excel and plotted on a graph showing the LTD age and LTD operating costs over time for each piece of equipment. The example that follows uses real-world data from seven 10-year-old motor graders of the same make and model that were similarly maintained, all of which were effectively doing the same operations (Table 3). Reports for each piece of equipment were run with blank start and end dates at the end of each calendar year (Dec. 31, 20xx). This provided LTD age and operating cost data for each piece of equipment. In this example, data were chosen for pieces of equipment that were at least 10 years in age and had accumulated at least 5,000 hours of operation. Calculation of A and B Coefficients for Economic Analysis Forecasting C H A P T E R   6

Calculation of A and B Coefficients for Economic Analysis Forecasting 31 Year Equipment ID LTD Operating Hours LTD Operating Cost 1 MG2001 460 $ 5,562 2 MG2001 1,576 $ 22,008 3 MG2001 2,465 $ 40,743 4 MG2001 3,489 $ 65,675 5 MG2001 4,404 $ 85,470 6 MG2001 5,107 $ 105,595 7 MG2001 5,868 $ 127,820 8 MG2001 6,734 $ 154,065 9 MG2001 7,303 $ 180,453 10 MG2001 7,653 $ 208,404 1 MG2002 326 $ 3,178 2 MG2003 1,416 $ 11,680 3 MG2004 2,404 $ 26,015 4 MG2005 3,537 $ 39,160 5 MG2006 4,437 $ 50,057 6 MG2007 5,471 $ 76,899 7 MG2008 6,036 $ 95,225 8 MG2009 6,396 $ 119,820 9 MG2010 6,969 $ 151,952 10 MG2011 7,310 $ 163,480 1 MG2003 416 $ 4,202 2 MG2003 1,600 $ 14,638 3 MG2003 2,491 $ 30,258 4 MG2003 3,493 $ 46,497 5 MG2003 4,174 $ 60,337 6 MG2003 5,244 $ 76,870 7 MG2003 5,531 $ 98,740 8 MG2003 6,045 $ 112,500 9 MG2003 6,560 $ 135,675 10 MG2003 6,864 $ 150,426 1 MG2004 454 $ 2,794 2 MG2004 1,072 $ 7,535 3 MG2004 1,778 $ 21,438 4 MG2004 2,702 $ 41,100 5 MG2004 3,711 $ 62,622 6 MG2004 5,025 $ 99,113 7 MG2004 5,422 $ 108,896 8 MG2004 6,039 $ 120,423 9 MG2004 6,086 $ 133,830 10 MG2004 6,426 $ 146,607 1 MG2005 340 $ 2,372 2 MG2005 1,516 $ 16,952 3 MG2005 2,304 $ 30,215 4 MG2005 3,028 $ 44,431 5 MG2005 4,044 $ 61,347 6 MG2005 5,031 $ 81,624 7 MG2005 5,457 $ 92,803 8 MG2005 5,746 $ 109,557 9 MG2005 5,940 $ 121,572 10 MG2005 6,164 $ 133,568 Table 3. Example LTD operational and cost data for seven representative pieces of equipment. (continued on next page)

32 Decision Making for Repair Versus Replacement of Highway Operations Equipment Once these data have been input into Excel, they can be plotted in a line graph that shows the relative age (x-axis) and LTD operating costs (y-axis) for each piece of equipment, as shown in Figure 7. A second order polynomial equation is then fitted to the data that goes through the origin (inter- cept value is zero), showing a curve for the set of historical data. In Excel, the equation and R-squared value (or the coefficient of determination) of this curve can be displayed, as shown in Figure 8. In this example, the A coefficient is determined to be 8.7875 and the B coefficient is 0.002. Mitchell (1998) applied regression techniques to field data to develop a mathematical model to represent repair costs in terms of equipment age in hours of use. Repair cost and use data were $0 $50,000 $100,000 $150,000 $200,000 $250,000 0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000 Age (hours) Note: The line graphs in Figure 7 show the seven equipment IDs from Table 3. Li fe -to -D at e O pe ra tin g C os t ( $) Figure 7. LTD age and LTD operational cost data. 1 MG2007 307 $ 4,301 2 MG2007 1,480 $ 17,763 3 MG2007 1,675 $ 28,152 4 MG2007 2,287 $ 41,253 5 MG2007 2,937 $ 58,483 6 MG2007 3,499 $ 73,889 7 MG2007 4,046 $ 86,492 8 MG2007 4,336 $ 105,794 9 MG2007 4,530 $ 124,628 10 MG2007 5,563 $ 143,259 Year Equipment ID LTD Operating Hours LTD Operating Cost 1 MG2006 488 $ 5,330 2 MG2006 1,527 $ 16,033 3 MG2006 2,270 $ 27,213 4 MG2006 3,035 $ 40,068 5 MG2006 3,766 $ 56,119 6 MG2006 4,383 $ 70,698 7 MG2006 4,888 $ 83,810 8 MG2006 5,423 $ 94,942 9 MG2006 5,596 $ 105,995 10 MG2006 5,757 $ 113,524 Table 3. (Continued)

Calculation of A and B Coefficients for Economic Analysis Forecasting 33 collected for 260 items of equipment operating in 17 different fleets that were managed by four construction companies. Regression analysis was used to evaluate 19 different equation forms, and a second order polynomial was found to provide the best fit to the data as follows: LTDOperating Cost A Age B Age2= × + × LTDOperating Cost cumulative cost of repair parts and labor from 0 hours to age hours= Age LTD hours worked by the equipment= This has come to be known within the realm of equipment management as the Mitchell Curve. The A coefficient reflects the fact that the cumulative cost of repairs grows, in part, in direct proportion to the hours worked by the equipment. The B coefficient reflects the fact that cumula- tive repair costs accrue more quickly as the piece of equipment increases in age. Costs associated with maintenance and actions that prevent equipment failures contribute to the A coefficient; repair actions resulting from failures contribute to the B coefficient (Vorster, 2009). This methodology benefits equipment managers seeking to estimate repair costs because it is straightforward and can be easily applied using common spreadsheet programs. Noted advan- tages to the methodology are that the results reflect the entire experience of an equipment item and are based on data collected directly from the equipment in the fleet (Mitchell et al. 2011). Equipment items often have periods of good, bad, and average repair cost accumulations throughout their life cycles; using data reflective of all experiences produces results that are both stable and representative of what can be expected. A coefficientB coefficient Age (hours) Li fe -to -D at e O pe ra tin g C os t ( $) Figure 8. Trendline curve and equation for LTD operating cost and age.

34 Decision Making for Repair Versus Replacement of Highway Operations Equipment The principal limitation to the methodology is the availability of quality LTD data needed to drive the analysis. LTD data are not available for equipment purchased on the resale market or where reliable cost data have not been maintained since the purchase of a new piece of equip- ment. In addition, the use of data from a disproportionate number of equipment items of similar age will bias the coefficients toward that age. Results are most accurate and representative when data are collected from equipment that are spread across the age spectrum.