Performance Efficiency
Group IV
John Jones
Karen Kennedy
Denyse Jones
Darrin Mallett
Quantitative Methods, MBAC5453
Dr. Juan Castro
October 18, 2001
Executive Summary
The study benchmarks the monthly production patterns and expenditures of
an Oriented Strand Board (OSB) facility located in Northeast Texas owned by
Louisiana-Pacific Corporation (LP).
Monthly production is captured at the point of payment or delivery to LP’s
OSB facility. All production is reduced to the average number of loads received
on a daily basis and to the cost to cords using local conversions to form a
common basis for comparison.
LP’s OSB facility consumes approximately 286,000 cords of pine and
hardwood annually, with a budgeted transfer cost this year of approximately $20
million dollars. The Forest Resources Division (FRD) must produce the wood
from three different sources: fee, purchase, and gate-wood timber. FRD must
deliver this wood in an orderly, sustained manner to meet changing mill seasonal
production schedules.
The OSB facility is responsible for the unloading, inventory rotation, storage,
and quality protection of the wood raw material with emphasis on minimizing fiber
loss from breakage and deterioration.
Cords of wood delivered per dollar spent is used as the measure of efficiency
for this study. Annual changes in total efficiency for the OSB facility are
monitored to measure changes in business strategies and in cost structures.
The model is utilized to develop composite classical economic measures of
average unit and marginal cost for the business. These, in turn, can be used to
assess the affects that efficiency and production levels within an OSB facility
have on business performance.
The analyses are intended to document the annual variation in cost and
efficiency of the OSB facility. The analyses also intend to document the changes
in equipment and work force and shifts in business strategies. Moreover, the
changes in total and partial economic efficiencies across years, along with major
causes of those changes shall be seen through the analyses. The findings can
be used to identify developing trends within the industry, to measure the effects
of weather, market, regulatory, and strategic changes on the productivity and
business performance of the OSB facility, and to test hypothesis concerning
factors that drive and affect the industry.
Introduction
Independent logging contractors provide an important service for the forest
industry and society. They are highly capitalized business professionals who
perform activities that manifest the forestry profession to the public (Stuart, Lebel,
Walter, and Grace 1996). Their actions benefit society and the forest industry by
providing the raw material used to make forest products so necessary to the
modern society. In doing so, independent logging contractors provide jobs to
communities, realize financial return on timber investments, accomplish
silvicultural objectives resulting in more productive forestland, and the salvage of
damaged timber (Walbridge and Shaffer 1990).
Independent logging contractors operate in a challenging business
environment. They are affected by increasing equipment prices, increasing fuel
prices, extremely high workers’ compensation insurance rates, the need to pay
competitive wages, and the need to hire and keep quality employees (Keesee
1997). In the past, these challenges have been met by gains in productivity. The
majority of the technology that generated these past gains in productivity is two
to three decades old. Therefore, it cannot be expected for logging contractors to
maintain those efficiency gains forever (Stuart, Lebel, Walter, and Grace 1996).
This inability to remain efficient could cause the southern logging force to be
surpassed by competitors in other parts of the world, jeopardizing the economic
and social benefits they provide.
Due to the complex operating conditions, (adverse forces encountered by
logging contractors and the important service they provide), it is critical to monitor
their performance. This measure of performance can then be used to assess the
economic health of logging contractors and the industry as a whole. Knowledge
of the economic health of the industry will in turn help point out areas of potential
improvement in the wood supply system, in business management, and in the
legal and regulatory environment. Improvements must be constantly made to
ensure that the U.S. forest industry will remain competitive in an increasingly
global environment.
A basic definition of performance is needed before proceeding. Performance,
as defined by Webster’s Ninth Collegiate Dictionary, is the act of completing or
accomplishing a task. While this definition of performance is adequate, dividing it
into different levels can further expand the meaning, depending on how fast,
effectively, or thoroughly the task is completed. Level of performance is very
important to the management personnel of any organization. If a firm is not
improving its level performance, it will likely be surpassed by competition; or it
may be on its way out of business. Management must clearly understand the
definition of performance in the process, and how to measure it, in order to
improve the performance of the activity (Triantis 1990).
Technical efficiency was the primary criterion used to measure performance in
this study. Technical efficiency measures how effectively the inputs of the forest
process are transformed into outputs. This translates into a measure that can be
used by management to identify factors and conditions positively or negatively
affecting performance.
Technical efficiency is an effective criterion to measure performance, but
business profitability must also be considered. It is important for the logging
contractors and the OSB facility to operate as efficiently as possible, while still
focusing on the ultimate goal of any business, profitability. When cords of wood
are considered the output of the process, efficiency and profitability are not
necessarily synonymous. Distinction between the two terms is important.
Therefore, both criteria will be considered in measuring the performance of the
OSB facility.
Literature Review
Much of the work done to date to measure performance in forestry operations
has included engineering techniques focusing on processes within a specific
operation or system. Common measures include production per man-day,
machine availability, or machine production per hour. These methods were
developed to make evaluations on performance in specific applications and often
contain broad assumptions (Stuart, LeBel, Grace, and Shannon 1997). These
assumptions may not be appropriate to the modern industry because of changing
business conditions and technology. The studies that focus on processes may
not be applicable to other applications and do not indicate whether the results
can be obtained on a broad scale (Cubbage 1988).
Another popular measure of performance has been the total cost per unit
measure. These measures use cost information obtained by accounting
methods. Therefore, broad assumptions are not needed due to the detail of the
data. Analyses using these data are not perfect (an example being the total cost
measure). Since an overall cost per unit in a specific time period is obtained, it is
difficult to make conclusions when comparing OSB facilities operating within the
same system type or region, due to lack of detail. Therefore, these measures
are incomplete measures at best because only one level of factors is observed
(LeBel 1996).
There are, however, alternative methods of measuring performance that
provide more insight into technical and economic performance within and across
harvesting or wood supply systems. Technical efficiency measures can
accomplish this when coupled with personal knowledge of the operations. These
methods can be used not only to examine performance on a total cost basis but
can be broken down into individual cost components. This is necessary due to
the diversity of business types and the legal environment of the timber industry.
There have been a few applications of efficiency in forestry-related fields.
These include Martin and Page (1983), Kao and Yang (1991), Carter and
Cubbage (1995), and LeBel (1996). Only the latter two applications focused on
southern U.S. timber operations. Martin and Page (1983) focused on production
forestry in Ghana, and Kao and Yang (1991) analyzed Taiwan forest
management. Carter and Cubbage (1995) and LeBel (1996) both focused on
efficiency measurement of southern U.S. timber operations.
Carter and Cubbage (1995) used econometric stochastic frontier estimation to
measure efficiency of southern U. S. firms between 1979 and 1987. Basic
differences between the Carter and Cubbage study and this study make a
comparison difficult. Carter and Cubbage (1995) was based upon aggregate
data, rather than individual observations, which is the primary difference between
the two studies.
LeBel (1996) performed a similar performance and efficiency study as the one
being discussed in this study. Additional discussion of the LeBel (1996) is
warranted at this time.
LeBel (1996) performed a similar performance and efficiency study using the
same analysis techniques, procedures, and types of data. The study included
production and cost data covering the period from 1988 to 1994. The data was
then used to determine how efficiently the process inputs were transformed into
outputs, or cords of wood delivered to market.
Even though profitability is the ultimate goal of any business and is directly
related to the cost of the wood, performance measured by efficiency is very
important. Efficiency is a prerequisite of profitability. Also, a procurement
organization cannot be expected to pay for the inefficiency of its logging
contractors, unless it is the source of the inefficiencies.
Data Collection and Analysis
The data collection for this project included performing a survey with logging
contractors associated with the OSB facility and the production and cost
information for the mill. The survey was mailed to 200+ logging contractors. The
purpose of the survey was to determine if there were variables involved that were
not associated with the logging contractor’s production and price. For example,
the attitudes within the scale-house might deter a contractor from delivering his
wood to the LP OSB facility.
The production data, the output used in this study, was obtained directly from
the OSB facility. Ten thousand six hundred and ninety-five data points were
involved in the production data information. The production information
represents the number of loads delivered during a particular time period and the
turn-around time involved in the unloading process. The goal was to obtain the
average number of loads per day hauled per month and the associated turnaround time for each load.
The input for this study was the cost in dollars associated with getting the
wood delivered to the mill. The objective was to collect monthly cost information
for the OSB facility. The information was obtained from the monthly profit and
loss statement of the mill.
Summary
The survey concluded that the turn-around time at the facility was the primary
concern of the logging contractors. The data collected as part of the study was
used to measure the efficiency of the OSB facility. Efficiency and cost was the
primary criterion used to evaluate the performance and overall economic health
of the logging contractors and the OSB facility.
Methodology
Many statistical tools can be utilized for the purpose of analyzing data.
Each method has unique criteria associated with it. The type of data that is to be
analyzed (i.e. categorical, ordinal, interval, or ratio) plays an integral role in
selecting the correct statistical tools for the analysis. This particular study
involves ratio data, or data that has a natural zero. If this particular fact is
coupled with the nature of this study (to investigate the relationship between load
turnaround time and cost), it can plainly be deducted that a simple linear
regression model provides the best avenue to examine the data.
“The simplest type of regression model is a linear relationship involving
one independent variable, X, and one dependent variable, Y,” (Evans & Olson,
2000, p.170). In laypeople’s terms, a linear regression shows how closely two
items are related. That relationship is then characterized mathematically by
using a linear equation. This linear regression line would be in the form Y = 0 +
1X + , where 0 is the y-intercept of the line,1 is the slope of the line, and  is
the error involved in the regression. The main focus of this analysis was to
uncover the effect truck turn-around time had on cost. Thus, the primary
regression employed in the study utilized truck turn-around time as the
independent variable and cost as the dependent variable.
Two extremely important aspects of a linear regression are the coefficient
of determination (R2) and the sample correlation coefficient (R). The coefficient
of determination is a number between zero and one, and it quantifies the amount
of variation in the regression model that can be explained by the independent
variable (Evans & Olson, 2000). Therefore, R2 truly tells how intimately the two
items in the regression are linked. As R2 approaches one, the relationship
between the two objects in the study becomes stronger. Moreover, if R 2 is close
to zero, then the relationship that exists is very weak.
The sample correlation coefficient is a number between negative one (-1)
and positive one (+1) and is the square root of the coefficient of determination. R
simply states whether the relationship in the regression is positive or negative. If
R is negative, then the relationship is negative (i.e. as one variable increases in
magnitude, the other decreases). However, if R is positive, the relationship is
also positive (i.e. as one variable increases, the other increases as well).
Another statistical tool that can be used in order to show if any relationship
at all exists is significance of regression. This particular method involves a null
hypothesis and an alternate hypothesis. In this case, the null hypothesis would
be that the slope of the regression line, 1, is equal to zero. If the slope of the
line is zero, then no relationship exists. The alternate hypothesis would be that
1 is not equal to zero. In other words, if the line has a slope, then a relationship
exists. The null hypothesis is then accepted or rejected on the basis of whether
or not a calculated F statistic is beyond the critical F for the regression. If the F
statistic is beyond the critical F, or outside of the acceptance range, then the null
hypothesis is rejected. In this instance, a relationship does exist. However, if the
F statistic is in the acceptance range, then the null hypothesis is accepted. In
this case, there is no relationship. Significance of regression does not in any way
reveal how closely the two items in a regression are related. It is simply a useful
tool to use to show whether or not a regression would be an adequate test to
utilize for the analysis of certain data.
Finally, there are a few assumptions that must be made when using a
linear regression model. The first and most paramount is that the relationship
between the two items is linear. A simple scatter plot could make this
determination (Evans & Olson, 2000). The second assumption is that the error
terms for each specific X are distributed normally and have a mean (average) of
zero and constant variance. A histogram plot of the error terms that is
scrutinized for normality (a bell shape) will satisfy this assumption (Evans &
Olson, 2000). The third supposition is homoscedasticity, or “that the variation
about the regression line is constant for all values of the independent variable,”
(Evans & Olson, 2000, p. 181). Last but not least, all the residuals should be
unconnected for each denomination of the independent variable (Evans & Olson,
2000).
Results
When a simple linear regression was ran on the results, the turn-around time
was used as the independent variable and the cost was used as the dependent
variable. Based on the results collected from the data, no significant correlation
existed between the turn-around time and the cost when a thirty-two month
period was considered.
The significance of regression test was run on the data. The calculated F
statistic for the data yielded a result of 1.5 and the critical value F resulted in of a
lesser value of 0.22. The calculated F statistic was not in the acceptance range;
thus, the null hypothesis was rejected. In other words, a relationship did exist.
The coefficient of determination for the primary regression model was
0.048. This suggests a very weak relationship between the independent variable
(turn-around time) and the dependent variable (cost). The sample correlation
coefficient was 0.22. This indicates that the relationship that existed was mildly
positive. The equation for the best-fit line of the data was Y= 65.54 + 0.04X.
Two other regression models were run with a fewer number of
observations. The first was turn-around time vs. cost with a duration of twenty
months. This period of time correlates to the period of time before LP purchased
a piece of machinery to increase efficiency. The coefficient of determination for
this regression was 0.193, which indicates that a slight relationship existed
between the variables. The sample correlation coefficient was 0.44. That
suggests that the relationship was positive. The linear equation for this data was
Y = 77.42 + -0.07X.
The next regression was turn-around time vs. cost for the time period after
LP purchased the piece of equipment. The coefficient of determination was 0.04.
This, as with the previous regressions, shows an extremely weak relationship.
The sample correlation coefficient was 0.21, which insinuates a positive
relationship between the variables. The linear equation for this regression was
Y= 60.49 + 0.03X.
.
The last regression in study was number of loads vs. turn-around time.
This regression was run just to see if any relationship existed between these two
variables. If there was no relationship, then a multiple regression using turnaround time and number of loads as independent variables could have been
done. The coefficient of determination for the regression was 0.131, indicating a
relationship. Thus, a multiple regression could not be done.
Conclusion
The analyses are intended to document the annual variation in cost and
efficiency of the OSB facility and to test hypothesis concerning factors that affect
the price LP must pay for a cord of wood. The analyses revealed no positive
correlation between variation in cost of the wood and the efficiency of the OSB
facility. This finding positively identified the market conditions established the
price of the wood. However, the survey and the knowledge LP’s buyers revealed
loggers are inclined to sell their wood for a price under the commodity price if the
turn-around time at a facility allows them to sell a larger quantity of wood The
objective was to collect monthly cost information for the OSB facility. The survey
concluded that the turn-around time at the facility was the primary concern of the
logging contractors.
References
Carter, R. C., F. W. Cubbage (1995). Stochastic frontier estimation and sources
of technical efficiency in southern timber harvesting. Forest Science, 41(3),
576-593.
Chiang, K., Yong Chi Yang (1991). Measuring the efficiency of forest
management. Forest Science, 37(5), 1239-1252.
Cubbage, F. (1988). Regional analysis of factors affecting southern pulpwood
harvesting cost. Forests Products Journal, 38 (11/12), 25-31.
Evans, James R. & David L. Olson (2000). Statistics, Data Analysis, and
Decision Modeling. Upper Saddle River, NJ: Prentice Hall.
Keese, K. C. (1996). Increasing utilization: A key to profitable operations.
Appalacian CFM News, 20 (3), 5.
LeBel, L. G. (1996). Performance and efficiency evaluation of logging contractors
using data envelopment analysis. Virginia Polytechnic and State University,
Blacksburg, VA. Ph.D. Dissertation, 201p.
Martin, J. P. & J. M. Page (1983). The impact of subsidies on x-efficiency in LDC
industry: Theory and an empirical test. The review of economic statistics 65,
608-617.
Stuart, W. B., L. LeBel, M. J. Walter & L. A. Grace (1996). The line and the
backfield: Teammates or competitors. Journal of Forestry 94 (7), 4-7.
Stuart, W. B., L. LeBel, L. A. Grace, J. T. Shannon (1997). The use of partial
economic efficiencies as a tool. Department of Forestry, Industrial Forestry
Operations, Virginia Polytechnic and State University, Blacksburg, VA.
Unpublished.
Triantis, K. (1990). An assessment of technical efficiency measures for
manufacturing plants. In People And Product Management In Manufacturing,
pp. 145-165.
Waldridge, T. A. & R. M. Shaffer (1990). Harvsting systems, tree processing,
skidding, forwarding, and yarding. New York: John Wiley & Sons.
Appendix
Contractor Vendor Market Survey
In September 2001, the Team prepared a survey form comprised of twelve questions
and mailed it to its 200+ logging contractors. The survey was intended to gauge the
perception of logging contractors towards LP’s scaling practices. Though the survey
was not scientifically designed, the Team feels that the answers do speak to scaling
problems that exist within LP. A summary of the results is included below.
I. Questionnaire:
1. Do you believe log specifications at LP, compared to other plywood mills, are:
A. Too Restrictive:
28%
B. Same
69%
C. Less Restrictive
3%
2. If haul distance and price were equal, would you haul your logs to:
A. LP:
54%
B. Other
46%
3. Are LP’s scaling procedures done fairly in accordance to the specifications?
A. Fair
78%
B. Unfair
22%
4. Are scaling procedures consistent, load to load, day to day, whether mill
inventory is full or empty?
A. Consistent
80%
B. Inconsistent
20%
5. Do scalers seem knowledgeable of the specifications and how to apply them?
A. Knowledgeable
59%
B. 70% Knowledgeable
34%
C. < 50% Knowledgeable
7%
6. What are the scalers attitudes towards you and your drivers?
A. Unfriendly
6%
B. Neutral
56%
C. Friendly
38%
7. The amount culled per defect is:
A. Fair
88%
B. Unfair
12%
8. Do scale tickets identify and itemize cull so that you know what is being culled
and the amount?
A. Yes
85%
B. No
15%
9. Is truck turn-around time in the mill:
A. Acceptable
55%
B. Unacceptable
45%
10. Are the Logging Contracts easy to understand?
A. Yes
94%
B. No
6%
11. Are changes in the contract delivery price given with enough prior notification
time?
A. Yes
65%
B. No
35%
12. Does the LP employee, with whom you negotiate your contract, deal with you
....
A. Fairly
75%
B. Courteously
73%
C. Knowledgeably
81%
II. Survey Written Comments:
Attesting to their intense feelings, many of the contractors went out of their way
to write additional comments.
1. The turn-around time at the facility has improved significantly, but for how
long?
2. Hauling rates change with little advance notice.
3. Forestry personnel are easily accessible and easy to talk with.
4. Most scalers are friendly and easy to deal with.
5. Turn-around time at the mill was horrible last year. I appreciate the
improvement.
6. You developed a bad reputation for getting trucks unloaded. This has
improved, and I appreciate your efforts.