This article was downloaded by: [University of Sussex Library]On: 06 September 2013, At: 22:03Publisher: RoutledgeInforma Ltd Registered in England and Wales Registered Number: 1072954Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

Construction Management andEconomicsPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/rcme20

Forecasting productivity by worksamplingV. K. Handa a & Osama Abdalla aa Department of Civil Engineering, University of Waterloo,Ontario, N2L 3GI, CanadaPublished online: 28 Jul 2006.

To cite this article: V. K. Handa & Osama Abdalla (1989) Forecasting productivityby work sampling, Construction Management and Economics, 7:1, 19-28, DOI:10.1080/01446198900000003

To link to this article: http://dx.doi.org/10.1080/01446198900000003

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoeveras to the accuracy, completeness, or suitability for any purpose of the Content. Anyopinions and views expressed in this publication are the opinions and views of theauthors, and are not the views of or endorsed by Taylor & Francis. The accuracyof the Content should not be relied upon and should be independently verifiedwith primary sources of information. Taylor and Francis shall not be liable for anylosses, actions, claims, proceedings, demands, costs, expenses, damages, and otherliabilities whatsoever or howsoever caused arising directly or indirectly in connectionwith, in relation to or arising out of the use of the Content.

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms& Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Construction Munugement and Economics, 1989,7, 19-28

Forecasting productivity by work sampling

V.K. HANDA and OSAMA ABDALLA Department of Civil Engineering, University of Waterloo, Ontario, Canada, N2L 3GI

The measurement of labour inputs to the productivity process is the major subject of this study. It is concerned with the simpl$cation of the operation of collecting labour productivity information on site where it is not a one-time task and should be a continuous effort to acquire as many measurements as possible to ensure constant field management control of the project. The study employs the simple methods of work sampling and learning curves forjeld managc?ment control to measure labour utilization during the construction process.

Work sampling technique is a simple and easy technique to use in the management of site resources, time and labour, but it is only an indirect measurement of actual productivity. Therefore, the study has concentrated on demonstrating the correlation between the work sampling percentages, unit rate productivities, and the learning rates ofglobal activities through the development of productivity projection models. Thus, it is possible to quuntfy productivity and learning rates directly through the simple technique of work sampling.

Keywords: Construction sites, productivity, management, unit rates, work sampling, regression analysis, learning curves.

Introduction

The construction industry has hardly made any significant improvements in overall performance during the last 2-3 decades. In spite of superficial improvements such as the use of computers for cost control, most of the industry still follows management techniques inherited from the past. New concepts and modern management practices are needed to boost its productivity levels. This study is focusing on the housing sector of the construction industry and the introduction of better management control techniques to improve its productivity levels.

Control is'the ability of the management team to predict, analyse and correct'routine operation in order to optimize three major attributes of the construction process: quality, schedule and cost. Control, in simple terms, is concerned with the detection of deviations of the actual from the planned performance and the correction of such deviations so that the plans can be fulfilled.

The input of manpower, i.e. labour productivity, to the management control process is the subject of this study.

The term 'productivity' is generally used to denote a relationship between output and the associated inputs used in the production process. The simplest definition of productivity is the ratio of outputs,ofgoods and/or services to inputs of basic resources, e.g. labour, capital,

014&6193/89 $03.00+.12 0 1989 E. & F.N. Spon Lrd

Dow

nloa

ded

by [

Uni

vers

ity o

f Su

ssex

Lib

rary

] at

22:

03 0

6 Se

ptem

ber

2013

20 Handa and Ahdalla

technology, materials and energy. The common expression of productivity is expressed as follows:

output productivity = -

Input

It is obvious from this mathematical expression that productivity can be increased either by increasing output using the same amount of inputs or decreasing inputs while keeping the original volume of output. In the construction industry, the reduction of man-hours in the completion of a unit of work is an example of increasing productivity by decreasing inputs.

In the construction industry, man-hours per unit produced is commonly used as a productivity index because of the concentration of manpower needed to complete a specific task. Unit rates provide the most objective and direct measure of construction productivity. However, the unit rate information may be determined too late really to affect project decision making.

Several other productivity measurement techniques have been used for measuring productivity on construction projects. These techniques are not mutually exclusive. In fact, productivity measurement can be most beneficial when several techniques are employed to identify problems that can be rectified by management.

This study employed two of the various methods of field management control to measure labour utilization during the construction process. These methods are work sampling and learning curves.

The study examined the effectiveness of work sampling in demonstrating labour performance and the reliability of work sampling data to predict productivity.

Work sampling measures time utilization and it is only an indirect measurement of actual productivity. Therefore, the purpose of the study is to establish whether the work sampling percentages and unit rate productivity measures are highly correlated statistically, and hence achieve the determination of labour productivity by measuring on-site work sampling instead of quantifying man-hours spent for a unit of work. The operation of collecting productivity information would, thus, be simplified especially as i t is not a one-time task and there should be a continuous effort to acquire as many measurements as possible to ensure constant control of the project.

Another general indication of field management effectiveness is the learning curve. For tasks or activities that are not hindered by some fixed productivity limitation, increased effort by management and workers can increase productivity. The learning curve provides one method for recording these improvements and for comparing management's effectiveness on different but similar repetitive tasks. The learning curve is a geometric progression which shows that there is a steadily decreasing cost or time for a given operation each time it is repeated, i.e. there is a productivity gain. This gain is the result of several factors includingjob familiarity, improvement in coordination, organization, management and supervision, and better methods and tools. Thus, learning curves demonstrate mathematically or graphically the increase in productivity output or the decrease in cost. Failure to improve, under fairly constant conditions, indicates lack of attention or effort by management.

Thus, this study intends, in addition, to demonstrate the statistical relationship between work sampling and learning rates, which indicates increasing or decreasing productivity gains. If work sampling data and learning rates are highly correlated, it would be possible to predict learning rates, i.e. increase or decrease in productivity gains, of site activities by on- site work sampling.

Dow

nloa

ded

by [

Uni

vers

ity o

f Su

ssex

Lib

rary

] at

22:

03 0

6 Se

ptem

ber

2013

Forecasting productivity by work sampling 2 1 I

Objectives

The present study has concentrated on demonstrating the correlation between work sampling percentages, unit rate productivities and the learning rates, of activities of the same overall nature, namely, the various framing activities in the housing industry, through the development of productivity projection models.

Case study

A field study was conducted to demonstrate how the techniques of field management control of labour productivity can be used on a construction site.

The study was conducted on a construction site of two-storey, 30-unit townhouses of two- and three-bedroom sizes in five block buildings, located in Woodstock, South-Western Ontario.

The cost of the project was $2.5 million and both the general contractor and his subcontractors are non-union.

The subject of this study was the various activities of framing on site which were carried out by the contractor's own forces. The framing crews consisted ofseven or eight framers and two or three labourers. Supervision of the framers was carried out by one working foreman who was also the owner of the contracting business. It is important to note that the foreman, the owner, was most of the time present on the site and that he usually had all his forces working on the same site at the same time. This general contractor usually contracts larger townhouse projects and has been primarily in the framing business for over I0 years. All of the framers employed by this general contractor have been employed for at least one construction season and at least one has been employed for more than five construction seasons. Hence, the majority of the framers were not very experienced and none of them was familiar with every type of framing construction in which this contractor has been employed.

The study was carried out for a period of 8 weeks during June and July 1986. The townhouses under construction were wood-framed structural buildings. Most of the

framing activities were classical. Preserved wood wall foundation (PWF) with concrete floor slab was used. It consists of wood-frame walls (pressure-treated wood framing members with treated plywood sheeting) supporting a conventional house. The preserved wood foundation walls were built using full-size (4 ft x 8 ft x $in.) plywood sheets laid with horizontal joints (face grain perpendicular to studs). The foundation walls were roughly 8 ft in height. The basement floor was of 3 in. concrete slab resting on 6 in. or 12 in. gravel pad.

The GNI-Beam produced by Gang-Nail Canada Inc. Wood Products Division were used for flooring. The beams were manufactured, off site, with Gang-Lam LVL flanges, oriented stand board web and assembled with waterproof glue, and convenient depths of 11; in. and 11; in. were used in this project.

Learning rates

A field study was conducted to predict the learning rates of the framing activities on site. The data were collected when the above mentioned activities were progressing in the first three block buildings of the project: Blocks A, B and C during June and July 1986. It is assumed

Dow

nloa

ded

by [

Uni

vers

ity o

f Su

ssex

Lib

rary

] at

22:

03 0

6 Se

ptem

ber

2013

22 Harzdu and Abdalla

that the learning rate is constant throughout an activity, and there are no effects from acquired experience, levelling off, o r degradation of unit rates at the end.

The cumulative average straight-line learning rates, their 90% confidence intervals, and the adjusted coefficients of determination for each data set were calculated using the least squares method of linear regression analysis to fit curve models to data and determine an equation where the sum of residuals is minimized. The results are given in Table 1.

Work activity sampling technique

The activity sampling data were collected concurrently with the learning rates data, for each of the activities studied.

Table 2 gives the sample proportion percentages of each activity classification and their calculated degree of accuracy percentage.

For each data set of work sampling, a maximum number of 400 observations are required for sample proportion of 507'0, 95% confidence and + 5% degree of accuracy.

Table 1 . Learning curve data for various framing activities using cumulative average straight-line model

Adjusted Learning 90% confidence interval for S coefficient rate

Activity Ra * S (YO) Upper (%) Lower (%)

Block A PWF walls 0.96 18 86.1 87.7 84.6 Block B P W F walls 0.625 92.1 113.4 88.1 Block C P W F walls 0.3409 104.5 109.3 100.0 All block P W F walls 0.9726 87.4 88.1 86.7 Block A basement 0.946 162.0 180.0 144.5

internal walls Block B basement 0.218 105.0 115.0 95.8

internal walls Basement 0.107 94.7 104.3 86.0

internal walls Block A tst floor 0.0672 104.0 117.3 91.8 Block B 1st floor 0.8032 92.7 95.7 89.8 Block A 2nd floor 0.959 113.5 119.4 107.9 Block B 2nd floor 0.9764 84.1 86.0 82.3 Block A 1st floor 0.2513 94.0 106.0 83.3

internal walls Block A 1st floor 0.9937 66.9 68.5 65.4

outside walls Block A 2nd floor 0.9696 67.7 74.5 61.6

internal walls Block A 2nd floor 0.6404 89.5 98.3 81.5

outside walls

Dow

nloa

ded

by [

Uni

vers

ity o

f Su

ssex

Lib

rary

] at

22:

03 0

6 Se

ptem

ber

2013

? Fa

0

E =. Table 2. Summary of each framing operation with the sample proportion ( O h ) of each activity classification and their calculated degree of Q 3

accuracy (%)

DW W TRA TRANS T & M lNST PLS U 2 P E

S A S A S A S A S A S A S A S A 2

Framing operation -. C.

Block A P W F walls 67.9 6.8 3.7 2.7 4.7 3.1 7.9 3.9 4.2 2.9 4.2 2.9 4.7 3.1 2.6 2.3 $ Block B P W F walls 70.9 5.7 3.9 2.4 4.7 2.7 4.3 2.5 6.3 3 1.2 1.3 6.3 3 2.4 2 k

Block C P W F walls 70.7 4.7 4 2 3.8 2 5.1 2.3 5.4 2.3 1.6 1.3 6.2 2.5 3.2 1.8 e All blocks P W F walls 69.9 4.7 3.4 1.7 3.4 1.7 6.4 2.3 4.8 2 3 1.6 6.4 2.3 2.7 1.5

2 ??

Block A basement int. walls 70.6 5.6 3.1 2.1 3.8 2.4 5.3 2.8 5 2.7 5.7 2.9 5 2.7 1.5 1.6 tl

Block B basement int. walls 66.7 4 4.4 1.7 6.4 2 6.9 2.1 4.4 1.7 2.8 1.4 3.4 1.5 5 1.8 'P, All blocks basement int. walls -. 66.6 3.5 4.2 1.5 5.9 1.8 6.5 1.9 4.4 1.5 4 1.5 4.2 1.5 4.1 1.5 Block A first floor 73.3 4.8 2.4 1.7 3 1.9 5.9 2.6 4.2 2.2 3.9 2.1 5.6 2.5 1.8 1.4 Block B first floor 69.4 4.7 3.6 1.9 5.9 2.4 6.7 2.5 4.4 2.1 1.8 1.3 3.9 2 4.4 2.1 Block A second floor 64.4 4.3 3.3 1.6 5.8 2.1 8.6 2.5 3.3 1.6 4.3 1.8 1.4 1.1 8.8 2.6 Block B second floor 63.8 4.7 5 2.1 7.2 2.5 9.8 2.9 3.1 1.7 2.6 1.6 5 2.2 3.4 1.8 Block A first floor outs. walls 60.6 5.2 6 2.5 8.6 3 7.4 2.8 4.6 2.2 3.4 1.9 2.9 1.8 6.6 2.65 Block A second floor outs. walls 66.5 5.2 4 2.2 6.2 2.7 10.5 3.4 2.5 1.7 4.6 2.3 1.2 1.15 4.6 2.3 Block A outside walls 63.4 3.7 5 1.7 7.4 2 8.9 2.2 3.6 1.4 4 1.5 2.1 1.1 5.6 1.8 Block A first floor int. walls 60.5 4.3 4.7 1.9 7.4 2.3 6.7 2.2 4.7 1.9 3.5 1.6 2.5 1.4 10 2.7 Block A second floor int. walls 66 4.7 4.4 2 5.8 2.3 10.4 3 3.2 1.7 3.6 1.8 1.7 1.3 4.9 2.1 Block A internal walls 62.9 4.5 4.6 2 6.7 2.3 8.3 2.6 4 1.8 3.6 1.7 2.2 1.4 7.7 2.4 Roof trusses 60.5 4.9 7 2.6 5 2.2 13 3.4 3.3 1.8 2.8 1.6 1.3 1.1 7.3 2.6

DW = Direct Work; TRA =Travel; T & M =Tools & Materials; PLS = Combined rates of Breaks, Personal & Late Starts; W = Waiting; TRANS = Transportation; INST = Receiving Instructions; U = Unexplained.

Dow

nloa

ded

by [

Uni

vers

ity o

f Su

ssex

Lib

rary

] at

22:

03 0

6 Se

ptem

ber

2013

24 Handa and Abdalla

Relationship between work sampling, unit rate productivity, and the learning rates for the various framing activities:

The data sets of the work sampling percentages and their corresponding unit rate productivity data points, given in Table 3, were analysed using a computer-aided technique of regression analysis which attempts to define the extent of movement in the dependent variable (unit rate productivity or learning rate) related to independent variables (direct work, waiting, travel, transportation, and so on) and to fit linear regression models to variables by the least squares method.

The analysis showed that the combined rate of breaks, personal, and late starts/early quits (PLS), tools and materials (T & M), travel (TRANS) and direct work (DW) rates are highly correlated with the unit rate productivities.

The projection models, obtained from this analysis, to predict unit rate productivity through work sampling information were as follows:

Model N o . 1 with probability level=0.091 I

Unit rate = 0.63 19 - 0.0033 (DW) + 0.2291 (PLS) R2 =0.3531

Model No. 2 with probability level =0.0972

Unit rate= -2.9063 $0.2022 (TRANS)+0.3745 (T & M ) + 0.278 1 (PLS) R"O.4535

Unit rate productivity predicted is in minutes per square foot and work sampling data are in percentages.

Table 3. Data point information of work sampling percentages with corresponding unit rate productivity

Unit rate DW W TRA TRANS T & M INST PLS U (min/sq. ft.)

73.3 2.4 3 5.9 4.2 3.9 5.6 1.7 1.27 69.4 3.6 5.9 6.7 4.4 1.7 3.9 4.4 1.077 64.4 3.3 5.8 8.6 3.3 4.3 1.5 8.8 0.888 63.8 5 7.2 9.8 3.1 2.6 5.1 3.4 0.982 70.6 3 3.8 5.3 5 5.7 5 1.6 0.879 66.7 4.4 6.4 6.9 4.4 2.8 3.5 4.9 0.615 60.6 6 8.6 7.4 4.6 3.4 2.8 6.6 1.278 66.5 4 6.2 10.5 2.5 4.6 1.1 4.6 1.023 68 3.7 4.7 7.9 4.2 4.2 4.7 2.6 2.704 70.8 3.9 4.7 4.3 6.3 1.1 6.2 2.7 2.085 70.5 4 3.8 5.1 5.4 1.6 6.4 3.2 2.517 60.5 4.7 7.4 6.7 4.7 3.5 2.5 10 0.469 66 4.4 5.8 10.4 3.2 3.6 1.7 4.9 0.449 60.5 7 5 13 3.25 2.75 1.25 7.25 1.208

DW = Direct Work; TRANS = Transportation; PLS = Combined rates of Breaks, Personal & Late Starts; W = Waiting; T & M =Tools & Materials; TRA =Travel; INST= Receiving Instructions; U =Unexplained.

Dow

nloa

ded

by [

Uni

vers

ity o

f Su

ssex

Lib

rary

] at

22:

03 0

6 Se

ptem

ber

2013

Forecasting producthity by work sampling 25

The data sets of work sampling percentages and their corresponding learning rate points, given in Table 4, were also analysed using the same procedure. It was established that transport (TRANS), and tools and materials (T & M) rates only are highly correlated with the learning rates but regression analysis showed that there was no significant level of probability or reliability for predicting learning rates through work sampling data.

A detailed review of the learning curves revealed that most of the learning process takes place during the operation-learning and routine-acquiring process. Therefore, it was decided to exclude the work sampling observations taken during the levelling-off phase of the learning curves. This breakdown of data resulted in obtaining only six reliable data points of work sampling percentages with their corresponding learning rates, as given in Table 5.

The same regression analysis was carried out again and' now showed a high correlation between the direct work, waiting, travel and personal/late starts and the learning rates. This analysis yielded a more reliable model to predict the learning rates through work sampling data. The best models obtained from the analysis were as follows:

Model No. 3 with probability level =0.0872

Learning rate = 34.307 +0.8037 (DW) - 1.083 1 (TRA) R 2 =0.8033

Model No. 4 with probability level = 0.01 53

Learning rate = 98.8698 -4.7235 (W) R 2 = 0.8052

Model No. 5 with probability level = 0.0508

Learning rate = 99.7081 - 3.4967 (W) - 1.4563 (U) R 2 =0.8628

Table 4. Data point information of work sampling percentages with corresponding learning rates

Learning DW W TRA TRANS T& M INST PLS U rate

DW =Direct Work; TRANS=Transportation; PLS= Combined rates of Breaks, Personal & Late Starts; W = Waiting; T & M =Tools & Materials; TRA =Travel; INST= Receiving Instructions; U =Unexplained.

Dow

nloa

ded

by [

Uni

vers

ity o

f Su

ssex

Lib

rary

] at

22:

03 0

6 Se

ptem

ber

2013

Hunda and Ahdullu

Table 5. Data point information of work sampling percentages, excluding observations at levelling-off phase, with corresponding learning rates

Learning DW W TRA TRANS T & M INST PLS U rate

DW = Direct Work; TRANS =Transportation: PLS =Combined rates of Breaks, Personal & Late Starts; W = Waiting; T & M =Tools & Materials; TRA=Travel; INST=Receiving Instructions; U = Unexplained.

Model No. 6 with probability level = 0.0698

Learning rate = 93.9383 -4.1 1 17 (W) + 2.6839 (T & M)- 2.2 (U) R 2 = 0.9528

Additionally, it was decided to verify the above findings by examining whether the levelling-off phase of the learning process and work sampling are also highly correlated. A data breakdown resulted as before in obtaining only six reliable data points ofwork sampling percentages with their corresponding learning rates during the levelling-off phase, as given in Table 6.

The same regression analysis was carried out again for the same parameters of work sampling, where they were highly correlated to learning rates during operation-learning and routine-acquiring phases, and the learning rates of the levelling-off phase. The results of the analysis showed that the learning rates of the levelling-off phase are highly correlated with the same work sampling percentages of direct work, waiting, travel and the unexplained

Table 6. Data point information of work sampling percentages only during levelling-off phase, with corresponding learning rates

Learning DW W TRA TRANS T & M INST PLS U rate

83.9 3.6 3.6 1.7 3.6 0.0 3.6 0.0 102.3 60.3 5.7 9.8 7.5 4.6 1.8 3.4 6.9 85.8 68.8 2.1 2.1 10.4 3 .O 7.3 4.2 2.1 88.2 57.3 7.0 5.7 10.2 3.2 1.9 8.3 6.4 82.9 60.8 6.3 7.4 7.4 4.5 5.1 2.2 6.3 66.9 83.9 3.6 3.6 1.7 3.6 0.0 3.6 0.0 100.0

DW = Direct Work; TRANS=Transportation; PLS = Combined rates of Breaks, Personal & Late Starts; W = Waiting; T & M =Tools & Materials; TRA =Travel; INST = Receiving Instructions; U =Unexplained.

Dow

nloa

ded

by [

Uni

vers

ity o

f Su

ssex

Lib

rary

] at

22:

03 0

6 Se

ptem

ber

2013

Forecasting productivity by work sampling

parameters. The projection models obtained from the analysis were as follows:

Model No. 7 with probability level =0.1678

Learning rate = 27.8753 +0.8731 (DW)-0.1087 (TRA) R 2 =0.6958

Model No. 8 with probability level = 0.1 776

Learning rate = 107.8568 -4.277 (W) R 2 =0.4002

Model No. 9 with probability level = 0.1803

Learning rate = 96.2397 + 1.0104 (W) - 3.6836 (U) R = 0.6809

The above analysis shows that it is possible to develop productivity projection models for either unit rate productivity or learning rates through work sampling because learning rates of either the operation-learning and routine-acquiring phases or the levelling-off phase are highly correlated with the work sampling percentages.

Recommendations and conclusions

This study established the correlation between the work sampling percentages, unit rate produtivities, and the learning rates of global activities through the development of productivity projection models. Thus, it is possible to quantify productivity and its learning rate directly through the simple technique of work sampling and accordingly to ensure constant control of the project by continuously acquiring such measurements.

The implementation procedure of collecting data, setting up, and developing either short- term or long-term productivity projection models is summarized as follows:

1. Define the project's major global activities to develop productivity projection models for each separate activity, i.e. framing, concreting, plumbing, and so on.

2. Collect at least 20-30 points of work sampling data with their corresponding unit rate productivity data and/or learning curves for each separate global activity and at as many stages of the project as possible.

3. Calculate learning rates data through regression analysis of cumulative average learning curves data.

4. Use regression analysis for fitting linear regression models to the data variables and to obtain the best model@) to predict future values of either unit rate productivity or learning rates through work sampling percentage(s). Use the best models of the highest confidence levels. Confidence levels for short-term projection models should not be less than 80%, i.e. they should give reliable results 80% of the time.

5. The long-term productivity projection models are developed from the short-term projection models over longer spans of time, i.e. 2- to 5-year periods. The data collection for the long-term models is made up of the various short-term site projects of the general contractor or the subcontractors. The data collection for the long-term models mainly involves the data set(s) of the global activities for the whole duration of each project. Thus,

Dow

nloa

ded

by [

Uni

vers

ity o

f Su

ssex

Lib

rary

] at

22:

03 0

6 Se

ptem

ber

2013

2 8 Handa and Ahdalla

the contractor will have a long-term data set(s) of work activity percentages with their corresponding unit rate productivities or learning rates representing the global activities carried out by the contractor for the wide spectrum of short-term projects executed by him over a long period, i.e. 2-5 years. Obtain the best projection model(s) to predict future values of either unit rate productivity or learning rates through work sampling percentage(s) by using a computer-aided forecasting technique of regression analysis. Confidence levels for long-term projection models should not be less than 90%, i.e. they should give reliable results 90% of the time.

The short-term productivity projection models developed in this study could be used in the housing industry for rapid analysis of various productivity changes due to changes of work patterns and/or techniques and management decisions, but contractors are advised to adopt the implementation procedure outlined above to collect more data points from their projects and recalibrate the models if possible.

In conclusion, productivity models can be a powerful management tool and should be applied t o the construction industry.

References

Ahuja, H.N. (1976) Corrstrttction Perforrnctnce Control, Wiley, New York. American Institute of Industrial Engineers (1972) American National Standard 2-94, Industrial

Engineering Terminology. Barnes, R.M. (1956) Work Sampling, Wiley. New York. Carlson, J.G.H. (1973) Cubic learning curves: precision tool for labour estimating. Murrr!facturirrg

Engineeri~rg and Managenlent, November, 22-5. Cochran, E.B. (1960) New concepts of the learning curve. Journal of hrdustrial Engineeri~rg,

July-August, 31 7-27. Gates, Marvin and Scarpa, Amerigo (1972) Learning and experience curves. Journal of Construction

Division, ASCE 98. March, 79-101. Heiland, R.E. and Richardson. W.J. (1957) Work Sampling. McGraw-Hill, New York. Jordan, Raymond, B. (1965) How 117 Use the Lrarrring Curve. Materials Management Institute, Boston. McNeill, Thomas F. and Clark. Donald S. (1 966) Cost Estitnatirlg Contract Pricing. American Elsevier,

New York, pp. 171-319. Parker, H.W. and Oglesby, C.H. (1972) Methods Itnprovernerrtfir Construction Marragers. McGraw-

Hill, New York, pp. 56-62. Preserved Wood Foundations, General Information Wood, WB-4. Canadian Wood Construction,

Canadian Wood Council, 85 Albert, Ottawa, Canada. United Nations Committee on Housing, Building, and Planning (1965) Eflecr of Reperitiorr on Building

Operations and Processes on Site. United Nations, New York, St/ECE/HOU/14. Ward, J.G. (1984) A validation of learning curve models available to the construction industry,

Department of Civil Engineering, The Graduate School, The Pennsylvania State University, August.

Wood Products Division, Gang-Nail Canada Inc., 725 Denison S1.E.. Markham, Ontario, Canada. Wright, T.P. (1936) Factors affecting the cost of airplanes. Jo~trnnl o/Aerorlautical Sciences. February.

Dow

nloa

ded

by [

Uni

vers

ity o

f Su

ssex

Lib

rary

] at

22:

03 0

6 Se

ptem

ber

2013