Mathew Smidt, Ph.D, Associate Professor and Extension Specialist, Forest Operations Phone: ,

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1 2009 Council on Forest Engineering (COFE) Conference Proceedings: Environmentally Sound Forest Operations. Lake Tahoe, June 15-18, 2009 Stump to mill logging cost program (STOMP) Authors Mathew Smidt, Ph.D, Associate Professor and Extension Specialist, Forest Operations Phone: , Robert Tufts, Ph.D., J.D., LLM, Associate Professor,Forest Engineering Phone: , Thomas Gallagher, Ph.D. Associate Professor, Forest Operations Phone: , School of Forestry and Wildlife Sciences, 602 Duncan Dr. Auburn University, AL Abstract In order to relate the change in trucking capacity to logging system costs, we developed a stump to mill harvesting cost program (STOMP). While the model results are affected by changes in trucking capacity due to average truck speed, haul distance, load weight, and truck utilization, the main feature of the model is to present the range in truck capacity across a range in probable mill unloading times. The model presents productivity, cost, and income for a range of unloading times dependent on the mill parameters selected by the user. The range times may either represent more likely (close to the median time) and less likely (farther from the median) conditions or could be used to examine the benefit and cost of current or expected mill conditions. The sample models of harvesting crews show there is usually one or more significant unloading time increases that cause significant decreases in total and net revenue. The magnitude of that decline is related to the size of the logging crew, haul distance, and other production limits (market and in-woods production). One example from a typical southern thinning crew shows the potential for a 10 to 15 decline in after tax income across a range in likely unloading times. A full range in unloading time (10 to 75 minutes) shows a drop in net income of 80 to 100 from the smallest to greatest unloading time. The models and user s manual are available at

2 Introduction The logging industry and the forest products industry have adapted and evolved in response to both internal and external financial pressure. Historically the most significant of these pressures have been the cost and productivity of labor which led to wide spread mechanization of logging which began in the 1960 s and continues today. Mechanization of logging has added risk from large capital outlays and the financial impact of poorly utilized capital costs and labor expenses. As a result variables in the wood supply system that inhibit efficient utilization of capital and labor resources have immediate consequences on wood supplier businesses. The most significant impacts are the strategies the wood suppliers employ to deal with internal and external production limits and the variability in system capacity for wood. Those strategies include extending equipment life, out sourcing trucking resources, or maintaining excess capacity in either harvesting or trucking resources. While those strategies make financial sense to individual wood suppliers, in total they may lead to inefficiencies and a general erosion of wood supplier capacity. The objective of this project was to integrate in-woods, trucking, and unloading capacities to examine how system variability affects logging productivity and costs. The resulting spreadsheet cost model uses monthly inputs for logging system capacity and productivity, annual inputs for labor and machines, and variables for mill unloading performance to determine the costs and productivity for specific logging crews. The model development was supported by the Wood Supply Research Institute (WSRI) was developed with significant interaction with the WSRI technical committee and six sessions with user groups. The project report describes the model approach and presents a scenario of a specific harvesting crew. Model approach System configuration The model represents the annual productivity for a static crew with fixed annual employment, machines, and owned trucking capacity. The model allows monthly changes in shift number, available market, haul distance, machine productivity, and harvest system. Also contract trucking capacity can be changed monthly since it does not affect the calculation of fixed costs. Production modeling Since one of the main objectives was to examine the interaction among harvest phases, the model represents Hot logging conditions. All products that are harvested that day or shift are assumed to be delivered during the same shift. Cold logging would involve removing constraints due to machine, harvest phase, or mill capacity so that each phase operates near the maximum utilization rate. The shift limit on productivity could be the quota or market available, the in-woods production, or trucking productivity. Once the limit is identified the number of machine hours and truck miles needed to accomplish the limiting production is calculated.

3 In-woods production Each machine used in the logging system has a production rate (tons per productive machine hour t/pmh) and a utilization rate (). Each machine is also assigned one or more harvest phases (felling, loading, processing, etc) that it can complete. The model uses the utilization rate and production rate (t/pmh) to calculate a production rate in tons per scheduled machine hour (t/smh) for each harvesting phase. The productivity (t/smh) available for each harvest phase is summed. The phase with the lowest production level sets the in-woods productivity. The scheduled and productive hours for the rest of the phases are calculated using the lowest production rate. The machine hours are then allocated to phases proportionally based on the hours needed in each phase and the hours available for each machine. Harvesting productivity of each phase can be adjusted monthly to represent poorer than average harvesting conditions. Phases like shoveling can be eliminated to represent seasonal changes in the harvest system (upland vs. wetland harvest). Only the phases included by the user are limiting for production. If the trucking production rate or the market (quota) is lower than the in-woods production rate, the hours used for each machine are lowered accordingly. Trucking production Trucking productivity is based on the number of complete round trips a truck can complete in one shift. The trucking shift can be longer that the in-woods shift reflecting the ability of truck to leave the woods with loads near the end of the in-woods shift. The round trip is composed of travel time (back and forth from the mill), loading time (in the woods) and unloading time (at the mill). Travel time is calculated with user inputs for distance from the woods to each mill and average travel speed. Loading time is a fixed number that equals the average time from when the truck arrives in the woods empty until it leaves loaded. Unloading time is determined using distributions generated from data collect by Deckard et al (2001). Inputs to the model identify mill type (pulpwood or sawtimber), unloading efficiency (benchmark or not), and inventory conditions (low, transition, and high). The model provides production and cost estimates for scenarios with unloading times across the distribution of possible unloading times. The mill turn time distributions and models are given in Appendix A. Each combination of inventory period and mill type yields unloading times from the distribution at the 20 th, 30 th, 40 th, 50 th, 60 th, 70 th, and 80 th percentiles. The unloading time for 20 th percentile infers that 20 of the unloading times are less than the one given. Taken together the estimates for unloading times represent more likely (50 th percentile) and less likely (20 th and 80 th ) outcomes or the general range in possible outcomes given the mill and inventory characteristics. Another way to interpret the results is to examine the average mill unloading time for each outcome. Contract Trucking For any month that the user specifies owned trucks and contract trucks as part of the fleet, the total production available will be divided equally among the trucks specified. Building excess

4 capacity with contract trucking will decrease the loads and miles operated by owned trucks and increase labor and fixed cost per ton. If model users rely on contract trucking only, hauling cost is a variable cost based on the loaded miles driven to deliver the loads. Trucking constraints can be lifted by adding more contract trucks on a monthly basis. Fixed Cost For capital equipment the model calculates the fixed costs, principle and interest payments on loan amounts, insurance cost, and market value decline. Principle and interest payments are calculated as a single annual payment dependent on term (years) and finance rate. The calculation assumes 100 financing of the purchase value. Insurance cost is a percentage of current value. Current value and market value decline are calculated using a double declining balance depreciation formula modified to represent the years of expected machine life defined by the user. If a machine is excluded from the harvest system for one month or more, the fixed costs for that machine are distributed only to the months the machine is utilized. After tax income and cost After tax costs are calculated given federal income tax guidelines for The depreciation calculation is simplified using only the half year convention and no consideration for Section 179 treatment. The simplification was implemented due to the likely variations in the tax code over time and individual depreciation circumstance of each machine. For depreciation road tractors are 3 year equipment and the rest of the equipment is assumed to be 5 year equipment. Formulas and assumptions related to after tax costing are presented in Appendix B. Applying the appropriate tax rate involves first entering all the production, cost, and income estimates and then observing taxable income. The tax rate is then determined based on the taxable amount. It is possible that the range in scenarios given for the same estimates might require different marginal tax rates. Variable costs The variable costs for machines are set by the number of productive machine hours. Maintenance costs are estimated by the user and entered directly into the spreadsheet. Fuel use is input for each machine and the monthly fuel price determines the hourly fuel cost. Lube and oil costs are estimated as a percentage of fuel costs. The model relies on users having reasonable estimates for variable costs. Overall variable costs of incidentals needed for management of a logging crew are included in the overhead cost. The overhead cost is estimated as a percent of monthly expenses. Labor Costs Considerable variability among logging firms exist in how labor rates are calculated for employees with numerous combinations of hourly or daily wages plus production bonuses. While in many cases these treatments are important to the analysis of labor cost as a fixed, semifixed or variable cost, the user input was viewed as too cumbersome to accurately express wage

5 cost in every way possible. Since the user inputs shifts per month rather than per week, total monthly hours are divided evenly by weeks per month to develop overtime costs. This means that given the same number of shifts per month the month with fewer days will record more overtime pay. Overtime rates can be set as straight wage or overtime (time and a half) depending on the firm size. Users set the benefit rate and workers compensation rate as a percentage of total wages. Revenue Model users enter revenue per ton for each product for each month. Users have the option of entering the cut, load, and haul revenue as one number or splitting the hauling revenue from the cut and load revenue. There is also an option that calculates trucking revenue based on haul distance. Users enter revenue per loaded mile and a base mileage. If the estimated haul distance exceeds the base mileage, revenue increases. Southern Thinning Crew The equipment data presented in Table 1 represents a typical high production pine thinning crew in the southern coastal plain. We assumed that the market available for each shift was 420 tons of all products and that all trucks could carry 26 tons of each product. The average loading time for trucks in the woods was 25 minutes. Table 1. Equipment and assumptions for cost scenario. Numb er Skidder 2 Knuckleboom loader w/ delimber Feller - Buncher 1 1 Make & Model John Deere 648 GIII Prentice 328 Prentice 2470 Beginni ng value ($) Tractors Prod. rate (t/pm H) Trailers Other equipment Util. rate Fuel Cons. (g/pmh ) Maint. & Repair ($/PM H) Lube rate ( of fuel cost) mpg $/mile

6 The total truck cycle time shows the monthly variance due to mill unloading times and haul distance (Figure 1). The numbers in the legend represent unloading time percentiles 20 through 80. Spikes in truck cycle time are due to longer haul distances for pulp wood in January, February, July, and October. The band or range in cycle times encompassed by all the lines represents a wide range in possible conditions. A more likely range would be encompassed by the three middle lines (40-60 th percentile). For April, the total range is from 1.28 to 2.01 hours per cycle and the more likely range is from 1.61 to 1.78 hours. Figure 1. Monthly variation in truck cycle time for unloading time percentiles. The legend shows unloading time in minutes from the lowest (80 th ) to the highest (20 th ) times. The model presents an annual financial summary (Table 2). The revenue used in the summary equals the before tax cost for the 50 th percentile scenario and includes a 10 return to capital. Increases in total harvest are accompanied by increases in total expenses needed to harvest and haul the additional wood. Annual after tax income ranges from $74,500 to $82,800. After tax income is presented using either principle payment or decline in market value. Contractors that are able to purchase more machines with cash or use older machines (from which the loans have been repaid) should use market value decline rather than principle payment as an indicator of machine cost and after tax income. In this example, assumptions for revenue ensured that all net incomes would be positive and return to capital ( ) would be greater than the 10 assumption in the before tax cost calculation. The model is designed so the user can apply additional trucking resources (owned or contract) and change the parameters (haul distance and type) of the mills and view the results on an annual of monthly basis. While this example demonstrates the relationship between net revenue and unloading time for a system balanced at the 50 th percentile. The model could also show the

7 affect on revenue of over-investment in trucking resources employed by many contractors to avoid in-woods production limits at long haul distances or long mill unloading times. Table 2. Annual financial summaries for the unloading time percentile distribution. Costs and losses are identified by ($). Unloading time percentile Unld. time (min) Total sales $1,156,868 $1,160,678 $1,173,378 $1,173,378 $1,173,378 $1,173,378 $1,173,378 Total expenses (with princ. pymt.) ($1,068,832) ($1,070,411) ($1,075,047) ($1,075,047) ($1,075,047) ($1,075,047) ($1,075,047) Gross profit (Loss) $88,036 $90,268 $98,331 $98,331 $98,331 $98,331 $98,331 Depreciation ($157,326) ($157,326) ($157,326) ($157,326) ($157,326) ($157,326) ($157,326) Taxable income $67,401 $69,633 $77,697 $77,697 $77,697 $77,697 $77,697 Federal tax liability ($13,480) ($13,927) ($15,539) ($15,539) ($15,539) ($15,539) ($15,539) After tax income (loss) $74,556 $76,341 $82,792 $82,792 $82,792 $82,792 $82,792 Beginning total capital value $499,094 $499,094 $499,094 $499,094 $499,094 $499,094 $499,094 Principle payment ($136,691) ($136,691) ($136,691) ($136,691) ($136,691) ($136,691) ($136,691) Market value decline ($114,888) ($114,888) ($114,888) ($114,888) ($114,888) ($114,888) ($114,888) After tax net income (less market value decline not princ. pymt.) $96,359 $98,144 $104,595 $104,595 $104,595 $104,595 $104,595 Return to capital References Deckard, D. L., R.A. Newbold, and C.G. Vidrine, An investigation of roundwood truck turn-time cost penalties to the wood supply system. Final Report to the Wood Supply Research Institute. 44p.