operations research & measure

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COST Action FP-0902

WG 2 Operations research and measurement methodologies

GOOD PRACICEGUIDELINES FOR BIOMASS

PRODUCION SUDIES


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Editors: Magagnotti N., Spinelli R.

Contributors: Acuna M., Bigot M., Guerra S., Hartsough B., Kanzian

C., Krh K., Lindroos O., Magagnotti N., Roux S., Spinelli R., albot B.,

olosana E., Zormaier F.

Reviewers: Bjrheden R., Kellogg L., LeBel L.

Tis publication is supported by COS

COS Vademecum (Part A) - Pay-as-you-go System

v05/05/2010 Page 44 / 67

ESF provides the COS Oce through a EuropeanCommission contract

COS is supported by the EU RD FrameworkProgramme

PEFC/18-31-124

PEFC certified

This book is printedon paper fromsustainablymanaged forests andcontrolled sources.

www.pefc.org

Graphic lay out: Comunicambiente.net

Illustrations: Giovanni ribbiani

Printed by: Litotipograa Alcione S.r.l.

Published by:

CNR IVALSA

Via Madonna del Piano, 10

I-50019 Sesto Fiorentino (FI)

IALY

www.ivalsa.cnr.it

Good practice guidelines or biomass production studies

Year o publication: 2012

ISBN 978-88-901660-4-4


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COS- the acronym or European Cooperation in Science and echnology-is the oldest and widest European intergovernmental network orcooperation in research. Established by the Ministerial Conerence inNovember 1971, COS is presently used by the scientic communitieso 35 European countries to cooperate in common research projectssupported by national unds.

Te unds provided by COS - less than 1% o the total value o the projects -support the COS cooperation networks (COS Actions) through which,

with EUR 30 million per year, more than 30 000 European scientists areinvolved in research having a total value which exceeds EUR 2 billionper year. Tis is the nancial worth o the European added value whichCOS achieves. A bottom up approach (the initiative o launching aCOS Action comes rom the European scientists themselves), lacarte participation (only countries interested in the Action participate),equality o access (participation is open also to the scienticcommunities o countries not belonging to the European Union) and

exible structure (easy implementation and light management o theresearch initiatives) are the main characteristics o COS.

As precursor o advanced multidisciplinary research COS has a veryimportant role or the realisation o the European Research Area(ERA) anticipating and complementing the activities o the FrameworkProgrammes, constituting a bridge towards the scientic communitieso emerging countries, increasing the mobility o researchers acrossEurope and ostering the establishment o Networks o Excellencein many key scientic domains such as: Biomedicine and MolecularBiosciences; Food and Agriculture; Forests, their Products and Services;Materials, Physical and Nanosciences; Chemistry and MolecularSciences and echnologies; Earth System Science and EnvironmentalManagement; Inormation and Communication echnologies; ransportand Urban Development; Individuals, Societies, Cultures and Health. Itcovers basic and more applied research and also addresses issues o pre-normative nature or o societal importance.

Web: http://www.cost.eu


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Table o contents

1. Introduction 7

2. Background 83. Work measurement 9

3.1 Statistics in work measurement 9

3.2 Study types 10

4. Beore you start 12

4.1 Study goal 12

4.2 Experimental design 124.3 Formulating a statistical model 15

4.4 Dening what to measure and how 16

4.4.1 Inputs 16

4.4.2 Outputs 17

4.4.3 Process variables 17

4.5 Practical rules 18

4.5.1 Saety 18

4.5.2 Ethics 19

5 Measurements in the eld 20

5.1 Measuring time input 20

5.1.1 Plot level 20

5.1.2 Shit level 21

5.1.3 Cycle level 21

5.1.4 Element level and work sampling 22

5.1.5 Units 27

5.1.6 Classication o time in orest studies 27

5.2 Measuring energy input 28

5.3 Measuring product output 30

5.3.1 Count 30

5.3.2 Solid Volume 30


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5.3.3 Bulk volume 30

5.3.4 Fresh weight 31

5.3.5 Dry weight 32

5.4 Measuring energy output 32

5.5 Measuring quality output 33

5.5.1 Product quality 33

5.5.2 Stand impacts 33

5.5.3 Soil impacts 34

5.6 Measuring process variables 34

5.6.1 Physical environment 34

5.6.2 Organization 36

5.6.3 echnology 36

6 Data analysis 37

6.1 Descriptive statistics 37

6.2 Checking or outliers 38

6.3 Checking or normality 39

6.4 Data transormation 39

6.5 Making comparisons 39

6.6 Modelling 40

7. Conclusive notes 43

8. Relevant bibliography 45Appendix 1 Work science: denitions 47

Appendix 2 - Example o the main parameters

most capable o afecting harvesting perormance 48

Appendix 3 - Classication o time

in orest work study (IUFRO 1995) 49


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Good practice guidelines on biomass workstudies

1. IntroductionForest Work Science is an important branch o Forest Science, which hasdeveloped into an independent eld since the late 1920s. Despite a stronginternational cooperation within the orest engineering community, the

evolution o the discipline has inevitably generated local adaptations inresponse to diferent work environments and individual preerences.Te mutual understanding once derived rom common study methodshas largely been lost. As a result, there is now much misunderstandingabout time study methods, both at the theoretical and the practicallevel. Ambiguity arises especially regarding the terminology, the unitso measure, the experimental design and the statistical treatment odata. Hence, there is the need or a good practice guideline (GPG), which

must be simple and concise enough to encourage widespread adoption.Te guide should ensure comparability o results and repeatability oexperiments, both undamental elements o the scientic method. Inturn, this will acilitate international network building and researchcoordination, which are the ultimate goals o the EU COS Programme.Te purpose o this guide is to answer this need. Tis is a simple andquick how-to guide that can help harmonize work study methods. It isdesigned or the eld researcher who needs quick access to sound studypractice, even when lacking strong theoretical skills in work science and/or statistics. Contrary to a scholarly book, this manual goes rom practiceto theory and not the reverse. In act, this manual does not replace themany scholarly books dealing with operational studies and the relatedstatistical methods. Readers are encouraged to consult them, i they


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wish to deepen their understanding o the subject. Te reerence sectioncontains a partial list o authoritative sources, which readers can use tothis very end.

2. Background

he origin o work studies is commonly credited to the paper Apiece-rate system being a step toward partial solution o the laborproblem published in 1895 by F.W. aylor on the ransactions o theAmerican Society o Mechanical Engineers. aylor was convinced thator each task there was a quickest time in which it could be perormedby a rst-class man, without depleting his work capacity. Te rst-class

man was the man best tted to perorm that task, through natural andacquired capabilities, including proper equipment. Te quickest time wasreerred to as the standard time, which could be determined throughscientic investigation and used or work management. Determining astandard time was considered crucial to setting a air piece rate and tond the one best way or perorming a given task. Standard time wassubdivided in three main categories: 1 - time actually spent working; 2 -time or overcoming atigue (rest); 3 - time or overcoming delays. Complex

Scientic management has its roots in an exploitative era characterised by rapid industrialisation


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tasks were simply treated as a sum o elemental tasks, and their standardduration was considered as the sum o the standard duration o eachelemental task. Tat is where elemental time studies comes rom. Someo aylors original concepts were criticised early on. Already in 1930,

the amous economist A.C. Pigou stated that everybody is continuouslylearning and that there is no one best way to perorm any given task.Another common criticism is that the time to perorm a complex task isnot necessarily the sum o the times to perorm its elemental sub-tasks,because there is oten interaction between correlated sub-tasks, leadingto time economies or diseconomies. Regardless o critics, aylors originalphilosophy has shaped work management as a discipline. His concepts arestill echoed in modern work study techniques even 100 years ater their

original ormulation.

3. Work measurement

Work science has multiple goals, achieved with diferent types ostudies (Appendix 1). In this guide we are mostly interested withwork measurement. Te objective o work measurement is to describe therelationship between work inputs and work outputs, and the inuenceo process variables on that relationship. One may consider many types

o inputs and outputs, depending on the goal o the study. In a simplework study, one may ocus on mass output and time input. Energy is alsoa very good choice or both input and output, especially when dealingwith energy biomass.Te direct relationship between product output and time input is calledproductivity. Te inverse is called time consumption (per unit product).Te variables afecting these relationships are many, and include suchactors as technology, work technique, operator skill and environmental

conditions. Some o these variables can be managed, while others arepassively received.

Determining the efect o process variables on the input-outputrelationship has many practical uses, such as: setting work rates,scheduling harvesting activities, and comparing technologies or workmethods.

3.1 Statistics in work measurementUnortunately, process variables come in almost endless combinations,which makes it dicult to determine the specic efect o the variable inwhich we are interested (target variable). Tat is where statistics come intoplay.


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Good experimental design and statistical analysis o data allow contrast-ing the efect o target variables against the general efect o all the othervariables combined (Figure 1). arget variables are oten called control-lable actors, on the assumption that what is known and predictable can

be managed, one way or the other. Te other variables are called nui-sance variables, and their efect background noise, or simply nui-sance. Experimental design ofers several techniques to dull backgroundnoise, so that the target variable efects can emerge through proper sta-tistical analysis.

Figure 1. A generic model o a work study system, afected by target and nuisance variables. Te

lower tier shows the main strategies to dampen nuisance.

All variables can assume discrete nominal values (or levels - e.g. machineA, B and C) or continuously changing numerical values. Variables o theormer type are commonly called actors, whereas variables o the lat-ter type are called covariates.

3.2 Study typesWork measurement studies can be classied according to their scope, goalsand characteristics, with diferent study types normally requiring diferentexperimental designs and statistical techniques.

Te scope o the study may be described ater careully dening the systemboundaries. In general a study can concern a single worker, a single machineor a whole system.

As to goal, we can diferentiate comparative studies rom modelling studies.Comparative studies aim at determining i and how productivity or time con-sumption is afected by two or more operational alternatives (e.g. machineA vs. machine B). Basically, comparative studies try to disclose the efects oxed actors. In contrast, modelling studies aim at determining the efect


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o continuous variables, or covariates (e.g. tree size, extraction distanceetc.). Normally, xed efects are represented as nominal variables, whereascovariates are represented as numerical variables. Studies oten involve acombination o comparative and modelling elements, with the specic goal

o the study normally determining how the study is classied (comparativeor modelling). For instance, modelling is oten used in comparative studieso the highly variable orest environment in order to enable comparisons tobe made under the same conditions (e.g. normalizing the comparison orthe same mean tree size).

Based on its experimental characteristics a study can be dened as eitherobservational or experimental. In an observational study, inuencingvariables cannot be controlled. Tat may result in a rather weak study de-

sign, which will provide indicative rather than conclusive evidence aboutthe efect o target variables. Many orest work studies are observationalin character, yet they ofer valuable insights into the studied processes andnd their way to the scientic press. In contrast, experimental studies in-volve a stronger capacity to control process variables, with levels that canbe suitably arranged in a strong experimental design. Te insights obtainedrom these studies are stronger and much more reliable than those obtainedrom observational studies. Simulated environments ofer an ideal oppor-

tunity to conduct the perect experiment. Nevertheless, researchers playingwith experiments must be careul not to build an experimental design thatis too articial to reect real operational conditions.

Perorming a work study is a complex job, involving several steps ( Figure 2).

Figure 2 Flowchart o the diferent steps required or a work study


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4. Beore you start

4.1 Study goalA clear study goal will guide researchers through all steps o a good study.

Te exact denition o this goal is afected by: 1) specic problem tosolve or knowledge to acquire, 2) oreseen use o study results and 3)available resources. For instance, the need or accuracy will difer be-tween a low-budget study aimed at obtaining a rough and ready estimateo expected perormance and a large-scale experimental study designedto produce reliable guidelines or ocial use. However, it is o the out-most importance to spend both time and consideration to ormulate thegoal, so that it meets both the expectation o the end user and the avail-

able budget. A clear goal statement is the oundation o a good study. Ahypothesis statement is oten part o the goal statement.

4.2 Experimental design

Experimental design is the process o planning a study to meet speci-ed objectives. Planning an experiment properly is very important inorder to ensure that the right type o data and a sucient sample sizeare available to answer the research questions o interest as clearly and

eciently as possible.

Once the goal has been dened and the hypothesis ormulated, the re-searcher is ready to draw his/her experimental design. Te design mustulll two conditions. First, the nuisance variables should be controlledeciently and logically. Second, the design should lead to simple analy-sis, since it is the experimental design that decides how the collecteddata should be analyzed statistically.

In order to know what nuisance to control, an important step in the ex-perimental design is to list all conceivable nuisance variables. Nuisancecan be handled by the ollowing methods: constant keeping, randomiza-tion and inclusion. By constant keeping the study is conducted underconstant conditions - e.g. only chipping logs o a given size and species,which corresponds to one given nuisance level (where log size representsa nuisance variable in the study). Randomization consists in randomlyallocating the treatments to the diferent levels o nuisance e.g. ran-

domly allocating the piles o similar, but not identical, material to bechipped. Inclusion is also called experimental control and consists otreating nuisance variables as additional target variables, measuringtheir levels and associating them to the corresponding levels recordedor productivity, time consumption or any other response variables.


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Box 1 Example o goal statement and hypothesis statementTe goal o this study was to compare the technical and economic

perormance o terrain chipping and roadside chipping, applied toshort-rotation biomass plantations. Te null hypothesis was thatthere was no signicant diference in the perormance o the twowork systems, when applied to short-rotation plantations.

Tese relationships are then used to correct the analysis. Depending onthe characteristics o the nuisance variable included in the analysis, onewill talk about blocking (or variables assuming xed levels) or intro-ducing a co-variate (or continuous variables).

Experimental design addresses the questions o how to combine thestudy treatments with the possible methods o nuisance control in sucha manner that no treatment is systematically avored.

Comparative studies will normally use some kind o actorial design.Te general similarities in actorial designs are that a certain numbero repetitions are conducted or each combination o treatments andblocks (Figure 3). It is advisable to aim or equally many repetitions or

all combinations, i.e. a balanced design. Tat acilitates simple and validanalysis. However, a balanced design is not compulsory in the analysis,and especially not so with large number o repetitions. Te capability toaccommodate or unbalance is very helpul, since unbalance oten occursdue to unoreseen events during the experiment.

A similar approach will be ollowed in modelling studies, with the difer-

ence that the ocus is on how measurable and/or quantiable nuisanceactors o interest inuence one or many controllable actors (treat-ments). Tus, instead o exerting passive statistical control on the nui-sance variables, the ones o interest should be actively selected so theyvary within a predened range. o improve analysis, the number o ob-servations should be balanced within the range o variation.

Each repetition o the same experiment is also called an observationalunit, sample or replicate. Te reason or observing several units that re-

ceive the same treatment is the expected variation in both response totreatments and in measurements. Tus, it is crucial to dene the obser-vational unit (i.e. what should be replicated) and the required number orepetitions. Tis can be calculated based on inormation about: expectedmean value, sample variation and desired (statistical) accuracy.


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Te procedure or such calculations varies with the type o experimentaldesign, and can be ound in standard statistical textbooks. However, weinclude the example below as a reerence. Te equation is used as a basisor determining sample size in harvesting studies conducted at the cycle

level (Murphy 2005):number o replications = t2 * V/(E*Mean /100)2 [1]

where: t = Students t-value (= 1.96 or a 95% condence interval t2 = 3.842)

V = expected variance o work cycle time

E = level o precision required (e.g. 5%,)

Mean = expected mean o work cycle time

Although not ormed by any kind o natural law, the precision level isgenerally set to 5%, which means that the researcher is willing to accepta 5% risk that the hypothesis is incorrectly evaluated.

Te expected mean value and variation is less easy to quantiy in ad-

Figure 3 Design o an experiment or comparing terrain chipping vs. roadside chipping in a short-

rotation poplar plantation. Plots are allocated randomly to the two treatment levels (i.e terrain

chipping = blank plots; roadside chipping = plots with a orwarder symbol). Te experiment is

blocked or two main clone types, i.e. Monviso and AF2. It is thereore a actorial 2 x 2 design,

where each o the 4 treatments is repeated 6 times (i.e. total o 24 replications). Hence the design

is balanced.


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vance o the study. Ideally, pre-studies could be used to provide appro-priate inormation or the calculation. Otherwise, this inormation canbe obtained rom previous similar studies. In act, the decision aboutthe number o repetitions is oten based on educated guesses. However,this is related with the risk o having too ew observations or detectingany diferences (large variation compared to the size o the treatmentefect) or to spend excessive resources on too many observations (verylimited variation and large treatment efect). Within a given experimen-

tal design, the sum o the samples (observational units) in all treatmentcombinations results in the total number o samples to be studied. Tisnumber is an important consideration in determining whether or notthe stipulated experimental design will t the study budget. I too re-source demanding, alternative designs will have to be considered and/or the number o samples will have to be decreased. Tus, the actual ex-perimental design will not only be the result o a preerred accuracy instatistical analysis, but also o the budget constraints.

4.3 Formulating a statistical modelSince it is the experimental design that decides how the collected datashould be analyzed statistically, it is natural that the researcher shouldbe aware o what statistical methods and models should be used or the

Box 2 Experimental design: example 1We want to determine the diference between two chipper models(= one treatment with two levels) operated by three diferent

operators (= one blocking actor with three levels) under identical(i.e. similar) conditions. Tereore, each operator will work witheach chipper, so that we shall have 2 x 3 = 6 combinations. Tis is aactorial design. We decide to conduct 5 repetitions per combination,so that the total number o samples will be 6 x 5 = 30. Te order inwhich we shall distribute samples should be random. So operatorsshall switch between machines randomly, until we have completed 5repetition o each o the 6 operator x machine combinations. Underreal conditions, a pure random sampling might be inconvenient, sothat one may assign operator x machine combination randomly, butconduct the ve repetitions sequentially or each combination. Tisprocedure is ormally incorrect, but it is oten accepted i one cannd other measures to mitigate the error deriving rom sequentialrepetition, or i it can be rationally explained why it is expected thatsuch violation o good practice does not represent a main source oerror.


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chosen design. Tus, it is important to ormulate the statistical modelthat will be used to analyze that data. I hypothesis and experimental de-sign are well aligned and the experiment includes ew treatments, blocksand co-variates, this is quite straight-orward. For instance, a design

with one treatment under constant conditions would have the statisticalmodel o:

yij

= + j

+ij

[2]

in which y is the response variable or observation i within treatmentlevel j (e.g. time consumption or a given log chipped by chipper A), isthe grand mean (total mean value), is the main efect o the treatmentj, and is the random error. Tis model would be evaluated in a one-way

analysis o variance (ANOVA). I the study employs statistical control o,or instance, diferences in log sizes, a co-variate is added to the model,according to

yij

= + j

+bxij

+ij

[3]

where b is the slope o the co-variate x. Tis model would be evaluated ina one-way analysis o co-variance (ANCOVA).

4.4 Dening what to measure and how

Te object o work studies is the relationship between work inputs andwork outputs, and its reaction to the efects o process variables. A wellplanned and implemented work study will determine the inputs, theoutputs and the process variables, trying to dene their possible rela-tionships with statistical methods. In particular, a work study will re-quire that all o the ollowing objects are measured:

4.4.1 Inputs

Being the characterizing element o work studies, time is obviously thevery rst object to be measured in a time study, e.g. the time input percycle, per cycle element or per plot. Another crucial input to be measured

Box 3 Experimental design: example 2When aiming to model the inuence o log size on chippingproductivity or a given machine, the design would be to dene arange o log sizes to be studied and the total number o logs to bechipped. Ideally, the number o logs should be spread evenly alongthe log size range (instead o having 95% small logs, 2% mediumsized logs and 3 % large logs).


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is the energy used by the process under study. Tis is especially impor-tant when the object o the process is the manuacturing o an energyproduct such as biomass uel.

4.4.2 OutputsAs work is assumed to produce outputs, these outputs should be deter-mined with sucient accuracy or productivity studies, since productiv-ity is dened as output divided by input. Outputs are both quantity andquality, equally essential to evaluating any work method and/or technol-ogy. In orest work, quality concerns two separate entities: product andenvironment.

Product quality is evaluated by comparing actual product characteristics

with market specications. In the case o uel chips, or instance, theseare: moisture content, particle size distribution, contamination level etc.Te environmental quality o a given work technique is generally denedby its stand and soil impacts. Hence, the main outputs o a orest har-vesting process are: product quantity, product quality, environmentalquality.

4.4.3 Process variables

Process variables that may afect time consumption and/or productivityshould be determined with accuracy, both in comparative and modellingstudies. In the ormer, determining work conditions is crucial to ensurethat all alternative treatments are applied under the same (controlled)conditions. In the latter, the validity o any model will depend on the ac-curate determination o the afecting independent variables.

Not all these objects must be measured in every study: object inclusion

and measurement accuracy will be tailored to the goal o the study, andto the resources allocated to conduct it.

At the outset, the research team should dene the goal o the particu-lar study and determine the variables that must be recorded in order tomeet these objectives.

I the study involves modelling, the researchers should generate a ma-trix o the relevant dependent variables (such as cycle time elements andproduction) and independent variables, anticipating which o the lattermay inuence the ormer. In Appendix 2 readers can nd an example othe many variables that could be explored, when studying a range o di-erent harvesting techniques.


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4.5 Practical rules

4.5.1 Saety

Saety is the rst and oremost requisite or all work activities, coming

beore productivity and environmental quality. Field researchers shouldalways make sure that they are not exposing themselves and others to

unnecessary risk. Teyhave the legal and socialobligation to comply withall saety requirements. Ithey are working in an en-vironment still relatively

casual about saety proce-dures, their obligation isalso moral, because theycan set an example thatwill help introduce a sae-ty culture where this isbadly needed. Regardlesso how condent we are

in our capacities, manyoperators look up to usbecause o our educationand status and we shouldset a useul example thatmay save lives. Wheneverentering an operation, re-searchers shall always:

- wear high-visibilityclothing (jacket or vest);

- wear a hard hat (with hearing protectors and visor when needed);

- ask the operator/s about the work routine, the sae zones and the riskzones, so that the researcher will always stay away rom risk zones andinside the sae zones. Whenever losing sight o the researcher, the op-erator should stop work immediately;

- agree with the operator/s on a system o communication, so as toquickly and unambiguously transmit urgent inormation (e.g. radiophones);

- abstain rom drinking alcohol and/or taking any drugs that may impair

Commercial GPS-tracking black-box unit


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ones alertness and judgment;

- and, more generally speaking, be compliant with any other require-ments coming rom regulations or internal rules adopted by the com-panies in charge o the orest operations.

Special attention should be paid when climbing into and out o contain-ers to collect chip samples, as container edges are tall and slippery. Whenclimbing is necessary, that should be done with caution, using the stepsnormally tted on most containers.

Work at a landing can oten be observed rom a xed station, includingthe researchers own car, appropriately parked in a sae zone where itdoes not hinder the operation. Tis can ofer much relie under rainy

and/or cold weather conditions.

When studying elling, processing or harvesting machinery, a sae dis-tance should be maintained in order to minimize the risk o injury incase o uncontrolled tree all or saw chain breakage. In certain oreststands, sae distance may make it impossible or the researcher to ob-serve the operation in such a detail as required or the study. Te onlysae place within the sae zone is in the machine cab, and it may happenthat the researcher rides in the cab together with the operator. However,that it is not advisable unless the cab has been designed to take a pas-senger. Otherwise, the eventual passenger may not t inside the internalsurvival volume remaining ater a possible roll-over or impact. In suchinstances, researchers should consider remote data collection, as allowedby video-recorders, on-board computers and commercial GPS-trackingblack-box units.

Field study researchers work outdoors and should take all precautions

required by outdoor work, including: wearing appropriate clothing (com-ortable, rainproo, warm, resh - according to need); wear work bootsor similar shoes; carry their own supply o water and ood, as needed;use insect repellent or carry an appropriate weapon i harmul insectsor animals may cause danger or discomort; and get the appropriate vac-cinations or any diseases that can be contracted in the specic workenvironment (tick-borne encephalitis, tetanus etc.).

4.5.2 Ethics

Work studies oten represent an intrusion into the personal workingspace o individuals, crews, and enterprises. Studies can be initiatedby companies that wish to know more about their own operations, bymachine manuacturers that wish to test or enhance their design, or by


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researchers who wish to investigate one or more aspects o machine orsystem perormance in an applied setting. Studies are almost never initi-ated by workers themselves.

Scientic management has its roots in an exploitative era characterisedby rapid industrialisation and a need to quantiy eciencies and costs.While work studies are important, the integrity o the personal workspace is protected by collective agreements, legislation and common re-spect.

Trough work studies, the researcher unavoidably gains insight into thephysical and intellectual capacities o the subjects involved. In motor-manual work this is explicit in the orm o heart and lung perormance

measurement. In mechanised operations it might be the subjects deci-sion making or concentration ability.

Tis short passage cannot deal with the complexities o labour law andhuman rights in the 34 participating countries. It is a simple reminderthat researchers should be amiliar with the legal and ethical rameworkwithin which they operate. Work studies should be ounded on dialogue,trust and condentiality. Subjects should be made aware o the purpose,methodology and intended use o the results. Teir consent should be

obtained beorehand. Te eld o ethics also includes the relationshipwithin your own study team and with other research teams. Tese aretreated extensively in the USDA Code o Scientic Ethics, which providesan excellent example and is reely available on the Internet http://www.s.ed.us/rm/analytics/ethics.htm

Similar attention must be paid to the relationship with the customer andall subjects involved in the study, and to the possible condentiality obli-gations imposed on sensitive data.

5 Measurements in the eld

5.1 Measuring time inputime consumption measurements ofer diferent resolution dependingon whether they are conducted at the shit, cycle or elemental level.

5.1.1 Plot levelIn plot level measurement the observation unit consists o a single plot,like those described in Figure 3. Hence, all the time input necessary orharvesting the plot is cumulated. ime input is measured directly by theresearcher observing the operation, or automatically by appropriate sen-


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sors connected to a data logger. Data can also be recorded manually by acooperative operator appropriately instructed.

5.1.2 Shit level

Shit level measurement implies that the observation unit consists o awhole shit, whose duration and organization should always be indicated(e.g., 8 hours total time, including 6 hours o actual work and two hourso maintenance). Shit level measurements are conducted manually orautomatically. In the ormer case, operators are given data collectionorms and are instructed to note daily on the orms data such as: date,place, job type, starting and ending hour, estimated output (turns, trees,m3 etc.), uel consumption and any major delays.

Te cause and estimated duration o all delays should also be noted.Much o the same inormation can be collected automatically throughappropriate sensors, connected to a data logger. Most dedicated har-vesters are already tted with the necessary equipment to capture thesedata, or the purpose o operational optimization and cost control. Shitlevel measurement is generally the main technique used or long-termollow-up studies aimed at determining machine utilization, long-termproductivity and incidence o delays.

5.1.3 Cycle level

In cycle level measurement, the observation unit is a single work cycle(e.g. the elling o a tree, the orwarding o a load etc.). Compared toshit-level measurement, cycle level measurement ofers more detail andcan help describe the work pro-cess with much more accuracy. Italso helps identiy the variabilityo a work process very quickly.Individual relationships can beisolated that could be dicult topinpoint with shit level meas-urements. A number o difer-ent tools can be used or manualmeasurements o time consump-tion at cycle level, including:standard wristwatch, stopwatch,stopwatch board, or hand-heldcomputer. All these instrumentscan determine the time elapsed

ime study board


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between the start and the end o a previously dened work cycle, andthis value is noted on paper or voice recorder by the researcher, or storedin the computer memory. ime consumption can also be captured auto-matically, i an action or a sequence o actions dening the start and the

end o a work cycle can be identied by appropriate sensors. Tese cantranser captured data to the storage o an on-board computer (i tted)or to an add-on external storage. Te MultiDA system developed by FPInnovations (ormerly FERIC) and distributed by Castonguay Electron-ique Inc. in Canada is an example o a proven automated data collectionsystem. Furthermore, modern eet control and management systemsofer similar capabilities and could be used or the purpose o collectingtime and motion data.

5.1.4 Element level and work sampling

Element level measurement consists o splitting the work cycle into unc-tional steps (elements) and then recording time consumption separatelyor each o them. Tis allows the work process to be described in more de-tail, which may contribute to a better understanding o process dynam-ics. In particular, the benets o elemental measurement are: 1) indicatingwhich specic process steps take more time, so that specic improvement

measures will primarily target these steps; 2) separating efective work timerom delay time, since thesetwo categories have diferentinternal variability and couldbe modelled in diferent ways;3) separating unctional ele-ments that react to diferentwork characteristics, so that

more accurate sub-models canbe developed.

Elemental measurement isconducted with the same in-struments listed in the pre-vious paragraph. When veryshort elements must be cap-tured, one may resort to video

recording: elemental time con-sumption is then measured inthe lab, using the slow/pauseunction and the time stampo the video recorder. In all

Collecting time data


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cases, it is crucial that the actions marking the beginning and the end oeach unctional step be clearly dened and described, so that the study canbe interpreted and eventually replicated by ellow researchers. I more ac-tions occur at the same time, they will overlap.

In this case, we shall have three options, depending on the goal o thestudy: 1) we may record their separate durations; 2) we may dene a new

combination element; 3) we may decide or a priority system that allocatesthe overlap time to one o the two separate activities. Let us consider thecase o a eller-buncher handling cut trees while rolling on its tracks. I weneed to separate the two activities, we shall ask a colleague or assistance sothat two separate persons will time the two unctions separately.

Box 4 - Subdivision o cycle time into unctional elementsAs an example, one may consider subdividing a chipper cycle (denedas the process o lling up a container o known volume) into the

ollowing time elements:- Moving the chipper along the wood pile or between adjacent wood

piles. Starts when the outriggers are lited of the ground andends when they are rmly positioned into the ground at the nextchipping station.

- Parking the container near to the chipper. Starts when the chipperis still, waiting or the container to be placed by its side and endswhen the chipper begins chipping again.

- Chipping. Starts when the rst wood load is moved to the chipperineed and ends when no more wood is being ed to the chipper.

- Other work. Any other work process (e.g. piling, handling woodwith the loader etc.)

- Delays. Any interruption o the work process (See next box).When subdividing a work cycle into unctional steps it is importantto resist the temptation o producing too many time elements, sincethat may detract rom recording accuracy, increase the possibility o

errors and complicate experiment replication by others.It is important to remember the purpose o the specic study and themain goal o elemental breakdown, which is the separation o processsteps that are diferently afected by diferent independent variablesand/or require diferent improvement measures.Separating more elements that are similarly afected by the samevariables and/or improvement measure will produce no practicalbenets. It is also helpul to look at other studies or ideas on

elemental breakdown and on relevant variables to measure.


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Te same result will be obtained by videotaping the operation and then

playing the tape twice, so that one person can record the two separatetimes. Otherwise, we can dene a special combination element (e.g. han-dle and roll) to contain overlap time.

Finally, we can allocate overlap time to either the handle or the roll

Box 5 - DelaysDelays are interruptions o the work process and are commonlysubdivided into three main categories depending on their origin.

Mechanical delays are caused by the need to service or repair themachine used or perorming the work task. Personal delays areinterruptions caused by the operator, and include rest breaks.Operational delays are related to organizational causes, such as apoor balance between the chipper and the supporting units (waiting)or an excessive concentration o machines on the same track (trac),work planning and site reconnaissance.A ourth delay type is represented by interruptions o the work cyclecaused by the study itsel (study delays). Tese are generally excludedrom the analysis. Te subdivision between evitable and inevitabledelays requires a subjective judgment and should be discouraged.Te main problem with delays is their large variability, due to erraticoccurrence.A reliable estimate o delay time (or overall time including delays) willrequire a very large number o replications and a comparably longobservation time. Tereore, two main solutions have been devisedto overcome this problem: 1) including into the study only thosedelay events that all within a maximum duration limit (e.g. 10 or 15minutes); 2) excluding delays rom data recording and accounting ordelay time through specic delay coecients applied to productivetime. Te rst strategy tends to underestimate the incidence odelays.For example, long-term studies o chipping operations have shownthat delay events shorter than 15 minutes represent over 80% othe occurrences, but only 32% o the total delay time. Te second

strategy is based on conducting a long-term ollow-up study o theoperation or on combining a large number o detailed time studiesinto a larger data pool, in order to extract the long-term incidenceo delay time (delay coecient). Tis should also be expressed as apercent o productive work time, and not o worksite time, since thelatter orm is awed by inter-correlation.


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unction. Te unction with priority will be the one clearly appearing in thestudy, whereas the other unction will be masked.

Te description o time elements should clearly report which unctionsmay overlap, and which will have priority when overlap occurs. Prioritywill be attributed on the basis o the study goal. I or instance our eller-buncher study aimed at predicting track wear by determining how muchtime a eller-buncher spent moving, then the roll unction should receivepriority and the handle unction would be masked whenever the twooccurred together.

Elemental time is recorded with two main techniques: continuous timingand snap-back timing. With continuous timing, the time o each element

shit is noted, and the duration o each time element is calculated by sub-

Box 6 - Handhelds computersime study data can be collected with handheld computers runningdedicated time study sotware. Tese computers are oten

ruggedized, so thatthey can withstandthe outdoor orest

environment. Diferentmachines are used bydiferent groups, butthe most common are:the Husky Hunter (andsubsequent models)running the dedicatedSiwork3 time study

sotware, which isstill very popular inmany English-speaking

countries, as well as in Denmark, France and Italy; the Latschbacheramily o eld computers, widespread in Austria and Germany; theRuco 900 in Finland. All these machines are relatively old and attimes they present interace problems with modern laptops, as mosto them use serial connection ports that have virtually disappeared

rom new personal computers. Modern potential replacements arenew portable machines such as Allegro, Psion, Ranger and oughbook.Interacing potential, sotware availability, reliability and battery lieare the main parameters to consider when choosing a handheld ortime study purposes.

Husky Hunter hand-held computer


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tracting two time marks (i.e. the time when the unction was completedminus the time at which it was initiated). Tis technique requires back-cal-culation but is the only viable option when using a wristwatch or timing.Snap-back timing consists in restarting rom zero at each element shit.

Tat is done using the lap unction available on most stopwatches. Teadvantage o this method is that one does not need any calculations to ob-tain the net elemental time.

Work sampling (also known as requency study) is another technique ormeasuring the elemental breakdown o time consumption. It consists o

observing the process at xed or random intervals, and noting in whicho the previously-dened unctional steps the work team is engaged inthat specic moment. At the end o the study, the researcher will obtaina total time (duration o the study) and a relative requency1 o the di-erent unctional steps which is one o the outputs expected o anyelemental time study.

Te advantage o work sampling is that it allows one researcher to ollowmore teams at a time, by organizing a sequence o observation intervalsor the diferent teams. Te disadvantage is that work sampling does notofer any inormation about cycle duration, since the observation inter-val cannot be synchronized with the variable duration o the work cycle.In act, one should careully avoid the synchronization o observationinterval with cyclic work, which would return a biased representation ocycle time distribution.

Irregular sampling intervals are preerable to regular intervals, because

they exclude the accidental synchronization with cyclic elements. Worksampling is oten used or quantiying equipment and people interactiondelays within a working team or work system.

1 whence the alternative denition o requency study

Box 7 Hawthorne efectIt is a well-known phenomenon, where workers modiy theirbehavior just because they know that they are being studied. Tismay determine perormance increases (or decreases) that are not

caused by the technical changes introduced with the experiment.Te Hawthorne efect may introduce a signicant bias in short-termwork measurements. For this reason, reliable productivity levels arebest determined with long-term ollow-up studies, or by analyzinglong-term production statistics.


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5.1.5 Units

ime consumption is generally measured in hours, minutes and seconds,depending on the resolution o the study. Occasionally, very short pro-cess steps can be measured in smaller units, like the tenth o a second.Work studies are oten conducted with clocks that measure minutes andcentiminutes i.e. hundredths o a minute instead o minutes and sec-onds. Tat is a compromise aimed at transorming into a quasi-decimalsystem the traditional sexagesimal time measurement system. It electsthe minute as the most representative unit and breaks it into hundredths,in order to simpliy the eventual data processing (by allowing decimalcalculation). It is a very efective measure, even in a time when comput-ers can easily transorm sexagesimal records into decimal records, andthe reverse. However, the second is the only SI unit or time measure-ment, although the hour and the minute have been ocially acceptedor use with the International System2. Hence, time study data can bereported in scientic publications in any o these three units, whereasthe use o centiminutes (cmin) could be rightly opposed by reviewers.In that case, an acceptable ormulation could be min*10-2 or 1/100 min

5.1.6 Classication o time in orest studies

ime consumption can be subdivided and/or grouped according to therole o the specic work steps within the whole process. A number oclassications have been produced over time, generally deriving romthe work o the Nordic Research Council and the American Pulp and Pa-per Association. Most previous classication eforts have already beenconsolidated and partly harmonized within IUFRO, and the best syn-thesis is still ofered by the IUFRO Forest Work Study Nomenclature,published in 1995. In Section D, the IUFRO document presents a clearand comprehensive classication o time in orest work study.

Tat same classication is reported in Appendix 3 o the present manual,and adopted or the purposes o our good practice guideline without anyurther changes.

Some scholars question the subdivision into time elements, because otheir possible inter-correlation. Correct statistical theory requires thatvariables are independent rom each other. Hence the statistical treat-

ment o separate time elements would be incorrect i these were oundto be inter-correlated. In that case, it would only be correct to analyze

2 ab. 6, Page 105 o Te International System o Units ( S I ) 1998, 7th edition 1998, OrganisationIntergouvernementale de la Convention du Mtre.


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total time consumption as a whole. However, elemental time studies arestill popular and useul.

5.2 Measuring energy input

Reliable measurements o the energy used or the supply o energy bio-mass are crucial to the compilation o Lie Cycle Analysis (LCA) studies,and ultimately to the ormulation o policy suggestions. Te work-studyresearcher is eminently well situated to provide accurate data on energyinputs. Direct energy consumption is normally measured by recordinguel consumption and then converting it to energy units through a con-stant that represents the energy content o the uel.

Fuel consumption studies can be carried out at various levels o resolu-

tion, depending on the goal o the study. able 1 lists the main tech-niques, with their pros and cons.

Any essential uel consumption study should always provide at least theollowing data:

- Engine model, make, year o manuacture and displacement (cm3);

- Litres or kilograms o uel used or the duration o the study;

- Amount o biomass produced or the duration o the study.Additional inormation could include: duration o the study, engine in-ormation relating to emissions (Euro standard or American tier sys-tem), etc.

Fuel is a main energy input


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Measurementresolution

Technology / Method Pros & Cons

Continuous Onboard ow meter (factorytted)

Onboard ow meter (tted forresearch)

Coupled with electronic impulserom hydraulic valve bank, it al-lows consumption analysis onseparate work elements, e.gboom movement, driving, chip-ping.

Requires relatively sophisticatedequipment

Operation orShit level

Onboard ow meterStandard ow meter on newmachines provides accurate

current and shit level data. Pump-tted ow meterFlow meter on an electricor manual pump is used torecord uel volume during re-uelling.

ScaleScale can be used to weighuel beore reuelling

Shit or operation level data canprovide more robust inorma-tion, evening out erratic peaks.

Requires less intensive observa-tion / data management

Short term retain machine op-erator enthusiasm

Lose individual work elements inthe analysis

Motivation - oten requires thatoperator must record and logdata alone

Daily or weekly Onboard ow meter

Pump tted ow meter

Scale

Similar to above, but less re-quent measurement is required.

Reduced accuracy in relation tooutputs

Not easy to keep operator moti-vated to ll orms - serious cor-ruption o data i error or omis-sion occurs

Monthly, quar-terly or yearly

ypically based on data ob-tained rom

Onboard ow meter

Fuel issued to machine (ac-counting system must iden-tiy machines)

Fuel purchase details frombulk supplier (or single ma-chine)

otal periodic uel consump-tion averaged by machines

Oten reasonably accessible data(due to accounting laws)

Robust data covering a widerange and depth o observations

Cannot be used to develop spe-cic models on operations

Risk o data loss or corruption

able 1 echniques to measure uel consumption


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5.3 Measuring product outputProductivity studies require that time consumption be associated with aproduct output, in order to determine the ollowing relationships: Pro-ductivity = Product output/ime input; Specic time consumption =

ime input/Product output. Specic energy use = Energy input/Productoutput. Product output can be measured using the diferent units listedbelow.

5.3.1 Count

Output can be measured just by counting the units produced, such astrees, logs, bundles, grapple loads or container loads. Unit count is a veryapproximate measure, which makes sense only when the units have a

regular standard size, which can be easily quantied into mass or volumegures.

5.3.2 Solid Volume

Solid Volume is a very reliable entity or estimating biomass output.Once the measurement technique and the eventual inclusion/exclusiono bark is dened, the measurement is relatively robust. Solid volume canbe measured with caliper and loggers tape, using many diferent tech-

niques (Huber, Smalian etc.). Otherwise, it can be determined using theharvester measurement system, provided this is correctly calibrated. An-other method to estimate solid volume consists o using volume tables,which return tree volume as a unction o tree diameter and tree height.In this case, eld measurements are limited to the diameter at breastheight (DBH) o the trees to be processed, and to a certain number otree heights, necessary to develop a diameter-height curve. However, allthese methods will produce solid volume estimates or the stem and the

main branches only, excluding the volume o smaller branches and twigs.Tat can be accounted or by using an empirical biomass expansion ac-tor (BEF), which increases the stem volume estimate by a certain per-centage, reecting the contribution o smaller branches3. However, BEFswill only provide approximate estimates, partly deeating the benet ousing solid volume as the reerence or estimating biomass output.

5.3.3 Bulk volume

Bulk Volume (or loose volume) is the volume physically occupied by a

3 See: eobaldelli M., Somogyi Z., Migliavacca M., Usoltsev V. 2009 Generalized unctions o

biomass expansion actors or coniers and broadleaved by stand age, growing stock and site index.

Forest Ecology and Management 257: 1004-1013.


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certain quantity o biomass. Tis unit is oten used with small logs, re-wood and chips. Loose volume is very easy to determine, since it justtakes a tape to measure the volume o the log stack or the internal vol-ume o the chip container.

Loose volume can be converted into solid volume or weight by usingappropriate coecients, which should be estimated case by case withsampling. Although very easy to use, loose volume ofers a somewhat ap-proximate estimate, since the actual product mass will vary with the sizeand the orm o the individual elements orming the stack or the pile.Furthermore, diferent chippers may pack chips with a diferent power,thus producing a more or less compact load, even or the same particlesize distribution. Finally, loose volume can be determined with good ac-

curacy only i the stack or the container has a regular shape, whereas de-termining the loose volume o chip piles may be dicult and will returnapproximate estimates.

5.3.4 Fresh weight

Fresh weight (or green weight) is considered the most direct measure-ment o actual mass output. However, its correct determination requiresthe use o accurate scales, oten unavailable in the surroundings o thestudy site. In that case, loads can be scaled at delivery (they generallyare) and their weight can be transmitted to the researcher, providingthat each load is clearly and unambiguously identied. As an alterna-tive, one can use portable scales o diferent types, applied to the loader

Plate scales used or axle weighing


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boom or installed on large metal plates and used or axle weighing. Bothmethods can ofer good results, providing that the plates are correctly cali-brated, that they are placed on solid level ground and that all axles are atthe same level when weighing.

5.3.5 Dry weight

Dry weight ofers a better representation o true product value comparedto resh weight, because it excludes the inevitable contribution o waterto mass output. Dry weight is an indirect measure obtained rom reshweight, ater determining moisture content. Existing European stand-ards dene the methods or sampling (EN 14778), sample preparation(EN 14779) and moisture content determination (EN 14774). Te ac-

curacy o dry weight estimates will be afected by the errors accumulatedduring sampling.

5.4 Measuring energy outputEnergy Content is another indirect measure o output value, and it hasthe merit o indicating the actual value or the end user, which simpli-es communications with plant engineers (who will call it lower heating

value).

Energy content is obtained by multiplying dry weight or an appropriateenergy density coecient, then subtracting the energy absorbed by thewater inside the product. A typical example or hardwoods would be the

Box 8 - Which units should one use to measure product output?Tat is indeed a big question. All units have their pros and cons, andmay be adopted depending on the goals and the circumstances o thestudy. Whenever indirect measures are provided (i.e. dry weight andenergy content) it is essential that the researcher reports: the methods

used or estimating them; the values actually measured or reshweight and moisture content; the equations and parameter values usedin the energy calculations. An indication o variability o the directmeasurements would also be useul.Please notice that the term weight applied here is ormally inaccurate:what we are really measuring is a physical quantity dened as mass.However, understanding with managers and operators will be easier iwe use the term weight, rather than mass. Hence, it is convenient to use

mass in scientic papers and weight in everyday speech. Ater all,language is a convention.


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ollowing one, which returns Giga Joule per metric tonne:

GJ/t = dry weight, t * 18.5 GJ/t water weight, t * 2.5 GJ/t

Tis estimate is indirect and based on coecients, and its accuracy is

afected by the reliability o the coecients and the eventual error withmoisture content determination.

5.5 Measuring quality outputJob quality reects on product quality and environment quality, the latterdened as the impact on the stand and the orest soil. In act, environmen-tal quality is a complex concept, going ar beyond a simple determinationo direct stand and soil impacts. However, determining the ull extent o

environmental impacts exceeds the scope o simple work studies.

5.5.1 Product quality

Product quality will be estimated in diferent ways, depending on tar-get specications. For the manuacturing o logs, measurement accu-racy and supercial damage could be important quality indicators. Teormer will be checked with tape and caliper, the latter through visualinspection, or by capturing and processing digital pictures with image

analysis sotware in order to estimate surace damage with more accu-racy. In the case o chips, the actual work process can impact productquality especially or what concerns contamination and particle size dis-tribution. Contamination with soil and stones can be estimated visually,or by separating wood and contaminants (manually or with the help oa solvent) and determining the weight ratio. Particle size distributionshould be determined according to European Standard EN 15149.

5.5.2 Stand impactsStand impacts are generally determined by inspecting the residualstand ater harvest, in order to detect and catalogue any eventual dam-age caused to residual trees and/or advanced regeneration. Inspectionis generally conducted on sample plots o varying shape and size. Tenumber and size o sample plots will be determined as a unction o sam-ple variability and desired accuracy. ree damage is generally attributeda severity class, oten related to wound size, type, position and depth.

Supercial wounds smaller than 10 cm2

are oten neglected, since theydo not seem to afect tree health, growth rate or wood quality4.

4 Whitney R. 1991. Quality o eastern white pine 10 years ater damage by logging. For. Chronicle

67: 2326.


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5.5.3 Soil impacts

Soil impacts are determined in a number o diferent ways, rom verysimple to very sophisticated. We may want to stick with the simplemethods, assuming that the sophisticated methods will only be deployedor studies specically devoted to the analysis o logging disturbance,and belong to soil science more than to work science. For our purposes,simple visual inspection could be enough, and it can be conducted sci-entically with a standard method, such as that described by McMahon(1995), which is rapid and easy. According to this method, the harvestsite is covered by a regular grid o inspection points with a mesh size su-cient to obtain the desired sampling intensity, on the basis o expectedsample variability and desired accuracy. Ten each point is visually in-spected and attributed a predetermined disturbance class. As a result,one will obtain a reliable estimate or the requency o diferent visiblesoil disturbance phenomena.

5.6 Measuring process variablesProcess variables that may afect time consumption or productivityshould be determined as accurately as possible, both in comparative andmodelling studies. Such variables can be grouped in the ollowing three

large categories.

5.6.1 Physical environment

errain and orest characteristics have a major efect on work peror-

ime study on a biomass operation


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mance, and can be described by a number o diferent indicators, gener-ally pertaining to the elds o orest mensuration and topography. Forthe specic purpose o orest operations, terrain characteristics can alsobe described using the Swedish errain Classication System5, which iswidely adopted in Scandinavia, as well as in Ireland and in the UnitedKingdom6 with local variations. Te system is simple, and the originalmanual ofers reerence pictures or the evaluator. It produces a singlesynthetic indicator capable o describing slope gradient, terrain rough-ness and ground bearing capacity.

When describing the physical environment it is important to distinguish

between those variables that are essential or the study and those thatare not, although potentially useul. Te ailure o many attempts at de-veloping general data collection protocols is likely to rest in the over-abundant requirements o such protocols, which put an unacceptableburden on the researcher, oten constrained by budget or time limita-tions. Tereore, it may be better to restrain data collection to those vari-ables that are most likely to afect the perormance o the work processunder examination. For instance, there is little need to dene ground

slope or residual stand density i the study concerns a chipper workingat a landing.

In this case, the measurement may include average piece size (easily ob-tained by counting the number o pieces needed to ll a container oknown volume or weight), landing surace, tree species and tree part(branches, logs, whole trees etc.), which have already been shown to havea signicant efect on chipping perormance. Other orest compartmentdata can help describe the general background o the experiment and

are welcome i they come or ree, but can also be omitted without muchprejudice to the quality o the research.

5 Berg S. 1992 errain classication system or orestry work. Skogsarbeten, Kista, Sweden. 28

6 UK Forestry Commission. 1995. errain classication. echnical Note 16/95. 5 p.

Box 9 - Measuring extraction distanceExtraction distance is a key independent variable in biomass extractionstudies. Distance can be measured with several instruments, including:

tape measure, hip chain, pacing, laser range-nder, machine odometer,GPS, map coordinates. It is always very important to indicate howdistance was determined and i the distance reported is the mapdistance or the actual slope distance. It is also important to speciywhether extraction occurred uphill or downhill.


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In general, one should record with greatest accuracy all those charac-teristics o the physical environment that could be used as independentvariables in the eventual data analysis, such as tree size in elling studiesor extraction distance in orwarding studies.

5.6.2 Organization

Operation layout and work organization have a strong inuence on pro-ductivity and time consumption. In general, it is enough to provide asimple description o how the whole operation is organized, how manyunits and crew members are involved, and what are their specic tasks.I the work organization generates specic risk or operator saety (e.g.intererence between operators), it may be useul to identiy this risks

and suggest solutions.Operator experience, skills and motivation have a major impact on pro-ductivity and time consumption. Operator efect has been shown to a-ect productivity or up to 40%, which accounts or the gap between theinexperienced and the very experienced operator. Ideally, work studiesshould be conducted with many diferent operators, in order to integrateoperator variability into the study design. Tis oten conicts with thetime and the nancial constraints o most research projects. Operator

rating would ofer a practical way to deal with operator variability, andcould be conducted with a number o diferent methods, oten accuratelycodied. Unortunately, no current method ofers both easy applicationand objective evaluation, so that operator rating is either too complex ortoo subjective. For this reason, most researchers have discarded operatorrating, and preer to use general habilitation criteria, based on opera-tor background. Tat leads to the exclusion o any operators consideredinexperienced, unwilling, clumsy or slow. Evaluation is done by exam-

ining the operator work history, interviewing the operator and his/hercolleagues and supervisors, and observing the operator at work. Teseprecautions will not prevent operator efect rom causing some variabil-ity in the results, but are most likely to contain the eventual error withinacceptable limits.

Te payment system can have a strong efect on operator motivation andhelp explain eventual inconsistencies between similar studies. Ideally, astudy should provide suitable inormation on the compensation system(i.e. hourly rate, piece rate etc.) or comparison purposes.

5.6.3 echnology

All studies should integrate a ull description o the technology being


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tive statistics are: mean, standard deviation or standard error, minimumand maximum. It is oten useul to add the lower and upper quartiles, aswell. Descriptive statistics will be updated at the end o the analysis, ithe data have been purged o the eventual outliers. Data distribution can

also be described graphically using Box Plots, which display the median(central line), the range and the 10th, 25th, 75th and 90th percentiles (Fig.4). Box Plots are especially useul or displaying potential outliers (anydots much urther away rom the 10th and 90th percentile lines).

6.2 Checking or outliers

Te data pool should be checked or outliers. Te easiest way to do thatis by extracting average and standard deviation or each data string, andchecking how these values match expected gures or that given datatype. Evident mismatches should arouse suspicion. For instance, i theaverage elling cycle has a duration o 40 seconds, it would be reasonableto suspect that a record indicating a duration o 4000 seconds is aulty.Ten, this record should be extracted and examined or possible errors(e.g. erroneous inclusion o more cycles in the same record, transcription

error, unwarranted inclusion o delay events etc.). A urther method todetect possible outliers is to plot the data and just look or any pointsthat seem unreasonably of the charts. Finally, there are ormal outliertests, essentially based on the criteria o distance rom the mean anddistance rom the nearest neighbor. Among these tests are the Grubbsest or the detection o single outliers and the ietjen-Moore est orthe detection o multiple outliers. Standard or modied Z-scores can alsobe used or the detection o potential outliers. Suspected outliers should

only be removed rom the data pool i there is an objective reason (prooo error) to justiy their exclusion. Otherwise we might be tamperingwith the data. When in doubt, a good strategy could be that o retainingthe potential outliers and adopting a robust statistical technique thatwill not be unduly afected by outliers (outlier accommodation).

Box 10 - Statistical packagesA number o diferent statistical packages can be used or analyzing thedata collected in orest work studies, and among them some o the most

common are: SAS, SPSS, Minitab, R or Excel. Tey ofer similar resultsbut vary in cost and user-riendliness. R or Excel is a good tool, witha large capability and is reely available to all, although it takes somelearning beore it can be used correctly. Te others are available at aprice (rom very moderate to high) and are generally quicker to master.


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6.3 Checking or normalityBeore analysis, data should be checked or normality by drawing a re-quency distribution graph. I the data is normally distributed, then wecan use parametric statistics; otherwise, we need to apply non-paramet-

ric statistics.

6.4 Data transormationI data is not normally distributed, one could also try to alter its distribu-tion through mathematical operations perormed on each observation.Tis procedure is called data transormation and it has the purpose obringing data distribution closer to normality. Common transorma-tions are: square-root transormation or count data, logarithm trans-

ormation or size data and arcsine transormation or percent data.ransormed numbers are then used in the planned statistical tests. estresults must be reconverted to the original through back-transorma-tion, by applying the inverse o the mathematical operation originallyperormed.

6.5 Making comparisonsTe statistical signicance o any diference o mean values returned by

comparative trials can be checked with diferent statistical tests, depend-ing on the number o treatments being compared, the relationships be-tween repetitions in the treatments (paired or not) and the distributiono the data. I the data are normally distributed, the best way to analyzea typical actorial experiment will be through the technique called Anal-ysis o Variance (ANOVA). Te ANOVA table will provide inormation

Efect DF SS % F-Value P-Value Power

min perod

reatmentCloneInteractionResidual

111

20

840.15752.62813.60637.184

89%6%1%4%

451.9028.317.32


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about the statistical signicance and the strength o the efects derivedrom the treatments under analysis. able 2 shows a typical ANOVA ta-ble calculated or the data obtained rom the actorial experiment repre-sented in Figure 3.

Te ANOVA table taken as an example shows that treatment type (i.e.terrain chipping vs. roadside chipping) has a strong (89% o total SS) andsignicant efect (p


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explained by the numerical relationship just produced. A regression withR2 = 0.8 will explain 80% o the total variation in the data pool, and willindicate a good predictor. Several indicators describe whether the efecto a given independent variable is statistically signicant, and among

Box 11 - CaveatsStatistics are a specialist eld and oresters are not always too versed orinterested in mathematics (although some are really good at it). Hence

there is a risk o making undamental mistakes with data analysis. Welist some o the most common mistakes encountered when examiningorest harvesting studies, so that you may avoid them and get yourmanuscript through peer-reviewing with as little damage as possible.- Productivity is a derived unit (output/time) and as such it is very

unwieldy. Averaging the individual productivity values or a number oobservations will return a value that will be diferent rom the sum ooutput values divided by the sum o time consumption values, due tothe skew in the distribution o single observations. Hence, it is alwayspreerable to use time consumption in all calculations, since thisgure is more stable and theoretically more appropriate. Productivityvalues are calculated in the very end o analysis, by inversing timeconsumption values. For instance, i time consumption is 0.2 hours/m3, the productivity is 1/0.2=5 m3/hour.

- In regression analysis, the predictors must be linearly independent,i.e. it must not be possible to express any predictor as a linearcombination o the others. Basically, collinear variables containinormation about the dependent variable and are redundant. Suchredundancy will conound the individual efects o the variables, thusweakening their predicting capacity.

- Te use o polynomial equations to describe machine perormance orany work related phenomenon is considered illogical by most oresters,and such equations should not be used just because they providebetter ts than other models. In act, the orm o the mathematicalmodel should be based on what is known about the mechanics o the

process. In many cases, linear models are also inappropriate, exceptas rst-order approximations over limited ranges o the independentvariables. For example, a linear model or orwarder travel time as aunction o distance is consistent with the mechanics o orwardertravel. On the other hand, a linear model or number o treesaccumulated by a eller buncher as a unction o average tree size isguaranteed to ail as tree size increases.


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them the p-value is most commonly used. Tis value is always individu-ally associated to each independent variable, and it can be interpretedas the probability that the efect described by the equation can happenby chance. Hence, a very low p-value (< 0.05) is a good criterion or the

inclusion o a variable in the regression equation. Beside R2, the analysiso residuals can provide useul inormation on model quality.

When handling more than one independent variable, multiple linear re-gression is applied. By denition, this technique works best with inde-pendent variables that change linearly. Non-linear variables can be lin-earized by appropriate transormation, or instance by raising them to

Box 12 - Reportingo ensure quality, clarity and repeatability, a report should include atleast the ollowing elements:- introduction and background o the study, leading to problem

statement- clear and direct goal statement (e.g. Te goal o this study was to)- description o the system under study, including a denition o system

boundaries- description o site conditions- description o the experimental design, including number o

replications and total study duration- description o the techniques employed or statistical analysis- denition o time concept used (what kind o delays were

included etc.)- denition o observational level used (shit level, cycle level etc.)- denition o cycles and/or time elements (with break points and

priority levels as needed)- denition o output units and all required calculations or estimating

indirect outputs- description o measurement methods- description o results, and comparison with the results rom other

similar studies- inerences that can be made rom the results o the study- report o known constraints or limitations with the study and/or

generalization o results

Use simple and clear language. No scientic report ever won the Pulitzerprize, so it is not worth trying now. I necessary, get the documentrevised by a proessional language editor. Be concise, avoid redundanttables and gures.


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the power o 2, i they show a quadratic behavior.

Regression equations can also be used to compare treatments. o thispurpose, one o the treatments will be taken as the base case and theothers will be congured just as other independent variables, reportingthe value 0 or 1 respectively or the absence or the presence o the spe-cic treatment.

For example, i we have a yarder working alternatively with standard andradio-controlled chokers, then we can add the variable radio-controlledchokers. Tis variable is set to 1 when the radio-controlled chokers areused, and to 0 when standard chokers are used. Since it indicates a treat-ment diference, this variable is dened as an indicator variable. In

act, the indicator variable is not a truly continuous variable (such asyarding distance), and or this reason it is also called a Dummy vari-able.

However dummy variables work well and their use in work studies is ac-cepted, widespread and very efective 7. Models should be veried andvalidated. A complete validation process normally includes several steps,but here we can recommend at least two o them: internal vericationand independent validation. Internal verication consists o using the

model to replicate some o the observations inside the data pool used orits construction.

Te same predictor values will be input into the model and the predictedvalue will be compared with the actual one, using statistical analysis todetect i the eventual diference is statistically signicant. Independentvalidation is a very similar process, whereas the observations being rep-licated come rom outside the original data pool used to calculate themodel. For this purpose, one may try and obtain data rom other stud-

ies, or partition the study data pool into two subsets, one o which willbe used or model construction and verication, and the other or modelvalidation.

7. Conclusive notes

M

uch has been written about work studies, and this short guide canneither summarize all the knowledge on the subject, nor replace the

many scholarly books that represent the oundations o work science. Inact, this guide only aims at providing a common platorm or all people

7 For a better explanation o the signicance, the justication or use and the benet obtainedrom dummy variables, see Olsen et al. 1998.


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approaching orest biomass work studies, so that misunderstandingscan be avoided and communication improved. Ultimately, the successo this efort will depend on the contribution o all people involved inthe COS Action, and on their adoption o this guide as the reerence

or their uture work. We believe that this handbook is simple, clear andcomprehensive enough or practical use in orest biomass work studies.What is more, this guide does not give prescriptions on how to do things,but rather ofers insights on what could be done, leaving everyone ree todevelop their own specic approach to the work. Harmonization is notstandardization. Tere is no need or everyone to do the same thing inthe same way. Tat is contrary to academic reedom and progress. Whatwe need to do is to understand what everyone has done, so that we can

track back the process to the original elements and eventually translatethe results. It is unrealistic to think that everyone should speak the samescientic language. On the contrary, it is more practical to developa dictionary that will allow efective communication regardless olanguage. Tat is the main purpose o this GPG, which ofers advice, notdirections, hoping that this advice can be useul and only i useul adopted by those who will read it.


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8. Relevant bibliography

Anumber o diferent manuals and articles are available on the subject,so that it may be dicult and conusing to compile an exhaustivebibliography on work studies. However, the ollowing texts may provideessential inormation on time studies, and their reading is stronglyrecommended:

Bergstrand K.G. 1991. Planning and analysis o orestry operation stud-ies. Skogsarbeten Bulletin n. 17, 63 pp.

Bjrheden R., Apel K., Shiba M., Tompson M.A. 1995. IUFRO Forestwork study nomenclature. Swedish University o Agricultural Science,

Dept. o Operational Eciency, Garpenberg. 16 p.Bjrheden, R. 1991. Basic time concepts or International comparisonso time study reports. Journal o Forest Engineering 2: 3339.

Day R. 1975. How to write a scientic paper. ASM News, 41: 486-494.

Gullberg . 1995. Evaluating operator-machine interactions in compara-tive time studies. International Journal o Forest Engineering, 7, 1, 51-61.

Harstela P. 1988. Principle o comparative time studies in mechanizedorest work. Scandinavian Journal o Forest Research n.3: 253-257.

Howard A. 1989. A sequential approach to sampling design or timestudies o cable yarding operations. Canadian Journal o Forest Research19: 973980.

ILO Guidelines or labour inspection in orestry Geneva, InternationalLabour Oce, 2006 ISBN 92-2-118081-6 (print) ISBN 92-2-118082-4

(web) Labour inspection, orestry, ILO Convention, comment, applica-tion. 04.03.5

Lindroos, O., 2010. Scrutinizing the theory o comparative time studieswith operator as a block efect. International Journal o Forest Engineer-ing 21: 20-30.

McMahon S. 1995. A survey method or assessing site disturbance.Project Report 54. Logging Industry Research Organisation, New Zea-

land, 16 pp.Murphy G. 2005 Determining sample size or harvesting cost estima-tion. New Zealand Journal o Forestry Science 35: 166-169.

Nuutinen Y., Vtinen K., Heinonen J., Asikainen A., Rser D. 2008 Te


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accuracy o manually recorded time study data or harvester operationshown via simulator screen. Silva Fennica 42: 63-72.

Olsen E., Hossain M., Miller M. 1998. Statistical Comparison o Meth-ods Used in Harvesting Work Studies. Oregon State University, ForestResearch Laboratory, Corvallis, OR. Research Contribution n 23. 31 p.

Samset I. 1990. Some observations on time and perormance studies inorestry. Communications o the Norwegian Forest Research Institute,n. 43.5, 80p.

Spinelli R., Visser R. 2009. Analyzing and estimating delays in woodchipping operations. Biomass and Bioenergy 33: 429-433.

Zerga, J. E. 1944. Motion and time study: a rsum and bibliography.Journal o Applied Psychology 28: 477-500.


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Appendix 1 Work science: denitionsWork science is the branch o knowledge associated with work and itsmeasurement, including: the work itsel, man at work, the machines,

tools and other equipment employed in work and the organization andmethods o work.

Work study is the systematic study o technical, psychological, physio-logical, social and organizational aspects o work. It provides or criticalexamination o existing and proposed ways o doing work. Work studyis based on objective, unbiased observation and analysis. It is applied toestablish or improve the eciency o production.

Organization study is the systematic and critical analysis o organiza-

tional structures and relationships, in order to describe and improve theorganization.

Method study is the systematic and critical analysis o ways o doingwork, in order to make improvements.

Work measurement is the application o techniques designed to meas-ure: 1) the input o resources into the productive process, 2) the meth-ods and motions o work and 3) the output o production. For man at

work the measurement may include: time consumption, movements andworking motions, physical and mental workload etc. For machines andtools: time consumption, wear, movements and maneuvers, energy con-sumption etc. In addition to this, it is common to include descriptions othe work object (tree size etc), the work environment (terrain, weatheretc) and the quantity and quality o production.

ime study is the measurement, classication and subsequent system-atic and critical analysis o time consumption in work, with the purpose

o eliminating useless time consumption.

Motion study is the systematic and critical analysis o working mo-tions with the purpose o describing the motions, eliminating uselessmotions, and arranging the remaining motions in the best sequence orperorming the operations.


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Operation

Options

Cycleparame

ters

Units

Stand/operationalparameters

Units

Fellingand/

orprocessing

Motor-

manual

Mechanized

T

reesize:

-

unitvolu

me/weig

ht

-

dbhorother

dimensions

-

num

bero

treespercycle

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Species

m3

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cm nrtreescycle-1

Species

code

F

ellingtype

(clearcut,

systematic,selectivethinning

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F

ellingintensity

S

tan

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(Even-aged

high

orest,coppice,etc.)

W

eathercon

dition

s

T

errainclass

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Fel

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de

Trees

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3ha-1,

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Stan

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Weatherco

de

Terrainclassco

de

,%slope

Extraction

Forwarding

Skidding

Yarding

S

pecies/p

roductsizes

L

oadtype

(Who

letrees,

branches

andtops,

bun

dles,

stumpwo

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dwoo

d)

P

ayload

E

xtraction

distances:

-

Access/m

ainroadun

loadedtrip

-

Striproad

un

loadedtrip

-

Access/m

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loadedtrip

-

Striproad

loadedtrip

-

Loadingd

istance

S

lope,isignifcant

-

Access/m

ainroadun

loadedtrip

-

Striproad

un

loadedtrip

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Access/m

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Striproad

loadedtrip

Species

code

Loadco

de

m3

dr

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humidtonnes

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No.o

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m m m m m %slope

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operations research & measure

Documents