Standards
for Time Studies
for the
South African Forest Industry
Project team:
Brad Shuttleworth
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Summary
This South
African Standard for Time-studies will provide a common and standard time-study
methodology for the South African Forest Industry; a protocol that does not
currently exist. Its implementation will serve the purpose of aligning the
South African Forest Industry with international forest operations development
and assist with the ÒmodernisationÓ of the IndustryÕs forest operations. The
concept of modernisation essentially includes updating forest operations in
terms of both mechanisation and other modern systems improvements with the goal
of improving wood/fibre yield, wood/fibre quality and reducing production costs
to remain locally and internationally competitive.
The
Standard has been compiled by those with specific expertise in work- and
time-studies, particularly the statistical analysis component and machine
costing. The Standard, with the inclusion of an internationally standardised
Machine Costing Model, was developed based on accepted and validated
international Time-study standards, protocols and literature. This Protocol is
envisaged to be a state of the art model to benefit the South African Forest
wood supply chain.
The
Standard will be web-based and will guide the user step-by-step through the set
up and execution of time studies and their application in Operations Research
analysis. The standard deals with the setting of time-study objectives to
ensure that time and resources are used efficiently and help to develop the
desired results. Three types of studies, observational, experimental and
modelling, are introduced. Different techniques are provided to control bias
(i.e., systematic error) including randomisation and blocking.
The Standard contains sections on
experimental study design, data collecting methodologies including sample size
calculations; time study models; selecting an appropriate time study technique;
statistical analysis and methods to best analyse the data collected; and ways
to use and proceed with the results achieved through linkage with a machine
costing model. The user will also be able to calculate machine availability,
utilisation and systems efficiency ratios that are useful in determining
systems efficiency. Background data forms, a terrain classification, templates
to create data collection forms for the userÕs study and a brief discussion on
available time study software and equipment are also included.
Included in
the Standard is a Time-concepts model developed by the International Union of
Forest Research Organisations (IUFRO), useful for the precise division of
common time elements included in all work and production systems.
The
Standard also describes in detail the six different scopes of time studies,
ranging from wide to narrow. These studies are shift-level, plot level, cycle
level, time and production count, working sampling and the element level. Each
study has different strengths and weaknesses and requires a specific technique
which is discussed. A statistical analysis manual is also in the drafting
stages and will aid the user through conducting their analysis and interpreting
the results.
Table of Contents
1.2 From Work Study to Time Study
2.1 Developing a Study Goal and
Objective
2.2 Study Classification and
Experimental Design
2.2.1 What type of study do you
need?
2.3 Experimental Study Designs
2.3.1 Mono-factorial Random Design
2.3.2 Multi-factorial Random Design
2.3.3 Mono-factorial Block Design
2.3.4 Multi-factorial Block Design
2.3.5 Mono-factorial Latin Square
Design
2.3.6 Multi-factorial Latin Square
Design
2.5.2 Approximating Sample Size
3.1.4 Visualisation of Time Concepts
4.0 Time Study
Techniques and Methodologies
4.1.1 Data acquisition methods
4.1.2 Advantages and Drawbacks
4.2.1 Data acquisition methods
4.2.2 Advantages and Drawbacks
4.3.1 Data acquisition methods
4.3.2 Advantages and Drawbacks
4.4.2 Advantages and Drawbacks
4.5.2 Advantages and Drawbacks
4.6 Work Sampling (Instantaneous
Observation, and/or Activity Sampling)
4.6.2 Advantages and Drawbacks
5.0 Machine Element
Standardisation
5.1 Standardised Element Lists by
Machine
Skidder/agricultural tractor with
winch or drawbar (a-frame or other):
Loader (either tracked or wheeled):
The purpose
of this protocol framework is to provide a standardised time study methodology
for the South African forest industry. This manual has been developed to work
in conjunction with the partner computer program to assist in work study
development. This program is developed specifically as an extension of this
manual and as a way to assist the user through navigating the concepts in the
manual relevant to their study objective.
This manual
will cover setting up a time study, selection of experimental design (2.0),
time models and time concepts (3.0), time study methods (4.0), standardised,
machine-specific time elements (5.0) and statistical analysis (6.0 –
still to be completed). Tools included with this manual are the above-mentioned
software, study forms – both generic and machine-specific, and a further
reading list.
The outputs
of time study analysis can then be inputted into a costing model developed by
the European Union Cost Action 0902. This costing model has been developed by
experts from around the globe, including South Africa, and provides an easy to
use and internationally regarded way to cost forest operations (Figure 1). The
model makes use of internationally accepted and current costing protocols and
has been validated by an expert panel. The costing model can be found on the
cost website at this link: http://www.forestenergy.org/pages/costing-model—machine-cost-calculation/?PHPSESSID=68b81c040f0688cadc1a350adda16c9c.
Figure 1: Screenshot
of costing model developed by the European Union Cost Action 0902. A
corresponding manual has been written to support the Microsoft Excel based
model.
Improving
operations efficiency is an on-going need for all industries, including
forestry. The South African forest industry faces unique challenges and
addressing efficiency in this context is complex. A key tool to address the
challenges of efficiency and productivity improvement comes from the discipline
of work science; to study work and productivity. Work science is the study of
work and its associated measurement including human elements, the machines and
other equipment used for work, the organisation of work and the methods of work
(Bjrheden and Thompson, 1995).
Work
science has a long history with forestry, having developed into an independent
field as early as 1920. The origin of work science is often attributed to F.W.
TaylorÕs (1895) paper titled ÒA piece-rate system being a partial solution of
the labour problemÓ published in the Transactions of the American Society of
Mechanical Engineers (Barnes, 1963). TaylorÕs emphasis on determining a
standard amount of time for a task under certain conditions of measurement
forms the basis for improving efficiency (Barnes, 1963). It is from this basis
that work study methodologies developed.
Work study
is the systematic examination of the methods of carrying on activities so as to
improve the effective use of resources and to set up standards of performance
for the activities being carried out (Kanawaty, 1992). The aim of work study is
to examine how activities are carried out to complete a task and the use this
information to simplify or modify and then use the activity to reduce
unnecessary or excess work (Kanawaty 1992).
1.2
From Work Study to Time Study
A work
study is typically broken up into two parts, the method study and then the work
measurement (Kanawaty, 1992). A method study is normally the first step in
order to determine what the optimal method for completing a task is. A method
study is defined as a study where the task is systematically recorded and
critically examined to find ways to make improvements to the task completion
(Kanawaty, 1992). An example of a change in method may be using three chokermen
rather than two with the increased productivity making up for the increased
cost of wages.
Once a
method has been established, then the work measurement can begin. Most
commonly, the time study is used to determine the standard time it should take
to complete the task using the optimised method. Different time study
techniques and scales of study exist; these are detailed in Section 4. The
outcome of the time study is typically a measure of productivity per productive
machine hour (i.e. 30 m3 hour-1). This output data is
incredibly useful allowing for the creation of machine or operation standards,
accurate inputs to pre-existing costing models, and potentially the creation of
models to predict a machine or operations productivity given certain inputs.
The
following section details the considerations before beginning a study and also
walks the reader through different potential experimental designs. This section
assumes the reader has already finalised the machine or operationÕs method. In
this case finalised means that the method may or may not yet have been
optimised; however, the vast majority of glitches have been identified and
resolved and the reader is therefore ready to determine productive machine
hours. At times, this section will likely be repetitive. This repetition was
specifically done as this section, and this manual in general, is designed to
be interpreted through the companion web-based application (in design). This
web-based application will be used to assist the user in designing their study
methodology and selecting an appropriate study technique. As such, this section
was not intended to be read as a whole, but rather the required segments would
be presented to the user based on their inputs into the web-based application.
2.1
Developing a Study Goal and Objective
Before any
study is undertaken, the objective needs to be determined. The development of a
clear objective ensures that time and resources are used efficiently and help
to develop the desired results. Examples of work study objectives are:
1.
Locate
inefficiencies in a particular harvesting system
2.
Determine
the productivity of a new operator
3.
Compare
two harvesting systemsÕ productivity
4.
Assess
a machineÕs downtime and find reasons for downtime
5.
Develop
a production model for a specific machine
Once an
objective is set, a study can then be designed to achieve this objective.
2.2
Study Classification and Experimental Design
A sound
experimental design makes it easier to achieve the objectives of the
experiment, as has already been mentioned. The early establishment of
experimental design makes it far easier to conduct an experiment, collect the
required data, and conduct the statistical analysis required. Although an
experimental design can be constructed to achieve most study objectives, for
the purposes of simplification, this manual will be divided into three study
types: observational studies, experimental studies and modelling studies.
Modelling studies in particular can be considered a sub-set of experimental
studies and it can be argued that a modelling study can be obtained from both
the observational and experimental studies and therefore is not an independent
study type. However, the proposed division of study types allows for greater focus
for the user of this manual to be spent on designing for the studyÕs objective.
This section is a guideline on how to design an experiment in the context of an
operation time study.
2.2.1 What type of study do you
need?
There
are three types of studies that can be used, although they are not mutually
exclusive. The first is the observational study. In an observational study,
variables are not controlled (Magagnotti and Spinelli, 2010; Kanawaty, 1992).
This study type serves to describe the current state of a machine, operation or
system. The second type of study is the experimental study. This type involves
greater control of variables and produces results that are more statistically
rigorous. The final classification is a modelling study. This type of study is
done to create a model for a given machine, operation or system. Modelling
differs mainly in the purpose of using the empirical information for modelling
and later simulation (computer implemented modelling). However to keep things
simple and for purposes of study, classification in this guideline will treat
the three study types as separate entities.
Standard
units of measure include (see Section 5.1 for standardised elements by machine
with units described and defined): m3, tons, tons/m3 pmh-1/smh-1/amh-1
An
observational study (not to be confused with activity sampling techniques and
which are discussed in Section 4.6), also called descriptive study, is
typically done to learn more about a specific machine, operator or system. This
is the simplest study design as it does not require comparisons with other
machines, operators or systems and where variables around the machine or
systems function are not controlled. In
essence an observational study draws inferences about the possible
effect of a treatment on subjects, where the assignment of subjects into a
treated group versus a control group is outside the
control of the investigator. This is in contrast with experiments, such as randomised controlled trials, where each subject is randomly assigned to a
treated group or a control group.
The
treatment unit is the desired machine, operator or system. Different
measurement methods can be used depending on the studyÕs final objective.
Example of Study Objective
Determine
the productivity of a feller-buncher.
What statistical analysis can be
done?
Basic
calculations include productivity and costs and are calculated using standard
units (see Section 5.1) for the given machine, operator or system. Basic
descriptive statistics (e.g. means, medians, minimum and maximum values and
standard deviations). The confidence intervals can also be determined.
What are the strengths and
weaknesses of this design type?
|
Strengths |
Weaknesses |
|
1. 2. 3. |
4. 5. 6. |
2.3
Experimental Study Designs
Experimental
designs compare different variables in order to determine differences or
establish cause and effect. Because more control of variation (such as slope,
machine type, etc.) is required, these designs are usually more complex.
Different techniques are used to control bias (i.e. systematic error) including
randomisation and blocking. Bias refers to a tendency to over represent or under
represent certain parts of the population (Ott, 1993). A factor (treatment) or
factors are applied to see the effects.
Determining
the effect of one factor is referred to as the Òmain effectÓ of the factor. For
example, if one wanted to see how skidder type, cable or grapple, influences
productivity, the main effect examined would be skidder type. When multiple
factors are involved, the interaction between the factors may also become
significant. For example, if one wanted to see how skidder type (cable or
grapple) and skidder engine capacity (e.g. <130 kW or >130kW) influence
productivity in combination, first the interaction between skidder type and
engine size would be tested, as the hypothesis tested is that there is no
factor interaction effects. If the interaction is not significant, the
hypothesis can be rejected and therefore it is sufficient to test the main
effects (Milton and Arnold, 1999). To put it in simpler language, if the two
(or more factors) do not interact statistically then the factors only need to
be examined individually.
Another key
concept to mention here is that of variance. Variance, in laymanÕs terms,
describes the spread of data around the mean (aka the average). For example,
Table 1 below shows two different sets of values. Both have the same mean of
2.75; however, the spread of values in set B is much wider than set A;
therefore, set B has the larger variance of the two sets. Greater explanation
of variance can be found in Section 6.0: Statistical Analysis.
Table 1: An
example of variance; two sample sets can have the same mean but different
variances.
|
Sample Set |
Values |
Mean |
Variance |
|
A |
2, 2, 4, 3 |
2.75 |
0.92 |
|
B |
1, 1, 3, 6 |
2.75 |
5.6 |
Experimental designs are described
below and each is discussed in terms of factors and the bias control technique
used as well as the strengths and weaknesses of each design. Three basic
assumptions need to be adhered to in the analysis with standard linear
statistics (i.e. t-test, ANOVA and ordinary linear regression): homoscedacity
(statistically similar variances) and independence of data. Should these basic
assumptions not be met, advanced statistical analysis is required. It is
recommended that the user seek the guidance of a statistical professional in
this case.
Care should
also be taken to either use one operator across all treatments or use similar
operators. A confounding factor can quite quickly develop if this factor is
ignored. Confounding means that it becomes impossible to find out whether the
relationship (or lack-there-of) is a result of the block or the treatments
themselves) (Clewer and Scarisbrick, 2001). Unless determining whether an
operator is more effective than another operator, always ensure any differences
between operators are minimal.
This
section draws on the work of Pretzsch (2009) as well as Clewer and Scarisbrick
(2001).
2.3.1
Mono-factorial Random Design
A
mono-factorial random design involves testing (or comparing) one specific
factor (Pretzsch, 2009). Bias is controlled through randomisation. This study
is conducted to compare one factor under the condition that the study site
conditions are homogenous (i.e. they do not vary drastically from each other).
Example of Design:
As an
example, a study could be designed to compare productivity between a grapple
and cable skidder. Operators have both been working for the same amount of
time, have the same amount of training and can be considered similar.
Alternatively, one can study the same operator on both machines to reduce the
potential of differences between operators. Site and stand conditions are
selected in a way that they do not differ for the two systems. The treatment is
therefore skidder type (Cable vs Grapple).
What statistical analysis can
be done?
Basic
calculations include productivity and costs and are calculated using standard
units for the given machine, operator or system. Basic descriptive statistics
and confidence intervals can also be determined. Treatment effects are tested
using an Analysis of Variance (ANOVA).
What are the strengths and
weaknesses of this design type?
|
Strengths |
Weaknesses |
|
1. 2. |
3. |
2.3.2
Multi-factorial Random Design
A
multi-factorial random design involves testing two or more factors (Pretzsch,
2009). Bias is controlled through randomisation. This study is conducted to
compare multiple factors and the study site conditions are homogenous (they do
not vary drastically from each other) (Pretzsch, 2009).
Example of Design
One can
design a study to examine the productivity of a cable skidder and a grapple
skidder as well as how productivity varies between morning and afternoon
shifts.
|
Cable Morning Shift |
Grapple Afternoon shift |
|
Grapple Morning Shift |
Cable Afternoon shift |
What statistical analysis can
be done?
Basic
calculations include productivity and costs and are calculated using standard
units for the given machine, operator or system. Basic descriptive statistics
and confidence intervals can also be determined. Treatment interactions as well
as individual treatment effects are tested using factorial ANOVAs.
What are the strengths and
weaknesses of this design type?
|
Strengths |
Weaknesses |
|
1. 2. |
3. 4. |
2.3.3
Mono-factorial Block Design
Mono-factorial
block design involves testing (or comparing) one specific factor (Pretzsch,
2009). Block designs are used to reduce known systematic variation, (e.g. known
changes in slope category, different shift times, etc.). This is done through
the technique of blocking where treatments are grouped across the different
categories (Cluwer and Scarisbrick, 2001). Systematic bias is therefore
controlled through a combination of the development of blocks and the remaining
bias is controlled through randomly placing treatments in blocks (Cluwer and
Scarisbrick, 2001). In other words it ensures that random effects are avoided
that lead to a clustering of repetitions of the same treatment, which would
lead to a bias in case of spatial correlations within the experimental site.
Example of Design
A study is
designed to compare the productivity of three operators (the treatment factor
is therefore operator), Abe, Bob and Carl. The sites vary depending on slope
and we split the experiment into two blocks (aka, blocking): slope less than
10% and slope greater than or equal to 10%. Abe, Bob and Carl will be studied
in both blocks and randomly allocated to sites in each block.
|
Block |
Operator |
||
|
Slope < 10%: |
Bob |
Abe |
Carl |
|
Slope ³ 10%: |
Carl |
Bob |
Abe |
What statistical analysis can
be done?
Similar to
the mono-factorial random design, basic calculations include productivity,
costs and are calculated using standard units for the given machine, operator
or system. Basic descriptive statistics and confidence intervals can also be
determined. Treatment effects are tested using an Analysis of Variance (ANOVA)
controlling for block error.
What are the strengths and
weaknesses of this design type?
|
Strengths |
Weaknesses |
|
1. 2. |
3. 4. 5. |
2.3.4
Multi-factorial Block Design
A
multi-factorial block design involves testing two or more factors (Pretzsch,
2009). Block designs are used to reduce known systematic variation, (e.g. known
changes in slope category, different shift times, etc.). This is done through
the technique of blocking where treatments are grouped across the different
categories (Clewer and Scarisbrick, 2001). Systematic bias is therefore
controlled through a combination of the development of blocks (aka blocking)
and the remaining bias is controlled through random treatment placement in the
block (Pretzsch, 2009).
Example of Design
A study is
designed to examine productivity of two operators (one of the treatment factors
is operator), Abe and Bob, and the use of a cable skidder or grapple skidder
(the second treatment factor). The site varies depending on gradient and the
experiment is split into two blocks: gradient less than 10% and gradient
greater than or equal to 10%. Abe and Bob operating each machine will be
studied in both blocks and randomly allocated to sites in each block.
|
Block: |
Machine and Operator |
|||
|
Slope < 10% |
Cable Abe |
Grapple Bob |
Cable Bob |
Grapple Abe |
|
Slope ³ 10% |
Grapple Bob |
Grapple Abe |
Cable Abe |
Cable Bob |
What statistical analysis can
be done?
Basic
calculations include productivity, costs and are calculated using standard
units for the given machine, operator or system. Basic descriptive statistics
and confidence intervals can also be determined. Treatment interactions as well
as individual treatment effects are tested using factorial ANOVAs controlling
for block effects.
What are the strengths and
weaknesses of this design type?
|
Strengths |
Weaknesses |
|
1. |
2. 3. 4. |
2.3.5
Mono-factorial Latin Square Design
A
mono-factorial Latin square design involves testing (or comparing) one factor
or treatment (Pretzsch, 2009). The site however varies in two or more ways and
this error is controlled through square (or rectangular) blocking (Clewer and
Scarisbrick, 2001). Block designs are used to reduce known systematic
variation, (e.g. known changes in slope category, different shift times, etc.).
This is done through the technique of blocking where treatments are grouped
across the different categories (Clewer and Scarisbrick, 2001). Blocks in a
Latin Square design can be thought of as moving in rows and columns (Pretzsch,
2009).
Example of Design
A study is
designed to compare the productivity of three operators, Abe, Bob and Carl (the
treatment factor is therefore operator). The sites vary depending on slope and
soil type. The experiment is therefore split into two rows (blocks) for slope
less than 10% and slope greater than or equal 10%. The experiment will also be
split into two columns (blocks) for clay type soil and sand type soil. Abe, Bob
and Carl will be studied in both blocks and randomly allocated to sites in each
block.
|
Blocks: |
Clay Soil |
Sand Soil |
||||
|
Slope < 10% |
Abe |
Carl |
Bob |
Bob |
Carl |
Abe |
|
Slope ³ 10% |
Carl |
Bob |
Abe |
Carl |
Abe |
Bob |
What statistical analysis can
be done?
Similar to
the mono-factorial block design, basic calculations include productivity, costs
and are calculated using standard units for the given machine, operator or
system. Basic descriptive statistics and confidence intervals can also be
determined. Treatment effects are tested using an Analysis of Variance (ANOVA)
controlling for block error both for rows and columns.
What are the strengths and
weaknesses of this design type?
|
Strengths |
Weaknesses |
|
1. |
2. 3. 4. |
2.3.6
Multi-factorial Latin Square Design
A
multi-factorial Latin square design involves testing (or comparing) two or more
factors or treatments (Pretzsch, 2009). The site however varies in two or more
ways and this error is controlled through square (or rectangular) blocking.
Block designs are used to reduce known systematic variation, (e.g. known
changes in slope category, different shift times, etc.). This is done through
the technique of blocking where treatments are grouped across the different
categories (Clewer and Scarisbrick, 2001). In a Latin Square design, blocks can
be thought of as moving in rows and columns (Pretzsch, 2009).
Example of Design
A study is
designed to compare the productivity of three operators, Abe, Bob and Carl (the
first treatment factor) and two skidder types, Cable and Grapple (the second
treatment factor). The site varies in terms of gradient and average tree size.
Two blocks will be formed for slope (less than 10% and greater than or equal to
10%) and two blocks for tree size (less than 1 m3 and greater than
or equal to 1 m3). Operators will be tested on both machines and
operators-machine combinations will be randomly distributed across all blocks.
The first
letter in the site refers to the Operator (A,B and C) and the second letter
refers to the machine type (C for cable and G for grapple).
|
Blocks: |
Average Tree Size Less than 1m3 |
Average Tree Size Greater |
||||||||||
|
Slope < 10% |
CC |
AC |
CG |
BG |
AG |
BC |
BG |
BC |
CC |
AG |
AC |
CG |
|
Slope ³ 10% |
AG |
CC |
AC |
BC |
BG |
CG |
CG |
AC |
BC |
CC |
BG |
AG |
What statistical analysis can
be done?
Similar to
the mono-factorial block design, basic calculations include productivity, costs
and are calculated using standard units for the given machine, operator or system.
Basic descriptive statistics and confidence intervals can also be determined.
Treatment interactions as well as individual treatment effects are tested using
factorial ANOVAs controlling for block error in both rows and columns.
Caution
must be noted because, for this design, the number of replications can rapidly
become very large (Clewer and Scarisbrick, 2001). For a solid design, every
treatment must be replicated across all blocks, otherwise confounding effects
can occur. Confounding can seriously diminish the strength of an experiment and
should be approached with caution.
What are the strengths of
this design?
|
Strengths |
Weaknesses |
|
1. 2. |
3. 4. 5. 6. |
Split plot
or split block designs are used in multi-factorial experiments when one
treatment can be applied on a large scale and the other treatment can be
applied on a small scale (Clewer and Scarisbrick, 2001).
Example of Design
As an
example, a study is designed to assess the effects of average tree volume (less
than 1 m3 or greater than or equal to 1 m3) and
skidder type (cable or grapple) across three Pine species. Since tree volume is
fixed by compartment, half the compartment is skidded with a cable skidder and
the other half with a grapple skidder. The plot is therefore organised by
average tree volume and then split by skidder type.
|
Pinus elliotti |
Tree Vol. <1m3 |
Tree Vol. ³1m3 |
|
Cable |
Grapple |
|
|
Grapple |
Cable |
|
|
Pinus patula |
Tree Vol. ³1m3 |
Tree Vol. <1m3 |
|
Grapple |
Cable |
|
|
Cable |
Grapple |
|
|
Pinus taeda |
Tree Vol. <1m3 |
Tree Vol. ³1m3 |
|
Grapple |
Cable |
|
|
Cable |
Grapple |
What statistical analysis can
be done?
Beyond
basic statistics and calculations, factorial ANOVAs would be used, although
output of interactions and main effects becomes difficult to interpret.
What are the strengths of
this design?
|
Strengths |
Weaknesses |
|
1. |
2. 3. 4. |
It is
highly recommended that for studies which require this type of experimental
design, a statistician should be consulted.
Similar to
observation studies, modelling studies are done to observe machines, operators,
or systems and create a production or cost model based on a series of input
factors. These input factors must be measurable and preferably are continuous,
meaning they are quantitative and within a range any number can exist. Examples
of continuous variables include DBH, slope (%), speed, etc.
Example of Design
Develop a
production model for a skidder in an operation. Inputs for this model include
slope (%), cycle time, choking time, dechoking time, travel empty and loaded
time, speed (loaded and unloaded), extraction distance etc. Some basic
assumptions that need to be adhered to are homoscedacity and independence of
data (see above).
What statistical analysis can
be done?
Productivity
and cost must be calculated in some way in order to develop the model. This can
be done using regression methods, including multiple regression, or analysis of
covariance.
What are the strengths and
weaknesses of this method?
|
Strengths |
Weaknesses |
|
1. |
2.
|
It is essential
that you have enough samples within your treatments and enough replications to
allow for differences (or lack there-of) to be determinable. The difficulty
that results is that in order to know the margin of error your sample will
produce, you need to know the within-treatment variation (σ2).
The generic formula for sample size calculation is shown in Equation 1.
|
|
(1) |
Where:
n = the minimum sample size
t = the t-value, as provided from a
t-table, usually selected with an error probability 0.05 (confidence level of
0.95)
σx2= the variance
It is
obvious from Equation 1 that apart from the confidence level, information about
the variation in the population is also needed.
One way of
determining this variation is to run a pilot study beforehand. Such a pilot
study allows a quick assessment of the variation (σ2). From
this variation, the full study sample size can be better approximated.
If running
a pilot study is not feasible, sample size can also be approximated from
similar studies. However, the pilot study is the preferable option as it gives
a more accurate picture of the variation.
Once the
pilot study has been completed, sample size for the study can be calculated by
using Equation 2 below. This formula calculates sample size using a 95.45
confidence level and a margin of error of ± 5% (Kanawaty, 1992).
|
|
(2) |
Where:
n = sample
size for study
n’ = number of readings taken in the pilot study
x = observed value
Σ = sum of values (i.e.: sum of observed values)
It is
important to note that if the minimum sample size determined is more than the
sample size of the pilot study, one cannot simply Òtop upÓ the pilot study by
sampling the difference of n and n’. Rather, n samples must be determined again
(Kanawaty, 1992).
2.5.2
Approximating Sample Size
Cochran (1977)
developed an equation for approximating sample sizes from a large population
based on proportions. Given that the number of cycles a machine works can be
considered a large sample, this formula can be used. The proportions referred
to are the approximate time the machine is working (p) and the approximate time
the machine is not working (1-p). Equation 3 below details the formula.
|
|
(3) |
Where:
Z =
Associated Z value for required accuracy (ie: 95%)
ME = Maximum allowable error (ie: 10%)
p = Estimated proportion of time machine is active and working
q = Estimated proportion of time machine is not active (aka 1 – p)
This method
is not as ideal as the above mentioned pilot study method as it relies on an
estimate of machine availability rather than the actual variance in shifts,
cycles, or elements. As a general rule of thumb, for cycles which are 1 minute
in duration, at least 30 samples are needed. This number increases
exponentially as cycle time decreases (Kanawaty, 1992).
Time is a
key element of production and is a crucial resource which must be managed.
Several models are in use and work to describe how forestry activities use
time. This standard will use the model and definitions proposed by the
International Union of Forest Research Organisations (IUFRO).
The IUFRO
model (Figure 2) divides Total Time (TT) into Non-Workplace Time (NW) and
Workplace Time (WP). Workplace time is further subdivided into Non-Work Time
(NT) and Work Time (WT). Work time is then divided into either Productive Work
Time (PW) or Supportive Work Time (SW). Productive work time includes Main Work
Time (MW) and Complementary Work Time (CW). Productive work time is where the
work elements would be considered. Elements will be discussed further in
Section 5.
Figure 2: IUFRO
time concepts structure (Bjrheden and Thompson, 1995) including abbreviations
for time components.
Supportive
work time is further split into Preparatory Time (PT), Service Time (ST) and
Ancillary Work Time (AW). From a time study perspective, the main objective is
typically to determine the productive machine hours (PMH). These hours are what
the IUFRO model refers to as Productive Work Time (PW). They are the portion of
time where the machine, or operator, is engaged in their primary work function.
For example, the productive machine hours for a chainsaw operator refer to the
time he is actively felling trees, including the time to walk from tree to tree
as this is fundamental to the felling process. In Section 5, detailed elements
which demonstrate the machine/operations productive work cycle are described.
From the time model, time can be divided up and used to calculate ratios which
are essential for accurate costing. These ratios are: mechanical availability,
machine utilisation and capacity utilisation.
The ratios
are calculated using time intervals developed from the IUFRO time concepts
structure. These are detailed below (Table 2).
Table 2: Description of time
concepts used to calculate usage ratios.
|
Term |
Calculation |
Description |
|
Scheduled machine hours (SMH) |
|
This is |
|
Available machine hours (AMH) |
|
Available |
|
Productive machine hours (PMH) |
Or:
|
The potion Referred |
Mechanical
availability refers to the portion of the workplace time (WP) during which a
machine is mechanically fit and able to conduct productive work (Bjrheden and
Thompson, 1995). Availability is dependent on machine required maintenance,
either preventative or otherwise (Pulkki, 2001). Equations 3 and 4 below detail
the formulas for calculating mechanical availability.
|
|
(3) |
Or
alternatively,
|
|
(4) |
Both
equations taken from Pulkki (2001).
Machine
utilisation refers to the portion of workplace time when a machine is used to
conduct the function intended for the machine (Bjrheden and Thompson, 1995).
It is dependent on the mechanical availability of the machine as well as on the
effectiveness of the operating method (Pulkki, 2001). Equations 5 and 6 below
detail the formulas for calculating machine utilisation).
|
|
(5) |
Or
alternatively,
|
|
(6) |
Both
equations taken from Pulkki (2001).
Machine
capacity utilisation refers to a measure of the extent of total time (TT) that
the machine is used for work. This includes all delay times, supportive work
time along with the actual productive work time (Pulkki, 2001). Equation 7
below details the formula for capacity utilisation.
|
|
(7) |
Equation
taken from Pulkki (2001).
3.1.4
Visualisation of Time Concepts
Figure 3
below shows a diagram illustrating how the time concepts come together for use
in ratio calculations.
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
100 % |
SMH Scheduled machine hours (12 hour |
|||||||||||
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
83% Availability |
AMH Available machine hours (10 hours) |
ST |
||||||||||
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
50% Mechanical Utilisation |
PMH Productive machine hours (6 hours) |
Operator rest time (2 hours) |
Other delays (2 hours) |
|
|
|||||||
|
|
|
|||||||||||
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
Figure 3: Time
concepts visualisation for a machine operating over a 12 hour shift. 2 hours
are spent on service time (ST), giving this particular machine a mechanical
availability of 83%, 2 hours of shift are spent on operator rest time and
another 2 hours are spent on other delays, such as answering personal cell
phone calls. The mechanical utilisation of this operation is therefore 50%.
4.0 Time Study Techniques and
Methodologies
Once an
objective and appropriate experimental design have been decided, the study
technique can be finalised. Study technique will be highly objective specific.
There are six different types of time study techniques that are commonly used
(Table 3). Each technique varies in its scope and duration. Cost of conducting
a study also varies depending on how much time and resources it takes to
conduct.
Table 3: Comparison
of the six time study techniques and their typical degree of scope and
duration.
|
Study Type |
Scope |
Observation Unit |
Duration |
|
Shift |
Wide |
Shift |
Weeks |
|
Plot |
Wide |
Plot |
Weeks |
|
Cycle |
Medium |
Cycle |
Days |
|
Time |
Narrow |
Cycle |
Hours |
|
Work |
Narrow |
Element |
Hours |
|
Element |
Narrow |
Element |
Hours |
Before
discussing the individual study types, it is important to discuss delays. As
shown above in the IUFRO model, Workplace time (WP) is divided into Work time
(WT) and Non-work time (NT). A delay is considered any time that is Non-work
time (NT). Delays can then be further classified depending on whether they are
work related (WD) or Disturbance (DT).
The
literature tends to handle delays in differing ways, and the suggestion is
often made that only delays greater than 15 minutes be recorded (Brown et al. 2010). We instead propose that
any delay greater than 30 seconds be recorded and classified appropriately.
Whether or not it is included in the analysis will depend on study length and
sample size (a 20 minute delay on a one-day study may be unrepresentative but
several 2 minute delays every day for a week could be) but it is felt it is
important to at least have an understanding of where and when the delays occur.
Whatever protocol is used, it is important that the person doing the study
clearly states which route was followed so that comparison studies in the
future become potentially possible.
A shift
level study examines production of a machine, operator or system with the
observational measurement being a fully completed work shift. This technique is
generally used for long-term observation, monitoring or follow-up studies
(Magagnotti and Spinelli, 2010).
4.1.1
Data acquisition methods
Data for a
shift level study can be acquired either manually or automatically if the equipment
is available. Manual shift-level studies involve giving a foreman or shift
supervisor a sheet on which to record their teamÕs performance every shift.
Specific data recorded should include:
1.
Shift
start and end time
2.
Record
of crew working
3.
Production
in appropriate unit
4.
Job
type
5.
Delays
and causes of delays
6.
Fuel
consumption
Some of
this data may be collected automatically with on-board data logging software
connected to appropriate sensors.
4.1.2
Advantages and Drawbacks
The major
drawback to a shift-level study is that it requires on-going data management,
particularly if done manually, and that it lacks the finer elemental detail.
Furthermore, shift supervisors need to support the study and understand their
role in the studyÕs success is crucial. Nevertheless, it is a powerful tool and
the analysis tends to be more straightforward, particularly when combined with
a simpler experimental design.
A plot
level study examines production of a machine operator or system with the
observational unit being a fully completed plot. A plot can be designed
specifically to meet the studyÕs objectives. An example of a plot would be 4
rows of 30 trees with consistent tree species, diameter, height and spacing.
The unit therefore is a completed plot and time is cumulative for the entire
plot (i.e. how long does it take Operator A to complete a plot versus Operator
B).
4.2.1
Data acquisition methods
Data
acquisition for a plot level study can be done manually or automatically
depending on how the plot is defined and on the technology available. If a plot
is smaller and contiguous with the next plot, it may not be possible to
differentiate one plot from the next using data logging. If; however, plots are
easy to separate then automatic acquisition is possible.
For manual
acquisition, the time study observer can time the duration of the plot and
record the respective production figures. Other information to specifically
include would be:
1.
Detailed
definition of the plot
2.
Machine
used, make and model
3.
Operator
4.
Species
4.2.2
Advantages and Drawbacks
The major
drawback to a plot level study is it becomes difficult to compare a plot level
study to other studies that do not use the same plot composition. Additionally,
as timing focuses on the plot completion alone, delays and elemental data are
not acquired. Furthermore, performance in a plot may be specific to the plot
itself and may not be able to be applied outside.
Nevertheless,
the advantage of a plot level study is it is a very good way to quickly compare
two very similar types of machinery or operators. Depending on the plot
composition, it may also be easier to design an experiment for other study
techniques.
A cycle
level study examines production on the cycle level and the observational unit
is a completed cycle. A work cycle is defined as a sequence of tasks that
perform a job or produce a unit of production (Kanawaty, 1992). A completed
cycle can be anything from felling a tree to trucking a round trip with a load.
Cycle level studies can be conducted manually or using automatic data
acquisition depending on the objective of the study and the equipment
available.
4.3.1
Data acquisition methods
For manual
acquisition, an observer in field would record time consumed per cycle and note
the relevant production figures. Delays should also be recorded and classified.
Data loggers may also provide an alternative if appropriate sensors can be
attached to the desired inputs (might be more difficult for chainsaws but
feasible for forwarders).
4.3.2
Advantages and Drawbacks
The major
drawback of a cycle level study is that it lacks the elemental detail of the
work process. The advantages are that it provides a quick way of seeing the
variability in the work process and allows delay information to be captured. It
is less intensive than an elemental study. Overall, cycle level studies are not
recommended.
One of the
simplest techniques for time and work study is time and production count. The
observation level is variable and can be anything from a cycle, series of
cycles or a shift. Time and Production Counts are designed to be very quick and
typically are done manually with an observer in the field over a few hours.
Data
collection is usually collected over a short period of time (i.e. few hours)
and is done through recording productive time and production in the preferred
unit (e.g. logs, volume, tons, etc.). Any delays should be recorded and
excluded from productive time. By dividing production by time, one can find a
quick estimate of performance. To calculate productivity, one of the following
formulae should be used:
|
|
(8) |
Or
alternatively,
|
|
(9) |
Equations 8
and 9 taken from Brown et al. (2010).
It is
helpful to record comments on any special situation during study time (delays,
work methods, etc.) as well as background information on the study conditions
such as tree size, stocking, slope, etc.
4.4.2
Advantages and Drawbacks
The
advantages of this technique are that it is quick and simple but the
disadvantages are that the result only reflects the performance for a
relatively short period of time and for a specific condition. In addition, it
is difficult to identify inefficiencies because of the lack of detail.
An element
study breaks down the work cycle of a machine or system into individual
functional steps called elements (Magagnotti and Spinelli, 2010). For the
purposes of standardisation, elements have already been defined by machine type
and are described in-depth in section 5.0.
An elemental
study is typically conducted manually and tools can range from basic clipboard
and stopwatch to complex handheld personal computers with detailed time study
software to video recording. Particularly when individual elements are very
short in duration, computer software and video recording can make capturing
these elements easier.
Elemental
timing can be recorded using two different timing techniques: snap back timing
or continuous timing (Magagnotti and Spinelli, 2010). In snap-back timing, the
clock is reset back to 0 at the end of every element. This can be done using
the lap feature of a stopwatch. The major benefit of snap back timing is that
recording the amount of time per element is very easy. A disadvantage is that it
requires a watch that has a lap function or the observer to reset the clock
every time which can increase the risk of timing mistakes.
Continuous
timing means that the time is recorded for every break point (transition
between elements) and time per element is then calculated after the fact.
Continuous timing is made simpler by the use of decimal time watches which
convert minutes into decimal minutes allowing for simpler math.
For
elements that are extremely short, a handheld computer program which can change
elements with one click can help to record very quick changes. The fastest
elements though will require video-taping and element times are established
through multiple replays after the fact (Magagnotti and Spinelli, 2010).
Other data
recorded includes:
1.
Any
delay greater than 30 seconds and cause of delay
2.
Production
unit
3.
Comments
per cycle or element
4.5.2
Advantages and Drawbacks
The main
advantage of an element study is the fine level of detail regarding the work
process it provides. Element studies allow for greater understanding of the
functional steps and can help directly pin down inefficiencies. The major
drawback of elemental studies is they are time consuming and can become costly
for acquiring large data sets. Experimental design has to be done to minimise
replications and keep the overall number of observations feasible. Furthermore,
element studies require the observer to be well versed in the element breaks
and understand what they are specifically looking for.
4.6
Work Sampling (Instantaneous Observation, and/or Activity Sampling)
While not a
true time study technique per say, work sampling is an important method of work
measurement and is therefore recorded here. Similar to an element study, Work
Sampling also records element-level data. Unlike time study; however, work
sampling determines the relative frequency of the elements over the total time
observed. During Work Sampling, a series of instantaneous readings of an
activity are taken over a period of time. Ideally, the readings are not taken
in time with the cycle as irregular sampling intervals.
The
observer collects data by sampling at either a fixed or random interval. Fixed
intervals (e.g. two minutes) should be used in conditions when the duration of
work activities are random. When the duration of activities are more systematic
or when there is uncertainty regarding the duration of activities, sampling
should be done at random intervals in order to avoid bias. With this technique,
the relative times of work activities are determined by assuming that the
percentage of observations recorded for each activity approximates the
percentage of each activity within the total time.
Each
activity that occurs during each sampling interval is tallied and tallies are
excluded from delays. To calculate the percentage of a particular activity
within a work cycle, divide the total tally for that element by the total tally
of the study.
4.6.2
Advantages and Drawbacks
Work
sampling is a simple and inexpensive way to conduct time and work study,
requiring only a wristwatch or stopwatch and a clipboard for equipment. No
special training or expertise is needed to conduct a study using this technique
and an observer may collect data on several pieces of equipment or operators at
the same time. This technique provides a general time distribution and
highlights efficiencies of a work cycle. However, it is difficult to apply to
other conditions because of its lack of detail.
A work
sampling study is most effective when used for an operation where a number of
activities are happening at once to complete a task. For example, a
merchandising operation at roadside where multiple workers are cross cutting
logs would be a good candidate for work sampling. Work sampling studies can
also be used to assist in method determination or to help the data collector
become familiar with a new machine or operation.
One
suggested use of work sampling is to couple it with an element study. The data
collector performs work sampling for the first hour or so of the study and then
switches to the element study. This first hour provides the benefit of allowing
the workers to become accustomed to the data collectorÕs presence as well as
provides a small work sampling dataset without any additional effort.
5.0 Machine Element Standardisation
A review
was conducted to determine the machines commonly used in the South African
forest industry. From this survey, elements in a normal machine cycle have been
established. An element breaks down to a basic, functional step which can be
measured throughout the duration of a normal work cycle. The pages below list
the standardised elements and data collection requirements for commonly used
harvesting machines in South Africa.
5.1
Standardised Element Lists by Machine
Please note
the following:
1.
Any
amount of additional detail can be added within each broad elements described
below. A provision is that the timekeeper has to be able to record the duration
of each additional element, all associated attributes are recorded and described,
that the additional detail fits into the fixed elements as listed in the tables
below, and that any additional breakpoints are properly described and recorded
within the elements.
2.
If
need be, one or more of the listed elements can be omitted for a study such as;
e.g., with chainsaw felling the element ÒconsiderationÓ. Or it may be necessary
to group Òconsideration Òand Òclear siteÓ; or leave them out altogether. If two
elements are grouped the recorder must make sure the breakpoints for start and end
include the start for the first element and the end of the second element.
However the timekeeper should consider the implication of this action before
doing so as it can seriously affect the integrity of the study to be
undertaken.
3.
The
column Òdetail requiredÓ outlines the minimum required data and these range
from time and distance to single tree dimensions. It is important in single
tree operations that individual cyclesÕ match a specific tree data. If in doubt
rather measure to smallest individual unit; e.g. single tree, log etc.
4.
It
is important to become conversant with the ÒTime ModelÓ described in section
3.0 for correct allocation of delays and systems operations. This is
particularly important in the calculation of machine availability, machine utilisation
and systems efficiency. Each delay must be adequately described and recorded.
Chainsaw:
|
Elements |
Break points |
Detail required |
|
Change position (walk ) to next |
From when |
Time (t) and distance (d) for walking to next |
|
Consideration (assess felling |
From when arriving |
Time (t) for considerations |
|
Clear site around stump and |
From when saw |
Time (t) taken to clear obstacles and creating |
|
Felling tree |
From when saw |
Time (t) taken to fell. Required: single tree dimensions |
|
Tree Cut to length -> delimbing and cross—cutting combined |
From when tree hits |
Time (t) taken to cross-cut, debranch, top. Record individual log data when practicing CTL
|
|
Delays |
||
|
Refuel time |
From when saw stops |
Time (t) for refuelling (RF) – refer Time Models |
|
Repair time |
From when saw stops |
Time (t) for repairs (RT) – refer Time Models |
|
Maintenance time |
From when saw stops |
Time (t) for maintenance (MT) – refer Time Models |
|
Other workplace |
From when work |
Time and reason (t) for delays – refer Time Models |
Specific
time-study information required
1.
Time: Measure time in minutes and
centi-minutes – i.e. hundredths of a minute.
2.
Distance: Estimate the distance the operator
moves from one task to the next. An approximate distance can be estimated in
un-thinned stands by using tree spacing. Otherwise the number of paces the
operator takes can be used for good measure. If more accurate data is required
use a tape measure. A GPS mounted to the operator could also provide a means of
determining distances travelled between tasks.
3.
Single standing tree dimension: Number each tree to be felled in the
study and pair this unique number to its associated dimensions. Also record
other tree attributes; e.g., form etc. (refer to background information forms).
Measure DBH (1.3m above ground level) and height of each tree. Use the
Schumacher and Hall model (South African Forestry Handbook, 2012) to determine
the volume of each tree. To convert to mass (tons) refer to the South African
Forestry Handbook (2012 & 2000).
4.
Measuring equipment: Callipers (digital or manual), vertex
(or other simple hypsometers – Suuntu) and tape measure or logging tape. For
measuring methodology refer to South African Forestry Handbook (2000 &
2012).
5.
Individual log data: Record number of logs, and their
dimensions (diameter – thin and thick-end – and length) cross-cut from
each tree, if of interest.
6.
Refer to IUFRO Time-models
|
Elements |
Break points |
Detail required |
|
Travel |
Begins when machine starts to move to new position and |
Time (t) and distance (d) for travel |
|
Boom-out |
Begins when the boom |
Time (t) and distance (d) for boom movement |
|
Felling |
Begins when the head |
Time (t) for felling. |
|
Boom-in |
Begins when the tree |
Time (t) and distance (d) for boom movement |
|
Processing (i.e. |
Begins when the feed |
Time (t). |
|
Delays |
||
|
Clearing (clearing |
Starts from the end |
Time (t) for clearing |
|
Moving |
Starts from the end |
Time (t) for moving logs, tops and branches (refer Time |
|
Stacking |
Starts from the end of a particular function (e.g., |
Time(t) |
|
Refuel time (in-shift) |
From when harvester stops |
Time (t) for refuelling (RF – refer Time Models) |
|
Repair time (in-shift) |
From when the harvester stops for repair to |
Time (t) for repairs (RT – refer Time Models) |
|
Maintenance time (in-shift) |
From when the harvester stops for maintenance to when current operation resumes |
Time (t) for maintenance (MT – refer Time Models |
|
Other workplace |
From when harvester stops |
Time and reason (t) for delays (refer Time Models) |
Specific
time-study information required
1.
Time: Measure time in minutes and centi-minutes
– i.e. hundredths of a minute.
2.
Distance: Estimate the distance the machine
moves between stops of tasks. An approximate distance can be estimated in
un-thinned stands by using tree spacing. Otherwise the number of rotations of
the wheels/tracks can be used for good measure (mark a point on the wheel/track
as reference point). Average speed for the move can be calculated as the
quotient of distance and time for the move. GPS/OBC/CanBus system is a good
alternative, if available.
3.
Boom movement: If machine has the ability to
measure boom movement distance i.e. CanBus system, recover this data, otherwise
exclude.
4.
Single tree
attributes: Number
each tree to be felled in the study and pair this unique number to its
associated dimensions and record. Also record other tree attributes; e.g., form
(refer background information forms). Measure DBH (1.3m above ground level) and
height of each tree. Use the Schumacher and Hall model (South African Forestry
Handbook, 2012) to determine the volume of each tree. To convert to mass (tons)
refer to the South African Forestry Handbook (2012 & 2000).
5.
Individual log data: Record number of logs, and their
dimensions (diameter – thin and thick-end – and length) cross-cut from
each tree. Calculate log size (m3/tons) from Huber, Samlain or
NewtonÕs equations (South
African Forestry Handbook 2012 & 2000). To convert to mass (tons) refer to
the South African Forestry Handbook (2012 & 2000).
6.
Measuring equipment: Callipers (digital or manual), vertex
(or other simple hypsometers – Suuntu) tape measure of logging tape. For
measuring methodology refer to South African Forestry Handbook (2012 &
2000).
7.
Refer to IUFRO Time-models
|
Elements |
Break points |
Detail required |
|
Move to next tree |
From where the previous accumulated bunch is dropped to when the saw (i.e. disc, chainsaw or shears) touches the next tree to be felled for next accumulated load |
Time (t) and distance (d) required for moving from |
|
Felling |
From when saw |
Time (t) required to fell each tree |
|
Move to next |
From when the tree is firmly |
Time (t) and distance (d) required to drive (or swing) |
|
Dump to stack |
From when the last |
Time (t), distance (d) and number of trees per |
|
Delays |
||
|
Refuel time (in-shift) |
From when machine |
Time (t) for refuelling (RF – refer Time Models) |
|
Repair time (in-shift) |
From when the machine stops |
Time (t) for repairs (RT – refer Time Models) |
|
Maintenance time (in-shift) |
From when the machine stops |
Time (t) for maintenance (MT – refer Time Models |
|
Other workplace |
From when the machine stops |
Time and reason (t) for delays (refer Time Models) |
Specific
time-study information required
1.
Time: Measure time in minutes and
centi-minutes – i.e. hundredths of a minute.
2.
Distance: Estimate the distance the machine
moves. An approximate distance can be estimated in un-thinned stands by using
tree spacing. Otherwise the number of rotations of the wheels/tracks or another
good approximation can be used for good measure (mark a point on the
wheel/track as reference point). Average speed for the move can be calculated
as the quotient of distance and time for the move. GPS/OBC/CanBus systems are
good alternatives, if available.
3.
Tree data: As single tree dimensions (DBH
specifically) do not affect time for felling operations greatly, single tree
dimensions are not required provided the individual tree dimensions are
relatively uniform throughout the work area. Use average tree volume/tons as a
measure. To gain average tree volume, sample the compartment following the
methodology outlined in the South African Forestry Handbook (2012 & 2000).
To convert to mass (tons) refer to the South African Forestry Handbook (2012
& 2000). To convert to mass (tons) refer to the South African Forestry
Handbook (2012 & 2000).
4.
Record the number of trees dumped: This will provide an estimate of the
bunch size (number of trees and volume) for the extraction operation. Use
average tree volume/tons as a measure. To convert to mass (tons) refer to the
South African Forestry Handbook (2012 & 2000).
5.
Measuring equipment: Callipers (digital or manual) vertex
(or other simple hypsometers – Suuntu) and tape measure or logging tape. For
measuring methodology refer to South African Forestry Handbook (2012 &
2000).
6.
Refer to IUFRO Time-models
Skidder/agricultural
tractor with winch or drawbar (a-frame or other):
|
Elements |
Break points |
Detail required |
|
Travel
|
From when the skidder starts |
Time (t) and distance (d) for travel along the road |
|
Travel |
From when the skidder enters |
Time (t) and distance (d) for travel in the compartment |
|
Choking |
From when the skidder has stopped to start the choking process to when it starts to move off with its |
Time (t) required to accumulate the load and number of |
|
Travel |
From when the skidder starts |
Time (t) and distance (d) for travel in the compartment |
|
Travel |
From when the skidder enters |
Time (t) and distance (d) for travel along the road loaded |
|
De-choking at |
From when the load makes |
Time (t) required to release the skidder from its load and |
|
Delays |
||
|
Refuel time (in-shift) |
From when skidder stops |
Time (t) for refuelling (RF – refer Time Models) |
|
Repair time (in-shift) |
From when the skidder stops |
Time (t) for repairs (RT – refer Time Models) |
|
Maintenance time (in-shift) |
From when the skidder stops |
Time (t) for maintenance (MT – refer Time Models |
|
Other workplace |
From when the skidder stops |
Time and reason (t) for delays (refer Time Models) |
Specific
time-study information required
1.
Time: Measure time in minutes and
centi-minutes – i.e. hundredths of a minute.
2.
Distance: Estimate the distance the machine
moves along the forest road and/or from stump site to roadside landing (m). A
close approximate distance can be estimated by staking the road/skid trail with
pegs (e.g. 20 m to 50 m apart) as reference points. GPS/OBC/CanBus system is a
good alternative, if available. Average speed for both the loaded and unloaded
can be calculated as the quotient of distance and time for the move.
3.
Tree/load
data: Record number of pieces contained in load dropped at the
landing. To determine load size (m3/tons) multiply average tree/log
volume/tons with number of pieces. Calculate piece volume for logs and for longer lengths using Huber,
Smalian or NewtonÕs equations (South African Forestry Handbook 2012 & 2000). Another option for
longer lengths is to clearly mark DBH on the stem so that it is visible on
arrival at roadside. Then record this DBH and the length of the tree and
applying the Schumacher and Hall model (South African Forestry Handbook, 2012)
for longer lengths or tree-lengths. To convert to mass (tons) refer to the
South African Forestry Handbook (2012 & 2000). It may not
be possible to measure each piece in high production operations. In this case
determine a sample size (refer to protocol manual). Failing that a good
estimate can be gained by measuring at least 30 pieces per day or per study and
calculating volume/tons using the equations mentioned above.
4.
Measuring equipment: Callipers (digital or manual), vertex
(or other simple hypsometers – Suuntu) and tape measure or logging tape. For
measuring methodology refer to South African Forestry Handbook (2012 & 2000).
5.
Refer to IUFRO Time-models
|
Elements |
Break points |
Detail required |
|
Travel
|
From when the skidder has dropped its previous load (load touches the ground) to when it enters the compartment |
Time (t) and distance (d) for travel along the road |
|
Travel |
From when the skidder |
Time (t) and distance (d) for travel in the compartment |
|
Loading |
From when the skidders grapple touches the bunched load to when the skidder starts to move with its final load is secured |
Time (t) required to accumulate the load |
|
Travel |
From when the skidder starts to move with full load to when it enters the forest road |
Time (t) and distance (d) for travel in the compartment |
|
Travel |
From when the skidder enters |
Time (t) and distance (d) for travel along the road loaded |
|
Dropping |
From when the load makes |
Time (t) required to release the skidder from its load |
|
Delays |
||
|
Refuel time (in-shift) |
From when skidder |
Time (t) for refuelling (RF – refer Time Models) |
|
Repair time (in-shift) |
From when skidder stops |
Time (t) for repairs (RT – refer Time Models) |
|
Maintenance time (in-shift) |
From when skidder stops |
Time (t) for maintenance (MT – refer Time Models |
|
Other workplace |
From when skidder stops |
Time and reason (t) for delays (refer Time Models) |
Specific
time-study information required
6.
Time: Measure time in minutes and
centi-minutes – i.e. hundredths of a minute.
7.
Distance: Estimate the distance the machine
moves along the forest road and/or from stump site to roadside landing (m). A
close approximate distance can be estimated by staking the road/skid trail with
pegs (e.g. 20 m to 50 m apart) as reference points. GPS/OBC/CamBus system is a
good alternative, if available. Average speed for both the loaded and unloaded
can be calculated as the quotient of distance and time for the move.
8.
Tree data: Record
number of pieces contained in load dropped at the landing. To determine load
size (m3/tons) multiply average tree/log volume/tons with number of
pieces. Calculate piece volume for logs and longer lengths using Huber, Smalian or NewtonÕs
equations (South African
Forestry Handbook 2012 & 2000). Another option for longer lengths is to
clearly mark DBH on the stem so that it is visible on arrival at roadside. Then
record this DBH and the length of the tree and applying the Schumacher and Hall
model (South African Forestry Handbook, 2012) for longer lengths or
tree-lengths. To convert to mass (tons) refer to the South African Forestry Handbook
(2012 & 2000). It may not be possible to measure each piece in high
production operations. In this case
determine a sample size (refer to protocol manual). Failing that a good
estimate can be gained by measuring at least 30 pieces per day or per study and
calculating volume/tons using the equations mentioned above.
9.
Measuring equipment: Callipers (digital or manual) and
vertex (or other simple hypsometers – Suuntu). For measuring methodology refer to South
African Forestry Handbook (2012 & 2000).
10.
Refer to IUFRO Time-models
|
Elements |
Break points |
Detail required |
|
Travel |
From when the forwarder starts to move after it has unloaded its load (crane secured) to when it enters the compartment |
Time (t) and distance (d) for travel along the road |
|
Travel |
From when the forwarder enters |
Time (t) and distance (d) for travel in the compartment |
|
Loading The loading element can be divided into sub-elements for 1. 2. 3. 4. |
Begins from when the |
Time (t) and number of logs loaded per grapple and in |
|
Driving |
Begins when the forwarder starts to move to the next stack/pile and ends when the forwarder |
Time (t) and distance (d) |
|
Travel |
Begins when the forwarder grapple comes |
Time (t) and distance (d) for travel in the compartment |
|
Travel |
Begins when the forwarder enters the forest road to when the grapple loader starts to move for unloading phase of the |
Time (t) and distance (d) for travel along the road |
|
Unloading The unloading element can be divided into sub-elements for 1. 2. 3. 4. |
Begins when the grapple starts to move to start the unloading phase and ends when the empty |
Time (t) and if the forwarder moved during unloading the |
|
Delays |
||
|
Refuel time (in-shift) |
From when forwarder stops |
Time (t) for refuelling (RF – refer Time Models) |
|
Repair time (in-shift) |
From when the forwarder stops for repair to when the current operation resumes |
Time (t) for repairs (RT – refer Time Models) |
|
Maintenance time (in-shift) |
From when the forwarder stops for maintenance to when current operation resumes |
Time (t) for maintenance (MT – refer Time Models |
|
Other workplace |
From when forwarder stops |
Time and reason (t) for delays (refer Time Models) |
Specific
time-study information required
5.
Time: Measure time in minutes and
centi-minutes – i.e. hundredths of a minute.
6.
Distance: Estimate the distance the machine
moves along the machine trail (m). A close approximate distance can be
estimated by staking the road with pegs (e.g., 20 m to 50 m apart) along the
road as reference points. In this case a GPS is a good alternative, if
available. Average speed for the move can be calculated as the quotient of
distance and time for the move. GPS/OBC/CanBus system is a good alternative, if
available. Average speed for both the loaded and unloaded can be calculated as
the quotient of distance and time for the move.
7.
Tree data Record
number of pieces contained in each grapple load. To determine load size (m3/tons)
use an estimation of average tree/log volume/tons by using Huber, Smalian or
NewtonÕs equations (South
African Forestry Handbook 2012 & 2000). Also record the number of grapple
loads to complete the loading of the forwarder. Total load size can be
estimated by multiplying the total number of logs in the full load with the
average piece size. To convert to mass (tons) refer to the South African
Forestry Handbook (2012 & 2000).
8.
It may however not be possible to measure each piece
loaded that makes up the total load. In this case a sample of logs must be measured
to determine an average log size and used throughout the study (if piece size
remains uniform). Determine the minimum sample size needed using the sample
size calculator outlined in the Protocol manual. Failing that a good estimate
can be gained by measuring at least 30 pieces per day or per study and
calculating volume/tons using the equations mentioned above.
9.
Measuring equipment: Callipers (digital or manual), vertex
(or other simple hypsometers – Suuntu) tape measure of logging tape. For
measuring methodology refer to South African Forestry Handbook (2012 &
2000).
10.
Refer to IUFRO Time-models
Loader
(either tracked or wheeled):
|
Elements |
Break points |
Detail required |
||
|
Moving from one |
From when loader starts |
Time (t) and distance (d) for travel |
||
|
Load The loading element can be divided into sub-elements for 1. 2. 3. 4. 5. |
From when crane and |
Time (t) and number of grapple loads and number of logs
Log specific dimensions required to determine individual |
||
|
Delays |
||||
|
Refuel time (in-shift) |
From when loader stops |
Time (t) for refuelling (RF – refer Time Models) |
||
|
Repair time (in-shift) |
From when the loader |
Time (t) for repairs (RT – refer Time Models) |
||
|
Maintenance time (in-shift) |
From when the loader |
Time (t) for maintenance (MT – refer Time Models |
||
|
Other workplace |
From when loader |
Time and reason (t) for delays (refer Time Models) |
||
Specific
time-study information required
6.
Time: Measure time in minutes and
centi-minutes – i.e. hundredths of a minute.
7.
Distance: Estimate the distance the machine
moves along the machine trail (m). A close approximate distance can be
estimated by staking the road with pegs (e.g. 20 m to 50 m apart) along the
road as reference points. In this case a GPS is a good alternative, if
available. Average speed for the move can be calculated as the quotient of
distance and time for the move. GPS/OBC/CanBus system is a good alternative, if
available. Average speed for both the loaded and unloaded can be calculated as
the quotient of distance and time for the move.
8.
Tree data Record
number of pieces contained in each grapple load. To determine grapple load size
(m3/tons) use an estimation of average tree/log volume/tons by applying
Huber, Smalian or NewtonÕs equations (South African Forestry Handbook 2012 & 2000). Also
record the number of grapple loads required to complete the loading of the vehicle.
Total load can be estimated by multiplying the total number of logs in the full
load with the average piece size. To convert to mass (tons) refer to the South
African Forestry Handbook (2012 & 2000).
9.
Measuring equipment: Callipers (digital or manual), vertex
(or other simple hypsometers – Suuntu) and tape measure or logging tape. For
measuring methodology refer to South African Forestry Handbook (2012 &
2000).
10.
Refer to IUFRO Time-models
|
Elements |
Break points |
Detail required |
|
Travel (relocating) |
Begins with the first |
Time (t) and distance (d) |
|
Boom-out |
Begins when the boom |
Time (t) and distance of boom movement (d) |
|
Boom-in |
Begins once it has a grip |
Time (t) and distance (d) for boom movement |
|
Processing |
From when the feed |
Time (t) |
|
Delays |
||
|
Refuel time (in-shift) |
From when processor stops |
Time (t) for refuelling (RF – refer Time Models) |
|
Repair time (in-shift) |
From when the processor stops for repair to |
Time (t) for repairs (RT – refer Time Models) |
|
Maintenance time (in-shift) |
From when the processor stops for maintenance to when current operation resumes |
Time (t) for maintenance (MT – refer Time Models |
|
Other workplace |
From when processor |
Time and reason (t) for delays (refer Time Models) |
Specific
time-study information required
11.
Time: Measure time in minutes and
centi-minutes – i.e. hundredths of a minute.
12.
Distance: Estimate the distance the machine
moves along the machine trail (m). A close approximate distance can be
estimated by staking the road with pegs (e.g., 20 m to 50 m apart) along the
road as reference points. In this case a GPS is a good alternative, if
available. Average speed for the move can be calculated as the quotient of
distance and time for the move. GPS/OBC/CamBus system is a good alternative, if
available. Average speed for both the loaded and unloaded can be calculated as
the quotient of distance and time for the move.
13.
Boom movements: If the machine has the ability to
measure boom movement distance, recover this data, i.e. CanBus, OBC etc., otherwise
exclude.
14.
Tree data: Record
number of logs produced from each processing event. Do not separate debarking
from cross-cutting as it is very difficult to define each operation separately.
If possible record passes with eucalyptus debarking if finite
15.
Measuring equipment: Callipers (digital or manual), vertex
(or other simple hypsometers – Suuntu) and tape measure of logging tape. For
measuring methodology refer to South African Forestry Handbook (2012 &
2000).
16.
Refer to IUFRO Time-models
|
Elements |
Break points |
Detail required |
|
Travel |
From when the truck |
Time (t) and distance (d) |
|
Loading Operation |
From when truck stops |
Time (t) and number of assortments or total mass of |
|
Load |
From when last load |
Time (t) |
|
Travel |
From when truck starts |
Time (t) and distance (d) |
|
Unloading |
Element starts when the truck stops and is in position waiting to be unloaded and ends when the |
Time (t) |
|
Delays |
||
|
Refuel time (in-shift) |
From when truck stops |
Time (t) for refuelling (RF – refer Time Models) |
|
Repair time (in-shift) |
From when the truck stops |
Time (t) for repairs (RT – refer Time Models) |
|
Maintenance time (in-shift) |
From when the truck stops |
Time (t) for maintenance (MT – refer Time Models |
|
Other workplace |
From when truck stops |
Time and reason (t) for delays (refer Time Models) |
Specific
time-study information required
1.
Time: Measure time in minutes and
centi-minutes – i.e. hundredths of a minute.
2.
Distance: Record distance travelled (km)
either from speedometer of by means of GPS data.
3.
Road class: Road class
can be added and recorded if desired; otherwise, note on background information
form.
4.
Tree data: Record load size by recording then
number of logs loaded multiplied with average tree/log volume/tons.
Determine log volume/tons using Huber, Smalian or NewtonÕs equations (South African Forestry Handbook 2012
& 2000). To convert to mass (tons) refer to the South African Forestry
Handbook (2012 & 2000). An alternative to determine load size is to use
recorded by the truck scales.
5.
Measuring equipment: Callipers (digital or manual), vertex
(or other simple hypsometers – Suuntu) and tape measure or logging tape. A GPS
is another useful tool for truck distance measurement, particularly when longer
distances are being studied. For measuring methodology refer to South African
Forestry Handbook (2012 & 2000).
6.
Refer to IUFRO Time-models
|
Elements |
Break points |
Detail required |
|
Yarder |
From when yarder |
Time (t) for set-up |
|
Carriage out |
From when the carriage starts to run out and ends when it stops/clamped in its designated position |
Time (t) and distance (d) |
|
Mainline out |
From when carriage is clamped |
Time (t) and distance (d) |
|
Choking |
From when the main-line |
Time (t) |
|
Mainline in |
From when the choker-setter are in the clear signal and ends when the carriage unlocks from the skyline |
Time (t) |
|
Carriage Return |
From when carriage |
Time (t) and distance (d) |
|
De-choking |
From when the load |
Time (t) and volume/tons of the load |
|
Yarder dismantling |
Starts with the |
Time (t) |
|
Delays |
||
|
Refuel time (in-shift) |
From when processor stops |
Time (t) for refuelling (RF – refer Time Models) |
|
Repair time (in-shift) |
From when need for repair |
Time (t) for repairs (RT – refer Time Models) |
|
Maintenance time (in-shift) |
From when need for |
Time (t) for maintenance (MT – refer Time Models |
|
Other workplace |
From when need the delay |
Time and reason (t) for delays (refer Time Models) |
Specific time-study information required
7.
Time: Measure time in minutes and
centi-minutes – i.e. hundredths of a minute.
8.
Distances: Estimate the distance the carriage
moves on the skyline loaded (m). A close approximate distance can be estimated
by staking the corridor with pegs (e.g., 20 m to 50 m apart) as reference
points. GPS can also be used. Average speed of the carriage return process can
be calculated as the quotient of distance and time for the move.
9.
Tree data: Record
number of pieces contained in load dropped at the landing. To determine load
size (m3/tons) use an estimation of average tree volume/tons using Schumacher and Hall model (South
African Forestry Handbook, 2012) or Huber, Smalian or NewtonÕs
equations for logs (South
African Forestry Handbook 2012 & 2000). To convert to mass (tons) refer to
the South African Forestry Handbook (2012 & 2000). It may not
be possible to measure each piece in high production operations. In this case
determine the minimum sample size needed to produce an acceptable estimate
(refer to protocol manual for sample size calculator). Failing that a good
estimate can be gained by measuring at least 30 pieces per day or per study and
calculating volume/tons using the equations mentioned above
10.
Measuring equipment: Callipers (digital or manual),
vertex (or other simple hypsometers – Suuntu) and tape measure or logging tape.
For measuring methodology refer to South African Forestry Handbook (2012 &
2000).
11.
Refer to IUFRO Time-models
|
Elements |
Break points |
Detail required |
|
Move to next stump |
From the time the machine completes a stump and starts moving to the time it starts destumping |
Time (t) and distance (d) required for moving |
|
Mulch / destump |
From when machine arrives at a stump and starts mulching / destump starts to when |
Time (t) required to mulch each stump |
|
Turn |
From the time the last stump is completed to the time destumping / mulching of the first stump in the new line starts |
Time (t) and distance (d) required to drive |
|
Delays |
||
|
Refuel time (in-shift) |
From when processor stops |
Time (t) for refuelling (RF – refer Time Models) |
|
Repair time (in-shift) |
From when need for repair |
Time (t) for repairs (RT – refer Time Models) |
|
Maintenance time (in-shift) |
From when need for |
Time (t) for maintenance (MT – refer Time Models |
|
Other workplace |
From when need the delay |
Time and reason (t) for delays (refer Time Models) |
Specific
time-study information required
12.
Time: Measure time in minutes and
centi-minutes – i.e. hundredths of a minute.
13.
Distances: Use a measuring wheel to measure
distance. This can be done afterwards if a starting point, the rows and the end
point are marked. Distance between stumps can be calculated using the
compartment spacing.
14.
Stump data: single stump
dimensions (height and diameter) are not required provided the individual stump
dimensions are relatively uniform throughout the work area. Use average stump
dimension as a measure.
15.
Record the
number of stumps treated
16.
Refer to IUFRO Time-models
The user is
strongly encouraged to use these pre-defined elements for both convenience and
the purposes of industry standardisation; however, in certain cases, developing
new elements may be required (e.g. the user is examining a machine which is not
on the list below). All elements are basic, functional steps that occur during
the work process, whether they contribute to the successful completion of work
or not (delays).
When
defining elements, a key consideration is defining element breakpoints.
Breakpoints refer to the exact start and exact end time of an element. For
example, a re-fuelling time element for a chainsaw begins when the saw stops
due to lack of fuel or fuel top up and resumes when the saw starts to continue
the operation. Elements also need to have defined measurement standards. This
may be just the length of time the element takes to complete but it may also
have other data requirements, such as the volume of load. When new elements are
used, the user is kindly asked to define these steps and forward this
information along to FESA in order to continue improving this protocol.
This
section is still in progress.
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Study – Design and Measurement of Work. 5th Edition. London:
John Wiley & Sons Inc.
Bjrheden R, Thompson MA. 1995. An
International Nomeclature for Forest Work Study. In DB Field (Ed.), Proceedings
of IUFRO 1995 S3:04 subject area: 20th World Congress (pp. 190-215).
Tampere, Finland: IUFRO.
Bredenkamp BV, Upfold SJ. 2012.
South African Forestry Handbook 5th Ed. Southern African Institute
of Forestry.
Brown M, Acuna M, Strandgard M,
Walsh D. 2010. Machine evaluation toolbox. Hobart, Tasmania: Cooperative
Research Centre for Forestry Australia.
Clewer AG, Scarisbrick DH. 2001.
Practical Statistics and Experimental Design for Plant and Crop Science.
London: John Wiley & Sons. 332 pp.
Cochran WG. 1977. Sampling Techniques (3rd edn)
.New York: John Wiley & Sons.428 pp.
Kanawaty G (Ed.). 1992. Introduction
to Work Study (4th Edn.). Geneva: International Labour Organisation.
Magagnotti N, Spinelli R. (Eds.)
2010. Good Practice Guidelines for Biomass Production Studies. Sesto Fiorentino:
CNR IVALSA.
Milton JS, Arnold JC. 1999.
Introduction to probability and statistics: principles and applications for
engineering and the computing sciences (2nd edn). New York:
McGraw-Hill.
Ott RL. 1993. An introduction to
statistical methods and data analysis (4th edn). Belmont: Wadsworth
Publishing Company. 1056 pp.
Pretzsch H. 2009. Forest Dynamics,
Growth and Yield. Berlin: Spring-Verlag. 663 pp.
Pulkki RE. 2001. Forest Harvesting
I: On the Procurement of Wood with Emphasis on Boreal and Great Lakes St.
Lawrence Forest Regions. 156 pp.