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EFFECTS OF DELAY FACTORS ON LABOUR PRODUCTIVITY ON
NIGERIAN CONSTRUCTION SITES
I.A. Jimoh
Department of Building,
Federal University of Technology
Minna, Nigeria
Abstract
To determine the factors affecting labour productivity, ninety six (96)
construction workers on two active construction sites in Minna were studied for fourteen days through Activity Sampling, application of Method
Productivity Delay Model (MPDM) and Foreman Delay Survey (FDS). Data obtained on the workers were analysed to obtain labour productivity and, the types and extent to which delay factors affect production. Activity
sampling gave 54% as measure of labour productivity and 21% as delay while MPDM resulted in 1:10 relationship between Ideal and Overall labour productivity, 1:3 between Ideal and Overall cycle variability and
3.5% as expected cumulative percentage of delay. The MPDM also assessed specific contributions to delay as 4.5%, 4.0%, 4.0% and 4.5% by
Job Environment, Equipment, Labour and Material related factors respectively. By FDS, waiting for other workers, waiting for information, waiting for materials and machine breakdown made significant
contributions of 25%, 24% and 17% to lost man-hours. It is therefore recommended that proper documentation, adequate information, efficient organization of resources and analysis of work environment be given
commensurate attention so as to raise level of labour productivity on construction sites.
Keywords: Construction site; construction workers; labour productivity;
delay; man-hour.
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1.0 INTRODUCTION
Productivity is the efficient and effective utilization of resources of
production for achieving, optimally, set organization objectives (Durdyev
and Ismail, 2012). Mbachu (2008) also asserted that improved productivity
correlates highly with increased profitability, competitiveness, achievement
of key stakeholder value and long-term growth and sustainability of an
organization, industry or an economy (Nation). Low profitability and
profitability, in the construction industry, has been a subject of concern
for long because the manufacturing industry has increased its productivity
by more than 100% whereas that of construction has been declining
(Chromokos and McKee, 1981; Briscoe, 1988). The reasons adduced for
the low level of productivity and which are related to peculiarities of the
construction industry include labour characteristics, varying project work
conditions and environment and the inherent non-productive activities
(Jerges, 2000; Oglesby, 1989; Talhouni, 1995). Labour cost in
construction was assessed by Kazaz and Ulibeyili (2004) to be between
20% and 50% of the total project cost. Calvert (1995) also confirmed that a
5-10% increase in productivity could have a tremendous favourable effect
on the profitability of construction works, by virtue of reduction in project
duration, if non-productive time could be reduced drastically or even
eliminated.
Lack of measurement methods for assessing productivity in the
construction industry, generally, is critical and this has shrouded the
nature of size of the productivity problem (Chapman and Butry, n.d). Most
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of the researches on productivity issues focused attention on
improvements (Tavakoli, 1985; Thomas and Yiakoumis, 1987; Proverbs,
1990). Issues like poor on-site management (Chromokos and McKee,
1981; Oglesby, 1989) and the impact of delays and interruptions by
Horner and Talhouni (1995) contribute to make the construction industry
unstable in its performance leading, often, to cost and time overruns aside
from poor quality of work and frustrations suffered by clients and other
stakeholders. It is therefore the aim of this study to evaluate the types
and effects of delay factors on labour productivity on construction sites.
Labour in the context of this study means the tradesmen or skilled
workers, involved directly on production activities on construction sites
and does not include workers in strategic or tactical levels, administrative
staff and the like. It is also worthy of mention that only productivity
indices on homogenous labour resource shall be determined through
activity based survey and measurement i.e. partial productivity at the
micro level.
2.0 METHODOLOGY
The population of this study is made up of workers on two construction
sites in Minna metropolis. The workers were employed by two
construction firms that handled the construction of National Examination
Council (NECO) headquarters (i.e. Site A) and Intercontinental Bank (ICB)
(i.e. Site B) in Minna, Nigeria. Observation and survey were made for
fourteen days on ninety six (96) workers on the two sites. At site A,
nineteen (19) concretors, eight (8) carpenters, seven (7) iron benders,
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twelve (12) masons, seven (7) domestic electricians, six (6) plumbers and
seven (7) labourers were engaged, actively, in construction works every
day. On site „B‟, seven (7) concretors, six (6) concrete mixers, seven (7)
masons, eight (8) carpenters, six (6) iron benders, four (4) domestic
electricians, three (3) plumbers and sixteen (16) labourers worked, on the
average, per day.
Methods used for data collection include direct observation, timing of
production activities and administration of questionnaire. The direct
observation was used to rate the effectiveness of the workers by Activity
Sampling while Method Productivity Delay Model (MPDM) involves the
timing of production cycles and Foreman Delay Survey (FDS)
questionnaires were distributed to Foremen on the two sites. The MPDM
and FDS were designed by the Construction Research Council (CRC) of
Canada for assessing the effects of delay factors on Labour productivity.
By Activity sampling, the major production activity in each of the two
construction sites was identified. Each crew carrying out the major
activity was observed randomly to collect activity level data on the
workers. The worker‟s activity was categorized and recorded as
productive, semi-productive and non-productive, which translate to
productive time, ancillary time and wasted time spent on production
respectively. All the observations made were recorded on a prepared form
using checkmarks under the appropriate mode of activity, as observed. In
order to obtain a sampling error of 5% and a level of confidence of 95%,
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the statistical criterion of 384 minimum number of observation was
satisfied. The data recorded are in Appendix I.
Application of MPDM entail observation and recording of production cycle
times, including source and period of delay on a special form. Two types
of production cycles recorded are the non-delayed and delayed cycles.
Sources of delay have been classified into six as Environmental,
Equipment, Labour, Material, Management and any other. The data
collected are in Appendix II and III.
In FDS, only the Foreman or Supervisor of each craft is questioned on the
extent and type of delays that affected the performance of the workers.
Considering his close contact with both the workers and management the
Foreman is assessed to be more competent in identifying the cause of any
delay and giving an accurate estimate of its duration. Only delays that are
beyond the control of the Foreman are recorded in terms of source, length
of time lost and the number of workers affected. The questionnaire was
administered daily, took about ten minutes of the Foreman‟s time at close
of work and involved all the key trades. Appendix IV contains the data
obtained from the survey.
3.0 DATA PRESENTATION, ANALYSIS AND DISCUSSION OF
FINDINGS.
The data collected were analysed with the use of SPSS and Microsoft Excel
packages to obtain descriptive and inferential statistics in ranked means,
and percentages.
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3.1 Activity Sampling
Eight four (84) rounds of observation on working modes of workers on the
two construction sites were recorded with each round containing ten (10)
observations. The three working modes are productive, semi-productive
and unproductive (idle). Summary of observations that gave the mean
values on productivity and delay in percentages are in Tables I and II.
Productivity Rating
Fig. 1- Labour Productivity by Activity Sampling- site „A‟
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Productivity Rating
Fig 2: labour Productivity by Activity Sampling- Site „B‟
Productivity Rating
Fig 3: Labour Productivity by Activity Sampling- Site „A‟ & „B‟ Combined
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Figures 1, 2, and 3 above indicate labour productivity measured by
proportion of time spent on production activities, as 51% for site „A‟; 57%
for site „B‟ and 54% for the two sites combined. By this result, labour
productivity in site „B‟ is higher, by 6%, then in site „A‟. If the result of the
two sites combined is taken as benchmark site „B‟ could be regarded as
more productive then site „A‟ by 3% albeit this could be contested because
the work environments of the two sites are not the same.
Proportion of unproductive time (idle time) for site „A‟ is 20.5% while for
site „B‟ it is 22% and the two sites combined had 21%- which interpretes to
“higher productivity created higher unproductive time”. With the two sites
combined, relationship between productive time and idle time is 54:21 (i.e.
39%). By this overall indicator of relationship between productive and idle
time and taking labour cost to be 35% on the average, of the total cost of
construction (Calvert, 1995), then a 21% delay tantamount to a saving of
N735,000.00 on a N10.0 million job.
3.2 Method Productivity Delay Model (MPDM)
The data obtained through records on cycles of production are in Appendix
II and were thereafter analyzed to produce the results in Table I below.
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Table I: Deductions from measurement of Labour Productivity with MPDM
Descriptors Construction Sites Mean
Site ‘A’ Site ‘B’
Mean overall cycle time (secs) 316.09 191.57 253.83
Mean non-delay cycle time (secs) 306.09 186.30 246.19
Expected percentage of delay (%) 4.00 3.00 3.50
Ideal cycle variability (%) 1.00 1.00 1.00
Overall cycle variability (%) 3.00 3.00 3.00
Ideal labour productivity 0.0033 0.0054 0.0044
Overall method productivity 0.003 0.005 0.004
Source: Researcher’s Fieldwork
Results in table I indicate that the Ideal labour productivity i.e. based on
non-delayed production cycles is ten times higher than that of the Overall
cycle time. Delays caused by the Environmental factors, Equipment,
Labour, Material and Management are as follow: in Table II and III.
Table II: Extent of delay on Site „A‟ based on Mean non-delay cycle time
(306.09) as Bench mark by MPDM.
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Table II: Extent of Delay on Site ‘A’ based on Mean Non-Delay Cycle
Time (186.30) as Benchmark by MPDM
Source of
Delay
Cycle
No
Cycle
Time
Delay per
Cycle
Percentage
Delay/ Cycle
Cumulative
delay
Ranking &
Remarks
Environmental
11
14
15
316
330
331
9.91
23.91
24.91
3.0
7.0
7.5
9.91
33.82
58.73
*Mean = 6%
* Rank = 1
*Highest
delay of 7.5%
in cycle 15
Equipment
13
19
20
24
26
324
324
319
324
316
17.91
7.16*2
12.91
17.91
9.91
5.5
2.0
4.0
5.5
3.0
17.91
25.07
37.98
55.89
65.80
*Mean = 4%
*Rank = 3
*Highest
delay of 5.5%
in cycles 13 &
24
*2 40% of
delay shared
Labour
2
10
12
19
29
30
315
319
318
324
327
322
8.91
12.91
11.91
10.75*2
17.911
20.912
3.0
4.0
4.0
3.0
6.0
5.0
8.91
21.82
33.73
44.4
65.39
81.30
*Mean = 4%
* Rank = 3
*Highest
delay of 6%
in cycle 29
60% of Delay
shared
Materials
3
4
5
16
17
18
310
322
325
321
331
328
3.91
15.91
18.91
14.91
24.91
21.91
1.0
5.0
6.0
5.0
7.5
7.0
3.91
19.82
38.73
53.64
78.55
100.46
* Mean = 5%
* Rank = 2
* Highest
delay of 7.5%
in cycle 17
From Tables II and III, delays caused to production activities in Site „A‟
were due to Environmental, Equipment, Labour and Material factors with
Environmental ranking highest. There was no delay caused by
management. On the other hand, three factors (Environmental, Labour
and Material) caused delay in Site „B‟ with labour and material ranked
equally in terms of specific contribution. In general, expected percentage
of delay is higher in site „A‟ than site „B‟ (4% vs. 3%) while production cycle
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differentials for Ideal and Over-all production runs remain the same (i.e.
1%) for the two sites as shown in Table I.
Table III: Extent of Delay on Site ‘B’ Site based on Mean Non-Delay Cycle Time (186.30) as Benchmark by MPDM
Source of Delay Cycle
No
Cycle
time
Delay
Per cycle
Percentage/
Delay Cycle
Cumulative
Delay
Ranking &
Remarks
Environmental
4
13
23
24
25
191
189
191
195
197
4.70
2.70
4.70
8.70
10.70
2.5
1.0
2.5
4.5
5.0
4.70
7.40
12.10
20.80
31.50
*Mean = 3%
* Rank = 3
*Highest delay
of 5% in cycle
25
Labour
6
7
8
9
17
18
19
26
29
30
195
197
190
198
194
192
191
201(60
%)*2
196
194
8.70
10.70
3.70
11.70
7.70
5.70
4.70
8.82
9.70
7.70
4.5
5.0
2.0
6.0
4.0
3.0
2.5
4.0
5.0
4.0
8.70
19.40
23.10
34.80
42.50
48.20
52.90
61.72
71.42
79.12
*Mean = 4%
*Rank = 1
*Highest delay
of 6% in cycles 9
*2 60% of delay
shared
Material
26
27
28
201(40
%)*2
192
198
5.88
5.70
11.70
3.0
3.0
6.0
5.88
11.58
23.28
*Mean = 4%
* Rank = 1
*Highest delay
of 6% in cycle
28
*2 40% of Delay
shared
3.3 Foreman Delay Survey (FDS)
The data obtained from Foreman Delay Survey (FDS) contain the total
number of production hours lost per week through the influence of ten
factors (Appendix III). The factors have been ranked in Table IV, in
accordance with level of disruption (lost hours) to production.
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Table IV: Factors and Extent of Delay on Construction Sites by Foreman Delay Survey (FDS)
S/N Factors Site ‘A’ Site ‘B’ Cumulative (Sites ‘A’ & ‘B’)
Lost
Man Hrs
% Rank Lost
Man Hrs
% Rank Mean lost
man hrs
% Rank
1 Waiting for information 96.00 35.00 1 36.00 13.00 4 70 25.00 1
2 Waiting for other workers 79.50 29.00 2 59.50 22.00 2 66 24.00 2
3 Waiting for materials 71.00 26.00 3 52.00 19.00 3 62 22.00 3
4 Machine breakdown 12.00 4.00 4 84.00 30.00 1 48 17.00 4
5 Design error (Redo work) 8.50 3.00 5 10.50 4.00 6 17 6.00 5
6 Production error (Redo work 8.00 3.00 6 26.00 9.00 5 10 4.00 6
7 Change in design (Redo work) 0.00 0.00 7 9.00 3.00 7 5 2.00 7
8 Waiting for machines 0.00 0.00 8 0.00 0.00 8 0 0.00 8
9 Waiting for tools 0.00 0.00 8 0.00 0.00 8 0 0.00 8
10 Unnecessary move 0.00 0.00 8 0.00 0.00 8 0 0.00 8
276.00 100.00 277.00 100.00 278 100.00
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From the results in table IV, waiting for other workers, information and
materials ranked first, second and third respectively. Dependency on other
workers is a common feature, in construction, albeit 22 – 25% idle time is
on the high side. Information and materials are also crucial at every stage
of construction work.
Lost hours on tools were not recorded because the artisans often possess
and work with their personal tools. The workers did not also depend on
machines and were not therefore affected by this factor, despite the 17%
lost time on breakdown of concrete mixer; they had enough material to
work with.
4.0 CONCLUSION
Labour productivity of construction workers is 54% by Activity Sampling
with 21% of the time wasted, MDPM gave a relationship of 1:10 between
Ideal and Overall labour productivity on the two construction sites and 1:3
between Ideal cycle variability and the Overall. The expected cumulative
percentage of delay thereof is 3.5%. In specific terms, delays were caused
by Environmental factors (4.5%), Equipment (4%), labour (4%) and
materials (4.5%). Environment and labour factors on Site „A‟ deserve close
attention while it is labour and material factors on Site „B‟. These
deductions confirm the assertion of Oglesby et al., (1989) and Jerges et al,
(2000).
Foreman Delay Survey (FDS) threw more light on delay factors by breaking
them down further into ten. Out of the ten factors, waiting for other
workers, waiting for information, waiting for materials and machine
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breakdown made significant contributions to lost man hours – 25%, 24%,
22% and 17% respectively. Delay of 22% on materials fall within 5.4 to
56.8 indicated in Thomas and Sanvido (2000). Re-do work in relation to
Design and Production documentation had little contribution to delay
while waiting for machine, waiting for tools and unnecessary move were
not recorded.
In order to raise labour productivity on construction sites, proper
documentation, adequate information, efficient organization of labour,
materials and equipment become crucial. There is also the need to
analyze the work environment well before commencement of work on site
to as to develop an appropriate production template for higher productivity
across board.
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REFERENCES
Calvert, R.E. (1995): Introduction to Building Management, 6th Edition,
Oxford, Butterworth-Heinemann.
Chapman, R.E. and Butry, D.I. (n.d). Measuring and Improving the
Productivity of the U.S. Construction Industry: Issues, Challenges and
Opportunities. Building and Fire Research Laboratory, National
Institute of Standards and Technology, Gaithersburg, MD 20899.
Chromokos Jr., J. and McKee, K.E. (1981): Construction Productivity
Improvement, Journal of the Construction Division, ASCE, 107 (1):
37-47
Dozzi, S.P and Abou-Rizk, S.M. (1991): Productivity in Construction.
Institute for Research in Construction, National Research Council,
Ottawa, Ontario, Canada p. 1
Durdyev, S. and Ismail, S. (2012). Pareto Analysis of on-site Productivity
Constraints and Improvement Techniques in Construction Industry.
Scientific Research and Essays. 7(7):824-833.
Horner, R.M.W. and Talhouni, B.T.K. (1995): Effects of accelerated
working, delays and disruptions in labour productivity by daily
visits. AAC International Transactions Prod. 05:1
Jerges, G.E., Christy, S. and Leitner M.J. (2000): Construction
Productivity: A Survey of Industry Practices, ACCE International
Transactions, PM 06.01
Kazaz, A. and Ulubeyili, S. (2004): A different approach to construction
labour: Comparative Productivity analysis, Building and
Environment, 39:93-100
Liou, F. and Borcherding, J.D. (1986): Work Sampling can predict Hint
Rate Productivity. Journal of Construction Engineering and
Management, 112(1):91-94.
Mbachu, J. (2008). Conceptual Framework for the Assessment of
Subcontractors‟ Eligibility and Performance in the Construction
Industry. Construction Management and Economics, 26(5):471-484.
Oglesby, C., Parker, H. and Howell, G. (1989): Productivity Improvement in
Construction New York, McGraw-Hill.
Proverbs, D.G. Holt, G.D. and Olomolaiye, P.O. (1999): European
Construction Contractors: a Productivity Appraisal of In-situ
Concrete Operations. Construction Management and Economics,
17:221-30
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Tavakoli, A. (1985): Productivity Analysis of Construction Operations.
Journal of Construction Engineering and Management, ASCE,
107(1):37-47
Thomas, H. and Yiakoumis, I. (1987): Factor Model of Construction
Productivity. Journal of Construction Engineering and Management,
ASCE, 113(4):427-9
Thomas, H.R. and Sanvido, V.E. (2000). Role of the Fabricator in Labour
Productivity. Journal of Construction Engineering and Management,
126(5), September/October. P.358.
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APPENDIX I
Data on Labour Productivity by Activity Sampling on Construct ion Sites
Rating Frequency Percentage Valid
Percentage
Cumulative
Percentage
Site ‘A’
30 3 7.30 7.30 7.30
40 5 12.30 12.20 19.50
50 20 48.80 48.80 68.30
60 10 24.40 24.40 92.70
70 3 7.30 7.30 100.00
Total 41 100.00 100.00
Site ‘B’
40 3 7.30 7.30 7.30
50 16 39.00 39.00 46.30
60 15 36.60 36.60 82.90
70 5 12.20 12.20 95.10
80 2 4.90 4.90 100.00
Total 41 100.00 100.00
Site ‘A’ & ‘B’ Combined
30 3 3.70 3.70 3.70
40 8 9.80 9.80 13.40
50 36 43.90 43.90 57.30
60 25 30.50 30.50 87.80
70 8 9.80 9.80 97.60
80 2 2.40 2.40 100.00
Total 82 100.00 10.000
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APPENDIX II
FIELD DATA ON APPLICATION OF METHOD PRODUCTIVITY DELAY MODEL (MPDM)
Data on Workers- Construction Site ‘A’
Prod. Cycle Cycle time Non-delay Enviro. Delay Equip. delay Labour delay Mat. Delay Mngt. Delay Processing Columns
1 305 305 1.0909 1,0909
2 315 * 8.9091
3 310 * 3.9091
4 322 * 15.9091
5 325 * 18.9091
6 309 309 2.9091 2,9091
7 304 304 2.0909 2,0909
8 306 306 0.0909 0.0909
9 304 304 2.0909 2.0909
10 319 * 12.9091
11 316 * 9.9091
12 318 * 11.9091
13 324 * 17.9091
14 330 * 23.9091
15 331 * 24.9091
16 321 * 21.9091
17 331 * 17.9091
18 328 * 12.9091
19 324 40% 60% 0.9091 0.909091
20 319 * 1.0909 1.0909
21 307 307 1.9091 1.909091
22 305 305 17.9091
23 308 308 0.0909 0.0909
24 324 * 9.9091
25 306 306 2.0909 2.0909
26 316 * 2.9091 2.0909
27 304 304 20.9091
28 309 309 15.9091
29 327 * 20.9091
30 322 * 15.9091
Source: Researcher’s Fieldwork
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APPENDIX III
FIELD DATA ON APPLICATION OF METHOD PRODUCTIVITY DELAY MODEL (MPDM)
Data on Workers- Construction Site ‘B’
Prod. Cycle Cycle time Non-delay Enviro. Delay Equip. delay Labour delay Mat. Delay Mngt. Delay Processing Columns
1 185 185 1.3000 1.3000
2 187 187 0.7000 0.7000
3 183 183 3.3000 3.3000
4 191 * 4.7000
5 185 185 1.3000 1.3000
6 195 * 8.7000
7 197 * 10.7000
8 190 * 3.7000
9 198 * 9.7000
10 195 8.7000
11 198 11.7000
12 192 5.7000
13 189 * 2.7000
14 185 185 1.3000 1.3000
15 188 18 1.7000 1.7000
16 191 191 4.7000 4.7000
17 194 * 7.7000
18 192 * 5.7000
19 191 * 4.7000
20 186 186 0.3000 0.3000
21 184 184 2.3000 2.3000
22 189 189 2.7000 2.7000
23 191 * 4.7000
24 195 * 8.7000
25 197 * 10.7000
26 201 60% 40% 14.7000
27 192 * 5.7000
28 198 * 11.7000
29 196 * 9.7000
30 194 * 7.7000
Source: Resources Fieldwork
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APPENDIX IV
FIELD DATA ON FORMAN DELAY SURVEY
Data on Workers Construction Site ‘A’
Survey
No
Sources
of Delay
No of
Men
Max Man
Hour
Lost Man Hrs
(Delay)
Lost Man
Hrs (in %)
A B C D E F G H I J
1 8 0 1.5 0 0 0 0 0 13 0 28 224 22 9.28%
2 0 0 0 14 0 0 0 21 14 0 21 168 49 29.17%
3 0 0 0 10 0 0 0 18 6 0 14 112 34 30.36%
4 0 0 2 12 0 0 4 7 8 0 21 168 33 19.64%
5 0 0 0 15 0 0 2 12 3 0 16 128 32 25%
6 0 0 5 20 0 0 6 28 20 0 35 280 79 28.21%
7 0 0 0 0 0 0 0 10 16 0 13 104 26 25%
Sum 8 0 8.5 71 0 0 12 96 80 0 148 1184 275 23.23%
Data on Workers Construction Site ‘B’
Survey
No
Sources
of Delay
No of
Men
Max Man
hour
Lost Man Hrs
(Delay)
Lost Man
hrs (in %)
A B C D E F G H I J
1 10 0 8 0 0 0 75 0 13 0 59 472 105.5 22.35%
2 6 0 0 20 0 0 9 3 15 0 19 152 53 34.87%
3 10 9 0.5 0 0 0 0 0 5 0 14 112 24.5 21.88%
4 0 0 0 16 0 0 0 8 16 0 24 192 40 20.83%
5 0 0 0 8 0 0 0 4 0 0 8 64 12 18.75%
6 0 0 2 8 0 0 0 5 6 0 14 112 21 18.75%
7 0 0 0 0 0 0 0 16 5 0 13 104 21 20.19%
Sum 26 9 10 53 0 0 84 36 60 0 151 1208 277 22.93%
KEY:
Classified Sources of Delay in Foreman delay Survey
A – Redoing work (Design error) B – Redoing work (Change in Design) C – Redoing work (Production error)
D – Waiting for materials E – Waiting for tools F – Waiting for machine
G – Machine Breakdown H – Waiting for information I – Waiting for other workers
J – Unnecessary move
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INTERDISCIPLINARY JOURNAL OF CONTEMPORARY RESEARCH IN BUSINESS
COPY RIGHT © 2013 Institute of Interdisciplinary Business Research 125
AUGUST 2013
VOL 5, NO 4
APPENDIX V
DATA COLLECTION FORM ON FOREMAN DELAY SURVEY (FDS)
Problem causing Area Person – Hours Lost
No of
Hours Lost
No of
Workers
Total Person-
Hours
(1) Redoing work (Design error)
(2) Redoing work (Prefabrication error)
(3) Redoing work (Field error or Damage)
(4) Waiting for materials (Warehouse)
(5) Waiting for material (Vendor/Supplier)
(6) Waiting for Tools
(7) Waiting for construction equipment
(8) Construction Equipment breakdown
(9) Waiting for information
(10) Waiting for other crews
(11) Waiting for fellow crew member(s)
(12) Unexplained or unnecessary move
(13) Other(s)
Comments:
Organization:
Date: