Multivariate approach to benchmarking quality prediction parameters in building maintenance works

Industrial Engineering Letters www.iiste.org
ISSN 2224-6096 (Paper) ISSN 2225-0581 (online)
Vol.3, No.6, 2013
21
Multivariate Approach to Benchmarking Quality Prediction
Parameters in Building Maintenance Works
Amusan L.M1
, Oluwunmi A.O2
., Owolabi J.D3
and Joshua O4
.
1,3&4 Building Technology Department School of Environmental Sciences College of Science and Technology,
Covenant University. Klm 10, Ota Idiroko Road, Ogun State. Nigeria.
2 Estate Management Department School of Environmental Sciences College of Science and Technology,
Covenant University. Klm 10, Ota Idiroko Road, Ogun State. Nigeria.
Abstract
Observing quality etiquette on maintenance work is of essence when satisfying clients’ requirement is a priority.
However, the quality etiquette comes in the form of framework and benchmarks. This study has therefore
presented succinctly, multivariate approach to benchmarking quality prediction parameters in building
maintenance works. The study used sixty-three (63) questionnaires retrieved which contains information on
benchmarked parameters. The study used factor analysis to reduce the parameters to a sizeable number based on
their coefficient and Eigen value. Resultant factors were used to dissect quality into quality dichotomies; the zero
defect, medium quality and high quality work status. The model would assist building maintenance practitioners
in quality monitoring on building maintenance works.
Key words: Benchmark, Quality, Multi-variable, Rotation, Prediction.
Introduction:
Quality system formulating and compliance enforcement at all facets of works in construction industry have
been areas with increased attention worldwide, over the decades. This has resulted in the awareness about
finished products quality assurance in the construction industry, the kind that provides clients’ satisfaction and
value on money invested (Chan & Tan, 2009; Roston and Amer 2006). Quality issues often arise from clients’
needs and specification, these often formed the basis of supervision as work progresses on construction sites
(Lings, 2005; Bamisile, 2004). Likewise, clause on compliance with specified quality standard is often stated in
building projects’ contract documents, commencing from brief stage to project commission stage, which has in
no way different from other sectors of the economy. This fact has therefore turned formulating quality policy
statement to a global best practice. However, certain school of thought believes quality to be work-state
dependent, that quality can be described as conformity with specified instructions as project progresses.
Bamisile (2004) and Oakland (1984) submitted that quality is fulfillment of specified requirements. Another
school of thought viewed quality from fitness for purpose point of view, that quality can be termed item of work
that is rightly formed to perform intended purpose (Chan & Tam, 2000). Similarly, in another submission,
quality can be seen from the perspective of agreement between goal, clients and builder (Fan 1999; Abdel-Rasek
et al; 2001).
However, non alignment with the goals in facilities maintenance that formed the direction through which various
plans and strategy should be driven could be linked to various failures often recorded in building maintenance
sector. Huge monetary resource goes into maintenance annually without lasting solution proffered, which is
often revealed through reworks. This trend however could be attributed to absence of standard procedure to be
followed in facility maintenance. To this end this study is set at developing a model for parameter that should be
follow to ensure quality maintenance work in Nigeria. sThere are institutions responsible for enforcement of
standard and procedure, such as Standard Organization of Nigeria (SON), British Standard Institutions (BSI) in
Great Britain including International Organization for Standardization ISO. BSI in 1979 issued BS 5750
‘Quality system’ ISO rolled out, ISO 9000 in 1987, all these are quality documents. Oladokun and Adegbenjo
(2008) submitted that standard Organization of Nigeria officially adopted ISO 9000 for quality management in
Nigeria, and since then widely publicized training workshop had been staged in an attempt to create awareness.
The structure and component of the ISO 9000 did not adequately provide framework to address detail aspect of
quality problem in construction industry. However, introduction of National Building code in August 2006
provided a silver lining out of the cloud of the problem, by providing a conceptual framework upon which
quality issues in building maintenance works can be based. Since then, there has been no attempt at modeling
the concept mathematically, which could provide a platform for further development and research, in aspect of
quality monitoring in facility maintenance in Nigeria. To this end therefore, this study is set at developing a
model of parameters that could be used as working guide in ensuring quality of facility maintenance operations,
and a real improvement in maintenance sector of Nigerian Construction Industry.

Vol.3, No.6, 2013
22
Review of Related Works:
A number of researches have been carried out in the direction of finding out detail about factors affecting quality
of works in built environment and generating model to describe related assumptions. Ling (1990) developed
quality assurance procedures manual, which summarizes essential tips in quality assurance in construction works.
Amusan et al; (2012a) described the role of building material manufacturers in quality assurance in building; the
study identified the unethical practices in the construction industry in relation with effect on quality and safety.
Similarly, Cham and Tam (2000) produced a model containing 77 sub-factors from six main factors in predicting
quality performance of building projects in Hong Kong. Furthermore, Oladokun and Adelakun (2008) Amusan
et al; (2012b) generated a quality model that attempt at describing existing relationship among project
parameters, it states that: Quality = 5.20 + 0.50 (project management) action of project team) + 0.80
(effectiveness of the construction team leader) + 0.30 (client emphasis on time). In El-Dosouky and Sulaiman
(2001), a model based on average weighted score of site staff, project execution, site layout, subcontractor,
equipment, labor material, contract and design was generated. This generated model was described as suitable
for use on Egyptian construction projects. A model for predicting quality of building projects in Singapore was
developed by Ling (2005). The study suggested parameters that could be used in enforcing quality in building
projects; it also identified the variables that affect quality scores of project in Singapore projects such as design -
bid- build (DBB) and design build (DB) project. Lastly, Rustom and Amer (2006) modeled factors affecting
quality of works in Gaza strip, using two different perspectives (i) Stepwise multiple regression analysis and
factor analysis.
Research Methods:
A platform was set for the research through comprehensive literature search to establish the current state of
knowledge in order to put the work into proper perspective. Random sampling technique was used to gather
information from population of site managers, project directors, construction managers, maintenance engineer
and facility manager. One hundred questionnaires were administered and sixty three were returned and used for
the analysis. Samples of respondents were taken from Lagos state, Ogun state, Abuja (F.C.T.) and Portharcourt.
These locations were chosen as a result of high concentration of construction activities taking place there. The
distributed questionnaire was designed in Likert scale 1 to 5, the respondent were requested to express their
opinion in the degree tabulated on the questionnaires. A scale 1 to 5 was adopted, with 1 representing “strongly
disagree (SD)” 2 – being disagree (D) 3 – being neither agree nor disagree (N), 5- being strongly agree (SA).
Agreement index of the respondents was generated using the relation M.A.I = 5S.A + 4A + 3S.D + 2D +
1N/5(S.A+ A+S.D+D+N)
( )
AijN
Aij
IAM
∑
∑
=
1
.. where M.A.I = Mean Agreement Index A= Agreement
variable i = Lower boundary, j = Upper boundary
N = Frequency of Variable Σ = Summation Notation.
Total maintenance operation management (TMOM) is advocated in this study. TMOM would enable total
management of all aspects of building maintenance operation. TMOM covers the technical and management
aspect of maintenance operation. In TMOM presented in this context, the following parameters were presented
in Likert scale structure for ease of response by respondents; quality policy, communication, work environment,
personnel management, performance monitoring, budgeting, resource allocation among others. Information on
the parameters is presented in Likert scale 1 to 5 on the questionnaires used in data collation on the set
parameters.
Model Development
Different researchers have used diverse methods to generate model to measure quality of construction operation
carried out. Chan and Tam (2000) used combination of multiple regression analysis and factor analysis. Roston
and Amer (2006) adopted weighted average, factor analysis, Pareto and stepwise multiple regression analysis.
Also, Abdel Rasaq et al; (2001); Ling (2005) used calculated Pearson’s correlation coefficient, and weighted
average approach. However for the purpose of this work, this research work adopted combination of stepwise
multiple regression methods and factor analysis for data reduction. The response from questionnaire was loaded
onto the statistical package for social science students (SPSS) software, the factors were subjected to factor
rotation so as to ensure emergence of stable criteria which would be used in modeling and represent relationship
among the thirty-nine variables regarded as ability parameters. The resultant factors were then subjected to
stepwise multiple regression analysis to establish pattern of relationships among them.
Factor Extraction:
Percentage of total variance obtained from each of the independent variables (the thirty-nine (39) variables (sub

Vol.3, No.6, 2013
23
factors affecting quality) were examined). Each variable was standardized to have variance of 1, while total
variance was given by the sum of each variable which totaled thirty-nine (39). Chan and Tam (2000), Ruston
and Amer (2006) adopted two approaches to determine the factors to be included in the model. They used
Screeplot and Eigen value approach, Chan and Tam (2000) submitted that in Eigen value approach, only variable
with Eigen value greater than one (1) should be included in the model formation. In screeplot approach, there is
differential relationship pattern among variables; there is always a distinct demarcation between large variables
on steep slope and gradual trailing off scores of the rest variables. This usually occurs at the variable, where K is
the true number of variables Chan and Tam (2000). However, this study adopted Eigen value and regression
coefficient approach as shown in Table 9. Eighty-two percent of (82%) the total variance is attributed to the first
20 variables where these variables have an Eigen value greater than 1. Other twelve (12) variables account for
only about 38.25% of the total variance. This shows that a model with 20 factors should be robust enough to
represent the data
Factors Rotation
Factors rotation is used to identify the relationship of individual variables to the set of common factor
synthesized; Oblim rotation can be used to achieve this. Therefore, Oblim rotation approach was adopted. On
the other hand, Rostom and Amer (2006), used variance rotation methods, and were able to discover each
variable with a single factor. Table 9 shows the relationship of the variables to the common factors, the new
factors and elements related to each factor. The new set of twenty (20) factors that emerged after rotation is
presented in Table 10.
Analysis of Results and Discussions .
Analysis of the sixty-three (63) collated questionnaires is scheduled in Tables 1 to 10 in this section. Table 1
presents information about quality policy, it revolves around the following items; maintenance policy details,
employee involvement in decision making, communication of standard expected of work done, formulation of
quality assurance team and periodic retrospective check on successful implementation of quality policy. Clearly
defining maintenance policy to be used was ranked first (1st
) among the five (5) variables with mean index
score(MIS) value 0.92. Open communication of expected standard of work and quality policy, retrospective
check on success of maintenance policy implemented was scored with MIS value 0.91 respectively. Implications
of these results points to the relevance of clearly defining quality policy to the employee, setting up the
enforcement and assurance team that will drive the quality vision and periodic review of success achieved. All
these are essentials of formulating, benchmarking and quality assurance on construction works.
Table 2 illustrates responses on communication, authority and responsibility, convening meeting on quality
issues periodically was scored high with MIS value 1.00, establishing line of command scores 0.89, effective
communication of information about maintenance quality to personnel scores 0.88 while responsibility
delegation has MIS value of 0.88.
The outcome implies that convening periodic meeting where maintenance issues would be discussed would
enable effective communication and understanding by all and sundry. Also, line of command need to be put in
place, this would likewise facilitates responsibility delegation for an effective leadership. Work environment
related issues were presented in Table 3, creating a work environment that conforms to international standard
was rated high with MIS value 0.9, provision of adequate ventilation first aid and personal protective items were
scored next with MIS value of 0.91. Likewise, provision of incentives to enhance productivity has MIS scored
0.91. In the modern day, creating a conducive environment that stimulates productivity is highly essential. It is
often regarded as an incentive that produces satisfying effects. Therefore creating an environment with state of
art work tools, first aid materials and personal protective devices has capacity to stimulate workers at performing
maximally.
Table 4 presents related factors on manpower, training and development. Organizing workshop, conference for
workers were strongly advocated and tagged with MIS value 0.96; organizing refresher courses on job-place
quality assurance scored 0.93, while mechanization of production process with automated tools scored MIS
value 0.92. Sometimes, mechanizing production process improves process quality, introduction of new tools
would warrant setting up refresher courses. In addition, knowledge upgrade through seminar, conference,
workshop among others increases quality and value of personnel.
Moreover, Table 5 contains information on measurement and precision as a benchmark for quality measurement.
Placing emphasis on getting quality work done once and at a time was scored on MIS scale 0.92 closely
followed with periodic measurement of quality index on the work done with MIS value of 0.92. Ascertaining
the frequency of rework also scored MIS value 0.92. This factor is also one of the quality parameters to measure
quality of work done. The greater the amount of returned job and breakdowns after maintenance the lower the
quality of product being turned out to consumer. However, placing emphasis on getting quality job done once

Vol.3, No.6, 2013
24
and all the time is an important issue in maintenance, this would reduce rate of rework and brightens customers’
hope. This can be achieved through periodic measuring of product quality index.
Furthermore, Table 6 illustrates analysis on performance monitoring. Performance monitoring on the
maintenance operation carried out is of importance, this could be achieved through use of conventional
techniques, teaching personnel on how to enhance their performance, teaching art of personal fault recognition
and assessing extent of maintenance work done. The use of conventional methods of performance monitoring
was ranked high with MIS value 0.91, teaching personnel art of failure recognition scores 0.91, replacing human
based inspection method with conventional methods has MIS score 0.88 while noting frequency of corrective
operation as performance index was scored 0.88.
In Table 7, factors on resource allocation and budgeting was scheduled, setting up budget for routine
maintenance has MIS value 0.90; resources to be allocated for works in every fiscal year has MIS value 0.86.
Benchmarking fund for maintenance work at every fiscal year is highly essential, it will enable avoiding
excessive spending and facilitate budgeting, and it would as well help in routine and periodic financial check.
Table 8 presents component of quality cost objective. Minimizing expenditure to maximize profit has MIS value
0.59, having maintenance expenditure base on machine, equipment age and utilization has MIS value 0.89 while
allowing contingency allowance tools, incidentals with bias for internal and external failure has MIS value 0.90.
Quality cost objective of any organization should make provision for minimizing expenditure while maximizing
profit and economic situation of machine and tools.
Moreover, in Table 9, Factor rotation analysis was conducted on thirty-two (32) factors; this was reduced to a
sizeable number. The co-efficient of the factor was used to select resultant factors that could be used as
benchmarked parameters. Correlation co-efficient and Eigen-value were used in selecting the best factor. After
rotation, variables with coefficient greater than 0.43 were preliminarily selected, the factors were later sized and
selection parameters benchmarked to coefficient value 0.75 to Eigen value of 1.00. Extracted values were
scheduled in Table 10. Table 10 presents the extracted coefficients of the factors; this action reduced the factors
from 32 to 20 factors, with strong Regression coefficients and Eigen values. Factors F1, F22, F6, F11, F14 and F21
emerged as favorable factors that constitutes benchmarked parameters. Factor 22 (F22) was tagged with two (2)
variables; F1 with 3 variables, F6 (3-variables); F11(3-variables); F14(4-variables) and F21 with five(5) variables.
This forms the nucleus of benchmarked quality parameters as presented in Fig 1.
In Fig.1, quality parameters were benchmarked into three (3) quality dichotomies, namely: zero defect, medium
quality and high quality. Zero defects occur when there is near-zero defect situations. The benchmarked quotient
for this status is 1.00. Medium quality is the second dichotomy; medium is benchmarked to occur at 80% quality.
Five (5) factors were tagged coded as Zero defect factors, these factors when observed would guaranteed zero
defect, the factors includes: F1( QP3-Clearly communicating standard and operation quality of maintenance work;
QP4- Formulating quality assurance policy and QP5- Periodic retrospective check on successful implementation
of quality policy. Also, CAR3- Setting up of quality implementation committee; CAR4-Delegation of
responsibility and CAR5- Establishing lines of command. Similarly, MTD1, MTD2 and MTD4 are tagged as
factors to be taken into consideration in ensuring zero defects. MTD1 Factor recommends skill workers’
sufficiency in maintenance operations; MTD2-Organizing workshop and conference for workers; MTD4-
Rotational job-bits for workers and mastering of craftsmanship.
Furthermore, F14 factors includes MTD3,MTD5, WE4 and QCO2. MTD3- Setting up refresher course for
personnel, MTD5- Mechanization of production process, WE4-Man-machine convenience, QC-Allocating
maintenance fund based on machine-tool age. `
Lastly, F21 is another factor for zero defect products. It consist of five (5) subfactors; PM5,RAB1,RAB2, RAB4
and QCO1. PM5- Noting frequency of corrective operation as index of performance monitoring, RAB1-Allocating
resources for emergencies; RAB4- Progressive auditions of operations and QC1-Minimizing expenditure to
maximize profit.
Conclusions and Recommendations
The study has presented a framework for benchmarking quality in maintenance operations. Quality parameters
have been benchmarked into three dichotomies: the zero defect parameters; 80% quality parameter (tagged
medium quality) while the third group is 90% quality parameters (tagged high quality). Therefore, when zero
defect is desired, the following parameters subsists:F1, F6, F11, F14 and F21. If Medium quality (80% quality) is
desirable the following parameters are applicable; F6, F11, F14 and F21. However, high quality job would be
achieved with the following parameters: F22, F11, WE4 and F21 combination of one or more of the parameter
would facilitate quality work in maintenance operation. The model would help maintenance practitioners in
formulating framework for quality conformance in maintenance work. The study has the capability of
contributing to the body of knowledge in the area of quality management in building maintenance operations this
study can also form a platform for further studies and working guide in quality prediction of different aspects of

Vol.3, No.6, 2013
25
construction works.
Reference
Amusan L.M; Anosike M.N; Opeyemi J; Adegbenjo A and Owolabi J.D (2012a) Entropy and Expert System
Approach to Modelling Cost smoothing system in Reinforced Concrete Office Building projects Procurement.
Journal of Civil and Environmental Research. 2(9). 27-35.
Amusan L.M; Oluwunmi A.O; Adegbenjo A; Tunji-Olayeni and Ogunde A.O (2012b) Target Output, Extended
Output and Site Productivity: Tales of the Expected. Journal of Environment and Earth Sciences. 2(10). 35-46.
Chan, A.P and Tam C.M (2009) Factors Affecting Quality of Building Projects in Hong-Kong. International
Journal of Quality Reliability Management 17(4), 43-441.
Rustom, R.N and Amer, M.I. (2006) Modelling the Factors Affecting Quality in Building Construction Projercts
in Gaza Strip. Journal of Construction Research.7(2), 33-67.
Ling,F.Y.Y (2005) Models for Predicting Quality of Building Projects. Engineering Construction and
Architectural Management.12(1),6-14.
Bamisile,A. (2004) Building Production Management. Lagos. Foresight Press Limited.
Oakland J.S. (1984) Total Quality Management, Oxford, Heinemann.
Fan, C.N (1995) Development of Quality Assurance in Hong Kong Construction Works. Hong Kong Contractor.
March/April.
Abdel- Razek, R.H; El-Dosouky,A.I and Solaimon, A.M. (2011) A Proposed Method to Measure Quality of The
Construction Projerct. International Exhibition Conference for Building and Construction.Egypt.
Oladokun M.G . and Adelakun A.D (2008) Multivariate Approach to Quality Prediction of Building Projects in
Nigeria. The Professional Builder.June.
Table 1: Analysis of Response on Total Maintenance Operation Management (T.M.O.M.) structure.
List of Tables
Table 1: Quality Policy
Quality Policy
1.Policy of maintenance method to be used should be clearly defined
2. Employee should be involved in decision making.
3. Standard of works and operational quality should be clearly communicated.
4. Quality assurance team should be formulated.
5. Period retrospective check on successful implementation essential.
Total
52
62
55
55
57
MIS
Val
0.92
0.85
0.91
0.92
0.91
Rank
1
5
3
1
3
Table 2: Communication, Authority and Responsibility.
Communication, Authority and Responsibility.
1. There should be effective communication of information on work
quality standard to the maintenance personnel.
2. Management should convey meeting on quality in maintenance issue
periodically.
3. Policy implementation committee need to be established
4. Delegation of responsibility is essential for over operation success
5. Establishing line of command is essential.
Total
61
57
59
57
54
MIS
Val
0.88
1.00
0.54
0.88
0.89
Rank
3
1
5
3
2
Table 3: Work Environment
Work Environment
1. Work environment should conform to international standard.
2. Adequate ventilation, first aid and personal protective items should be
available
3. Work schedule should be flexible to minimize error and accident.
4. Man-machine convenience should be given consideration
5. Provision of incentive to enhance productivity.
Total
45
55
43
45
55
MIS
0.92
0.91
0.88
0.86
0.91
Rank
1
2
4
5
2

Vol.3, No.6, 2013
26
Table 4: Manpower Training and Development
Manpower Training and Development
1. Skill workers should be sufficient in companies/ organizations maintenance
operations.
2. Workshop, Conference should be organized for workers (essential for on-job
development).
Total
44
43
43
MIS
Val
0.86
0.96
0.93
Rank
5
1
2
3. Rotational of job-bits for workers job-experience universality
4. Mechanization of production processes operation
43
48
0.89
0.92
4
2
Table 5: Measurement and Precision
E Measurement and Precision
1. Emphasis is usually on getting the work done correctly once and always
2. Periodic measurement of maintenance quality management.
3. Item repaired last long before developing faults.
4. Fault developing period on maintained items are as follows:
Below 5 months.
5-10 months.
10 months and above
Total
34
46
42
47
48
MIS
Val
0.50
0.55
0.85
Rank
5
3
4
2
1
Table 6 : Performance Monitoring
F Performance Monitoring
1. Conventional method of detecting faults should be in place.
2. Human-based inspection method should give way to conventional method
3. Personnel should be taught fault recognition techniques.
4. Personnel should be taught ways of assessing maintenance works done.
5. Frequency of corrective operation (rework) should be noted as performance
index
Total
59
65
55
56
71
MIs
Val
0.88
0.80
0.91
0.90
0.88
Rank
3
2
5
4
1
Table 7: Resource Allocation Budgeting
G Resource Allocation Budgeting
1 Resource should be allocated for works in every fiscal years.
2 Financial allocation should exist for emergencies.
3 There should be budget for routine maintenances.
4 Progressive auditioning of operations.
Total
50
40
48
51
MIS
Val
0.86
0.74
0.90
0.86
Rank
2
4
1
2
Table 8: Quality Cost Objective.
H Quality Cost Objective.
1 Minimizing Expenditure to maximize profit.
2 Having maintenance expenditure base on machine/equipment age/utilization
3 Allowing contingencies for tools and incidental: internals and external failure
Total
45
50
50
MIS
Val
0.59
0.85
0.90
Rank
3
2
1

Vol.3, No.6, 2013
27
Table 9: Factor Rotation of Quality Parameters
Variables F1 F2 F3 F4 F5 F6 F7 F8 F9 F10
QP1 1.00
QP2 1.00
QP3 1.00 1.00
QP4 1.00 1.00 1.00
QP5 1.00
CAR2 0.43 1.00
CAR3 1.00
CAR4 0.43 0.87 1.00
CAR5 0.91 1.00
F11 F12 F13 F14 F15 F16 F17 F18 F19 F20
WE1 1.00
WE2 1.00
WE3 1.00
WE4 0.52 0.91 1.00
WE5 1.00
MTD1 0.74 0.91 1.00
MTD2 0.82 1.00
MTD3 1.00 1.00 1.00
MTD4 1.00 0.52 0.91 0.82 1.00
MTD5 0.49 0.82 0.82 0.93 0.82 1.00
F21 F22 F23 F24 F25 F26 F27 F28 F29 F30
PM1 1.00
PM2 1.00
PM3 0.57 1.00
PM4 1.00
PM5 1.00 1.00
RAB1 0.82 0.91 0.58 1.00
RAB2 0.91 1.00
RAB3 0.52 0.52 1.00
RAB4 0.82 0.58 0.58 0.82 1.00
QCO1 1.00 1.00 1.00
QCO2 0.57 0.57 1.00 0.58 0.85 0.57
QCO3 0.90 0.91 0.52 1.00
Table 10: Extracted Factors Coefficients
FACTORS
F22 RAB2(0.91) QC3(0.91) ------------
F1 QP3 (1.00) QP4(1.00) QP5(1.00)
F6 CAR3(0.87) CAR4(1.00) CARS(0.91)
F11 MTD1(0.74) MTD2(0.82) MTD4(1.00) ---------------
F14 MTD3(1.00) MTD5(0.82) WE4(0.91) QCO2(1.00) -------------
F21 PM5(1.00) RAB1(0.82) RAB2(0.91) RAB4(0.82) QC01(1.00)
List of Figures
Benchmarked Quality Parameters
Fig. 1 Benchmarked Quality Parameters
Zero Defect: 1F1 + 1F6 + 1F11 + 1F14 + F21
80 % Medium Quality: 0.87F6 + 0.8F11 + 0.82 F14 + 0.82 F21
90% High Quality: 0.91F22 + 0.91F11 + 0.9WE4 + 0.91F21

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