OVERMANNING IMPACT ON
CONSTRUCTION LABOR PRODUCTIVITY
Awad S. Hanna1, Chul-Ki Chang2, Jeffery A. Lackney3, Kenneth T. Sullivan4
ABSTRACT
This paper details the impacts of overmanning on labor productivity for labor intensive trades,
namely, mechanical and sheet metal contractors. Overmanning in this research is defined as
an increase of the peak number of workers of the same trade over actual average manpower
during project. The paper begins by reviewing the literature on the effects of overmanning on
labor productivity. A survey was used to collect data from 54 mechanical and sheet metal
projects located across the United States. Various statistical analysis techniques were
performed to determine a quantitative relationship between overmanning and labor
productivity, including the Stepwise Method, T-Test, P-Value Tests, Analysis of Variance,
and Multiple Regression. The results indicate a 0% to 41% loss of productivity depending on
the level of overmanning and the peak project manpower. Cross-validation was performed to
validate the final model. Finally, a case study is provided to demonstrate the application of
the model.
KEY WORDS
Overmanning, Labor Productivity, Schedule Acceleration, Schedule Compression
INTRODUCTION
It is not uncommon for a contractor to find that he or she must accelerate a construction
schedule to meet a project completion date. Reasons for acceleration can vary and may be
caused by a late start, delays such as inclement weather, poor performance by previous work
crews, or additional work required to complete a project. When these circumstances arise, the
contractor is forced to accelerate the work progress in order to accomplish a “timely”
completion for the owner. This act of acceleration accomplishes what is commonly known
throughout the construction industry as schedule compression. Schedule compression is
defined as “a reduction from the normal experienced time or optimal time typical for the type
and size of project being planned within a given set of circumstance” (CII 1990). Schedule
compression is a common practice in today’s construction project. According to previous
1
Professor and Construction Engineering & Management Program Chair, Dept. of Civil and Envir. Eng.
University of Wisconsin, 2314 Engineering Hall, 1415 Engineering Drive, Madison, WI 53706 U.S.A.
hanna@engr.wisc.edu
2
Research Associate, Dept. of Civil and Envir. Eng, University of Wisconsin, 2304 Engineering Hall, 1415
Engineering Drive, Madison, WI 53706 U.S.A. chulkichang@wisc.edu
3
Assistant Prof., Dept. of Eng. Professional Development, University of Wisconsin, Room 825, 432 N. Lake
street, Madison, WI 53706 U.S.A. lackney@epd.engr.wisc.edu
4
Assistant Prof., Del E. Webb School of Construction, Arizona State University, P.O. Box 870204, Tempe, AZ
85287 U.S.A. Kenneth.sullivan@asu.edu
1
studies, time extensions for delay were not granted in approximately 75% of construction
projects (Leonard 1988), and more than 90% of contractors in a similar trade (electrical
construction) have experienced schedule compression of their original or normal project
duration (Noyce and Hanna 1998). There are various situations in which the owner and
contractor may consider compressing project schedule before or during construction.
There are two technical and legal terms associated with schedule compression or
acceleration: mandated acceleration and constructive acceleration. Mandated acceleration
occurs when the owner requests an earlier completion date than contractually agreed upon.
Constructive acceleration occurs when the contract end date stayed the same despite late start
delay or increase scope. For both situations, the most frequent initial reaction of contractors
to a schedule compression is to increase on-site labor force by working longer time,
implementing shift work, or adding more workers to increase the rate of progress. Among
these options, simply adding more workers to the project is the most common.
PROBLEM STATEMENT
Given the fact that labor costs for labor intensive trades such as mechanical and sheet metal
contractor typically range from 33-50% of the total construction cost (Hanna 2001),
understanding how and how much overmanning affects labor productivity is crucial. An
increase in productivity reduces labor costs in direct proportion.
Direct costs incurred by overmanning can be easily tracked, so it is usually not in dispute.
The more disputable and the greater cause of increased project costs is labor productivity loss
caused by overmanning. Lack of awareness on the part of the contractor of the impact of
overmanning may result in finger pointing between the estimating and execution teams.
There are few academic and building industry research studies that have quantified the
impact of overmanning on labor productivity. There is no precise way to compute the loss in
direct straight time labor and loss of productivity due to overmanning. What the building
industry needs is a quantitative equation that relates overmanning and labor productivity.
RESEARCH OBJECTIVE
This study examines the effects of overmanning on labor productivity and the possible causes
for productivity loss. This study reviews previous studies regarding the impact of
overmanning on labor productivity. The primary objective of this study is to provide a model
to quantify the impact of overmanning on labor productivity. Productivity multipliers for
various scenario of overmanning will be provided from a quantification model.
RESEARCH METHODOLOGY
FACTORS APPROACH
Understanding the effects of overmanning on labor productivity is quite difficult because the
factors affecting labor productivity in the schedule acceleration and compression situation are
numerous. In a situation of schedule acceleration and compression, a number of factors such
as overtime, shift work, and stacking of trades affect labor productivity. The cumulative
impact of these factors on the productivity of labor equate to the actual total manhour beyond
2
the budgeted level expended to complete the project. Waldron (1968) introduced factors
approach in which the researcher could theorize each of the factors contributing to a portion
of the total productivity loss. This approach has been adapted to determine the impact of
overmanning on labor productivity (Figure 1).
Δ1
Δ2
Δ3
Δ4
Δ5
100%
100%
Original Estimate
Δ 1 – Overtime Inefficiency
Δ 2 - Overmanning Efficiency Loss
Δ 3 - Remobilization Inefficiency
Δ 4 - Estimate Accuracy
Δ 5 – Premium Time costs over
Original Straight Time Cost
Accumulated
Labor Manhours
and/or Cost
Accelerated Schedule
Estimated
Actual
Completion Date
Time
Figure 1: The Factors Approach (Waldron 1968)
MACRO ANALYSIS VERSUS MICRO ANALYSIS
On a construction project, productivity can be analyzed on a micro or a macro scale. A
macro-analysis considers the project as a whole, while a micro-analysis looks at a specific
activity of a project (Hanna et al. 1999). Since it is difficult to quantify the impact of
overmanning on project as a whole through micro analysis, where productivity is measured
by a time per unit production, macro analysis was adapted to determine the impact of
overmanning on labor productivity.
LABOR COST VERSUS LABOR HOUR
In order to compare projects regardless of their geographic area, time of completion, size,
and labor hours were used as a basis. By using labor hours as the basis, all different projects
can be combined into a single database. All factors including productivity loss and project
size were defined by labor hour instead of labor cost.
DEFINITIONS
For the present research, definitions of overmanning and productivity are:
OVERMANNING AND PRODUCTIVITY
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Overmanning can be understood in two different ways. First, overmanning is defined as
putting more workers on jobs than optimal crew size. The optimal crew size is the minimum
number of workers required to perform the task within the allocated time frame (US Army
Corp of Engineers 1979). Second, overmanning can be referred to as an increase of the peak
number of workers over average number of workers of the same trade during project. This
second definition will be utilized in this study. Overmanning differs from stacking of trades
in that it considers only one trade while trade stacking deals with all the workers from all
trades on the job site. Additionally, for this paper, productivity has been defined as the ratio
between earned work hours and expended work hours, or work hours used.
WHY AND HOW OVERMANNING IMPACTS LABOR PRODUCTIVITY
Overmanning has advantage over overtime and shift work in that it can produce a higher rate
of progress without the physical fatigue problems associated with overtime and the
coordination problems realized with shift work. However, the problems associated with
overmanning are inefficiencies due to physical conflict, high density of labor, congestion,
and delusion of supervision. Due to increased number of workers, materials, tools, and
equipment shortage may occur, and engineering questions and requests for clarification may
not be provided in a timely manner due to greater demand within a given period.
Coordination and control become more difficult. Since more workers will have to spend time
familiarizing themselves with the job, there may not be an opportunity to take advantage of
learning curve effects. The demand for labor may introduce less productive workers. It
requires more intensive supervision in order not to degrade quality. There are some
influencing factors in implementing overmanning. For instance, adequately skilled workers
should be available in the market place, and there may be enough space in the work area for
added workers.
DATA COLLECTION
A data collection sheet was used in the acquisition of data for this study and consisted of two
parts: (a) information on the contractors’ background (company information and size); and
(b) information describing a specific project that experienced overmanning due to schedule
acceleration and compression. Data collection sheets were distributed to mechanical
contractors and sheet metal contractors in the U.S., with telephone and e-mail follow-up. In
some cases, the study team visited contractors to have better understanding of the project
utilized in this study. A variety of project factors were collected: project type, size, type of
owner, project delivery method, contractor’s role, type of contract, contractor’s project
management practice, productivity information, and project schedule along with estimated
and actual manpower loading graphs.
DATA CHARACTERISTICS
To see the impact of overmanning on labor productivity, the research team collected project
data and analyzed it. The research data was collected from geographically diverse specialty
mechanical and sheet metal contractors. The total databank contains 104 projects, 54 of
which meet both the criteria of having an efficiency loss and a Peak/Average ratio greater
4
than an optimal level that will be defined later in this paper. Of the 54 projects, 33 are from
the mechanical trade and 21 are from the sheet metal trade. These two trades are similar
because they are both labor intensive and connected trades. Connected trades mean there is a
distance between source of energy and its final destination. In addition, according to the
Occupational Outlook Handbook (2002) published by the Bureau of Labor Statistics, the
majority of sheet metal contractors are working for Heating, Ventilation and Air-conditioning
(HVAC), and plumbing in the construction industry. In addition to fabrication and
installation, some sheet metal contractors do maintenance work, such as testing, adjusting,
and balancing existing HVAC systems. To verify that these two groups are not different, a
Two-sample T-test was conducted for efficiency loss and overmanning related project
characteristics. The test result shows these two groups are not different statistically (Table 1).
Based on the similarity of characteristics of sheet metal work and mechanical work and the
result of Two-sample T-test, the projects done by these two trades were combined into one
databank and analyzed for this study.
Six different types of construction performed in 28 states are represented. The project
sizes in terms of manhour range from 700 to 208,451 total manhours. The average crew size
at peak manpower was 28. The largest crew size at peak was 90 workers, while he smallest
was 4. The average number of workers of trade during project ranged from 1.5 workers to 50
workers. The large diversity contained within the data set will allow for the final regression
model to be applicable to a wide spectrum of construction projects.
Table 1: Two Sample T-Test Result for Overmanning Model Predictor Variables of
Mechanical data and Sheet Metal Data
Characteristics
Tested
Efficiency
Loss
Actual Peak /
Average
Manpower
Actual
Manpower at
Peak
Group
Mean
Null Hypothesis
Mechanical
Sheet
Metal
Mechanical
Sheet
Metal
Mechanical
Sheet
Metal
0.140
0.146
μ(Mech.)μ(Sheet Metal) =
0
μ(Mech.)μ(Sheet Metal) =
0
μ(Mech.)μ(Sheet Metal) =
0
1.954
2.100
1.281
1.354
Alternative
Hypothesis
μ(Mech.)μ(Sheet Metal)
≠0
μ(Mech.)μ(Sheet Metal)
≠0
μ(Mech.)μ(Sheet Metal)
≠0
PValue
0.880
Result
Equal to
Zero
0.393
Equal to
Zero
0.490
Equal to
Zero
LOST EFFICIENCY
To determine productivity under a macro-analysis, estimated hours are taken as the measure
of output and actual hours are taken as the measure of input (Hanna et al. 1999). Lost
efficiency can be measured by the difference between the actual labor hours expended to
complete the project and the estimated base hours (including the approved change order
hours). A loss of efficiency may result from a contractor’s inaccurate estimate, exceptional
or poor performance, other contractor caused inefficiencies, and/or the impact of
productivity-related factors such as change orders, weather conditions, work interruptions,
etc (Hanna et al. 1999). To be able to compare projects of varying size, it is necessary to
normalize efficiency as a percentage. Percent Lost Efficiency is simply a project’s lost
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efficiency divided by actual manhours consumed to complete the project (Hanna et al. 1999).
As a mathematical expression, Percent Lost Efficiency (% lost efficiency) is given in
Equation 1, below (Hanna et al. 1999).
% Lost Efficiency 
Actual Total Manhours  ( Estimated Total Manhours  Approved Change Order Hours )
Actual Total Man hours
…. (1)
The strength of this method is its representation of the direct effects, as well as the
indirect effects, on productivity since actual labor hours are calculated after the completion of
project.
THE RATIO OF ACTUAL PEAK MANPOWER AND ACTUAL AVERAGE MANPOWER
The level of overmanning is typically measured by the ratio of Actual Peak Manpower to
Actual Average Manpower. Different values of the ratio were introduced by several studies;
1.35 for electrical, 1.50 for mechanical (Hanna 2001), and 1.6 for normal civil projects from
Allen’s study (Wideman 1994). Clark (1985) reported a ratio of 1.54, but failed to mention
the type of construction for which the ratio is applicable. For this study, Allen’s ratio was
selected because it represents the worst-case scenario for overmanning. Consequently, if
peak over average ratio is greater than 1.6 then we can say the project experienced
overmanning, and if peak over average ratio is equal to or less than 1.6 then the project
would be regarded as not having experienced overmanning. Unlike stacking of trades which
considers all the workers on site from all trades, overmanning deals with only one trade. The
number of workers at peak and average number of workers for the trade will be analyzed.
QUANTIFICATION MODEL DEVELOPMENT
Two variables, Ratio of Actual Peak Manpower over Average Manpower and Actual
Manpower at Peak, were selected through stepwise method.
Predictor Variables
1) Act. Peak/Avg = Actual Peak Manpower / Actual Average Manpower
2) Log (Act. Peak) = Log of Actual Manpower at Peak (the Number of workers of sheet
metal worker (or mechanical worker ) at peak)
Multiple regression analysis followed to determine a quantitative relationship between
overmanning and efficiency loss with putting Percent Lost Efficiency (formulated in decimal,
not percentage form) as the response variable and two independent variables (Act.
Peak/Avg., and Log (Act. Peak)) as predictors. A final regression model was developed and
is given as Equation 2.
%LostEff = - 0.305 + 0.116*Act. Peak/Avg + 0.163* Log (Act. Peak) …………. (2)
Table 2 shows the result of Analysis of Variance (ANOVA) information for the final
model. The R2 value of the regression is 45.5%, a high value for the type of data analyzed.
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The p-value of the regression analysis was 0.000, and p-values for predictors were also
statistically significant, indicating a relatively strong regression model. Table 3 shows the
result of the Hypothesis test performed on predicted variables: Act. Peak/Avg and Log (Act.
Peak). Low P-values for two predictor variables in hypothesis testing indicate that the
predictor variables are not equal to zero, which is against the null hypothesis.
Table 2: Analysis of Variance for Overmanning Regression Equation
Source
Degrees of Freedom
Sum of Squares
Mean Square
F
P
Regression
2
0.48857
0.24429
21.27
0.000
Residual Error
51
0.58587
0.01149
Total
53
1.07444
Table 3: Hypothesis Testing Result for Overmanning Model Predictor Variables
Coefficient
Tested
Act. Peak/Avg.
Log (Act. Peak)
Null Hypothesis
Equal to Zero
Equal to Zero
Alternative
Hypothesis
Not equal to Zero
Not equal to Zero
P-Value
Result
0.000
0.000
Not equal to Zero
Not equal to Zero
SCOPE OF THE MODEL
The data from the mechanical and sheet metal contractors ranged in project sizes of 700
manhours to 208,451 manhours and the Peak/Average ratio range extended from 1.70 to
3.76. Applicable range of actual manpower at peak is from 4 to 50. For projects that fall
outside of the ranges for either project size or Peak/Average ratio the model given in
Equation 2 is not applicable.
VALIDATION
Cross validation was used to validate the relation of regression function. In cross validation,
the collected data was randomly segmented into five subgroups. The model was refit using
four subsets, and then the remaining 20 percent of the data is inputted into the new model and
the predictions of the model are compared to the actual percent lost efficiency experienced.
This process was repeated for all the five subsets; three out of every four projects fell within
±13 percent of the actual value.
COMPARISON TO PREVIOUS STUDIES
Since overmanning in the current research is measured as a ratio of the Actual Peak
Manpower by the Actual Average Manpower, a comparison to past quantitative studies is
difficult. As previously mentioned, in past studies the measurement of overmanning is often
left undefined and taken as a percentage, making a comparison of the present research to the
past literature unsuitable and inconclusive.
CASE STUDY – COLLEGE OF PHARMACY PROJECT
An analysis of project data supplied by a sheet metal contractor from St. Louis, Missouri will
7
Workers
be examined as an example for the of the use of the overmanning model developed in an
actual scenario outside of academia. The objective of the example is to quantify the impacts
of overmanning due to schedule compression on the project through the use of estimated
labor hours versus actual labor hours.
The project began in January, 2002 and was expected to be completed by May, 2003. The
sheet metal contractor had to complete their mechanical scope of work by the end of January,
2003 due to variety of delays that were caused by other trades. The time available for the
contractor to complete the work was significantly reduced, and the sheet metal contractor had
to add more workers to meet the deadline.
Estimated
16
14
12
10
8
6
4
2
0
Actual
0
5 10 15 20 25 30 35 40 45 50 55 60 65 70
week
Figure 2: Manpower Loading Curve for Case Study
Figure 2 shows a week-by-week comparison of the actual and estimated manpower
spanning the entire 71 weeks of the project. The figure shows that severe overmanning was
experienced during for the period week 16 to week 50, almost during the whole project. The
actual peak manpower is 15 on the 40th week and the actual average manpower over the
course of the project is 6.7. The actual peak over average ratio is 2.24, a value greater than
1.60, implying overmanning was indeed present. The project size was 14,781 total labor
hours, which is between the regression’s applicable ranges. Since the project meets the
criteria of being within the specified size and possess a peak over average ratio greater than
1.70 it can be analyzed using Equation 2.
According to the final model, Equation 2, this project is estimated to experience a
productivity loss, or loss of efficiency, of 0.1465 or 14.65% as a result of overmanning. This
quantity is only applicable to portions of the project impacted by overmanning, not the entire
project, and represents only the lost efficiency caused by the overmanning. This is an
important distinction because of the possibility that Equation 2 may be misused. For
example, if a project has 6 workers present daily over the course of its duration with a peak
of 15 workers for one day, then returning to 6 workers the following day, the lost
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productivity calculated by Equation 2 would not be applicable to the entire project, only to
the impacted manhours occurring over the one day having 15 workers on site.
For the case study, the project experienced an actual Efficiency Loss of 31.1%
(calculated using Equation 1), or about 4,600 additional manhours beyond the estimate for
the entire project. Applying the results of Equation 2, 14.65% would be multiplied by the
portion of the project impacted by overmanning. Referring to Figure 2, this would be
primarily the manhours worked from Week 16 to Week 50. From the collected data, 11,694
manhours were worked during these weeks. Multiplying the results of Equation 2, 0.1465 by
the 11,694 manhours gives a value of 1,713 manhours that can be attributed to overmanning.
This represents 37.2% of total lost manhours of the project. The remainder of the hours lost,
approximately 2,887 would be due to other factors such as stacking of trades, or contractor’s
inefficiencies and poor field management, etc.
LIMITS OF STUDY

This study is limited to mechanical and sheet metal projects with lump sum contracts
and a traditional project delivery system.

Off-project costs may accrue when contractors are forced to reallocate resources from
other projects to a project which is experiencing schedule compression. Furthermore,
the commitment of schedule compression on a certain project will tie up important
resources and hence limit the company’s ability to undertake other work.
Consequently, the loss of productivity or even profit from a secondary project is not
considered in the analysis. (Hanna 1999).
CONCLUSIONS
The quantification model can be used to assist mechanical and sheet metal contractors not
only in understanding the labor productivity loss of overmanning, but also in calculating
productivity loss and labor cost. The study result shows that as overmanning increases, lost
labor productivity increases, indicating that a schedule with less overmanning is preferable.
If acceleration is required, overtime or shift work may be the more favorable work
acceleration technique due to the higher initial productivity losses experienced from
overmanning. Decision making on the selection of a schedule compression method is not
solely dependent on how much each method affects productivity. There are many other
factors to make a sound selection of method. For instance, if the job site has ample space to
accommodate more workers, and adequately skilled labors and more supervision are
available, overmanning may be preferred to overtime and shift work.
REFERENCES
Army Corps of Engineers (1979) “Modification Impact Evaluation Guide EP 415-1-3”
Army Corps of Engineers
Bureau of Labor Statistics (2002) “Occupational Outlook Handbook” Bureau of Labor
Statistics, US Department of Labor
9
Clark, Forrest D. (1985) “Labor Productivity and Manpower Forecasting” American
Association of Cost Engineers Transactions
Construction Industry Institute Research report 6-7 (1990) “Concepts and Methods of
Schedule Compression” Construction Industry Institute, Austin, Texas
Hanna, Awad S. (2001) “Quantifying the Impact of Change Orders on Electrical and
Mechanical Labor Productivity” Research report 158-11, Construction Industry Institute
Leonard, C.A., (1988) “The effects of Change Orders on Productivity” Masters Thesis,
Concordia University, Montreal, Quebec, Canada
Noyce, D.A. and Hanna, A.S. (1998) “Planned and Unplanned Schedule Compression: The
Impact on Labour” Construction Management and Economics, Vol. 16
Waldron, James A. (1968) “Applied Principles of Project Planning and Control”
Haddonfield, New Jersey
Wideman, Max. (1994) “A Pragmatic Approach to Using Resource Loading, Production, and
Learning Curves on Construction Projects” Canadian Journal of Civil Engineering, Vol.
21
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