Time_Study_Standards_First_Edition_13_3_2014

TANDARDS FOR

IME

TUDIES FOR THE

OUTH

FRICAN

OREST

NDUSTRY

First Edition

5 February 2014

Project team:

Pierre Ackerman

Elizabeth (Lise) Gleasure

Simon Ackerman

Brad Shuttleworth

Standard 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 “modernisation” of the

Industry’s 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 user’s 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

1.0 Introduction .

……………………………………………………………………………………………………..

1.1 Background .

…………………………………………………………………………………………………..

1.2 From Work Study to Time Study .

………………………………………………………………………

2.0 Setting up a 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? .

……………………………………………………………….

2.2.2 Observational study .

………………………………………………………………………………….

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 .

…………………………………………………………….

2.3.7 Split-plot designs .

…………………………………………………………………………………….

2.4 Modelling Studies.

………………………………………………………………………………………….

2.5 Sample size calculations .

………………………………………………………………………………..

2.5.1 Pilot Studies .

…………………………………………………………………………………………..

2.5.2 Approximating Sample Size .

………………………………………………………………………

3.0 Time Models .

……………………………………………………………………………………………………

3.1 Ratio Calculations .

…………………………………………………………………………………………

3.1.1 Mechanical Availability .

…………………………………………………………………………….

3.1.2 Machine Utilisation .

………………………………………………………………………………….

3.1.3 Capacity Utilisation .

………………………………………………………………………………….

3.1.4 Visualisation of Time Concepts.

………………………………………………………………….

4.0 Time Study Techniques and Methodologies .

…………………………………………………………

4.1 Shift Level Study .

…………………………………………………………………………………………..

4.1.1 Data acquisition methods .

…………………………………………………………………………

4.1.2 Advantages and Drawbacks .

……………………………………………………………………..

4.2 Plot Level Study .

……………………………………………………………………………………………

4.2.1 Data acquisition methods .

…………………………………………………………………………

4.2.2 Advantages and Drawbacks .

……………………………………………………………………..

4.3 Cycle Level Study .

…………………………………………………………………………………………

4.3.1 Data acquisition methods .

…………………………………………………………………………

4.3.2 Advantages and drawbacks .

………………………………………………………………………

4.4 Time and Production Count .

……………………………………………………………………………

4.4.1 Data acquisition .

………………………………………………………………………………………

4.4.2 Advantages and Drawbacks .

……………………………………………………………………..

4.5 Element Study .

……………………………………………………………………………………………..

4.5.1 Data acquisition .

………………………………………………………………………………………

4.5.2 Advantages and Drawbacks .

……………………………………………………………………..

4.6 Work Sampling (Instantaneous Observation, and/or Activity Sampling) .

…………………

4.6.1 Data acquisition .

………………………………………………………………………………………

4.6.2 Advantages and Drawbacks .

……………………………………………………………………..

5.0 Machine Element Standardisation .

………………………………………………………………………

5.1 Standardised Element Lists by Machine .

…………………………………………………………..

Chainsaw: .

……………………………………………………………………………………………………..

Harvester .

………………………………………………………………………………………………………

Feller-buncher: .

……………………………………………………………………………………………….

Skidder/agricultural tractor with winch or drawbar (a-frame or other): .

………………………

Skidder (grapple): .

……………………………………………………………………………………………

Forwarder: .

……………………………………………………………………………………………………..

Loader (either tracked or wheeled) .

…………………………………………………………………….

Processor .

………………………………………………………………………………………………………

Truck (timber transport) .

……………………………………………………………………………………

Yarder .

…………………………………………………………………………………………………………..

Mulchers and Destumpers .

………………………………………………………………………………..

5.2. User-defined elements .

………………………………………………………………………………….

6.0 Statistical Analysis .

……………………………………………………………………………………………

7.0 References .

……………………………………………………………………………………………………..

1.0 Introduction

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.

1.1 Background

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 (Björheden 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. Taylor’s (1895) paper

titled “A piece-rate system being a partial solution of the labour problem” published in the

Transactions of the American Society of Mechanical Engineers (Barnes, 1963). Taylor’s

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 m

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.

2.0 Setting up a Time-study

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 operation’s 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

help 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:



Locate inefficiencies in a particular harvesting system



Determine the productivity of a new operator



Compare two harvesting systems’ productivity



Assess a machine’s downtime and find reasons for downtime



Develop a production model for a specific machine

Once an objective goal 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 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 study’s

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): m

, tonnes, tonnes/m

pmh

-1

/smh

-1

/amh

-1

2.2.2 Observational study

An observational study (not to be confused with activity sampling techniques and which are

discussed in paragraph 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 study’s 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



Analysis is relatively straightforward



Likely to be quicker and easier to

conduct than experimental and

modelling classifications



Matches “real life” situations quite

well



Cannot be used to compare against

other machines, operators, etc.



Not statistically rigorous



Gives a picture for one

machine/operator in one set of

conditions; results might be drastically

different in other conditions

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 “main effect” 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 of

>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 layman’s terms

describes the spread of data around the mean (aka the average). For example, Table1 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

2, 2, 4, 3

2.75

0.92

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



Relatively straightforward analysis



Number of replications do not

necessarily have to be equal



If sites are not entirely homogenous,

results can be biased

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



Allows for examination of different

treatments and multiple interactions



Can determine how (and if) multiple

treatments interact



Analysis is slightly more complicated



May be difficult to replicate all

combinations

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 of 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 ≥ 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



Reduces systematic experimental

error (accounts for variation in site

quality)



Sampling can be done block by block



May be difficult to replicate all

treatments across all blocks



If sites are homogenous, less efficient

than random sampling



Large number of treatments may

make it difficult to find an appropriate

number of blocks

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 ≥ 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



Allows for treatment interactions to be

determined and controls for block

error



Analysis becomes more complicated



May be difficult replicating all the

necessary treatments and blocks



If sites are homogenous, extremely

inefficient as the design blocks what

could otherwise be a simple random

design

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

Carl

Abe

Slope ≥ 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



Relatively easy to control for two

differences in site type



Number of replications can very

quickly become too large to manage



Analysis is complicated



Analysis is not valid if there is

interaction between the blocking

factors and the treatment

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 m

and greater than or equal to 1 m

). 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 1m

Average Tree Size Greater than/Equal

Slope <

10%

Slope ≥

10%

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



Allows for control of two or more site

factors through blocking



Allows for assessment of both

treatment interactions and individual

factor effects



Number of replications can very

quickly becomes too large to manage



Analysis is complicated



Analysis is not valid if there is

interaction between the blocking

factors and the treatment



If all treatments are not replicated,

then confounding becomes a problem

2.3.7 Split-plot designs

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

or greater than or equal to 1m

) 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. <1m

Tree Vol. ≥1m

Cable

Grapple

Cable

Pinus patula

Tree Vol. ≥1m

Tree Vol. <1m

Grapple

Cable

Grapple

Pinus taeda

Tree Vol. <1m

Tree Vol. ≥1m

Grapple

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



Allows for greater freedom in Block

design



Analysis is much less precise



Analysis is more complicated than

factorial designs



May be difficult to find enough sites

for each block

It is highly recommended that for studies which require this type of experimental design, a

statistician should be consulted.

2.4 Modelling Studies

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 on 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



Development of predictive models

that can be used to describe the

relationship between the response

variable and the independent

variables



May be difficult to control for variation

from qualitative sources

2.5 Sample size calculations

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 (σ

). The generic formula for sample size calculation is shown in Equation

¤ ¥

¢ ¤ ¥

(1)

Where:

= the minimum sample size

= the t-value, as provided from a t-table, usually selected with an error probability 0.05

(confidence level of 0.95

= 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 (σ

). From this variation, the full study sample size

can be better approximated.

2.5.1 Pilot Studies

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).

¡ § ¨

©ª«

¯ ° ®±

(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 simple “top up” 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.

¡ ¢ £

¥¦

©ª

(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).

3.0 Time Models

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 (Björheden 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

)

¡¢ £ ¤¥¦§¨©ª«¬

This is the portion of the total time that an

operation or part of an operation is engaged at a

specific work task.

The normal shift time (e.g., a

9 hour shift per day). Referred to as Work Time

(WT) in the IUFRO model.

Available

machine

hours

(

AMH

)

¡¢ £ ¤¥¦§¨©ª«¬ ® ¬¦¯ª°¬¨©ª«¬

Available machine hours refers to the portion of

time that a machine is available to work. This is

the shift time minus time spent on routine

maintenance (i.e. fuelling). For example, 1 hour

out of a 9 hour scheduled shift.

Productive

machine

hours

(

PMH

)

±¡¢ £ ±¦¥²³°´ª¯¬¨¤¥¦§¨©ª«¬

Or:

±¡¢ £ ¡¢ ® ¬¦¯ª°¬¨©ª«¬

® µ´¶¬¦¨·¬¸¹º»

The potion of work time that is spent

contributing directly to the completion of a

specific work task.

Referred to as Productive Work Time (PW) in

the IUFRO Model.

3.1 Ratio Calculations

3.1.1 Mechanical Availability

Mechanical availability refers to the portion of the workplace time (WP) during which a

machine is mechanically fit and able to conduct productive work (Björheden 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.

¡¢£¤¥¦¢¤§¨©ª¤¦§¤«¦§¦¬¨®¯° ±

²³´µ¶§¤¢¡¨·¦¸¡¨®²¹° º »¡´ª¦¢¡¨·¦¸¡¨®»·°

²³´µ¶§¤¢¡¨·¦¸¡¨®²¹°

¼ ½¾¾

(3)

Or alternatively,

¡¢£¤¥¦¢¤§¨©ª¤¦§¤«¦§¦¬¨®¯° ±

©ª¤¦§¤«§¡¨ ¤¢£¦¥¡¨¿³À´Á¨®© ¿°

»¢£¡ÂÀ§¡Â¨ ¤¢£¦¥¡¨¿³À´Á¨®» ¿°

¼ ½¾¾

(4)

(Both equations taken from Pulkki (2001)).

3.1.2 Machine Utilisation

Machine utilisation refers to the portion of workplace time when a machine is used to conduct

the function intended for the machine (Björheden 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).

¤¢£¦¥¡¨Ã¬¦§¦Á¤¬¦³¥¨®¯° ±

¹´³ÂÀ¢¬¦ª¡¨²³´µ¨·¦¸¡¨®¹²°

²³´µ¶§¤¢¡¨·¦¸¡¨®²¹°

¼ ½¾¾

(5)

Or alternatively,

¤¢£¦¥¡¨Ã¬¦§¦Á¤¬¦³¥¨®¯° ±

¹´³ÂÀ¢¬¦ª¡¨ ¤¢£¦¥¡¨¿³À´Á¨®¹ ¿°

»¢£¡ÂÀ§¡Â¨ ¤¢£¦¥¡¨¿³À´Á¨®» ¿°

¼ ½¾¾

(6)

(Both equations taken from Pulkki (2001)).

3.1.3 Capacity Utilisation

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.

Ä¤¶¤¢¦¬¨Ã¬¦§¦Á¤¬¦³¥¨®¯° ±

²³´µ¶§¤¢¡¨·¦¸¡¨®²¹°

·³¬¤§¨·¦¸¡¨®··°

¼ ½¾¾

(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 shift)

83%

Mechanical

Availability

AMH

Available machine hours (10 hours)

Repair, etc.

(2 hours)

50%

Mechanical

Utilisation

PMH

Productive machine hours (6 hours)

Operator

rest time

(2 hours)

Other

delays

(2 hours)

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 level study

Wide

Shift

Weeks – Months

Plot Level study

Wide

Plot

Weeks

Cycle Level

Medium

Cycle

Days

Time and Production Count

Narrow

Cycle or Shift

Hours

Work Sampling

Narrow

Element

Hours – Days

Element Level

Narrow

Element

Hours – Days

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.

4.1 Shift Level Study

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 team’s performance every shift. Specific data recorded should include:



Shift start and end time



Record of crew working



Production in appropriate unit



Job type



Delays and causes of delays



Fuel consumption



Etc.

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 study’s 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.

4.2 Plot Level Study

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 study’s 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:



Detailed definition of the plot



Machine used, make and model



Operator



Species



Etc.

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.

4.3 Cycle Level Study

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.

4.4 Time and Production Count

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.

4.4.1 Data acquisition

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,

tonnes, 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:

¡¢£¤¥¦§¨§¦© ª

«¤¬®¡¯¢°¯¦¡®®± ² ³¨®¡³´®¯¦¡®®¯±§µ®

¡¢£¤¥¦§¨®¯¦§¬®

(8)

Or alternatively,

¡¢£¤¥¦§¨§¦© ª

«¤¬®¡¯¢°¯¶¢³£± ² ³¨®¡³´®¯¶¢³£¯±§µ®

¡¢£¤¥¦§¨®¯±§µ®

(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.

4.5 Element Study

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.

4.5.1 Data acquisition

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:



Any delay greater than 30 seconds and cause of delay



Production unit



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.

4.6.1 Data acquisition

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 interval 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 collector’s 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:



Any amount of additional detail can be added within each broad elements described

below.

A proviso 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.



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 “consideration”. Or it may be necessary to

group “consideration “and “clear site”; 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.



The column “detail required” 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 cycles’ match a specific tree data.

If in doubt rather

measure to smallest individual unit; e.g., single tree, log etc.



It is important to become conversant with the “Time Model” 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) to next tree

to be felled

From when

final cut

on the previous tree

(completed previous cycle) to

start of

consideration phase

(when operator

arrives at

the next tree)

Time (t) and distance (d) for walking to next

tree to be felled

Consideration

(assess felling

direction and potential

inherent dangers).

From when

arriving

at the tree to be felled

when

saw touches material to be

cleared

Time (t) for considerations

Clear site

around

stump and create

escape route

From when saw

touches material to be

cleared

to when

saw

touches stem for

felling process

Time (t) taken to clear obstacles and

creating escape route

Felling

tree

From when

saw touches

stem for first

cut

to when

tree hits the ground

Time (t) taken to fell.

Required: single tree dimensions

Tree length ->

delimbing

Cut to length ->

delimbing and

cross—cutting

combined

From when

tree hits the ground

to when

last branch, last log, or topping cut is

complete

(can include cutting the butt end

square)

Time (t) taken to cross-cut, debranch, top.

Record individual log data when practicing

CTL

Delays

Refuel time

From when

saw stops

due to fuel

starvation (or needs fuel top-up) to when

the

current operation resumes

(whatever

the operation was previously)

Time (t) for refuelling (RF) – refer Time

Models

Repair time

From when

saw stops

for repair to when

current operation

resumes

Time (t) for repairs (RT) – refer Time Models

Maintenance time

From when

saw stops

for maintenance to

when current operation

resumes

(whatever

the operation was previously)

Time (t) for maintenance (MT) – refer Time

Models

Other workplace

time

(refer to Time

Model) delays such

as planning, rests,

work preparations etc.

From when

work stops

due to delay

when current operation

resumes

(whatever

the operation was previously)

Time and reason (t) for delays – refer Time

Models

Specific time-study information required



Time:

Measure time in minutes and centi-minutes – i.e. hundredths of a minute.



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.

GPS mounted to the operator could also provide a means of determining distances travelled between

tasks.



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 (tonnes) refer to the South African Forestry Handbook (2012 & 2000).



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).



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.



Refer to IUFRO Time-models

Harvester

Elements

Break points

Detail required

Travel

Begins when machine starts to move to new

position and ends when the boom starts to

swing towards the next tree ti be felled

Time (t) and distance (d) for travel

Boom-out

(positioning head to

cut)

Begins when the

boom starts to swing

towards a tree to be felled and ends with the

head resting in position on the tree

for

the felling cut to commence

Time (t) and distance (d) for boom

movement

Felling

Begins when the

head is resting in

position

on the tree and ends when the

tree starts to fall.

Time (t) for felling.

Boom-in

Begins when the

tree is falling

and the

boom starts to swing towards and stops in

front of the base machine. The element

ends when the

feed rollers start to turn

the processing area at the machine front.

Time (t) and distance (d) for boom

movement

Processing

(i.e.,

delimbing, debarking,

cross-cutting

Begins when the

feed rollers start to turn

and ends when the harvester begins to

move to a new position

Time (t).

Delays

Clearing

(clearing of

disturbed

undergrowth and

processing of un-

merchantable trees

or pieces of trees

Starts from the

end of a particular

function

(e.g., processing – see above) and

ends with the end

function in boom-in

Time (t) for clearing

(refer Time Models)

Moving tops

and

branches (slash)

Starts from the

end of a particular

function

(e.g., processing – see above; and

ends when operation resumes again

Time (t) for moving logs, tops and branches

(refer Time Models)

Stacking logs

Starts from the end of a particular function

(e.g., processing – see above); and ends

when operations resumes again

Time(t)

Refuel time

(in-shift)

From when harvester

stops

due to fuel

shortage to when the current operation

resumes

Time (t) for refuelling (RF – refer Time

Models)

Repair time

(in-shift)

From when the harvester

stops

for repair

to when the current

operation

resumes

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

time

(refer to Time

Model) delays such

as planning, rests,

work preparations

etc.

From when harvester

stops

for the

particular delay to when current operation

resumes

(whatever the operation was

previously)

Time and reason (t) for delays (refer Time

Models)

Specific time-study information required



Time:

Measure time in minutes and centi-minutes – i.e. hundredths of a minute.



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.



Boom movement:

If machine has the ability to measure boom movement distance i.e. CanBus system,

recover this data, otherwise exclude.



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 (tonnes) refer to the South African Forestry Handbook (2012 & 2000).



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 (m

/tonnes) from Huber, Samlain or Newton’s

equations (South African Forestry Handbook 2012 & 2000).

To convert to mass (tonnes) refer to the

South African Forestry Handbook (2012 & 2000).



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).



Refer to IUFRO Time-models

Feller-buncher:

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 previous operation

Felling

From when

saw touches tree

to when the

cut tree is

firmly gripped within the

accumulating arms

of the feller-buncher

Time (t) required to fell each tree

Move

to next tree (or

swing to next tree)

From when the tree is

firmly in the

accumulating arms

to when the saw

touches the next tree for felling

Time (t) and distance (d) required to drive

(or swing) between trees

Dump

to stack

From when the

last tree

to be accumulated

firmly gripped within the accumulating

arms

to when the

machine releases

the

accumulated bunch on to the ground (bunch

hits ground)

Time (t), distance (d) and number of trees

per accumulation

Delays

Refuel

time (in-shift)

From when

machine stops

work due to fuel

shortage to when the

current operation

resumes

(whatever the operation was

previously)

Time (t) for refuelling (RF – refer Time

Models)

Repair

time (in-shift)

From when the machine

stops

for repair to

when the current operation

resumes

(whatever the operation was previously)

Time (t) for repairs (RT – refer Time Models)

Maintenance

time

(in-shift)

From when the machine

stops

for

maintenance begins to when current

operation

resumes

(whatever the operation

was previously)

Time (t) for maintenance (MT – refer Time

Models

Other workplace

time

(refer to Time

Model) delays such

as planning, rests,

work preparations

etc.

From when the machine

stops

for the

delay to when operation

resumes

(whatever the operation was previously)

Time and reason (t) for delays (refer Time

Models)

Specific time-study information required



Time:

Measure time in minutes and centi-minutes – i.e. hundredths of a minute.



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.



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/tonnes 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 (tonnes) refer to the South African Forestry Handbook

(2012 & 2000). To convert to mass (tonnes) refer to the South African Forestry Handbook (2012 &

2000).



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/tonnes as a measure.

To convert to

mass (tonnes) refer to the South African Forestry Handbook (2012 & 2000).



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).



Refer to IUFRO Time-models

Skidder/agricultural tractor with winch or drawbar (a-frame or other):

Elements

Break points

Detail required

Travel unloaded

along a forest road (if

applicable)

From when the skidder

starts

to travel back

to stump site after dropping its previous load

to when it

enters

the compartment

Time (t) and distance (d) for travel along the

road unloaded

Travel unloaded,

off-road

From when the skidder

enters

the

compartment to when it

stops

in a final

position to start choking process

Time (t) and distance (d)

for travel in the

compartment unloaded

Choking

From when the skidder has

stopped

to start

the choking process

to when it

starts to

move

off with its complete/full load after it

has been winched in

Time (t) required to accumulate the load

and number of trees in load

Travel loaded, off-

road

From when the skidder

starts

to move

fully

loaded to when it

enters

the forest road

Time (t) and distance (d)

for travel in the

compartment loaded

Travel loaded

along

forest road

towards

the landing

From when the skidder

enters

the forest

road to when the load (once the winch has

been released)

makes contact with the

landing surface

Time (t) and distance (d)

for travel along

the road loaded

De-choking

landing

From when the load

makes contact with

the landing surface

after release of the

winch cable to when the skidder

starts to

move off

to collect the next load

Time (t) required to release the skidder from

its load and to load all available choker

chains/cable – record number of

stems/trees/logs that made up the final load

that reached to landing.

Record single tree

dimensions (dbh, length, and species).

Dbh

recorded in experimental design since the

operation is a single stem system

Delays

Refuel time

(in-shift)

From when skidder

stops

due to fuel

shortage to when the current operation

resumes

(whatever the operation was

previously)

Time (t) for refuelling (RF – refer Time

Models)

Repair time

(in-shift)

From when the skidder

stops

for repair to

when current operation

resumes

(whatever

the operation was previously)

Time (t) for repairs (RT – refer Time Models)

Maintenance time

(in-shift)

From when the skidder

stops

for

maintenance to when

current

operation

resumes

(whatever the operation was

previously)

Time (t) for maintenance (MT – refer Time

Models

Other workplace

time

(refer to Time

Model) delays such

as planning, rests,

work preparations

etc.

From when the skidder

stops

for the delay

to when current operation

resumes

(whatever the operation was previously)

Time and reason (t) for delays (refer Time

Models)

Specific time-study information required



Time:

Measure time in minutes and centi-minutes – i.e. hundredths of a minute.



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.



Tree/load data:

Record number of pieces contained in load dropped at the landing.

To determine load

size (m

/tonnes) multiply average tree/log volume/tonnes with number of pieces.

Calculate piece volume

for logs and for longer lengths using Huber, Smalian or Newton’s 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 (tonnes) 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/tonnes using the equations mentioned above.



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).



Refer to IUFRO Time-models

Skidder (grapple):

Elements

Break points

Detail required

Travel unloaded on

forest road

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 unloaded

Travel unloaded off-

road

From when the skidder

enters

the

compartment to when the skidders grapple

touches

the first trees/logs that will

comprise the next load

Time (t) and distance (d)

for travel in the

compartment unloaded

From when the skidders grapple

touches

the bunched load to when the skidder starts

move

with its final load is secured

Time (t) required to accumulate the load

Travel loaded, off-

road

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 loaded

Travel loaded, on

forest road

From when the skidder

enters

the road to

when the load

makes contact

with the

landing surface after release from skidder

grapple

Time (t) and distance (d)

for travel along

the road loaded

Dropping load

landing

From when the load

makes contact

with

the landing surface after release of the

grapple to when the skidder

move off

for

next load

Time (t) required to release the skidder from

its load

Delays

Refuel time

(in-shift)

From when skidder

stops

due to fuel

shortage to when the current operation

resumes

(whatever the operation was

previously)

Time (t) for refuelling (RF – refer Time

Models)

Repair time

(in-shift)

From when skidder

stops

for repair to when

current operation

resumes

Time (t) for repairs (RT – refer Time Models)

Maintenance time

(in-shift)

From when skidder

stops

for maintenance

to when current operation

resumes

(whatever the operation was previously)

Time (t) for maintenance (MT – refer Time

Models

Other workplace

time

(refer to Time

Model) delays such

as planning, rests,

work preparations

etc.

From when skidder

stops

for the particular

delay to when current operation

resumes

(whatever the operation was previously)

Time and reason (t) for delays (refer Time

Models)

Specific time-study information required



Time:

Measure time in minutes and centi-minutes – i.e. hundredths of a minute.



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.



Tree data

: Record number of pieces contained in load dropped at the landing.

To determine load size

/tonnes) multiply average tree/log volume/tonnes with number of pieces.

Calculate piece volume for

logs and longer lengths using Huber, Smalian or Newton’s 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 (tonnes) 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/tonnes using the equations mentioned above.



Measuring equipment:

Callipers (digital or manual) and vertex (or other simple hypsometers – Suuntu).

For measuring methodology refer to South African Forestry Handbook (2012 & 2000).



Refer to IUFRO Time-models

Forwarder:

Elements

Break points

Detail required

Travel unloaded

forest road

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 unloaded

Travel unloaded in-

field

From when the forwarder

enters

the

compartment to when the forwarder

grapple begins to

move

from its position on

the forwarder bunk to start the loading of

the forwarder

Time (t) and distance (d)

for travel in the

compartment unloaded

The loading element

can be divided into

sub-elements for more

detailed analysis if

necessary:



Reaching to the

stack



Dropping load into

the forwarder bunk



Sorting and

handling logs on

the ground



Sorting and

handling the logs

on the forwarder

bunk

Begins from when the

forwarder grapple

starts

to move from the forwarder bunk to

when the forwarder grapple

come to rest

on the bunk after the last grapple load is

loaded into the bunk

Time (t) and number of logs loaded per

grapple and in total.

Stack number should

also be recorded. Log specific dimensions

required to determine individual log volume

and load volume

Driving while loading

(between loading

stops)

Begins when the forwarder

starts

to move

to the next stack/pile and ends when the

forwarder

stops

at the next stack/pile to

begin loading

from the next loading stop

Time (t)

and distance (d)

Travel loaded in-field

Begins when the forwarder grapple

comes

to rest

on the forwarder bunk after the last

grapple load of the last loading stop and

the forwarder bunk is full to when it

enters

the forest road

Time (t) and distance (d)

for travel in the

compartment unloaded

Travel loaded on

forest road

Begins when the forwarder

enters

the

forest road to when the grapple loader

starts to

move

for unloading phase of the

operation

Time (t) and distance (d)

for travel along

the road unloaded

Unloading at landing

The unloading element

can be divided into

sub-elements for more

detailed analysis if

necessary:



Lifting the grapple

load onto the landing

pile



Moving the empty

grapple loader back

onto the bunk



Sorting and handling

logs in the bunk



Sorting and handling

the logs on the

landing pile

Begins when the grapple starts to

move

start the unloading phase and ends when

the empty forwarder starts to

move

return to the field

Time (t) and if the forwarder moved during

unloading the distance between stops.

forwarder moved between stops record

number of logs unloaded per grapple and

per stop

Delays

Refuel time

(in-shift)

From when forwarder

stops

due to fuel

shortage to when the current operation

resumes

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 time

(refer to Time Model)

delays such as

planning, rests, work

preparations etc.

From when forwarder

stops

for the

particular delay to when current operation

resumes

(whatever the operation was

previously)

Time and reason (t) for delays (refer Time

Models)

Specific time-study information required



Time:

Measure time in minutes and centi-minutes – i.e. hundredths of a minute.



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.



Tree data

Record number of pieces contained in each grapple load.

To determine load size (m

/tonnes)

use an estimation of average tree/log volume/tonnes by using Huber, Smalian or Newton’s 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 (tonnes) refer to the South African Forestry

Handbook (2012 & 2000).



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/tonnes using the equations mentioned

above.



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).



Refer to IUFRO Time-models

Loader (either tracked or wheeled)

Elements

Break points

Detail required

Moving

from one

loading position to

next loading position

on landing

From when loader

starts moving

new

position and ends with

crane starting to

swing

to stack/pile of logs/trees

Time (t) and distance (d) for travel

Load

The loading element

can be divided into

sub-elements for more

detailed analysis if

necessary:



Reaching to the

stack/pile (grab

empty)



Boom from

stack/pile to truck

(grab loaded)



Dropping grab load

into the truck bunk



Sorting and handling

logs on the ground



Sorting and handling

the logs on the truck

bunk

From when

crane and grapple starting to

swing

to stack/pile and ends when the

last

logs/tree assortments are in place

truck and crane and grapple is stationary in

resting position

Time (t) and number of grapple loads and

number of logs per grapple of total number

to fill truck.

Log specific dimensions required to

determine individual log volume and load

volume

Delays

Refuel time

(in-shift)

From when loader

stops

due to fuel

shortage to when the current operation

resumes

Time (t) for refuelling (RF – refer Time

Models)

Repair time

(in-shift)

From when the loader

stops

for repair to

when the current

operation

resumes

Time (t) for repairs (RT – refer Time Models)

Maintenance time

(in-shift)

From when the loader

stops

for

maintenance to when current operation

resumes

Time (t) for maintenance (MT – refer Time

Models

Other workplace

time

(refer to Time

Model) delays such

as planning, rests,

work preparations

etc.

From when loader

stops

for the particular

delay to when current operation

resumes

(whatever the operation was previously)

Time and reason (t) for delays (refer Time

Models)

Specific time-study information required



Time:

Measure time in minutes and centi-minutes – i.e. hundredths of a minute.



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.



Tree data

Record number of pieces contained in each grapple load.

To determine grapple load size

/tonnes) use an estimation of average tree/log volume/tonnes by applying Huber, Smalian or

Newton’s 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 (tonnes) refer to the

South African Forestry Handbook (2012 & 2000).



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).



Refer to IUFRO Time-models

Processor

Elements

Break points

Detail required

Travel

(relocating)

and positioning

Begins with the

first movement of the

processor

and ends with

boom starting

its swing

to stack/pile for first processing

Time (t) and distance (d)

Boom-out

(positioning head to

grab tree or tree

section)

Begins when the

boom swings out

stack/pile and ends when it has

grip on the

stem

Time (t) and distance of boom movement

(d)

Boom-in

Begins once it has a

grip on the stem

and

then moves to front of base machine.

Element ends when the

feed rollers start

to turn

Time (t) and distance (d)

for boom

movement

Processing

(debranching

(debarking), and/or

cross-cutting and

stacking)

From when the

feed rollers start to turn

and ends when either the completed tree-

length or the

last

log cross-cut is

placed on

a stack

Time (t)

Delays

Refuel time

(in-shift)

From when processor

stops

due to fuel

shortage to when the current operation

resumes

Time (t) for refuelling (RF – refer Time

Models)

Repair time

(in-shift)

From when the processor

stops

for repair

to when the current

operation

resumes

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

time

(refer to Time

Model) delays such

as planning, rests,

work preparations

etc.

From when processor

stops

for the

particular delay to when current operation

resumes

(whatever the operation was

previously)

Time and reason (t) for delays (refer Time

Models)

Specific time-study information required



Time:

Measure time in minutes and centi-minutes – i.e. hundredths of a minute.



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.



Boom movements:

If the machine has the ability to measure boom movement distance, recover this

data, i.e. CanBus, OBC etc., otherwise exclude.



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



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).



Refer to IUFRO Time-models

Truck (timber transport)

Elements

Break points

Detail required

Travel Unloaded

From when the

truck moves off

after

unloading and ends when it

stops to be

loaded

at loading site

Time (t) and distance (d)*

Operation

From when truck

stops

at position to load

and ends when last load of assortments

have been

loaded

onto the truck load body

Time (t) and number of assortments or total

mass of assortments.

If number of logs are

recorded; record log dimensions for volume

determination of load

Load control

and

fixing load

From when

last load

of assortments have

been loaded and ends when the truck

starts to move

on transport route towards

the off-loading point

Time (t)

Travel Loaded

From when truck

starts to move

transport route towards the offloading point

the and ends when the truck

stops

and is in

position waiting to be unloaded with load

securing undone

Time (t) and distance (d)

Unloading

Element starts when the truck

stops

and is

in position waiting to be unloaded and ends

when the empty truck

moves

off top return

to loading site

Time (t)

Delays

Refuel time

(in-shift)

From when truck

stops

due to fuel shortage

to when the current operation

resumes

Time (t) for refuelling (RF – refer Time

Models)

Repair time

(in-shift)

From when the truck

stops

for repair to

when the current

operation

resumes

Time (t) for repairs (RT – refer Time Models)

Maintenance time

(in-shift)

From when the truck

stops

for maintenance

to when current operation

resumes

Time (t) for maintenance (MT – refer Time

Models

Other workplace

time

(refer to Time

Model) delays such

as Waiting to be

Loaded or Unloaded

etc.

From when truck

stops

for the particular

delay to when current operation

resumes

(whatever the operation was previously)

Time and reason (t) for delays (refer Time

Models)

Specific time-study information required



Time:

Measure time in minutes and centi-minutes – i.e. hundredths of a minute.



Distance:

Record distance travelled (km) either from speedometer of by means of GPS data.



Road class:

Road class can be added and recorded if desired; otherwise, note on background

information form.



Tree data

Record load size by recording then number of logs loaded multiplied with average

tree/log volume/tonnes Determine log volume/tonnes using Huber, Smalian or Newton’s equations

(South African Forestry Handbook 2012 & 2000).

To convert to mass (tonnes) refer to the South

African Forestry Handbook (2012 & 2000). An alternative to determine load size is to use recorded

by the truck scales



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).



Refer to IUFRO Time-models

Yarder

Elements

Break points

Detail required

Yarder set-up

From when

yarder arrives

at landing site

and end when the carriage first

starts to

move

on the skyline

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

(or

stationary) ready to take on load and ends

when the

main-line reaches the first

assortments

to be choked

Time (t) and distance (d)

Choking

From when the

main-line reaches the first

assortments

to be choked and ends when

the choker-setter are in the clear and

the

signal has been given

that the choked

load/turn can be hauled to the carriage

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 unlocks

from the

skyline in the infield position and ends when

the carriage stops at the landing and

the

load reaches the ground on the deck

Time (t) and distance (d)

De-choking

From when the

load reaches the deck

and

ends when the

carriage starts moving

return to the filed

Time (t) and volume/tonnes of the load

Yarder dismantling

Starts with

the last assortment load

being

de-choked

and ends when the

dismantled

yarder leaves

the deck.

Time (t)

Delays

Refuel time

(in-shift)

From when processor

stops due to fuel

shortage

to when the operation

starts again

Time (t) for refuelling (RF – refer Time

Models)

Repair time

(in-shift)

From when need for

repair begins

(breakdown) to when operation

starts again

Time (t) for repairs (RT – refer Time Models)

Maintenance time

(in-shift)

From when need

for maintenance begins

to when operation

starts again

Time (t) for maintenance (MT – refer Time

Models

Other workplace

time

(refer to Time

Model) delays such

as planning, rests,

work preparations

etc.

From when need the

delay begins

to when

operation

starts again

Time and reason (t) for delays (refer Time

Models)

Specific time-study information required



Time:

Measure time in minutes and centi-minutes – i.e. hundredths of a minute.



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.



Tree data

Record number of pieces contained in load dropped at the landing.

To determine load

size (m

/tonnes) use an estimation of average tree volume/tonnes using Schumacher and Hall

model (South African Forestry Handbook, 2012) or Huber, Smalian or Newton’s equations for logs

(South African Forestry Handbook 2012 & 2000).

To convert to mass (tonnes) 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/tonnes using the equations mentioned above



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).



Refer to IUFRO Time-models

Mulchers and Destumpers

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

between stumps (dependant on

compartment spacing)

Mulch / destump

From when machine

arrives at a stump and

starts mulching / destump starts

to when it

stops mulching / destumping

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 due to fuel

shortage

to when the operation

starts again

Time (t) for refuelling (RF – refer Time

Models)

Repair time

(in-shift)

From when need for

repair begins

(breakdown) to when operation

starts again

Time (t) for repairs (RT – refer Time Models)

Maintenance time

(in-shift)

From when need

for maintenance begins

to when operation

starts again

Time (t) for maintenance (MT – refer Time

Models

Other workplace

time

(refer to Time

Model) delays such

as planning, rests,

work preparations

etc.

From when need the

delay begins

to when

operation

starts again

Time and reason (t) for delays (refer Time

Models)

Specific time-study information required



Time:

Measure time in minutes and centi-minutes – i.e. hundredths of a minute.



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.



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.



Record the number of stumps treated



Refer to IUFRO Time-models

5.2. User-defined elements

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.

6.0 Statistical Analysis

This section is still in progress.

7.0 References

Acuna M., Heidersdrof E. 2008. Draft Technical Report – Harvesting machine evaluation

framework for Australia. Hobart, Tasmania: Cooperative Research Centre for Forestry

Australia.

Barnes RM. 1963. Motion and Time Study – Design and Measurement of Work. 5

Edition.

London: John Wiley & Sons Inc.

Björheden R., Thompson MA. 1995. An International Nomeclature for Forest Work Study. In

D. B. Field (Ed.), Proceedings of IUFRO 1995 S3:04 subject area: 20

World Congress (pp.

190-215). Tampere, Finland: IUFRO.

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

edn) .New York: John Wiley & Sons.428 pp.

Kanawaty G (Ed.). 1992. Introduction to Work Study (4

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 (2

edn). New York: McGraw-Hill.

Ott, RL. 1993. An introduction to statistical methods and data analysis (4

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.

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