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In a copper-molybdenum open pit mine in Chile, a collective labor dispute in April 2012 halted work on the site; the main problem was the negotiation of unified work schedules for contractors, subcontractors and their own staff. To avoid conflicts that could delay this project, the parties agreed to submit to arbitration by an external agency, to determine and quantify the existence of lost productivity caused by the change of work schedules. The external agency considered three basic elements: a) the temporal dimension to allow the comparison of two work schedules, b) the acceptable range of productivity losses associated with these schedules; and c) an unbiased mechanism to allocate a productivity loss within the range associated with each schedule. This paper presents the analysis of productivity loss when work schedules are extended, the method utilized, and the results obtained. This probabilistic model randomly assigns discrete values of productivity loss for each day of the work schedules, and uses inefficiency ranges from industry research; this allows a non-biased, easy to implement, comparison of work schedules.
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[ 9
2014, 13(1), 9 - 14 [ Arriagada, R. Alarcón, F. ] Revista de la Construcción
Journal of Construction
Quantification of Productivity Changes Due to Work Schedule Changes in
Construction Projects. A Case Study
Cuantificación de Cambios de Productividad debidos a cambios en las Jornadas Laborales en Proyectos
de Construcción. Un Estudio de Caso
Ricardo E. Arriagada
Pontificia Universidad Católica de Chile
Faculty of Engineering
rarriaga@ing.puc.cl
Casilla 306, correo 22, Santiago de Chile.
Luis F. Alarcón C.
Pontificia Universidad Católica de Chile
Faculty of Engineering
lalarcon@ing.puc.cl
Casilla 306, correo 22, Santiago de Chile.
Código: 0207
Fecha de Recepción: 01.01.2014.
Fecha de Aceptación: 01.04.2014.
Abstract
In a copper-molybdenum open pit mine in Chile, a collective labor dispute in April 2012 halted work on the site; the main problem was the negotiation of
unified work schedules for contractors, subcontractors and their own staff. To avoid conflicts that could delay this project, the parties agreed to submit
to arbitration by an external agency, to determine and quantify the existence of lost productivity caused by the change of work schedules. The external
agency considered three basic elements: a) the temporal dimension to allow the comparison of two work schedules, b) the acceptable range of
productivity losses associated with these schedules; and c) an unbiased mechanism to allocate a productivity loss within the range associated with each
schedule. This paper presents the analysis of productivity loss when work schedules are extended, the method utilized, and the results obtained. This
probabilistic model randomly assigns discrete values of productivity loss for each day of the work schedules, and uses ine fficiency ranges from industry
research; this allows a non-biased, easy to implement, comparison of work schedules.
Keywords: Probabilistic model, working day extended, productivity loss, mining project, case study.
INTRODUCTION
Chile is ranked as one of the leading producers of copper in the
world, and has the largest reserves of copper, gold,
molybdenum, silver potassium and other minerals. Mining in
Chile represent 15.2% of the GDP, and is the country´s major
economic activity (De Solminihac, 2012).
The sustained growth of the Chilean mining sector has
expanded into higher areas in the mountain range of Los Andes
(Cordillera de Los Andes), an area with approximately 80% of
the mining operations that are located in territories over 3,000
m.a.s.l., and are far away from urban areas (Carrasco and Vega,
2011).
Mining companies, as well as their contractors and
subcontractors, have chosen to build their camps close to the
mining site, and they use exceptional work schedules to ensure
productivity while providing sufficient recovery time for their
personnel. The local legal guidelines acknowledge two types of
unique work schedules, as described by the hours per day of a
daily work shift and the number of working days versus days
off. There are 1:1 shifts, with a maximum workday of 12 hours
per day; 1.3:1 shifts, with a maximum workday of 12 hours per
day, and 2:1 shifts, with a maximum workday of 9.6 hours per
day.
PROBLEM IDENTIFICATION
In 2010, an important Japanese consortium designed and began
construction on a copper-molybdenum open pit mining project
in the Atacama Chile region, at an altitude of 3,850 m.a.s.l. The
project has recently completed the construction phase,
employing more than five thousand workers at the mining site,
including personnel from contractors, subcontractors and direct
hires. In April 2012, a conflict with a group of laborers halted
the mining site; the main problem cited was the negotiation of
a unified work schedule, given the large variety of unique work
schedules among the contractor and subcontractor personnel.
The unique work schedule used mainly by the contractors´
personnel was 14x7x9,6: specifically 14 workdays of 9.6
hours/day and 7 days off. The other work schedule, used less
often by personnel from contractors and subcontractors, was
10x10x11: specifically 10 work days with a daily 11 hour shift,
and 10 days off.
In negotiations with the union labor, they agreed upon an
unique work schedule of 10x10x11 for all workers at the mining
site, in addition to other improvements. Contractors who used
to have 14x7x9.6 shifts asked management to acknowledge the
monthly production time difference per worker; in accordance
with the new legal guidelines, the 14x7x9.6 work schedule
equates to 192 direct-worker-hours-per-month and the
10x10x11 shift equals 165 direct-worker-hours-per-month.
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Management recognized the direct manpower loss created by
this change in work schedules, recognizing the 27 direct-
worker-hours-per month, but they didn´t acknowledge the
presumed productivity loss, since in their opinion, the increase
in the daily shift increased the direct production time.
Given this discrepancy, and so as to not generate any more
conflicts that could impact the mine´s work schedule, the
parties agreed to arbitration from a qualified external party,
who would clearly and consistently determine if there was or
wasn´t a productivity loss due to the work schedule change, and
the external party would also quantify that difference. This
document will determine if there are productivity changes
when a 14x7x9.6 work schedule replaces a 10x10x11 work
schedule, and we will also calculate the magnitude of this
change.
BACKGROUND
Productivity studies in the construction sector have been
debated over time due to their scope and complexity. Key
factors which affect labor productivity in the construction and
assembly sectors have been identified in papers by Oglesby
et.al. (1989), Borcherding & Alarcón (1991), Sanders & Thomas
(1991), Thomas (1992), Langford et.al. (1995), Motwani et.al.
(1995), Lim & Alum (1995), Baba (1995); Zakeri et.al. (1996),
Lema (1995), Kaming et.al. (1997), Olomolaiye et.al. (1998),
Thomas et.al. (1999), Makulsawatudom & Emsley (2002), Ibbs
(2005), Hanna et.al. (2005), Nepal et.al. (2006), Khoramshahi
et.al. (2006), Enshassi et.al. (2007), Alinaitwe et.al. (2007),
Weng-Tat (2007), Hanna et.al. (2008); y Kazaz et.al. (2008).
In addition to identifying key factors, there was also a need to
group them into categories. Borcherding & Alarcón (1991)
identified 50 factors and classified them into 8 categories; Rojas
& Aramvareekul (2003) identified 18 factors that affect labor
productivity and classified them into 4 categories; Liberda et.al.
(2003) identified 51 factors and classified them into 3
categories; Soekiman et.al. (2011) identified 113 factors
affecting productivity and grouped them into 15 categories.
In order to organize the different factors that affect of
productivity loss in a simple format, we highlight six main
factors, namely: organizational, motivational, operational,
climate, cognitive and physiological. We will assume a work
crew doing a specific type of work on the same project, with the
same supervision, with the same members and designation of
the internal roles, with the same salary and benefits, with the
same organization of daily work, and working in the same field
conditions; the only difference is the type of work schedule. We
can assume that other factors organizational, motivational,
operational, cognitive and climate - have the same influence on
both schedules, so we don´t observe any significant changes
when analyzing these variables for the given work schedules.
The only category we are analyzing is the physiological variable.
Analysis of physiological factors
The main physiological factors affecting labor productivity are:
physical fatigue, created by long work shifts; mental fatigue,
created by jobs with a high level of complexity; fatigue due to
stress, created by highly contaminated work environments; and
boredom, created by non-demanding tasks which are
excessively repetitive. In the case of the miners, and considering
the same crew of workers who do the same job, the only
physiological factor that should be considered is physical
fatigue.
Daily and weekly fatigue
Generally, demanding physical work can cause fatigue during a
work shift, and should be followed by a rest period to allow the
body to recover. Research in the industry has found that other
situations, that were not directly related to heightened
consumption of energy, also affected productivity in a part-time
shift as well as in a morning versus an afternoon shift.
Figure 1 shows that during a shift of an 8 hour day, peak
productivity is found near 11:00. and the peak afternoon
productivity is between 13:00 and 14:00. On the other hand,
productivity also varies with the day of the week.
Figure 1 Weekly productivity rates. Source: Oglesb y et all, 1989.
Figure 2 Weekly productivity rates. Source: Oglesb y et all, 1989.
Figure 2 shows the productivity graph indicating morning,
afternoon and average productivity for an 8 hour daily shift. The
peak productivity of the week is found on Tuesday, at
approximately 11:00; the lowest productivity is on Saturday at
18:00 hours.
It is worthy to note that productivity varies a lot throughout the
day and the week, so the values included in the productivity
charts and graphs indicate average values.
Fatigue in extended work shifts with overtime
Both the 14x7x9.6 shift and the 10x10x11 shift are shifts with
paid overtime. The 14x7x9.6 shift contemplates spending 9.6
hours per day at the worksite, for fourteen days straight; the
10x10x11 shift requires staying on the job for 11 hours per day,
for ten straight days. In the case of both schedules, the time
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2014, 13(1), 9 - 14 [ Arriagada, R. Alarcón, F. ] Revista de la Construcción
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spent on the job is counted from the arrival of the worker at the
worksite, up until the worker leaves to return to the mining
camp. They also both include one hour of lunch at the worksite
during the daily shift.
There are multiple studies which analyze productivity loss due
to extended work schedules and continuous working days over
five days per week. One of the most well-known is the National
Electrical Contractors Association (NECA study, which is also
referred to as the Mechanical Contractors Association of
America or MCAA), and another by Oglesby et.al. (1989). Even
though these are earlier publications, these studies are broadly
referenced by more current authors who study this topic, such
as Mubarak (2010); Schwartzkopf, (2004 y 2008); Hanna &
Haddad (2009); Dozzi & AbouRizk (2011) and Thomas (2012).
PROPOSED APPROACH TO THIS PROBLEM
Using the worksite´s control activities which collect and register
important data regarding the worksite´s progress, it is possible
to account for daily production units, as well as daily
attendance of the personnel. The quality of the data registries
and the appropriate breakouts of data will allow us to
determine the daily productivity of a work team at a specific
worksite. We can then add the daily events related to
productivity that are registered in the worksite´s log, such as
daily fluctuations in the movement of materials, equipment and
information; climate changes, etc. This would provide the ability
to adjust and normalize productivity of the work shifts so as to
be able to compare them.
In practice, the quantity and quality of data collected daily from
the worksite are not sufficient to do a robust analysis of
productivity for a specific worksite; the worksite log also may
not be precise, since the individuals in charge may only be
interested in registering events that could have an impact on
their own productivity or that of third parties. The lack of data
reflects the basic rule which states that the value of the
information must cover the cost of its collection.
Therefore, it is impossible to use the empirical data from the
worksite´s tracking log to support a comparative analysis of
productivity loss of two work schedules due to physical fatigue,
so another simple yet rigorous method was chosen; this
method, together with available research, allows us to get past
the limitations that were stated earlier. We determined that
this method should consider three major elements. The first is
the dimension of time, to be able to compare these two work
schedules; the second is the acceptable range of productivity
losses associated with the schedules; and third, a non-biased
mechanism that assigns productivity within a range associated
with each schedule.
Time Dimension
To compare these two work schedules, the time lapsed is
critical, since a 9.6 hour shift, done over 14 days straight, and
ending that cycle with 7 days of continuous rest, cannot be
directly compared with a different schedule of 11 hours/day, for
10 consecutive days, and ending that cycle with 10 days of rest.
In both schedules, the direct production time is completely
different, as are the effects of fatigue and the recuperating
effect of the rest periods.
When we graph these two schedules simultaneously, the two
shifts start on day 1. They are only comparable starting on day
57, through day 60 (see appendix). During this period, the 14x7
shift will have had three work periods, two rest periods and will
be entering their first day of rest for the third 7 day period. On
the other hand, the 10x10 shift will have had three work
periods, two rest periods and will be on day 7 of a 10 day rest
period.
Therefore, in this case, the comparative analysis of the two
work schedules must be done every two months, which is when
we can obtain the comparable monthly productivity losses.
Productivity losses are expressed in cost per work hours per
person assuming that the workers are paid on a monthly basis.
Productivity Loss Ranges
After analyzing the graphs and tables which quantify the
productivity losses, referred to as inefficiencies, we choose to
use table 9-2 from Oglesby et.al. (1989), given that it is based
on Figure 2 (Tabulated Results of the NECA´s Southeastern
Michigan Study, 1989), which provides the range of
inefficiencies for each scenario. The table is shown below, with
the necessary adjustments for the 14x7x9.6 and the 10x10x11
schedules.
As seen in Table 1, interpolation allowed us to generate a range
of inefficiencies for the 10 day work schedule with 11 hour daily
shifts (between 21% and 23%), and an inefficiencies range for
the other schedule with 14 days of 9.6 hour/day shifts (between
19% and 22%). Since a range of inefficiencies is indicated for
both work schedules, the inefficiency can be any discrete
number in this range. So, for the 14x7 work schedule, the values
may be 19, 20, 21 and 22; for the 10x10 work schedule : 21, 22
and23. Both ranges share inefficiencies 21 and 22.
Assigning Productivity Loss
Table 1 shows the range of possible inefficiency values, for both
the 14x7 schedule with 9.6 hours/day as well as the 10x10
schedule with 11 hour/day shifts. The use of either the values
from either of these ranges for the inefficiency calculation
should have a rationale, which we do not have. Given this
restriction, a probabilistic model which randomly assigns a daily
productivity loss for each work schedule provides the best
approximation to the actual situation, since the model assigns
an arbitrary inefficiency value to each day, taken from their
respective ranges, without human intervention and therefore
without the need for a rationale. Biases are eliminated by
assigning equal probability of occurrence to all of the values in
the ranges that are used for either work schedule.
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COMPARATIVE ANALYSIS OF TWO WORK
SCHEDULES
In the appendix of this paper, a probability model is presented
which was developed and run in Excel using the add-in @Risk.
The model uses a simulation and 10,000 iterations. A summary
of the results is presented in Table 2.
Table 2 shows average efficiency (100 - non-efficiency) for the
14x7 work schedule, this assigns 42 work day values randomly
chosen from a 57-60 day work schedule. Likewise, the average
efficiency for the 10x10 work schedule is calculated; it has 30
work day values, from the same 57-60 day period. Each of the
efficiency averages is applied to the amount of nominal hours
for each schedule, so as to obtain hours at 100% efficiency for
each work schedule. The effective loss of productivity when
changing the 14x7 work schedule to the 10x10 schedule is 61.41
hours; from that number, we subtract 54 hours of recognized
loss of manpower, for the two month period analyzed, resulting
in 7.41 hours of actual productivity loss for the two month
period. According to this, the productivity loss for the work
schedule change, not yet accounted for to date, is 2.32% per
month. Given the model assigns inefficiencies from the random
selection of discrete values for the range of possible
inefficiencies for both work schedules, a sensibility analysis will
let us observe the maximum possible range of comparable
inefficiencies as well as the inefficiency within this range which
can be randomly assigned by the model.
Figure 3 Sensitivity analysis. Source: self-elaboration.
According to table 1, the minimum value of a range of
inefficiencies, when comparing the two work schedules, is the
maximum inefficiency for each of the 42 days in the 14x7x9.6
work schedule (22%), and the minimum inefficiency for each of
the 30 days of the 10x10x11 work schedule (21%). The
maximum value of the inefficiencies, when comparing these
two work schedules, is the minimum efficiency for each of the
42 days of the 14x7x9.6 work schedule (19%), and the
maximum inefficiency for each of the 30 days of the 10x10x11
work schedule (23%). Figure 3 presents this analysis, including
the values obtained from the model.
Three collinear ranges are shown in Figure 3; this data has
already incorporated the acknowledged manpower losses.
The first range is 0.2,-0.06   3.10,0.98 which represents the
comparative values where the 10x10x11 work schedule is more
efficient than the 14x7x9.6 work schedule. The second is the
3.10,0.98   3.83,1.20 range representing the comparative
values of equal efficiency for either of the work schedules. And
the third range of 3.83,1.20  18.49,5.66 represents the
comparative values where the 14x7x9.6 schedule is more
efficient than the 10x10x11 schedule. The value generated by
our model (7.41,2.32) is located in the range where the
14x7x9.6 is more efficient than the 10x10x11 schedule, located
near the 25% point of this section and above 40% of the entire
range of possible values (-0.2,-0.06  18.49,5.66).
CONCLUSIONS
In December, 2012, the parties involved in the dispute
expressed their complete conformity with the recommendation
proposed in this analysis, stating that it was robust, easy to
understand and to recreate, and definitely non-biased. We can
therefore appreciate the generalization possible with the
proposed methodology for cases of comparative analysis of
productivities of different work schedules, as well for managing
productivity variability for the same work schedule in given
periods, for situations or scenarios where reliable information
is not available.
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Table 1 Productivity losses in extended working days . Source: Oglesby et all, 1989.
Table 2 Comparative analysis of working days by using a probabilistic model. Source: Self-Elaboration
Appendix 1 Probabilistic Analysis Comparing 14x7 and 10x10 Work Schedules. Source: Self-Elaboration
Appendix #1 Probabilistic Analysis Comparing 14x7 and 10x10 Work Schedules
Month 1 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue
14x7 schedule 80 79 78 78 80 79 80 79 80 78 79 81 79 78 of f off off off of f off off 78 80 78 78 81 80 81 79 79
10x10 schedule 78 79 79 79 78 78 77 79 79 79 off off off off off of f off off off off 78 77 79 79 77 79 78 77 78 77
Month 2 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60
Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri Sat Sun Mon Tue Wed Thu Fri S at Sun Mon Tue Wed Thu
14x7 schedule 81 78 80 78 79 off off off of f off off off 78 81 78 79 80 79 80 78 81 80 79 79 81 78 off off off off
10x10 schedule o ff off off off of f off off off of f 79 78 77 78 78 79 78 79 78 79 off off off off of f off off off of f off off
14x7 schedule 79,26 average ineff iciency bimonthly Total hours for the two month period, corrected for inefficiencies, for the 14x7 schedule
10x10 schedule 78,23 average ineffi ciency bimonthly
Total hours for the two month period, corrected for inefficiencies, for the 10x10 schedule
61,41 Hours of total productivity loss due to change of workday from 14x7 to 10x10 for two months of analysis
54,00 Acknowledgement of the manpower loss due to the change in work schedule for the two month period.
7,41 Hours that the supervisor should pay the contractor for a period of two months.
2,32 % equation of the monthly productivity loss due to the change in work schedule from 14.7 to 10x10
1 1 1 1 Equal probability
19 20 21 22 Range of inefficiencies for the 14x7 work schedule
1 1 1 Equal probability
21 22 23 Range of inefficiencies for the 10x10 work schedule
319,58
258,17
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2014, 13(1), 9 - 14 [ Arriagada, R. Alarcón, F. ] Revista de la Construcción
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Full-text available
Multiple studies have found that productivity in Chilean construction has been stagnated during recent decades; thus, creating the need to understand better what factors have led to these results in the construction sector. In the international literature, studies have found that changes are the leading cause of productivity losses in construction projects; however, limited studies have been done in Chile in this regard. This context is understood as an opportunity to learn from the existing literature about the impact of changes in construction productivity, more importantly, such learning can contribute to the discussion of productivity improvement in the Chilean construction sector. This study recommends that more studies are necessary to be done in Chile regarding the impact of changes in construction projects. Namely, future studies should be based on an extensive database of projects so that generalization can be drawn for the construction industry. Additionally, the data collection process of changes in construction should be improved, paying specific attention to the size of changes, the timing of changes, and the scale of assessment—namely activity, project, and industry levels. Ultimately, this study aims to contribute to the discussion about productivity improvement in Chilean construction as this remains one of the main challenges in the industry
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Human Resources Management in Construction fills an important gap in current management literature by applying general principles of human resources management specifically to the construction industry. It discusses and explores findings from research to supplement the theoretical and practical procedures used. It explores issues such as the technology used and the pattern of social and political relationships within which people are managed.
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Advancements in construction are concentrated largely on producing innovative practices to perform construction projects in an efficient and timely manner. Understanding the relationship between performance on the job site and level of productivity is important to develop best and innovative practices to improve construction productivity. This paper explains the ongoing research project within Alberta construction industry to identify the most critical aspects in terms of human, external and management issues that affect construction productivity. The pilot study interviewed very experienced personnel from the construction sector not only to prioritize the factors but also to identify the measurement criteria to improve future construction projects. The second phase of the research and the deliverables are also outlined in the paper.