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The impact of management practices on mechanicalconstruction productivityYongwei Shan a , Paul M. Goodrum a , Dong Zhai a , Carl Haas b & Carlos H. Caldas ca Department of Civil Engineering , University of Kentucky , 151C Raymond Building,Lexington, 40506, USAb Department of Civil Engineering , University of Waterloo , Waterloo, Ontario, Canadac Department of Civil, Architectural and Environmental Engineering , University of TexasAustin , Austin, USAPublished online: 09 Mar 2011.

To cite this article: Yongwei Shan , Paul M. Goodrum , Dong Zhai , Carl Haas & Carlos H. Caldas (2011) The impact ofmanagement practices on mechanical construction productivity, Construction Management and Economics, 29:3, 305-316,DOI: 10.1080/01446193.2010.538070

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Construction Management and Economics

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, 305–316

Construction Management and Economics

ISSN 0144-6193 print/ISSN 1466-433X online © 2011 Taylor & Francishttp://www.informaworld.com

DOI: 10.1080/01446193.2010.538070

The impact of management practices on mechanical construction productivity

YONGWEI SHAN

1

*

, PAUL M. GOODRUM

1

, DONG ZHAI

1

, CARL HAAS

2

and CARLOS H. CALDAS

3

1

Department of Civil Engineering, University of Kentucky, 151C Raymond Building Lexington, 40506 USA

2

Department of Civil Engineering,

University of Waterloo, Waterloo, Ontario, Canada

3

Department of Civil, Architectural and Environmental Engineering, University of Texas Austin, Austin, USA

Taylor and Francis

Received 8 July 2010; accepted 3 November 2010

10.1080/01446193.2010.538070

Over recent decades, sporadic advancements in machinery and construction materials have to some extentincreased construction productivity in the United States. However, there is evidence that additionalproductivity improvement opportunities exist. One way to improve direct work rates and likewise the potentialto increase construction craft productivity is through better planning and management. Utilizing a dataset fromthe Construction Industry Institute Benchmarking and Metrics programme with 41 sampled projects, therelationship between the level of implementation of different management programmes and mechanical craftproductivity is examined. The implementation of several management programmes, including pre-projectplanning, team building, automation and integration of information systems and safety had a positivecorrelation with improved mechanical productivity. In fact, the statistical results show that projects withadvanced implementation of the selected management programmes experienced significant mechanicalproductivity advantages over projects with weak implementation.

Keywords:

Automation, labour productivity, pre-project planning, safety, team building.

Introduction

The overall performance of many industrial construc-tion projects will often hinge on the specific perfor-mance of the mechanical trades. A primary reason forthis assertion is the relative risk involved with themechanical trades, since 40–60% of an industrialproject’s labour cost is attributed to the mechanicalcrafts (Hanna

et al

., 1999a, 1999b, 2002; RobertMorris Associates, 2000). For mega industrial projects(typically over $2 billion), mechanical constructioncosts can be even higher than this estimate.

While the productivity of the craft workers at theconstruction workface is the focus of this study, theanalyses examine the importance of the managementprogrammes that occur away from the workface.Errors, omissions, or other failures away from theworkface produce highly visible delays, cost overruns,and safety and quality problems at the constructionworkface. Oglesby

et al

. (1989) describe the away-

from-the-workface activities as the processes thatsupply the commitment, materials supplies, informa-tion, working space, staffing, methods, equipment andtools that are necessary for the craft workers to producethe final products. The specific away-from-the-work-face activities examined herein include the followingmanagement programmes, pre-project planning, teambuilding, alignment, materials management, informa-tion automation and integration, constructability andsafety policies. In industry, companies develop a seriesof practices under each programme for theprogramme’s overall implementation. Ultimately,the effectiveness of the overall programme hinges onthe implementation of critical practices that prove to bebeneficial to project performance.

While it is intuitive that better managementprogrammes are related to better craft productivity,the relationship has not been quantitatively exam-ined using actual project data. Utilizing project datafrom the Construction Industry Institute (CII)

*

Author for correspondence. E-mail: Yongwei.Shan@uky.edu

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Benchmarking and Metrics (BM&M) programme,this paper: (1) examines the relationship between thedescribed management programme and mechanicaltrades craft productivity on 41 sampled large indus-trial projects; and (2) verifies whether high use ofthese management programmes is related with bettermechanical productivity.

Background

Only a limited amount of previous research has focusedspecifically on productivity in the mechanical trades.Hanna

et al

. (1999a) developed a model to estimate theimpact on labour efficiency due to change orders. Themodel incorporated impact classification, amount ofthe change, number of the change orders and the timingof change orders to estimate their impact on mechanicallabour efficiency. Instead of only considering the char-acteristics of a change order itself, Hanna

et al

. (2004)identified the factors that affect productivity loss result-ing from changes in the project. The model includedsignificant factors, consisting of the change order’scharacteristics and its resultant management outcomes,specifically the percentage change, time required toprocess the change order, over-manning, and thepercentage of time the project manager spent on theproject. Using actual project data, Hanna

et al

. (2002)also developed benchmarking indicators (e.g. cumula-tive work hours, cost and peak manpower) for themechanical and electrical trades that are intended to beused for resource planning and progress tracking.Understanding how similar management programmescan influence mechanical labour productivity, which isa primary benchmarking indicator, is a specific focus ofthis paper herein.

A variety of management programmes have emergedand are dedicated to improving overall constructionperformance. The CII BM&M survey collects projectdata on the level of implementation among eightmanagement programmes, including pre-project plan-ning, team building, alignment, materials management,automation and integration of information system,constructability and safety. Owing to page and wordlimitations, only those programmes that were found tobe significantly related are described below and corre-sponding analyses are reported herein. The analysisresults of the other management programmes foundless significant will be discussed briefly in the conclu-sion section. There is a common perception thatmanagement programmes described earlier, if imple-mented correctly, can benefit craft productivity. Thepremise and scope of each of these programmes aredescribed below.

Pre-project planning

Pre-project planning involves developing and acquiringstrategic information early in the planning and designstages of a project’s development by prioritizingresources to maximize the chance for a successfulproject (Gibson

et al

., 1993). Pre-project planning isbased on the premise that decisions made during theearlier stages of a project’s development are paramountin influencing a project’s overall success (Gibson

et al

.,1995; Smith, 2000; Hartman and Ashrafi, 2004;Webster, 2004). Identifying and addressing potentialproblems early during a project’s development mini-mizes their impact on cost and schedule, since it affordsmore time for project management to respond andresolve them proactively (Hamilton and Gibson,1996). The CII BM&M survey quantifies the imple-mentation of pre-project planning in 10 aspects,including: (1) conveyance of project objectives to thefront-ending planning (FEP) team; (2) funding forFEP; (3) FEP team integration and alignment; (4) inte-gration of constructability feedback into FEP; (5) usageof a checklist for the consistency of FEP effort; (6) useof the CII Project Definition Rating Index (PDRI)(Cho and Gibson, 2001); (7) contingency funds; (8)definition of a project’s priorities; (9) timeliness of FEPmeeting schedule requirements; and (10) the quality ofFEP meeting project objectives.

Team building

The team building process is a project-focused processthat brings together key stakeholders, owner, designerand/or constructor, for the sake of better projectoutcomes. The process is intended to develop acommon mission statement of shared goals, a strongertrust and commitment among key stakeholders, greaterinterdependence and accountability among teammembers, and a unified agreed-upon process to removeroad blocks to improve stakeholder relations (Albanese,1995). Apart from improving stakeholder alignment,team building has also been shown to facilitate innova-tive construction methods, hence improving productiv-ity (Abudayyeh, 1994). The CII BM&M data quantifythe implementation of eight major practices related toteam building, including: (1) a formal team buildingprocess; (2) upper management support to the formalteam building process; (3) an external team buildingfacilitator; (4) documentation and definition of theteam building objectives; (5) achievement of the teambuilding objectives; (6) integration of new membersinto team building activities; (7) use of the team build-ing process during the stages of pre-project planning,design, procurement, construction and start-up; and(8) the party (owner, engineer, contractor, regulators,

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Management practices

307

supplier, subcontractor, construction manager andothers) involvement in the team building process.

Automation and integration of information systems

O’Connor and Yang (2004) define automation as theuse of an electronic or computerized tool by a humanbeing to manipulate data or produce a product, and theydefine integration as the sharing of information betweenproject participants or melding of information sourcedfrom separate systems. In order to measure the degreeof IT utilization on projects, Kang

et al

. (2006) estab-lished nine major work functions addressed by automa-tion and integration information systems: (1) businessplanning and analysis; (2) conceptual definition anddesign; (3) project definition and facility design; (4)supply management; (5) project management; (6)offsite/preconstruction; (7) construction; (8) as-builtdocumentation; and (9) facility start-up. Because of itsbroad scope, the fifth work function, project manage-ment, is subdivided into five work functions: (1) coor-dination; (2) communication; (3) cost; (4) schedule;and (5) quality. Several researchers have attempted toidentify the impact of automation and integration tech-nology including Yang

et al

. (2007) who showed that thelevels of automation and integration usage are positivelycorrelated with the levels of stakeholder success.

Safety

There have been numerous research studies that focuson safety culture and climate (Cooper, 2000; Mohamed,2002; Choudhry

et al

., 2007a, 2007b), safety leadership(Zohar, 2002; Wu

et al

., 2008) and safety management(Mohamed, 1999; Mearns

et al

., 2003; Choudhry

et al

.,2008), for the sake of an injury-free workplace. The CIIBM&M data quantify implementation of 18 major prac-tices related to safety, including: (1) implementation ofan overall safety plan; (2) prioritization of safety atpreconstruction and construction meetings; (3) pre-taskplanning for safety conducted by contractor foremen orother site managers; (4) safety toolbox meeting; (5)safety audits performed by corporate safety personnel;(6) safety supervisor’s time commitment to safety; (7)adequate worker to safety personnel ratio; (8) safetyorientation for new contractor and subcontractoremployees; (9) formal safety training for workers; (10)use of safety incentives; (11) use of safety performanceas criterion for contractor/subcontractor selection; (12)formal accident investigations; (13) formal near-missinvestigations; (14) senior company management’sinvolvement in investigation of accidents; (15)pre-employment substance abuse tests; (16) randomscreening for alcohol and drugs; (17) substance abuse

tests after accidents; and (18) substance abuse tests forreasonable cause.

Finally, numerous studies have claimed that theabove-described management programmes and theirrespective practices contribute to overall cost savingsand schedule reduction on construction projects. Thisstudy adds significant new evidence to those studies,while it also focuses on productivity in the mechanicaltrades, which is typically on the critical path of mostindustrial construction projects. The research describedherein also identifies the crucial practices within eachprogramme that correlate with the highest productivityperformance gains and thus may contribute the most tomechanical craft productivity improvement.

Research methods

Data source

In this research, the CII Benchmarking and Metrics(BM&M) Construction Productivity Database wasutilized. The CII BM&M programme provides themeans for its member companies to compare their capi-tal and maintenance projects with their peer members.The BM&M programme enables the participatingcompanies to visualize the gap in project performancewhen compared to the other projects among theircompetitors and allows them to identify the practicesthat contribute to project performance improvement.The dataset describes each project’s environment andcharacteristics (such as contract type, level of complex-ity and project cost). It also includes a quantitative(scaled) assessment of onsite management practicescorresponding to the programmes. Finally, the datasetalso includes labour productivity metrics in mechanicalconstruction. The BM&M dataset currently has projectperformance and practice data from 92 projects,containing project environment and characteristicsinformation (such as complexity), quantitative (scaled)assessment of onsite management practices, and labourproductivity metrics across the four major craft tradesof concrete, steel structure, mechanical and electrical.Recognizing that different companies use different prox-ies in defining their scope of work among constructiontasks and activities when they are collecting labourproductivity, the BM&M committee defined the stan-dard labour productivity metrics in the survey, whichensures the consistency of the productivity informationcollected across the members. It is readily acknowledgedthat the sampled construction projects were not imple-mented under controlled environments and each projectwas unique. However, the sampled projects were largeindustrial projects, thus the projects’ types of work wererelatively similar.

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Mechanical trades were exclusively studied in thispaper. The mechanical work encompassed hereinincludes piping and permanently installed equipment,thus the trades primarily represented are pipefitters,welders and boilermakers. Table 1 describes thespecific construction activities involved in the paper’sanalyses. Productivities were collected at both thesubcategory and element levels.

Productivity definition and normalization

The CII BM&M programme uses work hours perinstalled quantity as the measure of craft productivity(described in Equation 1).

It should be noted that a lower measure indicates abetter productivity associated with less time needed fora unit of work. To protect company proprietary perfor-mance information, all of the productivity rates werenormalized on a scale from 1 to 10, using the min–maxmethod of normalization (Hann and Kamber, 2000;Zhai

et al

., 2009) described in Equation 2:

where,

P

norm

is the normalized productivity,

P

actual

is theactual productivity measure;

P

actual

min

and

P

actual

max

arethe minimum and maximum actual productivity valuesin the construction task; and

P

norm

min

and

P

norm

max

arethe minimum and maximum normalized productivityvalues, equal to 1 and 10, respectively. The normalizedproductivity (Equation 2) is consistent with the paper’sactual productivity measure (Equation 1); a lower valueindicates better productivity. The normalized produc-tivity measures as described are dimensionless, whichenables the comparison of productivity among differentconstruction tasks.

Management programme use index

In order to examine how the already describedmanagement programmes were correlated with theproductivity performance among the mechanicaltrades, an implementation index was developed foreach programme to quantify how well the observedprojects implemented each respective managementprogramme. As mentioned, the CII BM&M commit-tee collects data on several different practices germaneto each management programme. Depending on howwell a project implemented each of these individualpractices within a programme, project respondentsrated their level of implementation according to theinstructions given in the survey instruction. Then anoverall programme implementation index was devel-oped and scaled from 0 to 10 based on the algorithmprovided in the survey. Ten (10) indicates a complete

Labour ProductivityActual WorkhoursInstalled Quantity

= ( )1

PP P

P PP P

P

normactual actual

actual actualnorm norm

norm

=−

− −

+

min

max min( )

( )

max min

min 2

Table 1

Construction activities in mechanical trades

Trade Subcategory level Element level

Piping Total small bore Carbon steelStainless steelChromeOther alloys

Total large bore (inside Carbon steelbattery limits) Stainless steel

ChromeOther alloys

Total large bore Carbon steel(outside battery limits)

Stainless steel

ChromeOther alloys

Equipment Pressure vesselsAtmospheric tanks shop

fabricatedAtmospheric tanks field

fabricatedHeat transfer equipmentBoiler and fired heatersRotating equipment (w/

drivers)Material handling

equipment (w/drivers)Power generation

equipmentOther process

equipmentModules and pre-

assembled skids

Total pulp and paper Woodyard equipmentequipment Pulp mill equipment

Bleach plant equipment

Stock preparation equipment

Wet end equipment (through the presses)

Dryer sectionsDry end equipment

including roll wrap/converter equipment

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Management practices

309

implementation of a programme, and zero (0) indi-cates that a programme was not implemented at all.Table 2 shows an example of the computation of theautomation of information system use index. Similarprogramme use indices were used to measure theeffectiveness of implementation among the sampledprojects in regards to pre-project planning, teambuilding, IT integration and safety.

For purposes of the analysis, projects scoring 5%above the overall median among all sampled projectswere classified as having a high level of implementationof each respective management programme, andprojects scoring 5% below the median were defined ashaving a low level of implementation. The projects fall-ing within the 10% mid-range were not used in thecomparison between the two groups. The reasonthe median was used rather than the mean was that theindices do not have a perfectly normal distribution.

The purposes of using such a 5% range below andabove the median are: (1) to create two groups withmore distinct differences in practice use levels; and (2)to ensure that the sample sizes are large enough toperform the statistical analyses.

Pearson’s correlation

Throughout the paper, Pearson’s correlation was usedto identify the critical practices within each manage-ment programme that were strongly correlated withmechanical productivity. It is readily acknowledgedthat all other practices are also very significant to otherproject performance areas; however, we wanted to zeroin on the critical practices that are correlated with craftproductivity among the mechanical trades the most.Serving as a tentative means to identify the significantpractices, the practices correlated with mechanicallabour productivity improvement at 0.2 significancelevel were further studied.

Hypothesis

An objective of this study is to investigate whether ornot a high level of implementation of managementprogrammes is associated with better productivity inthe mechanical trades. Both the null and alternativehypotheses can be described as follows:

P

H

denotes the mean productivity of the projects with ahigh level of implementation of certain managementprogrammes. Similarly, P

L

denotes the mean productiv-ity of the projects with a low level of implementation ofcertain management programmes. The productivitiesalluded to in the hypotheses are normalized productivity.The independent sample T-test was conducted to exam-ine the difference of sample means between groups.

Actual productivity comparison

To protect the confidentiality of CII member compa-nies’ productivity data, normalized productivities wereused in the T-test. Normalized productivity rates areunit free, though they indicate relative productivityperformance across the sampled projects. However,they obscure the absolute craft productivity rates. Thus,the absolute productivity difference between theprojects with low and high level of managementprogramme use was compared in addition to thenormalized measures. Equation 3 describes the absoluteproductivity comparisons:

H : P P

H : P P

0 H L

a H L

=

≠

Table 2

Automation of information technology programmeuse index example calculation logic

Automation task/work functions

Use level (low to high)

1(0) 2(0.25) 3(0.5) 4(0.75) 5(1) Score

Business planning and analysis

✓

0.5

Conceptual definition and design

✓

0.5

Project (discipline) definition and facility design

✓

0.5

Supply management

✓

0.75

Coordination system

✓

0.5

Communications system

✓

0.5

Cost system

✓

0.75Schedule system

✓

0.75Quality system

✓

0.5Offsite/

preconstruction

✓

0.5

Construction

✓

0.5As-built

documentation

✓

0.5

Facility start-up and life cycle support

✓

0.5

Total 7.25

Maximum score of 13, divide total by 1.3 to scale to 0–10 point range

Automation Programme Use Index 5.58

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where P

Actual H

denotes the actual productivity with ahigh level of management use in a particular index, andP

Actual L

denotes the actual productivity with a low levelof management in a particular index. The results werepresented as percentages, which not only protected theconfidentiality of member companies but also reflectedthe actual benefits of implementing specific managementprogrammes.

Results of analyses

Descriptive statistics

Mechanical trades are the focus of the analyses, encom-passing 156 activities involving the piping trades and135 activities involving the equipment trades from 41large industrial projects, whose total installed cost wasgreater than $5 million within the BM&M dataset.

Impact of management programmes

The primary objective of this paper is to examine thecorrelation of implementation of managementprogrammes and practices with the mechanical trades’productivity. The effect of each managementprogramme and practice was investigated separately,and yet it is clear that they may be highly interdepen-dent and cross-correlated in practice.

Pre-project planning

Using Pearson’s correlation (Table 3), the followingpractices were found to be correlated with mechanicalproductivity gains at the 80% confidence level orgreater:

(1) Integration and alignment of the front endplanning team.

(2) Use of checklists to ensure consistency of thefront end planning effort.

(3) Use of the CII Project Definition Rating Indexto determine how well a project is defined.

(4) Clear definitions regarding the front end plan-ning team’s priorities.

(5) Timeliness of FEP meeting schedule require-ments.

It should be noted that a smaller measure of productivityis better, thus negative correlations between productivityand practices performance are preferred.

Based on these five practices, a new composite indexwas developed to categorize the sampled projects intolow and high level of pre-project planning use. Table 4describes T-test results of the difference in normalizedlabour productivity between low and high level of pre-project planning in the mechanical trades. T-testsdescribed herein were performed with equal varianceboth assumed and not assumed. However, to providereaders with a better knowledge of the equality of vari-ance, tests for equality of variance were performed aswell, and the results are shown in the table. The actualproductivity difference (Equation 3) is presented in thesame table. As shown, projects with high levels ofinvolvement in pre-project planning indicated 3.0 unitsof time saving per installed quantity of overall mechan-ical work. This result is also statistically significant atthe 99% confidence level. The actual productivitydifference is compelling in that the high level useprojects showed a 74% productivity improvementwhen compared to their low level use counterparts.Table 4 also breaks down the analyses for both thepiping and equipment trades.

Team building

The BM&M survey quantifies implementation of teambuilding in a multitude of facets, including uppermanagement’s support of the team building process,the use of external team builder facilitators, documen-tation of team building objectives, integration of newteam members, and different parties and project phasesinvolved in team building. Using Pearson’s correlation(Table 5), engagement in team building during the pre-project planning, design and construction phases wasfound to have had the greatest influence on productiv-ity in the mechanical trades.

There were statistically significant differencesbetween the projects with low and high levels of teambuilding in the overall mechanical trades and in both

Percentage difference of actual productivity

Mean P Mean PMean P

Actual L Actual H

Actual L

=− ×( )

( )

100 3

Table 3

Pre-project planning practices correlated withmechanical productivity improvement

Practices Corr. Coeff. (sig.)

Integration and alignment of the front end planning team

−

0.30 (0.01)

Use of checklists to ensure consistency of the front end planning effort

−

0.16 (0.12)

Use of the CII Project Definition Rating Index to determine how well a project is defined

−

0.14 (0.18)

Clear definitions regarding the front end planning team’s priorities

−

0.41 (0.00)

Timeliness of FEP meeting schedule requirements

−

0.28 (0.01)

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the piping and equipment trades (see Table 6). Theprojects highly involved in the team building processthroughout the phases of pre-project planning, designand construction were associated with better produc-tivity. The comparisons of actual productivity measuresshow that on average the projects with high levels ofteam building involvement experienced a 40%improvement in labour productivity among themechanical trades.

Automation and integration of information systems

The BM&M survey quantifies the automation and inte-gration of information systems in terms of alreadydescribed 13 functions. Automations in coordination,communication and quality systems were correlatedwith productivity improvement at the 80% confidencelevel (see Table 7). Integration of information systemsin terms of work functions: coordination, communica-tion, schedule system, quality system, offsite precon-struction and as-built documentation was identified tobe correlated with productivity improvement as well(see Table 8).

Table 9 examines the relationship between the auto-mation use and mechanical labour productivity. Theresults were consistent across both the piping and perma-nently installed equipment activities. Projects with a highlevel of implementation of automation of informationsystems also experienced better labour productivity in themechanical trades. All of the results were statisticallysignificant at the 95% confidence level, except thespecific results among the piping trade, which was statis-tically significant at the 90% confidence level. Compar-isons of actual productivity measures across the

Table 4

Mechanical trade productivity vs. pre-project planning

Normalized productivity

Actual productivity Test for equality of variances

Equal var. assumed

Equal var. not assumed

Trade High level Low level Diff. Diff. (%) (Equation 3)

F Sig. t Sig. t Sig.

Mechanical trades** 2.3 (39) 5.3 (31)

−

3.0 73.6% 29.36 0.00

−

4.71 0.00

−

4.42 0.00Piping** 1.9 (18) 7.6 (9)

−

5.7 78.7% 8.41 0.01

−

7.38 0.00

−

6.03 0.00Equipment* 2.8 (4) 7.2 (5)

−

4.4 71.8% 0.09 0.77

−

3.20 0.02

−

3.29 0.01

Notes

:* denotes statistically significant at the 95% confidence level (i.e.

α

= 0.05).** denotes statistically significant at the 99% confidence level (i.e.

α

= 0.01).Numbers in parentheses denote the sample size of activities.

Table 5

Team building practices correlated with mechanicalproductivity improvement

Practices Corr. coeff. (sig.)

Engagement in team building during the pre-project planning phase

−

0.22 (0.00)

Engagement in team building during the design phase

−

0.15 (0.01)

Engagement in team building during the construction phase

−

0.15 (0.01)

Table 6 Mechanical trade productivity vs. team building

Trade Normalized productivity Actual productivity Test for equality of variances

Equal var. assumed

Equal var. not assumed

High level Diff. (%) (Equation 3)

Diff. Diff. (%) (Equation 3)

F Sig. t Sig. t Sig.

Mechanical trades**

2.4 (107) 3.3 (118) −0.9 40.0% 18.61 0.00 −3.10 0.00 −3.12 0.00

Piping* 2.1 (61) 3.2 (56) −1.1 45.1% 12.38 0.00 −2.49 0.01 −2.44 0.02Equipment* 2.5 (46) 3.7 (62) −1.2 35.0% 7.59 0.01 −2.24 0.02 −2.33 0.02

Notes:* denotes statistically significant at the 95% confidence level (i.e. α = 0.05).** denotes statistically significant at the 99% confidence level (i.e. α = 0.01).

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312 Shan et al.

mechanical trades showed that a 46% difference inlabour productivity existed with a high level of automa-tion use on projects compared with low level use projects.

Likewise, Table 10 presents the T-test results ofnormalized productivity difference and the compari-sons of raw productivity between low and high level useof integrated information systems. Similar results toautomation use were found in the integration of theinformation systems. A high level of integration ofinformation systems was correlated with bettermechanical productivity. Among the mechanicaltrades, statistically significant results were shown in theoverall mechanical at 99% confidence level, equipmenttrades at the 95% confidence level and piping trades atthe 90% confidence level. On average, the actuallabour productivity in the high-level use projects was51% better compared to the low-level use counterparts.

The BM&M survey quantifies the implementation of18 practices related to a project’s safety programme. Inthe interests of construction workers’ safety and health,all projects obviously should implement the 18 prac-tices. However, six particular practices were identifiedthat were related to better mechanical trades’ produc-tivity (Table 11):

Table 7 Automation work functions correlated withmechanical productivity improvement

Work functions Corr. coeff. (sig.)

Coordination system −0.09 (0.14)Communication system −0.13 (0.03)Quality system −0.17 (0.01)

Table 8 Integration work functions correlated withmechanical productivity improvement

Work functions Corr. coeff. (sig.)

Coordination system −0.18 (0.01)Communication system −0.20 (0.00)Schedule system −0.16 (0.01)Quality system −0.14 (0.02)Offsite preconstruction −0.15 (0.03)As-built documentation −0.17 (0.01)

Table 9 Mechanical trade productivity vs. automation of information system

Trade Normalized productivity Actual productivity Test for equality of variances

Equal var. assumed

Equal var. not assumed

High level Diff. (%) (Equation 3)

Diff. Diff. (%) (Equation 3)

F Sig. t Sig. t Sig.

Mechanical trades* 2.4 (136) 3.2 (98) −0.8 46.0% 24.19 0.00 −2.39 0.02 −2.23 0.03Pipinga 1.9 (60) 2.7 (42) −0.8 45.2% 14.37 0.00 −1.98 0.05 −1.76 0.09Equipment* 2.7 (50) 4.0 (39) −1.3 46.6% 17.63 0.00 −2.25 0.03 −2.12 0.04

Notes:a denotes statistically significant at the 90% confidence level (i.e. α = 0.1).* denotes statistically significant at the 95% confidence level (i.e. α = 0.05).Numbers in parentheses denote the sample size of activities.

Table 10 Mechanical trade productivity vs. integration of information system

Trade Normalized productivity Actual productivity Test for equality of variances

Equal var. assumed

Equal var. not assumed

High level

Diff. (%) (Equation 3)

Diff. Diff. (%) (Equation 3)

F Sig. t Sig. t Sig.

Mechanical trades**

2.3 (117) 3.1 (119) −0.8 50.7% 21.70 0.00 −2.63 0.01 −2.64 0.01

Pipinga 1.9 (52) 2.5 (47) −0.6 58.0% 6.47 0.01 −1.61 0.11 −1.56 0.12Equipment* 2.7 (44) 3.8 (48) −1.1 48.6% 11.37 0.00 −1.97 0.05 −2.00 0.05

Notes:a denotes statistically significant at the 90% confidence level (i.e. α = 0.1).* denotes statistically significant at the 95% confidence level (i.e. α = 0.05).** denotes statistically significant at the 99% confidence level (i.e. α = 0.01).Numbers in parentheses denote the sample size of activities.

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Management practices 313

(1) Safety preconstruction and construction meet-ings.

(2) Safety audits conducted by corporate safetypersonnel.

(3) Maintaining adequate ratios of craft workers tosafety personnel on site.

(4) Pre-employment substance abuse tests.(5) Post-accident substance abuse tests.(6) Substance abuse testing for reasonable cause.

These six safety practices were used to develop arevised safety composite index. Again, sampled projectswere categorized as having a low versus a high level ofsafety practice based on the median score. The resultsof the analyses involving safety and their correlationwith the mechanical trades’ normalized productivityare shown in Table 12. Projects with better safety prac-tices as described above experienced better labourproductivity among the mechanical trades.

The percentage difference in productivity betweenhigh and low level safety practice implementation projectgroups were calculated as well (see Table 12). The

results are very striking in that projects with greater safetycomposite index scores experienced on average 52%better labour productivity compared to other projects.

Discussion of results

Overall, the studied management programmes showeda positive impact on labour productivity in the mechan-ical trades through statistical analyses. Figure 1summarizes the results of the analyses on the overallmechanical trades’ productivity. Projects with a highlevel of use of these programmes are consistentlyrelated to better labour productivity. The results werealso statistically significant.Figure 1 Overall mechanical trades productivity vs. different management programmesTo clarify the results, the improvements of averagelabour productivity in the mechanical trades from thegroup with low levels of implementation of the keymanagement programmes to the group of projectswith high levels of use of these managementprogrammes were calculated for each programme andpresented in Figure 2. Based on the magnitude of thelabour productivity improvement, pre-project plan-ning ranks first. The result again has proved that deci-sions made during the pre-project planning phasehave significant influence on the overall performanceof the projects.Figure 2 Productivity improvement on high level practices use projectsIt has long been assumed that safety is positivelycorrelated with higher productivity; however, littleresearch has actually quantified the benefits of safetypractices on labour productivity. It is remarkable that,on average, mechanical trades’ labour productivity onprojects with high implementation of safety practiceswas 52% better than the projects with less implementa-tion of the observed safety practices. Once again, itshould be noted that the six safety practices used in theanalyses are not enough to secure a safe project. It isquite probable that, if the other 12 practices are notadequately used on a project, accidents and near-miss

Table 11 Safety practices correlated with mechanicalproductivity improvement

Practices Corr. coeff.(sig.)

Safety preconstruction and construction meetings

−0.15 (0.01)

Safety audits conducted by corporate safety personnel

−0.12 0.05)

Maintaining adequate ratios of craft workers to safety personnel on site

−0.38 (0.00)

Pre-employment substance abuse tests −0.34 (0.00)Post-accident substance abuse tests −0.38 (0.00)Substance abuse testing for reasonable

cause−0.30 (0.00)

Table 12 Mechanical trade productivity vs. safety

Trade Normalized productivity

Actual productivity Test for equality of variances

Equal var. assumed

Equal var. not assumed

High level Low level Diff. Diff. (%) (Equation 3)

F Sig. t Sig. t Sig.

Mechanical trades**

2.2 (112) 3.4 (103) −1.2 52.0% 30.71 0.00 −4.07 0.00 −4.00 0.00

Piping* 1.8 (60) 2.7 (49) −0.9 45.1% 8.60 0.00 −2.43 0.02 −2.29 0.03Equipment** 2.2 (52) 4.2 (54) −2.0 58.9% 23.62 0.00 −4.00 0.00 −4.04 0.00

Notes:* denotes statistically significant at the 95% confidence level (i.e. α = 0.05).** denotes statistically significant at the 99% confidence level (i.e. α = 0.01).Numbers in parentheses denote the sample size of activities.

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314 Shan et al.

incidents will occur and both will obviously hurt craftproductivity.

With the proliferation of information technology, theconstruction industry has recognized the benefit ofautomation and integration as well. Both a high level ofintegration and automation of information systems areassociated with better productivity in mechanicaltrades. Automation and integration are also clearlyinterdependent. The impact of automation on produc-tivity in both the piping and equipment trades variesonly slightly. Integration had a larger impact onmechanical labour productivity than automation,according to the magnitude of the productivityimprovement when high levels of automated and inte-grated information systems were employed on theprojects. This result concurs with O’Connor and Yang(2004), and Zhai’s et al. (2009) research that automa-tion is a prerequisite to integration and integration is anenhancement of automation.

It should be noted that the identified critical practicesunder each management programme that correlatedwith productivity gains are not necessarily independent.The other practices are also vital to the performance ofthe project, in terms of safety, quality, cost and sched-ule. However, the identified practices are statisticallysignificantly correlated with productivity gains.

Conclusion

Using the CII BM&M data, the potential impact onmechanical labour productivity on large industrialprojects through adoption of management programmesrelating to pre-project planning, team building,

Figure 1 Overall mechanical trades productivity vs. different management programmesNote: Numbers inside the bar denote the sample size.

Figure 2 Productivity improvement on high level practicesuse projects

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Management practices 315

automation and integration of information systems, andsafety was examined. The major findings are as follows:

(1) Among the sampled projects, projects with ahigh level of use of these programmes describedherein experienced at least a 40% better labourproductivity in the mechanical trades thanthose projects with a low level implementationof these programmes.

(2) Among the investigated managementprogrammes, pre-project planning had thegreatest impact on labour productivity in thepiping trades.

(3) Projects that were better implementers amongthe observed safety practices were associatedwith 52% better mechanical labour productivity.

The programmes, including alignment, materialmanagement and constructability were also studied,but no statistical significant correlations were foundbetween these programmes and mechanical productiv-ity. However, it does not necessarily mean that a corre-lation does not exist. Perhaps, due to smallermagnitude of correlations, statistical significancecannot be detected under the current sample size.What is clear is that the relative impact on mechanicalproductivity of alignment, material management andconstructability is less when compared to the impact ofpre-project planning, team building, automation andintegration of information systems, and safety.

Significant productivity advantages were observedamong high level management programme implement-ers. Nevertheless, the mechanical labour productivitystudied herein only considers the ratio of work hourinput to physical output. To industry practitioners,justification of a management programme and practicemust accompany implementation. Implementationcosts were not incorporated into this research, and thesavings that could be generated by implementing theselected programmes and practices were not consid-ered as well. Future research can examine the benefitsof management programmes from the perspective offactor productivity, which considers the monetaryinput in terms of labour, material and equipment. Thiswould provide the construction practitioners with amore persuasive argument to adopt the studiedmanagement programmes and ensure upper manage-ment’s buy-in.

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