Impact of Overtime on Electrical Labor Productivity: A

productivity enhancement

Impact of Overtime on Electrical Labor Productivity: A Measured Mile Approach

University of Wisconsin – MadisonAwad S. Hanna

ELECTRI International—The Foundation for Electrical Construction, Inc.

Impact of Overtime on Electrical Labor Productivity: A Measured Mile Approach

ELECTRI InternationalThe Foundation for Electrical Construction, Inc.

University of Wisconsin–Madison

Awad S. HannaDepartment of Civil and Environmental Engineering

iii

PRESIDENT’S COUNSEL$1,000,000 or more

Hugh D. ‘Buz’ Allison, d. ELECTRI Council 1995-2011Hugh D. ‘Buz’ and Irene E. ‘Betty’ Allison Trust, Washington

Richard W. McBride*The Richard W. and Darlene Y. McBride Trust, California

Albert G. Wendt*Cannon & Wendt Electric Company

Al and Margaret Wendt Trust, Arizona

National Electrical Contractors Association*

Square D/Schneider Electric

PROGRAM GUARANTOR$500,000 or more

Electrical Contractors Trust of Alameda County

McCormick Systems

The Okonite Company

DIPLOMAT$350,000 or more

Contractors

Timothy McBrideSouthern Contracting Company, California

NECA Chapters and Affiliates

Boston Chapter

San Diego County Chapter

Manufacturers

Accubid Systems

REGENTS

$250,000 or more

Contractors

H.E. “Buck” Autrey*Ron Autrey

Miller Electric Company, Florida

John R. ColsonHouston, Texas

Robert E. and Sharon Doran*Capital Electric Construction, Kansas,

In memory of Robert E. Doran, Jr.

Jerrold H. Nixon, d. ELECTRI Council 1995–2009

Eric F. NixonMaron Electric Co., Illinois


Chicago & Cook County

New York City*

Northeastern Illinois

Northern Indiana

Southeastern Michigan*

Western Pennsylvania

Manufacturers

Eaton Electrical

Maxwell Systems

GOVERNORS

$150,000 or more

Contractors

Arthur Ashley Ferndale Electric Co., Michigan

Stephen BenderBana Electric Corporation, New York

Brian ChristopherOregon City, Oregon

Larry CogburnRon L. Cogburn

Cogburn Bros. Electric, Inc., Florida

Rex A. FerryVEC Inc., Ohio

Clyde JonesCenter Line Electric, Inc., Michigan

Michael Lindheim*The Lindheim Family, California

Walter T. Parkes*O’Connell Electric Company, New York

Robert L. Pfeil, d. ELECTRI Council 1991-2007

Richard R. Pieper, Sr.*PPC Partners, Inc., Wisconsin

Dennis F. QuebeChapel Electric Company, Ohio

James A. RanckJ. Ranck Electric, Inc., Michigan

Stephen J. ReitenM. J. Electric, LLC, Michigan

Dan Walsh United Electric Company, Inc., Kentucky

ELECTRI CouncilELECTRI International—The Foundation for Electrical Construction, Inc.

As of January 2011

* denotes founding member of ELECTRI’21 COUNCIL (1989–1990)

Impact of overtIme on electrIcal labor productIvIty: a measured mIle approach

iv


Cascade

Illinois*

Kansas City

Los Angeles County

Northeastern Line Constructors

Northern New Jersey

Oregon-Columbia

Oregon Pacific-Cascade

Puget Sound

Santa Clara Valley

South Texas

Manufacturers and Distributors

Panduit Corporation

Thomas & Betts Corporation

FOUNDERS

$100,000 or more

Contractors

Carlos AnastasARS Proyectos, Mexico

Gina M. AddeoADCO Electrical Corporation, New York

John AmayaAmaya Electric, Washington

Ted C. AntonNewkirk Electric Associates, Inc., Michigan

Ted N. BakerBaker Electric, Inc., California

D. R. “Rod” Borden, Jr.*Tri-City Electric Co., Inc., Florida

Daniel Bozick Daniel’s Electrical Construction Company, Inc., California

Scott BringmannAlcan Electrical & Engineering, Inc., Alaska

Larry Brookshire* Fisk Acquisition, Inc., Texas

Jay H. BruceBruce & Merrilees Electric Co., Pennsylvania

Richard L. Burns*Burns Electric Company, Inc., New York

Lawrence H. ClennonClennon Electric, Inc., Illinois

Ben CookBen and Jolene Cook, Brownwood, Texas

Michael G. CurranRed Top Electric Company Emeryville, Inc., California, In honor of

George T. and Mary K. Curran

Tom CurranTom and Alana Curran, Piedmont, California

Ben D’AlessandroL.K. Comstock & Co., Inc. New York

Gene W. DennisUniversal Systems, Michigan

Robert DiFazioDiFazio Electric, Inc., New York

William T. Divane, Jr. Divane Bros. Electric Co., Illinois, In memory of William T. Divane,

Sr. and Daniel J. Divane III

Robert Egizii EEI Holding Corporation, Illinois

Randy Fehlman*Gregg Electric, Inc., California

John S. FrantzSidney Electric Company, Ohio

Bradley S. GilesGiles Electric Company, Inc., Florida

Darrell GossettERMCO, Indiana

Frank GurtzGurtz Electric Company, Illinois, In honor of Gerald Gurtz

John F. Hahn, Jr.*Peter D. Furness Electric Co., Delaware

Michael HansonHunt Electric Corporation, Minnesota

Michael J. HolmesHolmes Electric Company. Washington

Danny HortonLighthouse Electrical Group, Washington

Eddie E. HortonDallas, Texas

Mark A. HustonLone Star Electric, Texas

Brian Imsand*Dillard Smith Construction Company, Tennessee

Thomas G. IspasDaniel’s Electrical Construction Company, Inc., California

Nazeeh A. Kiblawi Truland Systems Corporation, Virginia

Max N. LandonMcCoy Electric, Oregon

Donald W. Leslie, Sr., d. ELECTRI Council 1994-2010Johnson Electrical Construction Corporation, New York

Richard J. Martin*Motor City Electric Co., Michigan

Roy C. MartinTriangle Electric, Michigan

Edward C. MattoxInland Electric Corporation, Illinois

Howard MayersMayers Electric Company, Ohio

Michael MazzeoMichael Mazzeo Electric Corporation, New York

James C. Mc AteeElectric Power Equipment Company, Ohio

Kevin McKoskyCoastal Electric Construction, New York

electrI council

v

Edward T. McPhee, Jr. McPhee, Ltd., Connecticut

Todd A. MikecLighthouse Electric Company, Inc., Pennsylvania

William R. MillerMiller Electrical Construction, Inc., Pennsylvania

James B. Morgan, Sr. Harrington Electric Co., Ohio

Harvey MorrisonPritchard Electric Co., West Virginia

Joel MorynParsons Electric Company, Minnesota

Skip PerleyTEC-Corp/Thompson Electric Co., Iowa

In memory of Alfred C. Thompson

David PinterZwicker Electric Company, Inc., New York

Carl J. Privitera, Sr.Mark One Electric Company, Inc., Missouri

Sonja RheaumeChristenson Electric, Inc., Oregon

Julia G. RogersWalker Seal Companies, Virginia, In honor of Michael H. Walker

and Frank W. Seal

Phillip G. RoseRoman Electric Company, Wisconsin

Franklin D. RussellBagby & Russell Electric Co., Alabama, In memory of

Robert L. Russell

Tim RussellR.W. Leet Electric, Inc., Michigan

Frederic B. SargentSargent Electric Co., Pennsylvania

Rocky SharpCarl T. Madsen, Inc., Washington

Travis A SmithJordan-Smith Electric, West Virginia

Herbert P. Spiegel A tribute in memory of Flora Spiegel, Corona Industrial

Electric, California

Robert SpinardiSt. Francis Electric, California

Greg E. Stewart Superior Group, A Division of Electrical Specialists, Ohio

Jeff ThiedeOregon Electric Construction, Oregon

Ronald J. ToomerToomer Electrical Co., Inc., Louisiana

Robert J. Turner II Turner Electric Service, Inc., Michigan

Angelo VeanesFerguson Electric Construction Co., New York

Steve WattsCSI Electrical Contractors Inc., California

Brad WeirKelso-Burnett Company, Illinois

Jack W. WelbornElectrical Corporation of America, Missouri

David A. WitzContinental Electrical Construction Co., Illinois

Robert M. ZahnChewning & Wilmer, Virginia


AlaskaAMERIC Foundation (Mexico)American Line Builders Chapter

ArizonaAtlanta

Canadian Electrical Contractors AssociationCentral Indiana

Central OhioEastern Illinois

Electrical Contractors Trust of Solano & Napa CountiesGreater Cleveland

Greater SacramentoGreater Toronto Electrical Contractors

Long IslandMichigan

MilwaukeeMinneapolis

Missouri Valley Line ConstructorsNorth Central Ohio

North FloridaNorth Texas

Northern CaliforniaPenn-Del-Jersey

San FranciscoSouth Florida

Southeastern Line ConstructorsUNCE-Union Nacional de Contructores

Electromecanicos, A.C.(Mexico)Washington D.C.

West Virginia-Ohio ValleyWestern Line Constructors

Manufacturers and Distributors

Advance/Philips ElectronicsCooper B-Line

Cooper Crouse-HindsCooper Lighting

Crescent Electric SupplyERICO

GE LightingGraybar

Greenlee / A Textron CompanyLegrand North America

LevitonLutron Electronics Co., Inc.

Milwaukee Electric Tool CorporationRuud Lighting

Thomas Industries, Inc.

Other Partners

MCA, Inc.Oles Morrison Rinker & Baker LLP

San Diego Gas & Electric


vi

The author wishes to thank several electrical contractors who participated in the advisory panel and provided data and/

or reviewed this document. The advisory panel includes:

Acknowledgements

This ELECTRI International research project has been conducted under the auspices of the Research Center.

©2011 ELECTRI International—The Foundation for Electrical Construction, Inc. All Rights Reserved

The material in this publication is copyright protected and may not be reproduced without the permission of ELECTRI International.

Matthew Doell, Sachs ElectricLarry Beltramo, Rosendin ElectricGene Ryley, Cooperative Electric

Ron Autrey, Miller ElectricWalter Parkes, O’Connell Electric Company

In addition, many other companies participated in providing data for the micro, macro analysis. This research would

not have been possible without their generous contributions of time in completing the survey and gathering productivity

data. The author would like to thank the following contractors for their participation in this research:

James Atkinson III, Chewning & Wilmer, Inc.Jeff Bartlett, Keystone Electric

Larry Beltramo, Rosendin ElecticNick Bernabe, Neal Electrical

Ken Brown, Dale C. Rossman, Inc.Patrick Campbell, Commonwealth Electric

Eric Cartier, Cartier ElectricJohn Colson, Quanta Services, Inc.

Gary Demmel, Commonwealth Electric CompanyMatthew Doell, Sachs Electric

Robert Egizii, EEI Holding CorpDavid Firestone, Commonwealth Electric

Barry Frain, Con J Franke ElectricalJeff Giglio, Inglett & Stubbs, Inc.

William Haugland, Hawkeye ElectricE. Milner Irvin, Riverside Electric Company, Inc.

Michael Joyce, Doan/Pyramid ElectricNazeeh Kiblawi, Truland Systems Corporation

William Koertner, MYR Group, Inc.Chris Jaskiewicz, Valley Electrical

Paul Latimore, Egizii ElectricPhil LaVallee, Hatzell & Buehler, Inc.

Frank Lizzardo, Meade Electric Company, Inc.

J. Robert Mann Jr., E-J Electric Installation CompanyTim McBride, Southern Contracting Company

Wayne McDonald, Fisk Electric CompanyJason Medaris, Schetter ElectricalMark Melton, Melton Electric Inc.

Jack Mueck, M.J. Electric, Inc.John Murphy, SM Electric Company

John Negro, Nelson ElectricWalter Parkes, O’Connell Electric CompanyTom Parkes, O’Connell Electric Company

James Peterson, Berwick ElectricalKen Priddy, Pick Electric

Jim Robertson, Fisk ElectricGene Ryley, Cupertino Electric

William Salzbrener, Intermountain ElectricClayton Scharff, Sachs Electric

Travis Smith, Jordan-Smith Electric CompanyTom Sorley, Rosendin Electric

John Stokey, Century ElectricalJames Stouffer, Stouffer Electric Company

George Troutman, MJ ElectricEd Witt, Sr., Miller Electrical Company

Dale Whaley, LE Myers Company

vii

Table of Contents

Summary ......................................................................................................................................................... 1

Introduction .....................................................................................................................................................3

Literature Review ............................................................................................................................................7

Qualitative Analysis ...................................................................................................................................... 13

Quantitative Analysis ................................................................................................................................... 19

Conclusion .....................................................................................................................................................37

Appendix A: References ................................................................................................................................39

Annex B: Data Collection Sheet.................................................................................................................... 41

Annex C: References .....................................................................................................................................43

1

Electrical contractors are frequently faced with the need to compress or accelerate the construction schedule as a

result of added scope, delays and/or a late start of activities. The most common approach to schedule acceleration is the

use of overtime. Past research studies concluded that placing workers on scheduled overtime reduces labor productivity.

Although several research efforts have studied the effect of overtime on labor efficiency, these studies have unknown data

sources and were conducted many years previously.

This study is focused on the analysis of scheduled and unscheduled (sporadic) overtime on labor productivity. There

are two components of this research; qualitative and quantitative. The qualitative aspect aims to document the views of

electrical contractors regarding the use of overtime and other schedule compression techniques such as overmanning

and shiftwork. The quantitative component deals with macro and micro analysis of overtime. The macro and micro

approaches are used for analyzing the impact of both sporadic and scheduled overtime. The macro approach is used to

analyze projects where no fixed overtime schedule is utilized or mixed work schedules are used throughout a week. The

micro approach is used to analyze projects that utilize a fixed overtime schedule, such as 5x10 or 6x10 throughout a certain

number of weeks. This study presents the results of a statistical analysis of productivity data collected from several projects

that used a variety of overtime scheduling techniques. The statistical analysis includes several productivity models that can

be used to estimate electrical labor inefficiency within a project, given both its scheduled overtime per week and the total

successive number of weeks of overtime.

The following conclusions and scope highlight the findings of this study:

■ Placing an electrical crew on scheduled or unscheduled overtime reduces labor productivity and increases labor

costs.

■ A greater number of hours worked beyond the regular forty hours per week is related to higher productivity

losses.

■ There is a direct correlation between labor inefficiency and the duration of overtime use.

■ Despite productivity losses related to overtime, placing an electrical crew on overtime schedule is more efficient

than overmanning.

■ The data collected for this study was limited to electrical workers for projects executed between 2004 and 2008.

■ The measured mile method was used in this study, which compares periods of time when the regular 40 hour

work week was utilized to overtime schedules of 50 hours or more. The measured mile technique is a widely ac-

cepted and accurate method of capturing labor inefficiency.

Summary


2

Use of This Report

It is widely and legally recognized that inefficiency factors cited in the National Electrical Contractors Association

(NECA) study and other similar studies should be used as a guide and not as absolute qualification of overtime impact.

This author believes that the best use of this study is for forward pricing, i.e. before the work is executed for the selected

overtime schedule (i.e. 5x10, 6x10, etc.). However, in case of sporadic use of overtime, one statistical model developed in

this study, the “Macro Model”, was produced using completed projects with a mix of overtime schedule, and can be used

“after-the-fact”.

This study enhances the ability of electrical contractors to rely on published scientific studies. It is recommended that

forward pricing for a change request be determined before expending cost. The authors of this study recommend request-

ing the appropriate change request, as most construction contracts require electrical contractors to do.

3

Background

In recent years, there has been an ever increasing need for electrical contractors to complete a construction project in a

shorter than customary time period. It is not uncommon for an electrical contractor to find that he or she must compress

or accelerate a construction schedule to meet the basic objectives of the project. In the United States system of competitive

contract construction, a prudent and capable contractor will plan the work economically within the scheduled time period

for any given project. If the contract is either compressed (reduced from the normal or typical time frame) or accelerated

(increased speed of progress from the normal or typical progress speed), the contractor will be required to perform the

same work in a substantially shorter timeframe, and the cost of the work will necessarily increase. Contractors and own-

ers are faced with few options to compress or accelerate the schedule. Among these compression techniques are overtime,

overmanning and shift work. This study and other similar research has indicated that the use of overtime with variable

lengths is the most common and preferred compression technique. This is an improvement over previous outdated studies

on the impact of overtime on labor productivity, many of which lack comprehensive information about the sources of data

collected.

Previous research has indicated that in approximately 73% of all construction delays, project owners refuse to grant

time extensions (Leonard et al. 1988). The lack of time extension and an accelerated completion date forces the contractor

to compress the construction schedule and complete the project in a less than customary time frame (for example, four

months rather than six months). There are many other potential causes of schedule variation that lead to the necessity of

schedule compression or acceleration. Some of these causes may be attributed to the following:

■ Contract change orders—most change orders (90%) are caused by increased scope (45%), design changes, design

errors, and design coordination (Hanna, 2001).

■ Poor performance by previous work crews on preceding activities in a set schedule—this is particularly impor-

tant among electrical contractors, as they typically perform as a “follow-up trade” and usually perform their work

“last-in-line”.

■ Anticipated weather and/or seasonal factors limit the available time for construction work.

■ The owner requires that the proposed project be completed in a less than normal time frame.

■ Planned and unplanned utility work requires power outages among customers to be minimized.

■ The minimization of shut-down time in plant operations during construction or maintenance work.

■ Construction delay and delay recovery—lack of design detail or incomplete design prior to the start of construc-

tion may also result in delay.

Introduction


4

Significance of This Research to Electrical Contractors

Electrical contractors generally allocate 33-50% of a project’s total budget to labor costs (Hanna, 2006). Of the typi-

cal project cost components (material, equipment, and labor), labor is considered the project element containing the

most risk. The other cost components (material and equipment) are predominately determined by market price and

consequently beyond the influence of the project management. As a result, the management of labor and its productivity

becomes paramount in determining the success of a project. Electrical contractors carry additional risk associated with the

fact that they are last-in-line and typically carry delays created by other trades and increased scope.

It should also be noted that long lead items such as switchgear, generators and light fixtures and copper products are

another risky element, being subject to cost escalations for early/late delivery, storage and extra handling. However, this

report will focus solely on the labor component.

Research Objectives

The purpose of this research is to analyze the impact of scheduled overtime on labor productivity for electrical con-

tractors. The research has five main objectives.

1. Determine what previous literature exists on the topic of overtime and its impact on labor productivity.

2. Conduct a qualitative survey to seek the input of electrical contractors who are experienced in the use of overtime

schedules to document its frequency, impact, and proper application.

3. Determine the impact of several overtime schedules on labor productivity. The study includes the impact of 5x10,

6x10, and 7x10 schedules (to be defined later).

4. Quantify the impact of using mixed and sporadic use of overtime schedules on labor productivity (macro analysis).

5. Develop a tool to aid electrical contractors in minimizing the impact of overtime scheduling and determine proper

applications and conditions for successful use.

Methodology

In order to achieve the project goals, three different data collection devices were utilized: qualitative analysis, quantita-

tive macro-analysis and quantitative micro-analysis.

For the qualitative analysis, an opinion survey was developed to ascertain the spread and the frequency of overtime

utilization among contractors.

For the quantitative analysis, two different data collection methodologies were utilized. First, for the macro analysis of

sporadic use of overtime schedule, data from completed projects was collected. Data was sorted by type of work, estimated

work-hours, actual work-hours, change orders hours, overtime hours, overtime schedule, percent of overtime used and

length of overtime. Second, for the micro analysis, several projects were tracked on a weekly basis to study the impact of

the of 5x10, 6x10, and 7x10 overtime schedules.

Measured Mile Method (MMM)

Historically, evaluating labor productivity losses has been difficult due to the many variables influencing productiv-

ity, such as overtime, schedule changes, scope changes, and less than perfect labor and management situations. In order

to evaluate labor performance, budget estimates have to be compared to past performance to determine efficiency. The

Measured Mile Method (MMM) is the gold standard for accomplishing this. This approach compares the unit productivity

IntroductIon

5

levels during unimpeded time (during the reference schedule) to those during impacted time (when using overtime) in

order to determine how significantly the project’s productivity was impacted (Zink, 1986). The theory is that the difference

between a contractor’s actual inefficient productivity and an identified normal productivity is the amount of excess cost to

the contractor as a direct result of labor inefficiencies and loss of productivity. As it is a reliable and widely accepted meth-

od, this research utilizes MMM to form the basis of a model to predict and forward price overtime labor inefficiencies. The

MMM was successfully used in the Natkin & Co. v. George A. Fuller Co. (347 F. Supp. 17(W.D. Mo. 1972)) case: the court

found that comparing the unit costs during the impacted and unimpacted periods was a reasonable manner in which to

compute the damages, and Natkin was awarded its lost productivity damages. Moreover, in Clark Concrete Contractors, Inc

v. General Services Administration (GSBA No. 14,340, 99-1 BCA (CCH) (1999)), the Board permitted Omni (the contrac-

tor) to recover damages of more than $1 million for lost productivity based on the measured mile analysis.

Scope/Constraints

This research is limited to data collected among electrical contractors. Any intersections to other industries should be

done with caution. Electrical construction is characterized as a labor intensive, follow-up and connected trade; electrical

work is last-in-line. It should also be noted that the productivity losses cited in this research are not exact: like any other

statistical models, they are subject to variability. The calculations are significant at 95% confidence intervals.

Definitions

For the current research, the following definitions are assumed:

Overtime: Work performed over 8 hours per day and/or 40 hours per week. Overtime can occur in a variety of sched-

ules, including 5 days of 10 hours worked per day (5x10), 7x8, 6x10, or 7x10.

5-10hr day/wk: An overtime scheduling technique where 10 hour days are worked 5 days per week, typically Monday

through Friday.

5-8hr day/wk: A scheduling technique where 8 hour days are worked 5 days per week, typically Monday through Fri-

day. This is considered normal work time.

Productivity: Economists and accountants define productivity as the ratio between total input of resources and total

output of product. Resource input includes labor, materials, equipment and overhead, while output can be measured as the

total dollar value of construction put in place. In contrast, project managers and construction professionals define produc-

tivity as a ratio between earned work-hours and expended work-hours or work-hours used. The latter definition is used in

this paper.

Why and How Overtime Affects Labor Productivity

As a schedule compression technique, overtime is often preferred because it can produce a higher rate of progress

without the coordination problems of shift work or the additional craftsmen needed for overmanning (Hanna, 2003).

However, overtime introduces additional problems, including fatigue, low morale, a higher cost per unit, a higher accident

rate, and pacing. Pacing is a phenomenon described by the U.S. Army (1979) where workers tend to pace themselves for a

longer work day or work week. The listed problems reduce labor productivity and present contractors with the problem of

increased costs.

Numerous studies have documented the capacity for overtime to reduce labor productivity. All concur that productiv-

ity is reduced, but the magnitude of the decline varies across studies. The most prominent and most widely used studies

are by Kossoris, the Business Roundtable, NECA, Adrian, and the Construction Industry Institute (CII).

Figure 1 displays Kossoris’ effect of overtime on productivity as reported in the 1947 Bureau of Labor Statistics Bul-

letin No. 917 (Kossoris, 1947). The figure shows that as hours per day and days per week increase, productivity decreases.

The figure is adapted from Long’s interpretation of the original as taken from the Bureau (MCAA, 1976). Nothing is

known about the source, nature, or quality of the data used by Kossoris (Hanna, 2001). It is probable that the data was not

taken from the construction sector but rather manufacturing (Smith, 1987).

Literature Review

7

Figure 1: Hours per Day and Days per Week versus Percent Productivity


8

In another study, the Business Roundtable (BRT) collected data over a ten-year period from a Proctor and Gamble

processing plant in Green Bay, Wisconsin. The productivity of the construction was recorded as the ratio of the standard

man-hours per unit to man-hours per unit achieved (which reduces to earned/actual hours) (Hanna, 2003). Thomas

(1992) reported that the Proctor and Gamble data was from a single project, consisting of shorter jobs with overtime, and

that the true type of construction performed is unknown. The BRT report mentioned that the operations of the project

were performed under excellent field management in times of tranquil labor relations (BRT, 1980). Figure 2 demonstrates

this relationship between lost productivity and weeks of overtime.

Additionally, the National Electrical Contractors Association released an overtime and productivity study in 1969

relating productivity loss to weeks of overtime. The study presented different scenarios of work days per week and hours

worked per day. A graphical representation of this study is given in Figure 3.

Figure 2: Business Roundtable’s Percent Productivity Loss vs. Weeks of Overtime (1980)

In a project using data that was very similar to NECA’s study, Adrian expanded on the same principles. Adrian’s data is

given in Figure 4 (next page) and was taken from concrete work performed in an amicable labor climate (1988). Adrian’s

data provided various combinations of days worked per week and hours worked per day. The scenarios of 10 and 11 hours

per day are presented in Figure 4 for work five through seven day work weeks.

In 1988, the Construction Industry Institute published the results of data collected from 25 different crews working

on seven independent projects. The majority of the crews (21) were involved in electrical and mechanical trades while

the remaining few (4) worked in concrete. The 25 crews consisted of three insulation, seven pipe, eleven electrical, one

formwork, one rebar, and two concrete crews. The seven projects entailed the refurbishment of different installations, with

one at a distillation unit at a refinery, two involving work at oil refineries, two at natural gas recovery plants, another at a

chemical processing unit, and a final project at a power plant (Hanna, 2001).

The data tracked material installed per man-hour over each workday. The different crews worked under various over-

time schedules including; 4x9s and 1x8s, 5x10s and 1x8s, 6x8s, 6x10s, and 7x10s (Hanna, 2001).

lIterature revIew

9

Figure 3: Productivity Loss (%) vs. Weeks of Overtime (NECA, 1969)


10

Figure 4: Adrian’s Productivity Loss (%) vs. Weeks of Overtime (1988)

Due to the varied forms of construction, averaging the data across all projects required the crew productivities to be

normalized. Normalization was achieved by first dividing each three week productivity period by that crew’s first week

(the first week was assumed as the base productivity for each crew), and then averaging the three week values for all crews.

This produced an average normalized productivity that could be plotted in relation to time (Hanna, 2001). Figure 5 plots

productivity against time, measuring the variation of the crews’ output in relation to their base productivity given as 1.0. A

productivity level greater than 1.0 implies increased productivity while a level below 1.0 equates to a productivity loss.

Figure 6 plots the best-fit regression line through all the comparable reviewed data. The regression line has an R2 value

of 53.4%, which is a measure of how well the plotted line represents the data (An R2 value of 100% would indicate a line

passing through every data point). In the figure, productivity falls rapidly over the first three weeks of overtime, and then

tapers off gradually, only decreasing slightly over the next few weeks. This graph demonstrates the average effect of over-

time; however, more accelerated shift schedules result in higher productivity loss. From the literature, a crew working a

6x11 schedule will be more impacted than a crew working a 5x10 schedule.

The validity and accuracy of past overtime studies is questioned by several authors, including Thomas and Larew.

Thomas (Thomas, 1992) concluded that the literature on scheduled overtime is dated, based on small sample sizes, and

largely developed from questionable or unknown sources.

lIterature revIew

11

Figure 5: CII’s Productivity vs. Time (1988)

Figure 6: Best-Fit Line through All Comparable Data

13

In order to assess the impact of overtime on labor productivity, data was qualitatively and quantitatively analyzed from

companies around the United States and Canada. The qualitative analysis component, which was aimed at studying the

current state and frequency of overtime use, was organized around a widely distributed questionnaire. The quantitative

analysis compared the efficiency of a fixed overtime schedule to that of the reference schedule (5x8). The effect of overtime

on labor productivity was calculated by collecting data from projects where contractors used fixed overtime schedules.

Data Collection

A survey was conducted to study the impact of overtime on electrical construction labor productivity. Two data col-

lection forms were developed and distributed to 400 electrical contractors across the United States and Canada. The first

was a questionnaire focused on qualitative measures, while the second questionnaire dealt with quantitative aspects of

overtime. The qualitative questionnaire conducted during 2007 and first half of 2008 covered opinions about the use of

overtime and its effects on productivity, reasons for the use of overtime, alternative approaches to extended overtime, and

the frequency and type of overtime used/preferred (Appendix A). The second form (Appendix B) collected data from

completed projects to capture information related to company contract information, project location, project type (e.g.

manufacturing, industrial, commercial), construction type (e.g. addition, new construction, or renovation), percent differ-

ence between lowest bidders, and relevant project information (e.g. budgeted manhours, overtime hours used, estimated

duration). Of the 400 qualitative surveys mailed, 51 were returned by contractors (response rate of 12.75%). The following

section will focus on the analysis of the qualitative survey.

Analysis

Overview

The geographic regions which were established to identify trends in the electrical construction industry of the United

State are shown in Figure 7 (next page).

Additionally, several responses from Canadian contractors were also received. Figure 8 (next page) shows the distribu-

tion of responses by region.

The companies surveyed were asked to report the percentage of their projects that use extended overtime, or work

more than 40 hours per week for more than four weeks. The responses indicate that 18.9% of all projects used extended

overtime. Among regions, the northeast had the highest use with 24.79% of its projects utilizing extended overtime. Re-

sults for individual regions can be found in Figure 9 (next page).

Qualitative Analysis


14

Figure 7: Division of the United States by Region

Figure 9: Percentage of Total Projects Using Overtime by Region

Figure 8: Distribution of Responses by Region

QualItatIve analysIs

15


Views on Overtime in Electrical Construction

Table 1 summarizes contractors’ responses regarding their opinions of overtime in electrical construction; an answer

of 1 means that the contractor strongly disagrees, whereas as an answer of 5 means that he or she strongly agrees.

Question NumberRegions

NE SW NW MW SE Canada Aggregate

1. Extended Overtime is used frequently in the

construction industry.

2.917 3.500 3.429 2.800 3.333 2.750 3.121

2. Extended Overtime reduces labor productivity. 4.083 4.900 4.571 4.667 5.000 3.750 4.495

3. Extended Overtime increases accidents. 3.833 4.100 4.429 4.133 4.667 3.500 4.110

4. Extended Overtime increases absenteeism on

projects.

4.000 4.300 4.571 4.067 4.667 2.750 4.059

5. Extended Overtime increases turnover on

projects.

3.500 3.400 3.000 3.429 4.000 2.000 3.221

6. Extended Overtime is necessary to achieve

project schedules.

3.167 2.500 2.857 2.500 3.000 3.500 2.921

7. Extended Overtime is necessary to attract

skilled crafts to projects.

2.667 2.900 2.714 2.133 2.667 2.500 2.597

8. Owners and contractors are using more

overtime than is required.

2.833 3.200 3.571 3.200 4.333 2.000 3.190

9. There are better alternatives that can be used

instead of extended overtime.

4.000 3.800 3.857 4.000 4.000 3.500 3.860

Table 1: Average Opinions of Overtime Responses

According to Table 1, contractors in the southwest, northwest, and southeast felt that extended overtime is used fre-

quently in the construction industry. Coincidently, contractors in the northwest and southeast also felt that owners and

contractors are using more overtime than is required, indicating a discrepancy between the preferred and actual work

schedules utilized by these contractors. The aggregate responses for questions 1 and 8 indicate that contractors do not feel

that excessive overtime is being used in the construction industry. However, the strong affirmative responses to question

2 across all regions signify that contractors, as a whole, agree that overtime reduces labor productivity. According to the

generally high responses to questions 3 and 4, contractors in the southeast and northwest were more likely to blame the re-

duction of productivity on an increase in accidents and absenteeism than in other regions; however, the remaining regions

still recognized that more accidents and absenteeism may lead to lower productivity. The low rating for question 5 sug-

gests that contractors do not attribute productivity reduction to a greater learning curve, since they reported that extended

overtime does not increase labor turnover. From questions 6 and 7, we can conclude that overtime is not commonly used

to meet project schedules or attract qualified labor. Finally, the high responses for question 9 demonstrate that the south-

east, mid-west, and northeast believe that better alternatives to extended overtime exist. However, the southwest, northwest


16

Table 2: Reasons for Using Extended Overtime

and Canada were the least likely to agree that better alternatives were available. Coupled with the reported highest use of

overtime, the contractors in these regions may have learned through experience that overtime is the best alternative.

Reasons for the Use of Extended Overtime

The unique circumstances surrounding each project suggest that the reasons for using extended overtime can vary

widely. Table 2 shows the results for six common reasons for using extended overtime. The reasons were ranked 1 through

6 (without duplication), where 1 indicated “most commonly used” and 6 referred to “last resort”.

MW SW SE NW NE Canada Aggregate

Aggressive Schedule 2.133 2.300 3.000 2.571 2.583 3.250 2.640

Reduce Plant Outages 2.133 3.700 2.333 2.143 2.667 2.750 2.621

Scope Changes 2.533 3.100 3.333 2.000 2.583 2.250 2.633

Attract Skilled Labor 4.667 4.200 5.667 5.000 3.917 5.000 4.742

Make up for Labor Related Delays 5.200 4.600 6.000 5.857 3.833 3.250 4.790

Make up for Missed Milestones 2.933 2.700 2.667 2.286 2.583 2.000 2.528

From the results above, the contractors in the mid-west were the most likely to use overtime because of an aggres-

sive schedule and to reduce plant outages. The southwest also used overtime mainly due to aggressive schedules; this may

relate in part to the increase in construction in California and Las Vegas from 2007-2008. The southeast relied on overtime

mainly to reduce plant outages, which could be driven by the oil refineries in the Gulf of Mexico. The northwest relied on

overtime to reduce plant outages and to compensate for scope changes. All of the regions reported that attracting skilled

labor or making up for labor-related delays was not a main motivator for overtime. Finally, nearly all regions reported the

frequent use of overtime as a means to make up for missed milestones and to meet project schedules.

Alternative Approaches to Extended Overtime

Given the significant costs associated with extended overtime and the possibility of significant productivity losses,

contractors were asked to rank their experience regarding the effectiveness of several overtime alternatives. Table 3 shows

the various alternatives which were ranked from 0-10, where 0 signified “Does Not Work” and 10 meant “Highly Recom-

mended”.

Table 3: Alternative Approaches to Extended Overtime

MW SW SE NW NE Canada Aggregate

2nd Shift 7.067 6.600 5.333 6.286 7.333 4.250 6.145

Overmanning 2.333 0.700 3.000 2.714 2.333 2.750 2.305

Rolling Crews/Shifts 4.867 5.000 5.333 4.714 4.750 4.000 4.777

3rd Shift 4.800 4.400 2.000 3.857 5.417 2.750 3.871


17


The use of a second shift was the most common alternative to overtime. This was followed by the use of rolling crews

or shifts, which allows for constant labor on a project and decreases the number of craftsmen used at a given time. Over-

manning was perceived as the least effective, most likely due to increased costs associated with additional labor; when ad-

ditional labor are present on site, maintaining adequate access to tools, materials, and space is much more difficult.

Commonly Used Ways to Handle Schedule Acceleration/Compression

Given the responses collected in question 9 of Table 1, contractors do not always feel that extended overtime is the best

way to handle schedule acceleration or compression. Table 4 shows methods which contractors prefer and typically use to

meet more demanding schedules. In the table, 1 indicates “Most Preferred”, while 3 is “Last Resort”.

Prefer MW SW SE NW NE Canada Aggregate

Overtime 1.867 1.400 1.667 1.429 1.667 1.500 1.588

Overmanning 2.667 3.000 2.333 2.714 2.833 2.500 2.675

Shift Work 1.533 1.600 2.000 1.857 1.500 2.750 1.873

Typically MW SW SE NW NE Canada Aggregate

Overtime 1.400 1.100 1.000 1.143 1.500 2.000 1.357

Overmanning 2.133 2.600 2.000 2.000 2.750 2.000 2.247

Shift Work 2.400 2.300 2.667 2.571 1.667 2.750 2.392

Table 4: Preferred and Typical Methods of Schedule Acceleration

Similarly to Table 3, overmanning was the least preferred method to meet schedule change. Considering the difficulty

associated with shift work management and coordination, it is the least implemented method of schedule acceleration out-

side of the southwest and northeast. Likely due to the ability of overtime to keep crews small and precluding the need for

additional tools or craftsmen, it is the most popular method of schedule acceleration. Conversely, the greater crew size, ad-

ditional tools, and increased project congestion related to overmanning are likely the reason that made it a distant second

as a preferred schedule acceleration method.

Overtime Schedules

Given that extended overtime is the most commonly used method of schedule acceleration and compression, it is

necessary to examine the different types of overtime schedules that are commonly utilized. Table 5 (next page) shows the

various overtime schedules typically used or preferred by contractors.


18


Table 5: Typical and Preferred Overtime Schedules

Typical MW SW SE NW NE Canada Aggregate Percent

5x10 7 4 0 2 7 1 21 41.18%

6x8 1 2 0 1 1 1 6 11.76%

6x10 1 1 1 2 3 0 8 15.69%

6x12 1 2 1 0 1 0 5 9.80%

7x10 1 0 0 1 0 1 3 5.88%

7x12 0 0 0 0 0 0 0 0.00%

Other 4 1 1 1 0 1 8 15.69%

Prefer MW SW SE NW NE Canada Aggregate Percent

5x10 9 6 1 4 8 1 29 56.86%

6x8 2 2 0 1 2 1 8 15.69%

6x10 0 1 1 1 1 0 4 7.84%

6x12 1 0 0 0 1 0 2 3.92%

7x10 0 0 0 0 0 1 1 1.96%

7x12 0 0 0 0 0 0 0 0.00%

Other 3 1 1 1 0 1 7 13.73%

5x10 can be implemented by simply altering the labor break schedule, and it allows for labor and management alike to

enjoy a full weekend. Not surprisingly, it was the most preferred and most typically used overtime schedule for nearly half

the respondents. However, surprising was the number of contractors who preferred to use either 5x10 or 6x8 but reported

typically using a 6x10, 6x12, or 7x10 schedule, indicating a need for a substantially larger number of working hours per

week. With a higher number of hours needed, the 6x8 was less often used, but it remains a common schedule because it

increases the number of hours worked per week while limiting the number of hours worked per day. This helps minimize

productivity loss by eliminating the pacing endemic to a longer day. The final category of overtime schedules, other, in-

cluded 5x9, 5x9+1x8, and 5x10+1x8; these were largely uncommon compared to the other styles.

Overtime Policies

Only 31.3% of the contractors had a company policy regarding the use of overtime. Of the companies with overtime

policies, 44% required management or the owner to pre-approve the use of overtime. An additional 31% restricted the

type of schedule or the maximum number of hours which could be worked per day.

19

Quantitative Analysis

As stated earlier, the use of overtime on electrical construction projects is often unavoidable; thus, it is important to

establish a firm understanding of its potential effects on labor productivity. Overtime can be used in two different forms:

sporadic or scheduled. As a result, the quantitative analysis will address these two forms.

Schedule and Sporadic Use of Overtime

There are generally two forms of overtime utilization. One form, sporadic or spot overtime, refers to sporadic work

hour increases that are usually assigned to a limited number of workers and/or utilized for a short time within a mix of dif-

ferent overtime schedules. Sporadic overtime may be used to “catch up” when certain activities or areas fall behind sched-

ule. On the other hand, scheduled overtime refers to a planned decision by the project manager to schedule more than 40

work hours per week for an extended period of time for most electricians. Scheduled overtime may be used for the follow-

ing reasons: to constructively accelerate the schedule, maintain or improve the completion date, take advantage of favor-

able weather conditions, make maximum use of rented equipment (such as cranes), maintain schedules in limited work

space, avoid contract penalties or win incentive awards, complete emergency construction, avoid strike deadlines, and/or

avoid higher wage rates. The most prevalent use of overtime is to accelerate a work schedule, either due to owner mandate

or constructively accelerated contractor schedules. The most common causes of schedule acceleration are:

■ Late start, such as late access to site and late issue of notice to proceed.

■ Delay of any sort, such as weather delay, late delivery of owner-purchased items, delay by subcontractors, late

response to RFIs, and late approval of submittals and shop drawings.

■ Incomplete prerequisite work by other contractors, resulting in delay. Conversely, accelerated work by others also

causes schedule acceleration.

■ Change orders, when the scope of work is added to without extending project duration. In this case, the electrical

contractor is forced to do more work within the same pre-established time frame.

Macro- vs. Micro-analysis of Overtime

The macro and micro approaches are used for analyzing the impact of overtime during sporadic and scheduled over-

time. The macro approach is used to analyze projects where no fixed overtime schedule is utilized, or mixed work schedules

are used throughout a week on a project (sporadic overtime). The micro approach is used to analyze projects that utilize a

fixed overtime schedule (scheduled overtime), such as 5x10 or 6x10 throughout a certain number of weeks.


20

Macro Analysis Collection and Analysis

For the macro analysis, the research methodology is segmented into data collection and data analysis. Data was col-

lected through the distribution of a data collection sheet to electrical contractors. The data analysis was conducted through

statistical regression and a comparison of the results to the findings of previous research.

The data collection sheet was prepared and distributed to a large number of electrical contractors across the United

States and Canada. It was also published in Electri International Magazine. The data collection sheet is presented in Ap-

pendix B and included two sections: general background information about the contractor and projects that experienced

overtime. Project data collected by the data collection sheet included information related to the project type, the type of

construction (addition or expansion, new construction or renovation), the difference between the two low bids for the

project, and the actual peak number of electrical craftsman. Additional project data was collected, including budgeted

work hours, actual work hours used, change order hours, and length (duration) of overtime used.

Definition of Productivity and Loss of Efficiency

For the purpose of the macro analysis (project level), productivity is defined as the ratio between the actual labor

hours expended to complete the project and the earned base hours (Hanna, 2006). Loss of efficiency or productivity is de-

fined as the difference between actual hours utilized from earned hours as a percent of total actual hours utilized. It should

be noted that data related to the difference between the lowest bid and the second lowest bid were collected and if the dif-

ference between these two bids was more than 10%, the project was be disqualified from being included in database.

Lost efficiency may result from a contractor’s poor performance or the impact of productivity related factors such as

overtime, overmanning, shift work, and work interruptions. The strength of this method is its representation of the direct

and indirect effects on productivity, since actual labor hours are calculated after the completion of a project. To compare

projects of varying size, percent lost efficiency (%LostEff) is given by following equation:

%Lost Eff =Total Actual Direct Labor Hours – Earned Hours

* 100Total Actual Direct Labor Hours

The total actual direct labor hours are all field personnel and field supervision labor hours for a project. Earned hours

are the hours that were originally in the contract along with all additional earned hours received for approved changes.

Table 6 shows the performance data for the macro analysis.

Data was collected for 50 projects with average weekly hours range from 37.48 to 58.10. All projects in our database

experienced the use of sporadic overtime. Regression techniques were used to arrive at a quantitative relationship between

loss of productivity (Percent Lost Efficiency) and average hours per week.

QuantItatIve analysIs

2121

Table 6: Performance Factor and Hours per Week for the Macro Analysis

Hours per Week Performance Factor Hours per Week Performance Factor

37.48 107.51 45.63 88.26

37.65 106.55 46.13 59.23

37.98 97.52 47.05 91.18

38.07 104.94 47.05 94.08

38.32 88.17 47.13 85.05

38.82 103.34 47.71 94.09

38.90 104.95 48.46 74.10

38.90 110.11 49.04 88.30

38.90 108.18 49.38 81.21

38.98 102.05 50.21 68.32

39.23 102.05 51.21 78.97

39.40 108.18 51.37 66.40

39.48 100.76 51.70 82.85

39.56 93.02 52.20 56.73

39.81 114.64 52.20 74.47

40.06 100.13 52.87 79.00

40.56 100.78 53.12 47.06

40.98 94.98 54.12 47.08

41.06 96.59 54.78 71.92

41.64 111.11 55.20 75.15

41.81 101.12 56.03 49.36

42.14 100.15 57.19 61.31

43.72 100.82 58.02 70.35

44.47 100.18 58.11 69.06

45.14 82.12


22

Macro Analysis

A regression line (Y = -2.38052 X + 196.3959) or approximately (Y = -2.38 X + 196.4) is fitted to the data (accumulat-

ed from different sources) with an R2 squared value of 72.1%. R2 values are a measurement of how well the model fits the

data, with an R2 value of 100% indicating a perfect fit and 0% indicating no fit. If a vertical line is drawn from 40 hours per

week, the performance factor is equal to one. This confirms the assumption that the standard 40-hour work week achieves

100% productivity. This result validates using 40 hours per week as the benchmark. Figure 10 compares performance fac-

tor against the hours worked per week.

Figure 10: Performance Factor versus Hours per Week

The macro method can be used to estimate productivity loss when:

■ When sporadic overtime is being used (no scheduled overtime).

■ When the work hours per worker vary during the week and between weeks.

■ When the project experiences above-average absenteeism and turnover, resulting in sporadic overtime.

■ When the average overtime hours are above 40; however, some workers on the project are working overtime hours

above 40 hours per week and others are not.

■ When the contractor is not measuring productivity in terms of input/output.

■ When workers work non-standard overtime hours, such as 8 ½ hours, 9 ½ hours, 11 hours per day, etc.


23

Projects often use mixed overtime schedules (i.e. 5x10 and 6x10) or fail to utilize a set overtime schedule throughout

the project duration. The crews working overtime on a project often change, and periods of extended overtime work may

be interrupted by periods of straight time work. These circumstances, coupled with a lack of detailed record keeping by

contractors, establish the need for a macro approach to determine the impact on productivity, instead of using an earned

value approach for a specific overtime schedule.

Figure 11 shows the 95% confidence interval for the model developed from the Macro Approach. If the above model is

used 100 times to compute the loss of efficiency, 95% of the time the results will be within the confidence interval. Figure

11 shows the original data with the best fit line, along with the upper and lower limit of the confidence interval.

Figure 11: 95% Confidence Interval for the Macro Model

Table 7 (next page) shows the data used to develop the model from the Macro Approach, along with the data for the

95% confidence interval.


24

Table 7: PF, Hours per Week and Confidence Interval Data for the Macro Analysis

Hours per Week Performance Factor DELTA Fitted Model Upper Limit Lower Limit

37.48 107.51 20.01 107.17 127.16 87.19

37.65 106.55 19.99 106.77 126.76 86.78

37.98 97.52 19.97 105.98 125.97 86.00

38.07 104.94 19.96 105.77 125.76 85.78

38.32 88.17 19.94 105.17 125.16 85.19

38.82 103.34 19.91 103.98 123.97 84.00

38.90 104.95 19.91 103.79 123.78 83.81

38.90 110.11 19.91 103.79 123.78 83.81

38.90 108.18 19.91 103.79 123.78 83.81

38.98 102.05 19.90 103.60 123.59 83.62

39.23 102.05 19.88 103.01 122.99 83.02

39.40 108.18 19.87 102.60 122.59 82.62

39.48 100.76 19.87 102.41 122.40 82.43

39.56 93.02 19.86 102.22 122.21 82.24

39.81 114.64 19.85 101.63 121.61 81.64

40.06 100.13 19.84 101.03 121.02 81.05

40.56 100.78 19.81 99.84 119.83 79.86

40.98 94.98 19.79 98.84 118.83 78.86

41.06 96.59 19.79 98.65 118.64 78.67

41.64 111.11 19.76 97.27 117.26 77.28

41.81 101.12 19.76 96.87 116.85 76.88

42.14 100.15 19.75 96.08 116.07 76.09

43.72 100.82 19.70 92.32 112.31 72.33

44.47 100.18 19.69 90.53 110.52 70.55

45.14 82.12 19.69 88.94 108.93 68.95

45.63 88.26 19.69 87.77 107.76 67.79

46.13 59.23 19.69 86.58 106.57 66.60

47.05 91.18 19.69 84.39 104.38 64.41

47.05 94.08 19.69 84.39 104.38 64.41

47.13 85.05 19.70 84.20 104.19 64.22

47.71 94.09 19.71 82.82 102.81 62.83

48.46 74.10 19.72 81.04 101.02 61.05

49.04 88.30 19.74 79.66 99.64 59.67

49.38 81.21 19.75 78.85 98.83 58.86

50.21 68.32 19.78 76.87 96.86 56.88


25

Example using the Macro Approach

To validate and demonstrate the use of the overtime model, an analysis of project data supplied by an electrical con-

tractor in the Midwest will be examined. The focus of the example is to quantify the impacts of overtime on the project

through the use of Average Hours Worked per Week.

The project was a multi-story parking structure that served a gambling casino. The original project duration was 64

weeks. It was very important for the contractor to finish on time so the casino could be fully operational. As a result of

several delays, including obstruction during the pile driving operation, bad weather delay, and redesign of some of the pre-

stressed concrete beams, the project fell substantially behind. The contractor was forced to accelerate the schedule, starting

in week number 43. The contractor used sporadic overtime from week 43 to week 64. The average overtime hours used

during this period was 51.65. The contractor utilized a total actual hours of 43,666 during that period.

The total estimated hours for the job was 92,770 manhours. Using the developed model, we get a Performance Fac-

tor of 0.735 (since -2.38 x 51.65 + 196.4 = 73.5%), which translates to a 26.5% loss of efficiency. This leads to a total of

11,571.49 manhours (since 43,666 x 0.265 = 11,571.49) lost to inefficiency caused by overtime.

Ratio of Total Overtime Hours to Total Hours Worked

The ratio of Total Overtime Hours to Total Hours Worked is an indicator of the amount of overtime experienced on

the project. The greater the value of the ratio, the more overtime has been used. This differs from scheduled overtime,

which is measured in terms of weeks under a specific schedule, i.e. 5x9 or 6x10. The strength of measuring overtime as a

percentage of total hours rather than in terms of weeks is that contractors can quantify the impact of overtime on labor

efficiency during spot or sporadic overtime.

Hours per Week Performance Factor DELTA Fitted Model Upper Limit Lower Limit

51.21 78.97 19.83 74.49 94.48 54.50

51.37 66.40 19.84 74.11 94.09 54.12

51.70 82.85 19.86 73.32 93.31 53.34

52.20 56.73 19.89 72.13 92.12 52.15

52.20 74.47 19.89 72.13 92.12 52.15

52.87 79.00 19.93 70.54 90.52 50.55

53.12 47.06 19.95 69.94 89.93 49.96

54.12 47.08 20.02 67.56 87.55 47.58

54.78 71.92 20.08 65.99 85.98 46.00

55.20 75.15 20.11 64.99 84.98 45.00

56.03 49.36 20.19 63.02 83.00 43.03

57.19 61.31 20.31 60.25 80.24 40.27

58.02 70.35 20.40 58.28 78.26 38.29

58.11 69.06 20.41 58.06 78.05 38.08

Table 7: PF, Hours per Week and Confidence Interval Data for the Macro Analysis (continued)


26

Table 8: Example Calculation of Performance Factor

Activity Cost Description Completed (%) Estimated Hours Actual Hours Earned Hours PF

1 UG Conduits 100 624 984 624.0 0.63

2 SiteBranch Wire 90 39 39 35.1 0.90

3 OH Feeder Conduits 85 1500 1009 1275.0 1.26

4 Feeder Conduits 80 1500 769 1200.0 1.56

5 Outdoor Meter Section 100 23 31 23.0 0.74

6 MSB Switchboard 100 93 72 93.0 1.29

7 Switchboards 80 146 318 116.8 0.37

8 Panels/ATS 85 523 794 444.6 0.56

9 Transformers 100 238 135 238.0 1.76

10 CKT Breakers/Starters 100 78 5 78.0 15.60

11 Generator 100 93 127 93.0 0.73

12 Patient SER Modules 85 660 759 561.0 0.74

13 MedicalGas Riser 80 214 50 171.2 3.42

14 Temp Lighting/Power 90 59 147 53.1 0.36

15 Demolition-1st Floor 80 115 54 92.0 1.70

16 Demolition- 2nd Floor West 80 100 83 80.0 0.96

17 Demolition- 2nd Floor East 70 497 262 347.9 1.33

18 Demolition- 3rd Floor 85 115 53 97.8 1.84

19 Deliveries/MH 88 800 1734 704.0 0.41

20 Supervision/Layout 85 2050 2733 1742.5 0.64

21 Conduit 85 8868 9140 7537.8 0.82

22 Thin Wire 77 1996 1441 1536.9 1.07

23 MCCable 75 1454 1955 1090.5 0.56

24 Fixtures/Lamps 63 3204 2029 2018.5 0.99

25 Trim 58 1112 985 645.0 0.65

26 Equipment Connections 56 1228 737 687.7 0.93

27 Fire AlarmConduit 82 850 1439 697.0 0.48

28 Rehab 60 180 152 108.0 0.71

Totals 28359 28036 22391.28 PF =0.79


27

Productivity Measurement

One of the main objectives of this research is to determine the relationship between productivity and scheduled

overtime. In order to determine the relative productivity levels of different overtime schedules, contractors were asked to

track their productivity using an earned value approach as shown in Table 4.1. This information was used to calculate the

weekly performance factor (PF) for different overtime schedules. For the purpose of this study, the performance factor was

defined as the sum of earned hours divided by the sum of the actual hours utilized, as shown in the following equation:

For example, in Table 8, PF = 22391.28/23036 = 0.79. A PF of 1 represents a project that required exactly the number

of hours estimated to reach completion. A project that was more productive than estimated would have a PF greater than

1, while a PF less than 1 represents a project that achieved productivity below the estimate.

Microanalysis (Scheduled Overtime)

Table 5 (page 18) showed that most electrical contractors are using 5x10, 6x10, 7x10 and 7x12 overtime schedules.

Thus, this study focuses on quantifying the negative impact of these four overtime schedules.

Scheduled Overtime Impact Charts

The following section illustrates different overtime schedules, showing the impact of overtime on the performance

factor in an attempt to shed light on overtime’s effect on labor productivity. Where data for an unimpeded project is avail-

able, the overtime schedule is compared to the reference schedule using the Measured Mile Method (MMM). The qualita-

tive survey showed that the 5x10 overtime schedule was typically used, present in 41% of overtime projects and the most

preferred schedule in 57% of cases. As a result, an in-depth investigation was conducted on a 5x10 schedule.

Impact of 5x10 Schedule on Labor Productivity

Electrical labor productivity was tracked weekly for a newly constructed power plant project in the state of Alabama.

The project consisted of three heat recovery steam generators, three combustion turbine generators, one steam turbine

generator, and one cooling tower. The project proceeded normally for several weeks until they discovered red clay in the

area (different site conditions). As a result of this and heavy rain in the month of November, electrical work fell behind

schedule. The electrical contractor was authorized to use a 5x10 overtime schedule to accelerate the project schedule. Pro-

ductivity data for the 5x10 schedule was collected over a period of 20 weeks and is shown in Figure 12.

PF =Earned Hours

Actual Hours


28

Figure 12: 5x10 Model with Comparison to 1969 NECA Model

The data was fitted to two different regression models, one from week 1 to week 11 and another from week 12 to week

20. It was observed that productivity loss increased steadily from week 1 to week 11. After week 11, productivity loss was

relatively flat. The blue lines indicate productivity loss published by NECA in 1969. The new model shows slightly higher

productivity loss than the old NECA model for the first ten weeks; however, the new model shows lower productivity losses

than the old NECA model during the last ten weeks. The data for the 5x10 model is recorded in Table 9. A cumulative

weekly performance factor was calculated by dividing the cumulative earned hours by the cumulative actual hours.

Table 10 gives the efficiency values for each week for the first 20 weeks of using 5x10 overtime. The values in Table 10

were obtained from the following two regression functions:

■ Week 1 to 11: y= 0.0042x2 – 0.0808x + 1.0945

■ Week 12 to 20: y= 0.0035x + 0.6818


29

Old NECA Model

(1969)

Week

Number

Revised Week

Number

Cumulative

Productivity% Low Avg High

14 1.22 122%

15 1.16 116%

16 1.44 144%

17 1.33 133%

18 1.34 134%

19 1.09 109%

20 1 1.00 100% 95% 98% 100%

21 2 .95 95% 92% 95% 97%

22 3 .94 94% 89% 92% 94%

23 4 .82 82% 86% 89% 91%

24 5 .77 77% 83% 86% 88%

25 6 .77 77% 80% 83% 85%

26 7 .74 74% 77% 80% 82%

27 8 .71 71% 74% 77% 79%

28 9 .70 70% 71% 74% 76%

29 10 .70 70% 69% 72% 74%

30 11 .71 71% 68% 71% 73%

31 12 .73 73% 67% 70% 72%

32 13 .73 73% 66% 69% 71%

33 14 .73 73% 65% 68% 70%

34 15 .72 72% 64% 67% 69%

35 16 .73 73% 63% 66% 68%

36 17 .75 75%

37 18 .75 75%

38 19 .75 75%

39 20 .75 75%

Table 9: Data for 5x10 Model

Week Productivity

Productivity

Rounded to

2 Digits

1 1.10179 1.1

2 0.9497 0.95

3 0.8899 0.89

4 0.8385 0.84

5 0.7955 0.8

6 0.7609 0.76

7 0.7347 0.73

8 0.7169 0.72

9 0.7075 0.71

10 0.7065 0.71

11 0.7139 0.71

12 0.7238 0.72

13 0.7273 0.73

14 0.7308 0.73

15 0.7343 0.73

16 0.7378 0.74

17 0.7413 0.74

18 0.7448 0.75

19 0.7483 0.75

20 0.7518 0.75

Table 10: Productivity Losses Using 5x10 Model


30


The following data corresponds to a 36-story condominium in Florida. Overtime was used because incomplete fram-

ing introduced delays; the increase in manpower and working hours was attributed to the framing contractor accelerating

the work activities. Furthermore, poor management practices enabled subcontractors to work slowly, without concern

for a concrete deadline. Figure 13 plots productivity over a 20 week period, shifting from an initial 5x8 schedule to a 6x10

overtime schedule.

Productivity in the 6x10 model decreases overtime. The steepest decrease in productivity was in the final 4 weeks.

From weeks 1- 16 (disregarding week 4 with its substantial jump), productivity declined an average of 1-2% each week,

decreasing from 100% before the implementation of overtime to 77% in week 16. After week 17, the drop in productivity

was steeper, declining to a low point of 55% in the final week. Table 11 shows the productivity losses for 20 weeks based on

the regression function developed for this model. The new 6x10 model shows higher productivity losses than the NECA

model estimates.

Figure 13: 6x10 Productivity Model


31

Week 6x10 NECA

0 100.00 100.00

1 98.07 94.33

2 96.13 90.64

3 94.20 86.82

4 92.26 82.99

5 90.33 79.02

6 88.39 75.34

7 86.46 71.51

8 84.52 68.54

9 82.59 66.55

10 80.65 64.71

11 78.72 63.29

12 76.78 62.16

13 74.85 61.31

14 72.91 60.32

15 70.98 60.32

16 69.04 60.32

17 67.11 N/A

18 65.17 N/A

19 63.24 N/A

20 61.30 N/A

Table 11: Productivity Losses for 6x10 Model


32


The model showed a sharp decrease in productivity in the first 3 weeks, followed by a fairly stable decrease over time,

stabilizing at around 1% per week for the rest of the project. Productivity declined every week that overtime was used until

it reached its final, lowest point of 46% in week 20.

Table 12 records the cumulative productivity estimates based on the two regression functions developed for this

model. The actual model estimates the productivity at a lower value than the NECA model for weeks 2 to 6, with a higher

estimate than the NECA model for the remaining weeks.


The following model corresponds to a project in Florida. The project consisted of converting an existing paper ma-

chine from white paper to a linerboard and corrugated medium. The project included the following activities: reconfigur-

ing the pulp mill to accommodate producing the linerboard pulp, reconfiguring the bleach plant and other stock prepara-

tion for the existing fluff pulp paper machine, reconfiguring the stock preparation area to accommodate the pulp for the

linerboard paper machine, shutting down the existing paper machine, putting in new piling, replacing the majority of the

rolls, reconfiguring the winder and roll handling, reconfiguring the warehouse and rail loading, and performing major

power distribution changes concurrently with the other work. Different work schedules were used for this project. A 5x10

overtime schedule was implemented for pre-shutdown, 6x10 and 7x10 were used consecutively for shut-down, and a 5x10

schedule was used for post-shutdown. Data for the 5x10 schedule was decreased by a factor of 10% in order to obtain the

cumulative productivity for a 5x8 (Hanna: The Effectiveness of Innovative Crew Scheduling Techniques, 2003). Figure 14

(next page) plots productivity over a 20 week period for the 7x10 overtime schedule.


33

Week 7x10 NECA

0 100.00 100.00

1 87.44 84.83

2 77.37 80.02

3 69.79 75.34

4 64.69 70.24

5 62.08 65.28

6 58.35 61.03

7 57.47 57.34

8 56.59 54.51

9 55.71 52.38

10 54.83 50.12

11 53.95 48.56

12 53.07 47.42

13 52.19 46.57

14 51.31 45.58

15 50.43 45.02

16 49.55 44.31

17 48.67 N/A

18 47.79 N/A

19 46.91 N/A

20 46.03 N/A



34


The following project was a Flue Gas Desulphurization (FGD). It included a selective catalyst reactor installation along

with existing boiler modifications on two existing 800 Megawatt coal fired units. An overtime schedule of 7x12 was utilized

a result of the overall compressed schedule, brought on by the other activities. The cumulative productivity data provided

for this project was calculated by dividing the cumulative earned hours by the cumulative actual hours. A suggested model

was generated from the data plot in Figure 15.


Cumulative productivity in the actual model dropped quickly the first three weeks, and then stabilized around an

initial plateau at around 78%. Productivity began a slow descent of about 0.5 to 1% following week 5, reaching its lowest

point of 69% in week 20. Two regression functions were used for this model – a second degree polynomial for the first part

and a simple linear regression for the latter part, as show in Figure 15.

Table 13 compares the data for the 7x12 model with the estimated productivity losses from the NECA model. The

NECA model followed a similar trend, with the highest decreases in productivity occurring during the first few weeks, and

then slowly decreasing towards the end (roughly 0.5% between the last two weeks). However, the productivity losses esti-

mated by NECA are significantly lower than the suggested model.


35

Week 7x12 NECA

0 100.00 100.00

1 89.95 75.14

2 81.19 70.02

3 78.90 65.89

4 78.33 60.91

5 77.76 56.07

6 77.18 51.66

7 76.61 48.95

8 76.04 46.53

9 75.47 44.25

10 74.90 42.96

11 74.33 41.67

12 73.76 40.24

13 73.19 39.67

14 72.62 38.66

15 72.05 37.80

16 71.47 37.65

17 70.90 N/A

18 70.33 N/A

19 69.76 N/A

20 69.19 N/A


37

Conclusion

In recent years, there has been an ever increasing need for electrical contractors to complete a construction project in a

shorter than customary time period. If the contract is either compressed (reduced from the normal or typical time frame)

or accelerated (increased speed of progress from the normal or typical progress speed), the contractor will be required to

perform the same work in a substantially shorter timeframe, and the cost of the work will necessarily increase. Contrac-

tors and owners are faced with few options to compress or accelerate the schedule. Among these compression techniques

are overtime, overmanning and shift work. This study indicated that the use of overtime with variable lengths is the most

common and preferred compression technique in the construction industry.

The qualitative analysis found that 18.9% of all projects by surveyed contractors used extended overtime. A 5x10

schedule is the most frequently used schedule for contractors seeking to accelerate a schedule. At the same time, contrac-

tors as a whole agreed that overtime reduces labor productivity.

The quantitative analysis presented two approaches for estimating productivity losses resulting from overtime. The

macro approach was used to analyze projects where no fixed overtime schedule is utilized, or mixed work schedules are

used throughout a week on a project (sporadic overtime). The micro approach was used to analyze projects that utilize a

fixed overtime schedule (scheduled overtime), such as 5x10 or 6x10 throughout a certain number of weeks.

The following conclusions and scope highlight the findings of this study:

1. Placing an electrical crew on scheduled or unscheduled overtime reduces labor productivity and increases labor

costs.

2. A greater number of hours worked beyond the regular forty hours per week is related to higher productivity losses.

3. There is a direct correlation between labor inefficiency and the duration of overtime use.

4. Despite productivity losses related to overtime, placing an electrical crew on overtime schedule is more efficient

than overmanning.

5. The data collected for this study was limited to electrical workers for projects executed between 2004 and 2008.

6. The measured mile method was used in this study, which compares periods of time when the regular 40 hour

work week was utilized to overtime schedules of 50 hours or more. The measured mile technique is a widely accepted and

accurate method of capturing labor inefficiency.


38

These results are particularly significant to electrical contractors, who generally allocate 33-50% of a project’s total

budget to labor costs (Hanna, 2006). As a result, the management of labor and its productivity becomes paramount in

determining the success of a project.

39

Appendix A

Qualitative Questionnaire


40

41

Appendix B

Data Collection Sheet

Adrian, J. J. (1988). Construction Claims: A Quantitative Approach. Trenton, NJ: Prentice-Hall, Inc. Business Roundtable.

(1980). Scheduled overtime effect on construction projects, Author, New York, NY.

Construction Industry Institute (CII). (1988). The effects of scheduled overtime and shift schedule on construction craft pro-

ductivity, CII, Austin, TX.

Clark Concrete Contractors, Inc v. General Services Administration (GSBA No. 14,340, 99-1 BCA (CCH) (1999))

Hanna, Awad S. (2001). Quantifying the impact of change orders on electrical and mechanical labor productivity, Research

report 158-11, Construction Industry Institute, Austin, TX.

Hanna, Awad S. (2001). Quantifying the impact of change orders on electrical and mechanical labor productivity, Research

report 158-11, Construction Industry Institute, Austin, TX.

Hanna, Awad S. (2003). The effectiveness of innovative crew scheduling techniques, Research report 185-11, Construction

Industry Institute, Austin, TX.

Hanna, Awad S. (2006). Using the Earned Value Management System to Improve Electrical Project Control, Electri

International.

Horner, R. M. W. and Talhouni, B. T. (1995). Effects of accelerated working, delays, and disruptions on labour productivity,

Chartered Institute of Building, Ascot, Berkshire, U.K.

Kossoris, M. (1947). “Hours of work and output.” Bureau of Labor Statistics Bulletin No.917, U.S. Department of Labor.

Larew, Richard E. (1998). “Are any construction overtime “studies” reliable?” Cost Engineering, 40(9), 24-27.

Leonard, C.A, (1988). The Effects of Change Orders on Productivity, Thesis, M.Eng. (Building), Centre for Building Studies,

Concordia University, Supervisors: P. Fazio and O. Moselhi.

Mechanical Contractors Association of America (MCAA). (1976). Bulletin No.58, Management Methods Committee,

MCAA, Rockville, MD.

Natkin & Co. v. George A. Fuller Co. (347 F. Supp. 17(W.D. Mo. 1972))

Appendix C

References

43


44

National Electrical Contractors Association (NECA). (1969). Overtime and productivity in electrical construction, NECA,

Washington D.C.

Smith, Andrew G. (1987). “Increasing onsite production.” Transactions of the American Association of Cost Engineers, AACE,

Morgantown, WV, k.4.1-k.4.14.

Thomas, H. R. (1992). “Effects of scheduled overtime on labor productivity.” Journal of Construction Engineering and Man-

agement, 118(1), 60-76.

U.S. Army Corps of Engineers. (1979). Modification Impact Evaluation Guide EP 415-1-3, Department of the Army, Wash-

ington, D.C.

Waldron, James A. (1968). Applied principles of project planning and control, 2nd Edition, Haddonfield, New Jersey.

Zink, D. A. (1986). “The measured mile: Proving construction inefficiency costs.” Cost Eng., 28(4), 19–21.

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