Automatic Time Study Method for Recording Work Phase Times of Timber Harvesting

Automatic Time Study Method for Recording Work Phase Times ofTimber Harvesting

Teijo Palander, Yrjo Nuutinen, Arto Kariniemi, and Kari Vaatainen

Abstract: The objective of the present study was to develop an automatic time study method based on aprocess-data model for single-grip harvesters, with inputs based on data automatically collected by theharvesters onboard computer. The method integrates the phases of the work cycle into components underconditions in which the work phases may overlap to varying degrees. During the work phase analysis, we foundthat process-data models differed under similar work conditions because work phases could not be completelyseparated during the automatic recording of data. We therefore used the combined data provided by manual andautomatic timing to develop a new process-data model for a single-grip harvesters work. We also analyzed theoverlapping and simultaneous work phases to optimize the improved process-data model. The results weresatisfactory, and the method can be systematically used in time studies based on automatically recorded data bymodifying the process-data model using the approach described in this article. Adjustment of the model toimprove data-recording accuracy compared with manual time studies has great potential, but this must beconfirmed through additional harvesting experiments during work studies with different machines and indifferent forests. FOR. SCI. 59(4):472483.Keywords: time and motion studies, single-grip harvester, process data, data management system

S INCE THE INTRODUCTION of the first single-grip har-vesters (hereafter, harvesters) in the Nordic coun-tries in the 1970s, studies of the harvesters workcycle have relied on the time study method. Since then,these studies have expanded from the testing of new modelsto determining the influence of the operating environment,the operational efficiency of the integration of harvestingwith downstream processes such as forwarding harvestingchains, operator skills, and the dynamics of human-machinesystems. Research methodologies have evolved greatlysince the introduction of these machines (Figure 1).

In the 1970s and 1980s, time studies were mainly con-ducted using digital watches (International Labour Office1981). In the mid-1980s, field computers started to replacedigital watches and paper forms in time studies because theyprovided opportunities for measuring the time elements of awork cycle in more detail and more accurately (Harstela1988). During the 1990s, numerous time studies of harvest-ers were conducted using handheld field computers (e.g.,Kellogg and Bettinger 1994, Eliasson 1998), and thesedevices remained essential into the 2000s (Karha et al.2004, Puttock et al. 2005, Kariniemi 2006, Spinelli andVisser 2008, Ovaskainen 2009). Since the 1990s, digitalvideo cameras have been used to record harvester perfor-mance and working techniques (Vaatainen et al. 2005,Ovaskainen et al. 2006, Nurminen et al. 2006, Nakagawa etal. 2007). In the 2000s, it became possible to collect timestudy data automatically using a harvesters computer con-nected to channels such as the controller-area network

(CAN) hardware (Vaatainen et al. 2005, Kariniemi 2006,Tikkanen et al. 2008, Ovaskainen 2009, Nuutinen et al.2010, Palander et al. 2012). The automated time studymethods used for monitoring the performance of harvestersin cut-to-length systems have also been used to monitortree-length harvesting systems (McDonald and Fulton2005).

The CAN bus was developed and launched by RobertBosch Corporation in 1986. It was designed specifically forautomotive applications and is a multiplexed wiring systemthat can be used to connect intelligent devices such aselectronic control units in vehicles, allowing data to betransferred in a low-cost and reliable manner (CAN inAutomation 2011). The benefit of the CAN bus for timestudies of harvester work is that it lets researchers recordlarge amounts of time study data with a high level of detailand accuracy during the harvesters work on each processedstem (Figure 2). A variety of dataloggers have been devel-oped that can automatically record this information, includ-ing the Forestry Engineering Research Institute of CanadasMultiDAT (FPInnovations 2012) and the PlusCan data-logger (Plustech Oy, Tampere, Finland).

For harvesters, Plustech Oy developed a datalogger toautomatically record the information flow in the CAN buschannels. Their first device for recording the CAN businformation (the PlusCan datalogger) recorded detailed in-formation concerning the harvesters operations, such as thestem dimensions and time consumption during each ofthe harvesters work and movement phases (Peltola 2003).

Manuscript received February 1, 2012; accepted September 1, 2012; published online October 4, 2012; http://dx.doi.org/10.5849/forsci.12-009.Teijo Palander ([email protected]), University of Eastern Finland, Joensuu, Finland. Yrjo Nuutinen ([email protected]), Finnish Forest ResearchInstitute. Arto Kariniemi ([email protected]), Metsateho Oy. Kari Vaatainen ([email protected]), Finnish Forest Research Institute.Acknowledgments: The research reported in this article was partially funded by the Finnish Funding Agency for Technology and Innovation (decision number40197/09) and Metsateho Oy.Copyright 2013 by the Society of American Foresters.

472 Forest Science 59(4) 2013

The successor to the PlusCan datalogger, the TimberLink(developed by John Deere), is a more advanced monitoring

system that has been available as an option on all new JohnDeere harvesters since 2005. TimberLink comprises hard-ware and software that collect and process CAN bus dataabout the machines and the operators condition and per-formance (John Deere 2008, Tikkanen et al. 2008, Nuutinenet al. 2010, Palmroth 2011, Palander et al. 2012).

The first Forest Work Study Nomenclature (Nordic For-est Study Council 1978) was published in 1963 and revisedin 1978. It represented an agreement between Denmark,Finland, Sweden, and Norway about terminology and meth-odology that was worked out by the Nordic Work StudyCouncil. The Nomenclature aimed to improve the compa-rability of international time study reports (Samset 1990).The second international Forest Work Study Nomenclature(Bjorheden 1991) was launched in 1995 (InternationalUnion of Forestry Research Organisations 1995) for prac-tical testing aiming at refining it for final acceptance at theInternational Union of Forestry Research OrganisationsWorld Congress in Malaysia in 2000. These nomenclatureswere the first steps toward developing a common universaltime study methodology. They contained a collective pro-posal for the basic time concepts and phases that should beused for time measurements in forestry work to serve as abasis for any study that hoped to achieve internationalsignificance.

Spinelli et al. (2010) have proposed that general modelsshould be developed for harvesters and processors instead

Figure 1. The development of time studies in harvester operations.

Figure 2. The ideal work cycle of a single-grip harvester.

Forest Science 59(4) 2013 473

of using more accurate stand-level models for a separatehuman-machine system. The goal of such a generalizedmodel would be to eliminate the time-consuming and ex-pensive process of creating a specific model for each har-vesting machine and stand. Recently, we developed anadaptive work study method to support stand-level studies(Palander et al. 2012). The method uses a detailed model ofa machines work cycle and work-phase data provided bythe machines automatic monitoring system to identify themost important work phases in a human-machine system.However, the only time study standard intended specificallyfor forest machines is the Standard for Forest Data andCommunication (Skogforsk 2010), which is a de facto stan-dard for all forest machines manufactured in the Nordiccountries.

The first version of StanForD was published in 1987 inSweden. In the early 1990s, Finnish researchers also joinedin the development of the StanForD standard (Arlinger et al.2008). The goal of the standard was to enable analyses ofthe technical and organizational factors that affect the op-eration of forwarders and harvesters. The latest version ofStanForD was issued in 2010 (Arlinger et al. 2010). In thecurrent version of StanForD for harvesters, the effectivework time is divided into processing and travel time at thehighest level. During StanForDs development process, in-formation technology provided a number of possibilities topromote the use of a standard method and overcome thelimitations of previous international standards for time stud-ies. StanForD was specifically developed to account for theheterogeneous timing devices that have been used, the re-search topics these techniques support, and the approachesused in these studies (Figure 1).

Kariniemi and Vartiamaki (2010) developed a general-ized model of the work phase classification for use inautomatic time studies of harvesters. This is a process-datamodel in which pause times are considered in addition to theeffective work time. Despite the common consensus, theprotocols that have been implemented, and their continuoususe within the forest engineering community, there is still awidespread misunderstanding of automatic time study ap-proaches at the conceptual, theoretical, and practical levelsin the context of cut-to-length harvesting systems. Theprocess-data model of Kariniemi and Vartiamaki (2010) isbased on an idealized work cycle for the harvester whereinthe work phases follow regularly repeating steps (Figure 2).However, unforeseen situations that lead to deviation fromthe normal procedures can occur during the work, and thiscan lead to difficulties in correctly identifying how the timeconsumed by these activities should be allocated among theharvesters work phases, especially when automatic ratherthan manual recording of the time data is used. This isproblematic because it is important to ensure that all humanand machine actions are included in the right work phasesand that all work time is recorded as effective work. Un-foreseen situations that can confuse the automatic recordingof effective time consumption include the following possi-bilities for each level 1 work phase in the model:

1. Gripping the stem. Automatic recording can correctlyrecord the removal of undergrowth surrounding a tree

(clearing) within the work phase in which the oper-ator swings the harvesters felling head toward the treeand fells the undergrowth using the heads saw. How-ever, undergrowth may also be removed by pressingthe felled tree against the vegetation or by draggingthe tree instead of using the harvesters felling saw. Inthese cases, the operation is not registered correctly. Aprefelled tree can also cause confusion between thegripping the stem phase and the felling phase becausethe felling cut was made before the processing phase.If the tree to be felled is sufficiently branchy, the buttmust be delimbed before the felling cut so that theharvester head can grip the stem. This operationshould be included in the gripping the stem phasebecause it is involved in the preparation for felling andis not part of the felling.

2. Felling. When the felling cut must be repeated severaltimes for a tree that is too large to fell in a single cut,multiple felling cuts will be required, and these mustbe combined into a single felling phase. It is alsopossible to fell several small trees consecutively if thefelling head has an accumulator arm and then processthem simultaneously, which does not follow the idealwork cycle shown in Figure 2. After the felling cut andbefore stem feeding, damage to the butt of the treemust be removed by sawing off a short piece of thestem, and this operation should be included in theprocessing work phase rather than the felling phase.

3. Processing. Several operations can confuse the divi-sion of the recorded times during processing. The stemcan break during felling, in which case a small piecemust be cut from the first log to remove the damagedpart of the stem, and the activity should be includedunder processing, not felling, despite the activation ofthe heads felling saw. Furthermore, a tree that dividesinto two stems near the stump must be separated intotwo stems by a new felling cut. During cross-cutting,the first cut might not suffice, leading to a requirementfor additional cuts. When the top of a stem or a wholeunmerchantable tree is fed through the felling headand the diameter is too small to produce a merchant-able log, the time should not be recorded as processingbecause there is no output (defined as producing avolume of merchantable wood). Sometimes stem feed-ing is conducted using only boom movements withoutactivating the feed rollers, and, in this case, the feed-ing time and the length of the stem cannot be recordedby the automatic systems. The endpoint of processingcan also be defined in two ways: the final cross-cut ofthe stem or return of the harvesters felling head to anupright position. Which of the two definitions shouldbe used has not yet been defined in the existing timestudy standards and is still being debated.

These and other reasons why automatic recording maybe difficult to use with a process-data model should beclarified so that these problems can be accounted for duringmodel development and use. Another problem with auto-matic recording is the large amount of time study data


obtained for each processed stem, which must be systemat-ically organized (i.e., divided among the work phases whichmay overlap to varying degrees). The overall purpose of thepresent study was to examine both automatic and manualrecording of the time consumption during a harvesterswork cycle to increase our understanding of the potential forautomatic recording during time studies and to identifysituations in which manual recording can provide insightsinto better ways to allocate automatically recorded timedata. The study also aimed to identify the advantages ofautomatic time studies.

We conducted a study in which the focus was to findcommon characteristics, models, and new theoretical ideas,methods, and concepts by analyzing two recording alterna-tives (automatic versus manual) under similar work condi-tions. In an experimental study strategy, tests are used as ameans of researching different phenomena. In our study, weidentified the key components that should be quantifiedempirically (based on observations of the harvesters work)instead of assuming from the start what those componentsshould be (Eisenhardt 1989, 1991, Dyer and Wilkins 1991).We used the PlusCan datalogger for automatic recordingand visual observation by a researcher using a handheldfield computer for manual recording. We used the process-data model of Kariniemi and Vartiamaki (2010) as the basisfor the automatic recording, because this model had beendeveloped especially for use with automatic timing.

Our objective was to develop a systematic method forrecording details of the effective work time of a harvesterthat would support the use of a process-data model. The factthat harvesting machines operate so rapidly that accuratemanual time study is impossible, combined with the pres-ence of many overlapping time elements, makes an auto-matic solution essential. The fact that the CAN bus data arereadily available permits such a solution. In this article, wealso describe the modifications required to the process-datamodel to allow the use of the CAN bus data and especiallythe allocation of all (overlapping) time elements within themodels hierarchical data structure. We used principal com-ponents analysis in the final stage of the work phase anal-ysis to optimize the allocation of times among work phases.One goal of our study was to develop a general model thatmet the criteria of Spinelli et al. (2010) and would thereforefacilitate the use of automatically recorded data withoutrequiring researchers to develop a specific model for eachcombination of machine and stand conditions.

Materials and MethodsThe work phase analysis was performed in three stages.

In the first stage, we developed an original version of theKariniemi and Vartiamaki (2010) process-data model fortime studies of a harvesters work by automatically collect-ing time study data using the PlusCan datalogger and man-ual recordings taken using a Husky-Hunter handheld fieldcomputer. In the second stage, we performed three fieldtests (three different time studies) to obtain data that couldbe used to improve the process-data model. Test 1 examinedthe activities during the processing phase (automatic record-ing). Test 2 examined the activities that occur before the

processing work phase (manual recording). Test 3 examinedall three level 1 work phases (gripping the stem, felling, andprocessing) in the model (automatic and manual recording).In this second stage, the adjustment of the model was basedon automatic and manual time study data obtained in aprevious field study (Vaatainen et al. 2005). In the study ofVaatainen et al., automatic timing was conducted using aPlusCan datalogger as a recording device. Manual timingwas conducted by an observer using a Rufco handheld fieldcomputer.

In the third stage, we developed an automatic time studymethodology by combining the manual and automatic tim-ing data to develop an improved process-data model of theharvesters work. Test 3 can be used to examine all threelevel 1 work phases (gripping the stem, felling, and pro-cessing) in the model and to develop the general model. Anexperimental research strategy was applied for identifyingthe components of the harvesters work cycle, and theseresults were used to modify the model. Specifically, weperformed an exploratory factor analysis to compress thedurations of the work phases and the subphases of theharvesters work into the most significant factors and toidentify any latent structures in the processing phase.

Process-Data ModelThe process-data model of Kariniemi and Vartiamaki

(2010) was developed specifically to use the data providedby the CAN bus of a harvester; in this study, we refer to thisas the process data. Process data include detailed infor-mation about harvester operations such as the stem dimen-sions, time consumption during each phase of the harvest-ers work, machine movements, and fuel consumption. Wedefined the time phases in the process-data model using theidealized work cycle (Figure 2). The time study materialwas recorded from Ponsse, Timberjack, and Valmet har-vesters and comprised 200 stems per harvester. The struc-ture of the original process-data model is described inFigure 3.

The model consists of three hierarchical levels: the level1 work phases in the hierarchy, the work cycle elementswithin these phases (level 2 phases), and the components ofthese work cycle elements (level 3 phases). The total worktime for each processed tree equals the combined timeconsumption of the level 1 work phases. In levels 2 and 3,the level 1 work phases are subdivided into smaller workcycle elements, and the time consumption of each level 1work phase equals the sum of the work cycle elements atlower levels of the hierarchy. In the original model, all workphases are considered to be separate, which means that thetime consumptions do not overlap.

In level 1 of the hierarchy, the work phases are grippingthe stem, felling, and processing. Tables 1 and 2 definethe start and end points of the level 1 work phases andtheir work cycle elements. The time consumption duringgripping the stem is calculated as an average value for theprocessed trees at each working location or in each stand,whereas felling and processing times are recorded foreach tree. Here, we used the definition of Kariniemi(2006), who described the working location as an idealized


area within the reach of the boom, in which a skilledoperator can fell and process all trees without moving theharvester.

In level 2, the level 1 work phases are subdivided into

five shorter work cycle elements and four pauses. Tables 1and 2 provide details of the level 2 time elements. Ifpositioning occurs while the harvester is moving, the wholeworking time is registered as part of the moving phase.

Figure 3. Flowchart for a process-data model describing the relationships between the different workphases during a harvesters work (Kariniemi and Vartiamaki 2010).

Table 1. Definition of the time phases used for manual recording.

Work phase Definition

Moving forward This phase was recorded when the harvester was driving forward, but not when theharvester was in motion during the felling or processing work phases.

Extend the boom and grip This phase started when the boom began to swing toward a tree and ended whenthe chain saw began the felling cut; thus, this phase also includes positioning theharvester head at the base of the tree.

Felling This phase started when the felling cut began and ended when the feeding anddelimbing of the stem started. Felling included the duration of boom movementwhile the head was holding a cut tree to move it to a processing site at amaximum distance of 3 m from the base of the tree.

Felling and bunching This phase included the duration of felling the tree and moving the felled tree to aprocessing site located more than 3 m from the base of the tree.

Processing (delimbing and cross-cutting) This phase started when the stem began feeding through the harvester head andended when the operator lifted the harvester head to an upright position after thefinal cross-cut through the stem.

Clearing This phase included removal of undergrowth and unmerchantable trees fromaround standing trees that must be felled.

Stacking logs This phase included gathering the logs into piles along the extraction trail.Piling of slash This phase was recorded whenever slash was piled as a separate work phase (i.e.,

not as part of the processing phase).Bringing the top to the extraction trail This phase included bringing unmerchantable tops of stems to the extraction trail

after the final cut to produce the last log.Moving backward This phase included the period when the harvester was moving backward, but not

when the harvester was in motion during the felling or processing work phases.Position the boom forward This phase occurred when the operator moved the harvester head to the front of

the machine before moving forward.


During the gripping the stem phase, when the boom or theharvester head is motionless due to reasons such as workplanning by the operator or a machine breakdown, the timeis included in the pause 1 phase. Felling is split into fellingand pause 2. Felling starts when the felling cut begins andends when the stem feeding starts. Pause 2 is defined as atime phase during which the machine, boom, and harvesterhead are motionless. Processing is divided into stem feed,pause 3a, arrangement of the products, and pause 3b. Theprocessing time of each log is split into feeding the stem andpause 3a. Feeding the stem starts when the stem beginsmoving through the harvester head and ends when thefeeding of the log stops. Pause 3a is the time phase duringlog processing when the machine, the boom, and the har-vester head are motionless. Arrangement of the productsincludes operation of the boom and harvester head, andpause 3b is the time phase without any harvester operations.Arrangement of the products and pause 3b occur immedi-ately after the processing of each log.

In level 3 of the hierarchy, the moving phase in level 2 isdivided into forward and backward movements. The posi-tioning work phase equals the sum of the extend the boomphase and the other 1 phase. Extend the boom beginswhen the boom starts to swing toward a tree and ends whenthe harvester head is resting against a tree. Other 1 includesphases for clearing undergrowth and for arrangement of theproducts around a tree to be felled or repositioning the headto avoid an obstacle such as a large rock that prevents thehead from reaching the tree. The felling phase includesthe felling cut and felling control phases. Felling cut meansthe cut that fells a standing tree, whereas felling controlmeans moving the felled tree to the position where it will beprocessed. The feeding the stem work phase is broken downinto four phases: delimbing, reversing, cross-cutting, andother 2. During delimbing, the branches are removed byfeeding the stem through the harvester head while the har-vester head is moving forwards. Reversing occurs when theharvester head is moving backward along the stem. Cross-cutting includes time consumption during the cross-cut thatproduces each log. Other 2 includes work not involvingdelimbing or cross-cutting, such as piling logs. The arrange-ment of the products phase is split into bunching and other3 phases. Bunching includes moving the stem to the mostconvenient position for cross-cutting so that the logs willform a single pile. Other 3 includes sorting the logs after the

feeding the stem phase and moving tops and branches. Thepause 1, pause 2, pause 3a, and pause 3b phases are splitinto shorter phases based on their duration: break for apause 3 seconds, rest for a pause longer than 3 secondsbut shorter than 30 seconds, and stoppage for a pause30seconds.

Adjustment of the Process-Data ModelIn the three tests that provided the data used in our

second and third analyses, we minimized the variability inthe harvesting conditions in the study stands: the terrain wasflat and the tree size, tree species composition, and stemdensity (number per unit area) were as similar as possible.Time study plots in which 45 or 60 minutes of operatingtime were monitored were assigned to six harvester opera-tors. Six time study plots from two first-thinning standsand three time study plots from a clearcutting stand wereselected for each operator. We used data provided byVaatainen et al. (2005) for the time for each work phase ofthe harvesting system. We observed and timed each phaseof the harvesters work for each processed tree (for a total of1,776 stems). The effective work time was measured to thenearest 0.1 second using both the handheld field computerand the harvesters automatic datalogger.

In the first manual recording test, the observer followedthe steps of the work cycle presented in Figure 4. Thedefinitions of these work phases are presented in Table 1.The extend the boom and grip, felling, felling and bunching,processing, and bringing the top to the extraction trailphases were defined as the level 1 work phases for each tree.The moving forward, clearing, stacking logs, piling of slash,reversing, and positioning the boom forward phases werecomplementary work phases that were not analyzed for eachprocessed tree. In the manual time study, the time for eachof these work phases was recorded separately.

In the second test, automatic recording followed the stepsof the work cycle without including the pause work phasesthat are included in the process-data model in Figure 3. Thelevel 1 work phases were gripping the stem, felling, andprocessing. The test concentrated on the processing phase,and the definitions of the phases are described in Table 2.Total processing time included driving, sawing during pro-cessing, and boom use. Simultaneous driving and using the

Table 2. Definition of the time phases used for automatic recording.

Work phase Definition

Gripping the stem Starts when the harvester or boom start to move and ends whenthe felling cut begins.

Felling Begins when the felling cut starts and ends when the stemfeeding starts.

Sawing during felling Duration of the felling cut during felling.Processing Time from the start of feeding of the first log to ejection of the

final log from the head.Driving during processing Harvester movement during the processing work phase.Sawing during processing Total time for processing the stem into logs.Using the boom during felling and processing Using the boom during the felling and processing phases but

excluding moving.Simultaneous driving and using the boom during processing Using the boom at the same time as processing and moving.


boom during processing was also included in the totalprocessing time.

Automatic Time Study MethodologyA good research hypothesis and research questions are

integral elements in conducting experimental research be-cause they steer the process of collecting study material,producing results, making syntheses, and writing the studyreport (Dyer and Wilkins 1991). To modify the originalprocess-data model, we asked the following researchquestions:

1. How does the process-data model describe the auto-matically recorded components of the work cycle?

2. Which time consumption components that we re-corded manually can be added into the originalprocess-data model?

3. What aspects of the process-data model should beimproved based on the answers to research questions1 and 2?

In this study, we used principal components analysis toadjust the process-data model. We used this approach toreduce the variation contained in the measured variablesinto principal components that were not correlated witheach other but that explained as much as possible of theoverall variation. Our goal was to find combinations ofwork cycle time elements that could be interpreted as di-mensions of the overlapping work phases. This analysisgives every variable a weight that reveals its position withinand its impact on the overall work cycle. The component is

also given an eigenvalue, which represents the relativeproportion of the overall variation that a component canexplain. We chose solutions that included components withan eigenvalue greater than 1 (Kaiser 1960) and used thescree test to determine which factors to retain (Cattell 1966).We used the Varimax rotation provided by SPSS-X soft-ware (SPSS, Inc. 1988) to minimize the number of variableswith high loadings (i.e., high weights) for each factor andthereby simplify interpretation of the factors.

ResultsAutomatically Recorded Times

In the first test, we examined the work phases of theconventional process-data model (Figure 3). Table 3 sum-marizes the principal components of these work phases inthe improved process-data model. The overall work com-ponents in the actual automatic recording can be dividedinto gripping the stem, manual processing, and automaticprocessing. The gripping the stem component had only oneseparate work phase that was not selected for any othercomponent (gripping the stem), whereas the manual andautomatic processing components both included severalwork phases. These work components were not congruentwith the level 1 work phases of the original process-datamodel (gripping the stem, felling, and processing). In addi-tion, two of the three level 1 work components includedoverlapping work phases.

The manual processing component was split into drivingduring processing, using the boom during felling and pro-cessing, simultaneous driving and using the boom during

Figure 4. Flowchart for work phases describing the manually recorded work cycle.


processing, and felling. The felling phase within the manualprocessing component was equivalent to the felling phase inthe process-data model. The simultaneous driving and usingthe boom during processing phase was congruent with thelevel 2 phase of log processing in the original model. Thedriving during processing phase was an important variablein this analysis, but it was not defined in the original model.

The automatic processing component was split into pro-cessing, using the boom during felling and processing,sawing during processing, and sawing during felling. Thesawing during processing phase was equivalent to the cross-cutting phase in the original model. The using the boomduring felling and processing phase was congruent with thebunching phase of the original model. However, the usingthe boom during felling and processing phase did not fit intothe original model because it included simultaneous ma-chine operations with the manual processing. The sawingduring felling overlapped both the manual processing andthe gripping the stem phases.

Manually Recorded Time ConsumptionIn the second test, we examined the manually recorded

components of time consumption to determine whether

they could be added in the improved process-data model.Table 4 summarizes the principal components of thesework phases. The level 1 work phases in the originalmodel (gripping the stem, felling, and processing) werecongruent with three of the components revealed in themanual recording. However, clearing was revealed as animportant additional work component. In level 2 of theoriginal model, the moving and positioning phases in-cluded the same operations observed in the manual record-ing: position the boom forward, moving forward, and mov-ing backward. At the same level, the felling phase includedthe felling phase and the felling and bunching (3 m)phase. The processing phase in the original model includedone of the same operations revealed by the manual record-ing (cross-cutting and delimbing). However, the manualobservations included the extend the boom and grip phasethat was included in the positioning phase of the originalmodel.

In the original model, the extend the boom phase (level3) and the positioning phase (level 2) diverged from theposition the boom forward phase of the manual recording(Figures 3 and 4). This is because the positioning phase inthe original model only includes boom movements to fell a

Table 3. Results of the principal components analysis of the timber-harvesting phases with automatic recording.

Variable

Component

CommunalitiesI II IIIGripping the stem 0 0 0.925a 0.859Driving during processing 0.924a 0 0 0.874Using the boom during felling and processing 0.568 0.651a 0 0.795Simultaneous driving and using the boom during processing 0.905a 0 0 0.826Felling 0.661a 0 0 0.528Sawing during felling 0 0.498a 0.373 0.397Sawing during processing 0 0.813a 0 0.667Processing 0 0.859a 0 0.753Eigenvalue 2.4 2.1 1.1 Total of components IIIIProportion of the variation explained (%) 36.2 21.5 13.5 71.2A Varimax rotation with Kaiser normalization was used in the principal components analysis (weights of 0.3 have been replaced with a weight of 0).Interpretation of the principal components: I, manual processing; II, automatic processing; III, gripping the stem.a The highest weightings for each main component.

Table 4. Results of the principal components analysis of the timber harvesting with manual recording.

Variable

Component

CommunalitiesI II III IVMoving forward 0 0 0.788a 0 0.664Extend the boom and grip 0 0 0 0.781 0.615Felling 0 0.921a 0 0 0.866Cross-cutting and delimbing 0 0 0 0.723a 0.539Clearing 0.0765a 0 0 0 0.628Bringing the top to the strip road 0 0 0 0.205 0.058Moving backward 0 0 0.612a 0 0.394Position the boom forward 0 0 0.752a 0 0.589Felling and bunching (3 m) 0 0.910a 0 0 0.862Clearing and positioning 0.862a 0 0 0 0.766Clearing and felling 0.708a 0 0 0 0.505Eigenvalue 1.8 1.7 1.6 1.2 Total of components IIVProportion of the variation explained (%) 17.1 15.6 14.7 11.5 58.9A Varimax rotation with Kaiser normalization was used in the principal components analysis (weights of 0.2 have been replaced with a weight of 0).Interpretation of the principal components: I, clearing; II, felling; III, gripping the stem; IV, processing.a The highest weightings for each main component.


tree, whereas in the manual recording, the position the boomforward phase was recorded separately when the operatorsteered the harvester head to the front of the machine beforemoving to the next working location. The principal compo-nent analyses included this phase in the gripping the stemcomponent (Table 4).

Process-Data ModelIn the third test, we investigated the changes required to

improve the original process-data model based on the an-swers to research questions 1 and 2. The potential workphases that could improve the model were identified bymeans of principal components analysis (Table 5), whichallowed us to combine the important work phases from themanual and automatic recordings. The main work compo-nents were automatic processing, manual processing, clear-ing, moving, gripping the stem, felling, positioning, andarrangement of the products. The level 1 phases in theoriginal model (gripping the stem, felling, and processing)were congruent with the main work components revealed bythe principal components analysis. In addition, the manualobservations revealed manual processing, clearing, moving,positioning, and arrangement of the products as additionallevel 1 work phases. In level 2 of the original model, themoving and positioning work phases included the extendthe boom and grip (manually recorded [M]), moving for-ward (M), and moving backward (M) phases.

The position the boom forward (M) phase occurs beforethe harvester starts to move to the next working location.This phase could not be incorporated in the original modelbecause the definition of positioning in the model only

includes boom movements to fell a tree. In our improvedmodel, it was included in the gripping the stem component.The bringing the top of the stem to the extraction trail phasecould be included under the processing phase (level 1) ofthe original model. In our improved model, this work phasewas under the arrangement of the products work compo-nent. Furthermore, the extend the boom and grip the stem(M) work phase was included in the positioning workcomponent.

The felling component included the felling (M) workphase and the felling and bunching (M) work phase. On theother hand, the felling (automatically recorded [A]) phasewas included in the manual processing component. Thesawing during felling phase was included in the felling cutphase of the original model, but in the improved model, itwas included in the clearing component. These results in-dicate different timing allocation between the manual andautomatic recordings. In the original model, the manuallyrecorded clearing phase was included in the other 1 phase.This was possible because in the model, the total grippingthe stem time is usually calculated as the average value forthe stems at each working location or for the whole stand. Inour model, clearing (M), clearing and positioning (M), andclearing and felling (M) were included in the clearing com-ponent. These results indicate a different hierarchical struc-ture between the original and improved models. The hier-archical structure of the improved model did not includecomplementary work phases.

The processing phase (level 1) in the original modelincluded work phases from the start of feeding of the firstlog to ejection of the final log from the head. Therefore, the

Table 5. Results of the principal components analysis of the timber harvesting with both manual and automatic recording.

Variables

Component

CommunalitiesI II III IV V VI VIIMoving forward, M 0 0 0 0.793a 0 0 0 0.690Extend the boom and grip, M 0 0 0 0 0 0.814a 0 0.699Felling, M 0 0 0 0 0.950a 0 0 0.942Cross-cutting and delimbing, M 0.917a 0 0 0 0 0 0 0.849Clearing, M 0 0 0.714a 0 0 0 0 0.591Bringing the top to the extraction trail, M 0 0 0 0 0 0 0.977a 0.960Moving backward, M 0 0 0 0.540a 0 0 0 0.350Position the boom forward, M 0 0 0 0.649a 0 0 0 0.590Felling and bunching (3 m), M 0 0.415 0 0 0.834a 0 0 0.928Clearing and positioning, M 0 0 0.792a 0 0 0 0 0.682Clearing and felling, M 0 0 0.750a 0 0 0 0 0.587Gripping the stem, A 0 0 0 0.762a 0 0.397 0 0.812Driving during processing, A 0 0.897a 0 0 0 0 0 0.873Using the boom during felling and

processing, A0.575a 0.504 0.495 0 0 0 0 0.855

Simultaneous driving and using the boomduring processing, A

0 0.896a 0 0 0 0 0 0.834

Felling, A 0 0.639a 0 0 0 0.502 0 0.685Sawing during felling, A 0 0 0.475a 0 0 0.313 0 0.421Sawing during processing, A 0.795a 0 0 0 0 0 0 0.636Processing, A 0.910a 0 0 0 0 0 0 0.843Eigenvalue 2.8 2.5 2.3 2.1 1.7 1.5 1.1 Total of components IVIIProportion of the variation explained (%) 14.7 13.3 12.1 10.9 8.8 7.7 5.3 72.8A Varimax rotation with Kaiser normalization was used in the principal components analysis (weights of 0.3 have been replaced with a weight of 0).The highest weightings are presented in boldface for each main component. Interpretation of the principal components: I, automatic processing; II, manualprocessing; III, clearing; IV, gripping the stem; V, felling; VI, positioning; VII, arrangement of the products.a The highest weightings for each main component.


driving during processing (A) work phase could be addedinto either the other 2 or other 3 tertiary phases. Further-more, the sawing during processing (A) phase could beadded in the cross-cutting tertiary phase. In the improvedmodel, the automatic and manual processing componentssystematically replace the processing level 1 phase. There-fore, the driving during processing (A) phase was includedin the manual processing component and the sawing duringprocessing (A) phase was included in the automatic pro-cessing component. Furthermore, the using the boom duringfelling and processing (A) phase did not fit into the originalmodel because it included significant overlapping durationsdue to simultaneous machine operations but could be in-cluded in the improved model.

DiscussionAssessment of the Study

We recorded the work phases of a single-grip harvesterin parallel using automatic and manual recording tech-niques. This let us compare the information value of bothtechniques. Statistical methods were successfully used inanalysis, although the experiment of this study does notoffer the possibility for statistical generalization. The ex-perimental study strategy applied in our study was to de-scribe the potential method for automatic and manual timingto reach a better understanding of the automatic time studymethod. Although we collected enough data to have confi-dence that our results are statistically valid (i.e., that theycan be generalized to other machines and stands), the pur-pose of our study was not to collect statistics on cycle times,but rather to identify the optimal allocation of cycle timeswithin an improved process-data model. Using the strategyof Dyer and Wilkins (1991), we were able to analyze theautomatic and manual timing data from the studies byVaatainen et al. (2005) and Kariniemi and Vartiamaki(2010).

The problem of discrete work phases in the manualtiming was avoided by analyzing additional subphases ofthe level 1 work phases using data collected automaticallyby a datalogger attached to the machines data bus. Therewas significant overlap among the work phases. We there-fore used principal components analysis to provide a statis-tical basis for the use of partial scales (i.e., partially over-lapping work phases). This analysis revealed several keyunderlying factors that would not have been identified usingthe original process-data model or by using completelyautomated time study methods such as the one introducedby McDonald and Fulton (2005).

Improved Process-Data ModelFor cut-to-length harvesting systems, Table 5 presents an

improved version of the process-data model presented inFigure 3. The new work phase classification was indepen-dent of the manual timing techniques, because the time perwork phase was not recorded separately and because certainsecondary work phases were omitted in the manual record-ing. For example, the automatic time study method recordedthe duration of using the boom during felling and process-

ing. This overlapping operation could not be included in theoriginal model because the work phases in that version ofthe model are constructed separately for use in modelingstudies (Olsen et al. 1998, Spinelli et al. 2010) aimed todetermine the general relationships between time con-sumption and parameters of the working conditions; suchstudies require that the work cycle in the time study beconstructed from regularly repeating elements without over-lapping subphases. Our improved process-data model isbased on a more systematic and clear hierarchical structureof the work phases, which can account for both separate andoverlapping work phases (Table 5). There will always bedifferences between data-collection techniques, making itchallenging to produce time consumption information in aconsistent format.

One advantage of the improved process-data model wasthat it enabled combinations of the information obtainedusing automatic and manual recording. An additional ben-efit of our analysis is that it includes an analysis of delays,which are one of the major factors that limit productivityand are, therefore, an integral part of most time studies (e.g.,Spinelli and Visser 2008). The division of work cycle timeelements in the improved model allows pause times to berecorded by a datalogger and included in the model as newwork phases. Furthermore, automatically recorded pausetimes can be incorporated in the hierarchy of the improvedmodel.

As Table 5 shows, the duration of the using the boomduring processing and felling phase indicated the existenceof overlapping and simultaneous work phases. This con-firms the results of Vaatainen et al. (2005), Kariniemi(2006), and Ovaskainen (2009). In those studies, the over-lapping durations of simultaneous work phases were impor-tant indicators for explaining operator performance andmotor sensory abilities. The simultaneous work phasescould also be used to identify the human factors that influ-ence the performance of a human-machine system (Palanderet al. 2012). Therefore, the proportion of simultaneous workphases should be more carefully accounted for in futureresearch. However, measuring simultaneous phases with ahandheld field computer is a challenging task because thesimultaneity of different operations requires the presence oftwo or more observers (Nuutinen et al. 2011). In this re-spect, our improved model allows a highly detailed workcycle projection and increases our ability to understand thestructure of the human-machine system. The findings fromsuch a study could be used in operator training to make theoverall system more efficient.

The rapid evolution of information technology can allowmanagers to generate local adaptations of process-data mod-els in individual stands. Our adjusted model provides both aconceptual-theoretical basis and a practical basis to recon-cile the results of time studies based on different methods.Therefore, the adoption of our harmonized time study pro-tocol would prevent misunderstandings. Adoption of thisapproach would also facilitate the production of more in-ternationally comparable work study reports. These sugges-tions are in accordance with recent methodological experi-ence gained from the adaptive control of a human-machinesystem (Palander et al. 2012). For advanced work study


techniques, it is necessary to adjust the process-data modelto local work conditions using an automatic time studymethod. The advantage of the improved model is that it canbe adapted to human-machine systems, depending on thestudy subject or measurement technique. For example, forshort monitoring or control periods, the level 1 work phasescould be broken into segments of the effective work time,excluding pause times.

Advantages of the Automatic Time StudyMethod

In our study, the automatic time study method was de-veloped by analyzing the work phases under similar work-ing conditions. It must be noted that, to date, the presence ofa researcher has usually been required to detect unexpectedsituations during harvesting. The use of automatic recordingsolves this problem and gives the researcher extra time toinvestigate matters such as conditions at the logging site thatexplain overall productivity and the operators workingtechnique. For example, Vaatainen et al. (2005) includedboom movements and moving between working locations inthe gripping the stem phase recorded by the PlusCan data-logger. These components could be used to describe theeffect of the working conditions. For example, the grippingthe stem time increases when the terrain is difficult totraverse or when tree density is low.

The phases of gripping the stem and felling that aredefined in level 3 of the model can be recorded manuallyusing a handheld field computer, except for work phaseswith short durations (Figure 3). Nuutinen et al. (2008) foundthat an observer cannot reliably measure work phasesshorter than 3 seconds. This is also true for the log-leveloperations during processing in level 3 of the modelshierarchy, which cannot be recorded manually, especiallywhen large stems and a large number of stems are measured.Such observations are limited by observer fatigue during thecourse of a long time study (Nuutinen et al. 2008). Ourautomatic recording let us analyze short work phases, suchas the sawing during processing phase. This possibility wasalso used by Nuutinen et al. (2010) to compare the effi-ciency of different feed rollers in a harvester head.

We found differences in the durations of the work phasesbetween the manually and automatically collected data.These results indicated that the automatic time studymethod provides more systematic and accurate recording ofthe work phases. These differences should be taken intoaccount when the advantages of different data collectionmethods are compared. Nuutinen et al. (2008) found that anobservers skill and experience affected measurement accu-racy in manual time studies and thereby affected the results,especially during intensive time studies of harvester opera-tions. Therefore, the accuracy of manual timing is limitedwhen such machines are monitored. In addition, manualrecording cannot produce sufficiently detailed and diverseinformation for the log-level durations of work phases.

There is no doubt that automatic recording enables thecollection of larger volumes of data at lower cost than withtraditional manual observations. Palander et al. (2012) dem-onstrated this by automatically recording more than 50 work

study variables and using computerized data mining toselect the most important work conditions and work phases.Nuutinen et al. (2010) also performed a highly detailed andaccurate projection of fuel consumption and processingtimes for 7,400 stems using a harvesters automated data-logger. Our approach worked well because the time studymethod allowed efficient adjustment of the original process-data model. As recent advanced studies have suggested(Nuutinen et al. 2010, Palander et al. 2012), the entiredata-mining phase, including the transfer of data for furtheranalysis, can be automated using automatic dataloggerscombined with methods such as the one developed in thepresent study. The insights provided by our study suggestthat the methodology represents a powerful core for a datamanagement system and that it has great potential to sig-nificantly improve the efficiency of time studies of human-machine systems.

ConclusionsIn this article, we presented the results of three represen-

tative time studies of single-grip harvesters. We used theresults to develop an automatic time study method with animproved ability to capture the key components of theharvesters work cycle. This method can be used to adjustthe original process-data model of Kariniemi and Var-tiamaki (2010) to account for different time studies undersimilar harvesting conditions. To adjust the model for dif-ferent stands, managers can reorganize the accurate timedata gathered by this systematic method. The benefit of ourapproach is that it can identify the most important workphases from large amounts of time study data. The principalcomponents analysis identified the optimal reorganizationof the model using an objective and statistically validmethod. The improved process-data model is superior to theoriginal because it can record simultaneous work phasesthat overlap to varying degrees. This is an important advan-tage for mechanized and semiautomated work operations,for which both the machine operator and the harvesterscomputer may control certain actions. Adjustment of themodel to improve data recording accuracy has great poten-tial in future forestry work, but this must be confirmedthrough additional time studies under different workingconditions.

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Automatic Time Study Method for Recording Work Phase Times of Timber Harvesting