Logging Efficiency and Cost

As fully mechanized logging became widespread, a primary concern for cost control was efficient utilization of capital by optimizing equipment use. Increasing the system production per shift, week, or month lowers the capital, or fixed cost, per unit of operation. To achieve a balanced system, the contractor changes the operational system (machine kind and number) or operational method (technique) to maintain system balance and achieve high capital efficiency. The decline in fixed cost per ton (figure 1) has reduced the value of using fixed machine cost as a driving factor for system organization. The decline is due to a large number of factors such as delayed investment in machines to minimize capital costs and an increase in contracting to reduce capital cost and liability. This cost control was imperative due to the disparity between rising logging costs and decreasing logging contract rates over the past decade. Stagnant incomes, highly variable fuel costs, and rising labor costs have increased the complexity of effective cost control. Contractors need to develop methods to project cost and revenue in a way that provides a tool for planning and negotiation. Those methods should result in the development of performance measures that can be applied at the appropriate scale (e.g., tract, week, month, or year).

Performance measures that describe the system can be powerful tools to track changes over time and track progress and setbacks in response to system changes. The basic measures start at the machine level with utilization rate, operational efficiency, mechanical availability, and production rate. At the system level, important measures are production rate, production cost, labor productivity, and capital productivity. Performance measures can be used as benchmarks to determine the status of an operation. The benchmarks are carefully considered to ensure improvement in the system. A good set of performance measures and benchmarks is like a machine instrument cluster that helps the operator determine whether the system is operating correctly and where the problem areas might be.

Machine Measures

Operational Efficiency

The sources of operational delays are numerous. Table 1 identifies a number of reasons for delay or missed loads, the percent of total production missed as a result, and the frequency of the delay. The list provides a good guide for the relative importance of planning issues that could be addressed to improve operational efficiency. Relative rankings on this list would change due to season, geography, and market conditions.

Table 1. Delay Sources: Frequency and Impact on Average Firm Production

Source	Loads (% missed)	Frequency (%)
Weather: roads, woods	5	26
Planning: stumpage, access, harvest plan, equipment, trucking	4	22
Market: quota, mill closed, mill handling	3	38
Mechanical: scheduled, unscheduled	2	22
Stand and tract issues	2	12
Labor	1	15
Vacation	1	6
Regulations	<1	1
Total	18	100
Data from Greene, W. D. et al., 2004. Causes and costs of unused logging capacity in the southern United States and Maine. Forest Products Journal 54(5):29-37

Most of the sources in the table are directly or indirectly related to planning and scheduling. The remainder can be influenced by either planning or execution. Improvements in planning can be accomplished by establishing and achieving a number of planning benchmarks:

Acquire long-term plans from consumers for wood consumption and specification changes (12 months).

• Determine expected wood flow and harvesting conditions from known future harvests.
• Establish reasonable lead time to plan future harvests.
• Establish minimum known tract acreage and volume in the planning horizon (3 months).
• Communicate current tract status with wood buyers.
• Acquire written harvest plans for tracts.
• Insure adequate communication with customers and consumers.

Mechanical Availability

Utilization Rate

A benchmark utilization rate can be set using an approach such as the one in table 2. The approach is based on a typical timber sale to calculate the effect of moving time on UR and OE. Scheduled and unscheduled mechanical downtimes and operational delays are calculated as a proportion of machine hours. Scheduled machine hours (SMH) are defined as total scheduled operator time per day, per harvest, or per year. The productive machine hours (PMH) equals SMH multiplied by UR.

Table 2. Estimated Operational Efficiency, Mechanical Availability, and Utilization Rate for a Three-Machine Harvesting System

Item	Formula	Skidder	Feller buncher	Loader
Production (tons/PMH)	A	28	40	35
Sale size (tons)	B	3000
Minimum hrs/sale	C = B/A	107	75	86
Move and prep hrs/sale	D	5	3	5
Scheduled maintenance (hr/hr)	E	0.05	0.05	0.05
Unscheduled maintenance (hr/hr)	F	0.05	0.1	0.05
Operational delay (hr/hr)	G	0.05	0.05	0.1
Delay overlap %	H	0%	0%	0%
Total loss (hr/hr)	I = (E+F+G)*(1-H)	0.15	0.2	0.2
Minimum scheduled hrs	J = C*(1+I)+D	128	93	108
Maximum UR possible	K = C/J	84%	81%	79%
Mechanical availability	L = 1-(((E+F)*C)/J)	92%	88%	92%
Maximum operational efficiency	M = 1-((G*C+D)/J)	92%	93%	87%
Maximum UR required	N = C/maximum of J	84%	58%	67%
Observed operational efficiency	M = 1-(L-N)	92%	71%	75%

As OE and MA get smaller, it is more likely that the time spent in repair might overlap time spent waiting for trucks or moving. For example, if the loader broke down on Friday, the operator might have run out of quota after one more load anyway.

The maximum utilization rate possible is the rate that could be achieved if each machine were working independently. The situation is commonly referred to as cold (versus hot) operations where the next operation processes or transports logs from log storage. As long as buffers are not exhausted, machines can operate at the maximum utilization rate. In hot logging, where buffers are minimal or absent, the slowest production rate sets the production rate of the operation. In table 2, the maximum UR required reflects that skidding sets the overall production rate since the maximum UR required equals the maximum UR possible. Any additional delays in skidding would reduce the UR of the other machines. The limit on productivity in the skidding function adds operational delay to the other machines. The change decreases operational efficiency of the feller buncher and loader to 71 percent and 75 percent, respectively. The loader and feller buncher could suffer lower mechanical availability with little effect on system production.

Production Rate

There are a few tools or production models available to estimate machine production given certain stand and machine attributes. On occasion, these models are incorporated into computer models. The USDA Forest Service Operations Work Unit maintains references and links to many of these models (see http://www.srs.fs.usda.gov/forestops/home.htm). In general, the models should be used with caution because they are often based on older equipment, include limited range of conditions, or are heavily influenced by specific operators and systems. Contractors tracking their own production over a range of conditions can develop estimates that are better than most models.

System Variables

System Cost

Another important aspect of cost is the tax consequence. Contractors pay income taxes on the net income they receive. All expenses, including interest payments, can be deducted from gross income to reduce net income and therefore tax liability. Capital costs as they are incurred cannot be deducted, but owners can deduct a depreciation amount from gross income. The depreciation amount is determined by Section 179, the MACRS depreciation rule and economic life specified for each type of equipment. In 2008, equipment purchases under $250,000 could be expensed entirely under Section 179 from taxable income. Depreciation and expense rules in the federal and state tax code have changed regularly over the past decade.

The typical approach for cost analysis is to calculate the fixed costs (interest, principal, insurance, and taxes) and variable costs (fuel, lube, tires, maintenance, and repair) separately (table 3). The fixed cost per unit is the annual production divided by the annual fixed cost. The variable cost per unit equals the production per PMH divided by the cost per PMH. Fixed cost per ton is sensitive to annual production and, therefore, annual scheduled hours and the utilization rate (figure 2). Variable cost per ton is related only to variable costs and the hourly production rate.

Table 3. Before-Tax Machine Cost Calculations for a $200,000 Machine

			Year
			1	2	3	4	5
Variable cost ($/PMH)	Fuel and lube Maintenance and repair	A			20
		B			15
	Total	C=A+B			35
Fixed cost ($/yr)	Interest payment	D	14989	12189	9126	5576	2111
	Labor	E	47190	47190	47190	47190	47190
	Insurance	F	10000	7714	5810	4286	3143
	Total expense	G=D+E+F	129929	124843	119875	115001	110194
	Principal payment	H	29849	32649	35712	39062	42727
	Total	I=G+H	159778	157492	155588	154064	152921
Scheduled machine hrs/yr		J			2200
Utilization rate		K			75%
Productive machine hrs/yr		L=J*K			1650
Production rate (tons/PMH)		M			25
Variable cost ($/ton)		N=C/M			1.40
Fixed cost ($/ton		O=I/(L*M)	3.87	3.81	3.77	3.73	3.70
Total cost ($/ton)		P=N+O	5.27	5.21	5.17	5.13	5.10
1The machine has a 5-year life and is financed with 10 percent down, 9 percent APR, and sixty monthly payments.

Variable costs are best estimated using operation records but can also be estimated using several rules of thumb that have been published. Fuel consumption rules of thumb are often based on machine horsepower. General maintenance and repair rules of thumb are often linked to a depreciation formula. More specific information is on repair is available in resources like the CAT® Performance Handbook. In addition, machine manufacturers may have guidance on machine hours to rebuild/replace major components given the operating conditions.

Labor Productivity

In 2001, a survey of logging contractors in Alabama found labor productivity (of woods workers only) had a very wide range, from 1.4 to 10.5 tons per scheduled hour (figure 3). The scheduled labor hours include all workers on the operation (3 workers × 40 hours/week = 120 hours per week). The average was nearly 5 tons per hour. What makes the variability so large? Better mechanical availability from newer or better-kept machines; better operational efficiency from larger tracts or better planning; and higher machine production rates from bigger machines, fewer sorts, bigger trees, less hardwood, or better terrain all play a role in changing this number. The goal of an individual contractor would be to define labor productivity based on his or her system and sites and find cost-effective ways to improve. The improvement always has to consider the limiting factor in the operation. For example, if the market is limiting, that means reduced labor hours rather than increased production. The target for annual improvement in labor productivity should be equal to or greater than the annual growth in wages.

Capital Productivity

Capital and labor productivity combined gives a more complete view of the operation. Consider that an operation designed to accomplish a specific goal has a potential level of capital productivity. If that operation is well managed, that system could have higher levels of labor productivity, too. Figure 5 shows the range in both numbers for that same group of contractors. Operations with low capital productivity could be operations in difficult situations (terrain) or those with high processing demands (chipping). Either situation limits labor productivity. Increasing capital productivity in the same system also yields potential increases in labor productivity. The maximum labor productivity occurs when capital productivity is between 0.2 and 0.3 (see figure 5). This level of capital investment is favored because more than 60 percent of contractors are in this range (see figure 3). At high levels of capital productivity (>0.3), contractors are typically 30 to 50 percent below the maximum labor productivity achieved in this sample. This is likely due to a combination of the factors mentioned previously that lower utilization rate. To achieve high capital productivity, contractors operate older equipment or have partially manual operations. Either way, labor productivity is capped due to low production rates or low utilization rates, which are caused by low mechanical availability.

Conclusion

Resources

Brinker, R. W., J. Kinard, R. Rummer, and R. Lanford. 2002. Machine Rates for Selected Harvesting Machines, Circular 296. http://www.ag.auburn.edu/aaes/communications/circulars/cir296machine.pdf

Caterpillar Corp. 2007. Caterpillar Performance Handbook. Edition 37.

FERIC. 2009. FPInnovations Web site: http://www.feric.ca/index.cfm

Miyata, E. S. 1980. Determining Fixed and Operating Costs of Logging Equipment. USDA Forest Service General Technical Report NC-55. http://www.ncrs.fs.fed.us/pubs/gtr/gtr_nc055.pdf

Smidt, M. F., R. A. Tufts, and T. V. Gallagher. 2009. Stump to Mill Cost Program (STOMP): https://fp.auburn.edu/auforestops/stomp/

Stuart, W. B., L. A. Grace, C. B. Altizer, and J. J. Smith. 2008. 2006 Preliminary Logging Cost Indices. http://www.fwrc.msstate.edu/pubs/WSRI-R11.pdf

USDA Forest Service. 2009. Forest Operations Work Unit Web site: http://www.srs.fs.usda.gov/forestops/home.htm

Web Only, New August 2009, ANR-1347. Mathew Smidt, Extension Specialist, Associate Professor; Robert Tufts, Associate Professor; Thomas Gallagher, Associate Professor; all in the School of Forestry and Wildlife Sciences with Auburn University.

For more information, contact your county Extension office. Visit http://www.aces.edu/counties or look in your telephone directory under your county's name to find contact information. Issued in furtherance of Cooperative Extension work in agriculture and home economics, Acts of May 8 and June 30, 1914, and other related acts, in cooperation with the U.S. Department of Agriculture. The Alabama Cooperative Extension System (Alabama A&M University and Auburn University) offers educational programs, materials, and equal opportunity employment to all people without regard to race, color, national origin, religion, sex, age, veteran status, or disability.