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The impact of Engineering, Procurement and Construction (EPC) Phases on Project Performance: A Case of Large-scale Residential Construction Project

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The Construction Industry is a complex and fragmented industry worldwide with regards to its supply chain, products, and processes, and is faced with a similar dilemma as faced by manufacturers during its time in past decades. Scope, time, and cost are the triple constraints of project management and leading factors in defining the project performance. Productivity and efficiency of each construction project is measured through its triple constraints, therefore the factors that affect project success are significantly important. Despite the importance of understanding project performance indicators, few empirical studies have been conducted over the last decade in terms of analyzing the factors that determine the performance of high-rise buildings in Engineering, Procurement, and Construction (EPC) projects. Hence, the aim of this paper is to analyze and rank EPC critical activities across large-scale residential construction projects in Iran, by using the TOPSIS method as a multi-attribute group decision-making technique. Results indicate that engineering design, project planning and controls are significant factors contributing to the project performance. In addition, engineering has a pivotal role in project performance and this significance is followed by the construction phase. On the contrary, all believe procurement is more important than Construction phase.
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buildings
Article
The impact of Engineering, Procurement and
Construction (EPC) Phases on Project Performance:
A Case of Large-scale Residential
Construction Project
Kamyar Kabirifar * and Mohammad Mojtahedi
Faculty of Built Environment, University of New South Wales; Sydney 2052, Australia;
m.mojtahedi@unsw.edu.au
*Correspondence: kamyar.kabirifar@student.unsw.edu.au; Tel.: +61-478-195-323
Received: 23 November 2018; Accepted: 2 January 2019; Published: 8 January 2019


Abstract:
The Construction Industry is a complex and fragmented industry worldwide with regards
to its supply chain, products, and processes, and is faced with a similar dilemma as faced by
manufacturers during its time in past decades. Scope, time, and cost are the triple constraints of
project management and leading factors in defining the project performance. Productivity and
efficiency of each construction project is measured through its triple constraints, therefore the factors
that affect project success are significantly important. Despite the importance of understanding
project performance indicators, few empirical studies have been conducted over the last decade in
terms of analyzing the factors that determine the performance of high-rise buildings in Engineering,
Procurement, and Construction (EPC) projects. Hence, the aim of this paper is to analyze and
rank EPC critical activities across large-scale residential construction projects in Iran, by using
the TOPSIS method as a multi-attribute group decision-making technique. Results indicate that
engineering design, project planning and controls are significant factors contributing to the project
performance. In addition, engineering has a pivotal role in project performance and this significance
is followed by the construction phase. On the contrary, all believe procurement is more important
than Construction phase.
Keywords: EPC projects; project Performance; triple constraints; TOPSIS
1. Introduction
The project is a short-term attempt that seeks to create a product or service. The aim of the
project is to identify and achieve its respective owner’s goals. Projects are frequently carried out by
the project team as a means of attaining the organizations crucial plan or service production [
1
].
Project management forms the foundation of every construction project. Construction projects
are a multi-faceted and highly organized operation, consisting of many tasks focused solely and
in conjunction with the singular purpose of constructing a building or structure [
2
]. Cost, time,
and scope have been the triple constraints of Project Management Triangle (PMT) for many years.
These constraints have been linked with measuring the project management success [3,4].
The construction industry represents a significant percentage of many countries Gross Domestic
Product (GDP). According to World Bank, developing countries are responsible for approximately
6–9% of the GDP [
5
,
6
], therefore the success of the construction industry often leads to the promotion
and maintenance of long-term economic growth and stability. In recent years, multiple attempts
have been made to improve construction project productivity and success rates, which frequently
represent the fundamental principles for the successful implementation of the projects management
Buildings 2019,9, 15; doi:10.3390/buildings9010015 www.mdpi.com/journal/buildings
Buildings 2019,9, 15 2 of 15
and optimization. The construction projects success is the main foundation of management and control
procedures of the current project and detailed planning for future projects [7].
Construction projects generally involve complex and fragmented multi-tasks, which are carried
out by several professionals and non-professionals within the Project Life Cycle (PLC), which include
engineering, procurement, and construction (EPC) phases. Construction projects comprise building
and infrastructure projects and need accurate coordination to meet project success. Accordingly,
the construction industry is often confronted with dilemmas in its processes which cause poor
performance. As such, the construction industry is left embattled by the resulting flow-on effects of
low efficiency and productivity [8].
The significance of these inefficiencies within the construction industry is heightened in terms of
cost and time overruns. Hussin, Rahman [
9
] revealed that 14% of project contract sum is consumed by
cost overruns, while time overrun happens to more than 70% of all construction projects, and 10% of
projects materials end up as waste material.
The successful implementation of construction projects in the competitive construction market
plays a significant role in the company’s success. Meanwhile, the construction companies that are able
to manage their resources (material, human, financial, equipment, and time) achieve high performance
efficiency. Construction projects are complex with regard to variety of works, budget, duration, and the
number of parties involved [10].
The construction industry, as any other industry, needs to be continuously improved. The principle
behind this continuous improvement has come from the PDCA cycle (Plan, Do, Check, Act) which was
initially introduced in manufacturing and was later utilized in the construction industry [
11
]. PDCA is
highly dependent on continuous measurement. It is an iterative four-step management method applied
in enterprises for the control and continual improvement of processes and products [
12
]. There have
also been a lot of other approaches towards efficiency enhancement in the construction industry,
which is the preventive factor from poor performance. One of these trends is derived from the Toyota
Production System (TPS) that is looking for waste minimization, effort maximization, and secure
profit to end users. TPS has originated from the approach which is called Lean Production (LP).
The international group for lean construction identified lean construction (LC) to define a method for
the purpose of designing and implementing construction activities to minimize waste in construction
industry in terms of time, cost, and quality [
13
]. In addition to LC, there have been other approaches
towards better management of construction projects including adoption of Total Quality Management
(TQM), which is a management theory focused on improving an organization’s ability to deliver
quality to its customers on a continuously improving basis. Six Sigma and ISO 9001:2000 can also
enhance the organization’s efficiency by reducing the number of defects [14].
The construction industry is a project-specific industry and assessment of the overall performance
of construction projects is difficult due to the lack of development of standard procedure. The project
nature, the effective project management tools, and the adoption of innovative management approaches
are the Critical Success Factors (CSF) for construction projects [
15
]. Meanwhile, CSF should be
determined at the inception of the project, therefore, by focusing on these factors which are the
main inputs of the project management system, the likelihood of project success is most likely
increased. CSF explicitly influence the main goals of the project including time, cost, and scope [
16
20
],
however, CSF depends on the nature and type of construction projects and includes cost, time, quality,
satisfaction, management, safety, technology, organizations, environment, and resources [
21
,
22
]. Time,
cost, and quality are, however, the three predominant performance evaluation dimensions in the
construction industry, also known as the Iron Triangle or Project Management Triangle [22].
Despite the application of various theories, techniques, and tools, the construction industry is still
suffering from inefficiency in terms of time and cost overruns and poor quality globally, which can
threaten the entire life of the projects and lead to delays, disputes, and losses. [
23
]. Iran’s construction
industry has also not been an exception and suffers from inefficiencies which arise from several factors
that finally affect time, cost, and scope of the projects [18,23].
Buildings 2019,9, 15 3 of 15
There is lack of comprehensive research to explore factors causing poor performance of large-scale
residential construction projects (residential construction projects above 5000 square meters) with
regard to project phases (EPC) in Iran. Meanwhile, the prioritization of these factors and their
interaction with project performance have also not been studied. Therefore, this research aims
to identify and prioritize the factors that affect construction project management triangle (CPMT)
with regards to project phases (EPC) in constructing large-scale residential buildings in Iran’s
construction industry.
2. Literature Review
2.1. Management Practices in Construction Project Triangle Success
The concept of lean construction (LC) continues to expand. LC has been defined in numerous
ways, however the following explanations are among the most updated ones [
24
]. Co-founders of the
Lean Construction Institute (LCI), Greg Howell and Glenn Ballard, see lean construction approach
as a construction management procedure [
25
,
26
]. Lean construction has its roots in TPS and is a
novel way of designing and implementing construction projects that are uncertain and complex [
27
].
The Construction Industry Institute (CII) has defined lean construction as the constant process of
waste elimination, fulfilment of customer expectations and requirements, concentration on whole
value stream, and seeking perfection throughout all aspects within the operation of constructing a
project [2830].
Several initiatives play a significant role in order to yield improvement for construction projects.
These initiatives include; Lean Construction (LC), Total Quality Management (TQM), Six Sigma,
and ISO 9001:2000. These initiatives have a close connection to Critical Success Factors (CSF), which is
a management term through which the success of a company or an organization is ensured and is the
most important factor that is related to project performance. According to [
31
,
32
], project performance
is determined by performance measurement, which is identified as evaluation of performance relevant
to project success in terms of time, cost, and quality.
2.2. Factors Affecting Construction Project Triangle Success
Many researchers have highlighted the causes and effects of poor construction project
management. Ogunde, Joshua [
33
] have highlighted the most important criteria of construction
projects, which include monetary stability, work progress, quality standard, health and safety,
relationships with stakeholder, resources, management capabilities, contractual and claim disputes,
and reputation.
Among the aforementioned factors, time and cost measurement are increasingly important due to
its capability to establish a crucial benchmark for the purpose of assessment of the project performance
and project efficiency [
34
]. It is also mandatory to determine the reasons for incomplete tasks as
planned. Often, the analyst role might have been assigned to a project scheduler or other staff who
have been educated in the principles of the construction lean methodology, however, traditional
measurements are no longer applicable [
35
]. Traditional construction management tools do not
address productivity, because they encompass cost overruns and schedule slippages [28,30,35].
Time, cost, and quality are the three most essential elements of construction projects, which are
used to determine and measure the efficacy of project success. These three elements exist throughout
the entire project lifecycle, commencing with the planning and design stages and culminating with
the final handover stage [
36
]. Ensuring a sustainable balance across these elements with reference
to the construction projects success is critical, particularly so in the execution of duties required and
targets set for the main stakeholders attached to the project, most especially with sub-contractors.
These stakeholders are often left at the mercy of the deadline imposed by the construction project
and the considerable financial burden yielded when the agreed upon targets are not met [
37
].
According to [
38
], there are a number of risks that can affect the project’s success. These relate
Buildings 2019,9, 15 4 of 15
to time and cost overruns, including, but not limited to, accidents, fluctuation of price, material
inadequacy, and inclement weather.
Chou, Irawan [
39
] conducted research about the construction professional’s knowledge of project
management. In this study a model was suggested, where the effects of various factors on the project’s
success where correlated against the areas of knowledge which were studied. These areas of knowledge
included project scope, time, cost and quality of the project, procurement management, risk, human
resources, and communication [
40
42
]. Poor performance of construction projects, especially in terms
of time overruns and delays, cost overruns, and quality defects has drawn the attention of many
construction practitioners and researchers [43].
Several researchers have identified Stakeholder satisfaction as an additional, yet major index for
measuring the prosperity of the construction project [44]. They have gone further in recognizing that
this index is equally as important as the previously mentioned elements of time, cost, and quality in
relation to the measurement of construction performance [45]. They have cited this index as a crucial
component of mutual stakeholder satisfaction [46].
Other researchers have since noted a clear distinction between the “projects success” and “projects
management success”, where the first phrase places emphasis on measurement against overall success
of the overall objectives of the project and the second phrase relies more on measurement against the
traditional measures of project performance, such as time, cost, and quality [47].
Numerous studies in recent years have been carried out to identify the factors influencing time and
cost overruns in construction projects worldwide [
48
,
49
]. These factors include deficiencies in contract
management, payments for the completed works, materials which are imported, alteration in design,
and deficiencies in subcontractors and supplier’s performance. In addition to the aforementioned
factors, a combination of variables inclusive of poor labor productivity, material shortages, inaccuracies
in the estimation of required materials, fluctuations in the cost of materials, in addition to insufficient
experience with the project type and location have been identified as the main reasons for project time
and cost overruns in the construction of a high-rise building in Indonesia. Other factors which caused
poor efficiency relating to the construction project were identified in Hong Kong, including mistakes
and discrepancies in design, poor site management and supervision, and delays in approvals [50].
There have also been several studies within the construction industry focused on project
control [
51
,
52
]. The aim of project control is to confirm that projects finish on-time, within budget,
and meet the agreed upon objectives. Project control in practice is undertaken by project or construction
managers and comprises continuous measurement of the project progress and taking correction
actions wherever necessary. In the past few decades several project control techniques have been
adopted, such as Gantt Bar Chart, Program Evaluation and Review Technique (PERT), Critical Path
Method (CPM), and Graphical Evaluation and Review Technique (GERT). Meanwhile, several software
packages have become accessible that support the methodology behind the mentioned techniques,
such as Microsoft Project, Primavera, and more [50,53].
Generally, planning and scheduling are a required necessity for all construction projects. Each
construction activity includes several tasks; therefore, planning is a regular technique that identifies
which tasks should be completed, the resources (labor, materials, and equipment) that are needed,
and by the time they are needed. Each schedule indicates the whole plan in graphical form, which
would be in the format of a bar chart. This chart shows activities on a horizontal time scale (on the
basis of days, weeks, months, or even years, which actually depends on the complexity of the project).
The master scheduling plan is typically generated before the commencement of the construction phase
by the experienced estimators [28,54,55].
A study conducted by [
56
] on the influence of deviations from specific standards of delivered
materials in construction projects indicated that lack of communication (communication failure) among
all relevant parties included in a construction project led to deficiencies in the construction performance.
Generally, since the construction industry is a labor intensive industry and laborer’s are
getting paid on a regular basis, time management can assist in controlling the costs of wages [
57
].
Buildings 2019,9, 15 5 of 15
Meanwhile, working with any delays or behind schedule can retard the overall duration of the project,
especially when a group of workers should execute a specific task, or any material should be used in
the construction site. Inevitably, if construction projects are not completed within their allocated time
span, then the contract can be terminated because of the breach of duty, therefore construction disputes
may arise and payment loss will be imposed to the construction company [
58
]. Based on studies
conducted by [
58
,
59
], two of the most important causes of poor project performance are manageable
and non-manageable, which are illustrated in Table 1.
Table 1. The most important factors causing construction project’s inefficiency.
Factors
1. Manageable
1.1 Flows (Resources and information inadequacy)
1.2 Conversion (Poor planning, poor design, improper implementation and execution,
insufficient quality)
1.3 Management (Ineffective control, poor allocation, poor dispensation)
2. Non-Manageable 2.1 Failure in external methods
2.2 Environmental issues
In addition to prior descriptions of wasted time, construction poor performance is caused by
several factors, including contractor, consultant, or labor related, such as inefficient site management,
problems with sub-contractors, poor scheduling, monetary problems, and inexperienced crews, as well
as absenteeism [
60
63
]. Moreover, there are other reasons that create delay which are not under
control of project participants, such as economy instability, natural disaster, revolutions, and inclement
weather, and there are causes that are created by the owners (clients), such as changes in design or late
payments [6467].
Poor performance can happen due to unexpected events. Unexpected events can influence
construction performance severely. One study [
68
] has highlighted the three main categories of delay
caused by unexpected events; delay to commencement, extension of the time span, and suspension of
work during the execution of the project.
The main causes of construction project management poor performance are different in different
countries and depend on their construction culture. Some researchers have highlighted the most
important causes of poor performance that are common in many countries. According to [
66
,
69
,
70
],
the major causes of delays in construction projects are inadequate and poor supervision of construction
site, problems due to inefficient working of subcontractors, planning and scheduling problems,
contractors lack of experience, changes in design during construction phase, late delivery of materials,
unpredictable geological conditions, difficulties and shortages in providing materials, equipment,
and manpower, delays in payment from owners, contractors’ monetary difficulties, design deficiencies,
excessive bureaucracy and paperwork in obtaining work permits, harsh weather conditions, economic
loss due to inflation or fluctuation, and slow pace toward decision making processes.
2.3. Factors Affecting EPC Project Success
A study conducted by [
71
] indicates the differences and similarities between Iranian and Nigerian
construction culture regrading causes and effects of delay. This study highlights the effects of strong
communication among parties from both consultant and contractor views and how this affects
construction efficiency. Another study conducted by [
72
] revealed the identification and prioritization
of the key success factors of mass construction projects in Iran. One study [
73
] has identified and
evaluated the factors influencing success of gas, oil, and petrochemical contractors. This study has also
considered the projects of a well-known oil and gas company in Iran and presented a model for the
success of such types of projects.
In another study, project success has been predicted and evaluated by using the indexes of the
business environment and development model. Determination of the importance of the key factors
Buildings 2019,9, 15 6 of 15
influencing project success in oil and gas projects by identifying them has also been carried out by
another researcher [74].
In addition, another study has conducted by [
75
] based on evaluation of key factors of the success
of the project management in the South Pars Project, the largest gas project in Iran. The identification
and evaluation of the key success factors in project-based organizations was performed by [
76
].
There have been other studies regarding the identification of success factors of healthcare projects in
Iran [77].
EPC phases in projects are complex due to transactions involving a series of construction tasks
to complete a specific asset within a certain time. EPC phases are the most critical phases of
the construction projects, which are related to project success. Some researchers have identified
three aspects of project success in EPC phases of projects; execution process, the project value,
and client satisfaction. Another researcher has emphasized on the importance of time, cost, quality,
and satisfaction of customers in EPC phases [
78
]. Generally, the success of complex construction
projects is strongly related to their lifecycle performance and the performance of each EPC phase
can be attributed to the triangle of time, cost, and quality [
79
]. Several studies have explored the
ways that construction project stakeholders affect the performance of the project. In these studies
the relationship among owners, contractors, consultants, suppliers, and sub-contractors have been
studied [
41
]. Collaborative relationships among construction parties, information sharing and
communication, continual improvement, mutual objectives, dynamic problem solving, equitable
risk allocation, supplier and subcontractor selection criteria, trust, and measuring project outcomes in
EPC phases of construction projects have been considered by other researchers [
80
]. The use of time,
cost, and quality as critical success factors of construction projects for the purpose of construction
project performance evaluation have widely been studied by several researchers [
42
], however, there is
great need to understand these critical success factors with regard to EPC phases of the construction
projects and to identify and prioritize the factors that can affect critical success factors of the project in
the different phases of EPC and affect project performance.
Although there have been several studies investigating construction project management success
factors in Iran, there have been few studies identifying and prioritizing the factors causing poor
performance in residential construction projects. In addition, the evolution of one model for all
construction projects is not reasonable because of dissimilarity in size, nature, and level of complexity
of the projects. Regardless of the valuable research, it should be noted that the accurate identification
and prioritization of factors causing poor performance depends on comprehensive analysis and
investigation of the projects, expert’s judgements, and literature review. Therefore, the identification
and prioritization of the factors causing poor performance of residential projects in Iran has not been
studied specifically, and such research is necessary more than ever.
While all the above studies, to various extents, helped with better understanding the problems
associated with poor efficiency in construction projects, there are some limitations.
1.
Although several studies have highlighted the causes and effects of poor performance in the
construction industry, only a limited number of them have focused on Iran’s construction industry,
especially for residential buildings.
2.
Identification, prioritization, and interaction of factors causing poor construction performance
with regard to engineering, procurement, and construction (EPC) in constructing residential
buildings in Iran has been far from the researcher’s attention.
3. There is a significant need for up-to-date data.
This paper identifies and prioritizes the most relevant factors that cause construction project
management poor performance in terms of time, cost, and quality in constructing residential buildings
in Iran.
Buildings 2019,9, 15 7 of 15
3. Theoretical Framework
Some researchers have studied factors that affect construction project poor performance in
Iran’s construction industry, yet there has not been adequate study on identification, categorization,
and prioritization of these factors according to engineering, procurement, and construction (EPC)
phases of the project. EPC includes three steps in each construction project: (1) Engineering (design);
(2) procurement; and (3) construction. Each of these three phases include factors that affect construction
project performance regarding the project triangle (time, cost, and scope).
The formation of a conceptual framework has been illustrated in Figure 1.
Buildings 2019, 9, x FOR PEER REVIEW 7 of 14
prioritization of these factors according to engineering, procurement, and construction (EPC) phases
of the project. EPC includes three steps in each construction project: (1) Engineering (design); (2)
procurement; and (3) construction. Each of these three phases include factors that affect construction
project performance regarding the project triangle (time, cost, and scope).
The formation of a conceptual framework has been illustrated in Figure 1.
Figure 1. Conceptual diagram for a decision making model.
4. Materials and Methods
Residential buildings in Iran have the greatest number of users among all construction projects
which have been the focus of this research. There were several Iranian entities that participated in
this research, including public construction companies, private construction companies, city councils,
and construction engineering organizations. Therefore, they were selected as a sampling frame in this
research.
The research methodology began with formulating a problem statement and identifying
objectives of the study. The first step of conducting this research was formed based on reviews of
literature to identify main factors that influence poor performance in constructing residential
buildings in Iran’s construction industry. Then, operationalization of established factors into a
questionnaire was carried out. Subsequently, pilot testing of the questionnaire was carried out and
the developed format of the questionnaire was formed. The developed questionnaire included the
factors causing poor performance of residential buildings in Iran with regard to EPC phases of the
project.
4.1. Step 1: Identify Factors
A systematic investigation will identify most of the relevant critical factors in the literature based
on the developed conceptual framework that construction contractors need to implement for EPC
project management and achieve better performance for large-scale construction projects. The list of
factors identified is presented in Table 2. This study draws critical factors from previous studies as
potential critical factors for the project performance for EPC projects.
Table 2. Attributes and Initial Measurement indicators.
Project Phase
Indicator
EPC Project Performance Attributes
Reference
Engineering (X1)
X11
1. Poor design
[33, 38, 45, 46]
X12
2. Poor project planning
X13
3. Poor estimation
X14
4. Design incompletion
Procurement
(X2)
X21
5. Insufficient stakeholder engagement
[56-58, 62]
X22
6. Dispute
X23
7. Reputation loss
X24
8. Long-lead item delivery
Construction
(X3)
X31
9. Poor site supervision
[60, 61, 63, 64, 66, 67,
69]
X32
10. Poor project control
X33
11. Changes in project execution
Figure 1. Conceptual diagram for a decision making model.
4. Materials and Methods
Residential buildings in Iran have the greatest number of users among all construction projects
which have been the focus of this research. There were several Iranian entities that participated in
this research, including public construction companies, private construction companies, city councils,
and construction engineering organizations. Therefore, they were selected as a sampling frame in
this research.
The research methodology began with formulating a problem statement and identifying objectives
of the study. The first step of conducting this research was formed based on reviews of literature
to identify main factors that influence poor performance in constructing residential buildings in
Iran’s construction industry. Then, operationalization of established factors into a questionnaire was
carried out. Subsequently, pilot testing of the questionnaire was carried out and the developed format
of the questionnaire was formed. The developed questionnaire included the factors causing poor
performance of residential buildings in Iran with regard to EPC phases of the project.
4.1. Step 1: Identify Factors
A systematic investigation will identify most of the relevant critical factors in the literature based
on the developed conceptual framework that construction contractors need to implement for EPC
project management and achieve better performance for large-scale construction projects. The list of
factors identified is presented in Table 2. This study draws critical factors from previous studies as
potential critical factors for the project performance for EPC projects.
Buildings 2019,9, 15 8 of 15
Table 2. Attributes and Initial Measurement indicators.
Project Phase Indicator EPC Project Performance Attributes Reference
Engineering (X1) X11 1. Poor design
[33,38,45,46]
X12 2. Poor project planning
X13 3. Poor estimation
X14 4. Design incompletion
Procurement (X2) X21 5. Insufficient stakeholder engagement
[5658,62]
X22 6. Dispute
X23 7. Reputation loss
X24 8. Long-lead item delivery
Construction (X3) X31 9. Poor site supervision
[60,61,63,64,66,67,69]
X32 10. Poor project control
X33 11. Changes in project execution
X34 12. Late delivery of onsite construction materials (late or on time)
X35 13. Poor quality of construction materials
X36 14. Redo of deficient tasks
X37 15. Inadequate or inefficient equipment or machinery
X38 16. Sub-contractor’s poor conditions
X39 17. Skilled workforce
X40 18. Changes in workforce
X41 19. Accidents or incidents
X42 20. Excessive bureaucracy
X43 21. Inclement weather
4.2. Step 2: Collect Data and Evaluate EPC Contractors
Data was collected from local EPC companies accredited by Iran Construction Engineering
Organization to apply to the model developed in Step 1. The questionnaires were then distributed to
relevant parties of Iran’s construction industry. The questionnaire’s structure is based on two parts.
The first part is to attain the respondent’s background and experience in the construction industry,
including qualification, position in the company, years of experience, business activity, and the nature of
the company. The second part was framed based on major causes of poor performance in constructing
residential buildings in Iran’s construction industry.
Data achieved using questionnaires from respondents was gathered and quantitatively analyzed.
A total number of 100 questionnaires (hard and soft copies) was distributed to the all parties involved
in the construction industry in Iran, including clients, consultants, contractors, sub-contractors,
and suppliers, who have been engineers, architects, project managers, engineer assistants, quantity
surveyors, and foremen. The respondents’ working experience ranged from less than three years
to more than 30 years and they had different levels of education from Diploma to PhD. However,
only 40 questionnaires were returned, which constitutes a sum of a 40 percent response rate. EPC
contractors were asked to rate individual questions on a seven-point Likert scale pertinent to their
project performance approaches developed in Table 2.
4.3. Step 3: Develop a Group Decision-Making Model and Data Analysis
A mathematical optimization model based on multi-attribute group decision-making was
developed to combine the factors identified in Step 1 and collected in Step 2 into a composite
decision-making matrix that best represents the range of approaches used in project performance by
EPC contractors in Iran. Multi-attribute group decision-making is an optimization technique which
can address the problem of conflicting conditions [
81
]. The aim of multi-attribute decision-making is to
select the most desirable project management approaches that have the highest degree of performance
for all of the relevant EPC contractors. In multi-attribute decision-making, decision-makers need to
select or rank the alternatives that are associated with commensurate or conflicting attributes. In order
to index the various factors, a multi-attribute decision making technique is required [8183].
In this paper, a non-compensatory approach is introduced for the ranking of project management
approaches in terms of their impact on project performance, using the original TOPSIS, known as
the elimination and choice translating reality method, which is a widely used multi-attribute group
decision-making method [
84
]. This approach provides solutions to performance activities and selection
Buildings 2019,9, 15 9 of 15
problems of transport infrastructure involving multiple conflicting objectives, particularly when
compensation among the criteria is not allowed. By producing a decision matrix and a criteria
sensitivity analysis, TOPSIS can be applied to perform a reasonable strategy selection for a particular
application, including a logical ranking of considered EPC contractors [8186].
TOPSIS is an effective method for analyzing and ranking alternatives and uses the Net
Concordance (NC) value from the best solution and Net Discordance (ND) value from the worst
solution [
85
,
86
]. TOPSIS concurrently takes into account both NC and ND distances to calculate a
Net Concordance Dominance (NCD) value [
87
]. The NCV notion is derived from prospect theory,
which is used to identify the ideal point from which a compromised solution would have the shortest
distance. In this paper, TOPSIS and the notion of NCV is used to develop score values for each project
management approaches in each engineering, procurement, and construction phase to rank the most
critical factors for project performance.
5. Results
Table 3presents the respective Net Concordance Dominance (NCD) value obtained from the
TOPSIS procedure. The table shows that FR2-project planning (NDC = 0.92), FR10-project control in
procurement (NDC = 0.84), and FR1-detailed design (NDC = 0.79) have a greater focus than other
critical factors for project performance based on EPC head contractor’s perspective.
Table 3.
Ranking EPC critical factors on project performance in large-scale residential construction
projects by head contractors.
Indicator ID EPC Performance Related Indicators NC ND NCD RANK
X11 FR1 Poor design 0.82 0.75 0.79 3
X12 FR2 Poor project planning 0.91 0.92 0.92 1
X13 FR3 Poor estimation 0.41 0.32 0.37 20
X14 FR4 Design incompletion 0.54 0.42 0.48 14
X21 FR5 Insufficient stakeholder engagement 0.76 0.54 0.65 6
X22 FR6 Dispute 0.5 0.33 0.42 15
X23 FR7 Reputation loss 0.31 0.44 0.38 18
X24 FR8 Long-lead item delivery 0.6 0.15 0.38 18
X31 FR9 Poor site supervision 0.34 0.75 0.55 11
X32 FR10 Poor project control 0.89 0.78 0.84 2
X33 FR11 Changes in project execution 0.37 0.45 0.41 16
X34 FR12 Late delivery of onsite construction materials 0.5 0.55 0.53 12
X35 FR13 Poor quality of construction materials 0.75 0.82 0.79 3
X36 FR14 Redo of deficient tasks 0.46 0.52 0.49 13
X37 FR15
Inadequate or inefficient equipment or machinery
0.35 0.45 0.4 17
X38 FR16 Sub-contractor’s poor conditions 0.46 0.66 0.56 10
X39 FR17 Skilled workforce 0.55 0.58 0.57 9
X40 FR18 Changes in workforce 0.79 0.35 0.57 8
X41 FR19 Accidents or incidents 0.66 0.89 0.78 5
X42 FR20 Excessive bureaucracy 0.55 0.69 0.62 7
X43 FR21 Inclement weather 0.48 0.24 0.36 21
In addition, the TOPSIS analysis shows that the engineering phase has a pivotal role in project
performance. Table 4shows the ranking and the significance of EPC phases on project performance.
Table 4. Ranking EPC phases and their impact on project performance.
EPC Phase NC ND NCD RANK
Engineering 0.670 0.603 0.636 1
Procurement 0.655 0.550 0.454 3
Construction 0.553 0.403 0.572 2
Buildings 2019,9, 15 10 of 15
Table 4shows that the engineering phase of EPC projects has a leading role in project performance
and on the contrary of the clients’ perspective, construction is more important than procurement phase
in EPC projects for well-performed projects.
6. Discussions
Although several researchers have studied some causes of construction project’s poor performance
in Iran, there is a vital gap in identification, categorization, and prioritization of these factors in
residential construction projects which have been the focus of this study [
88
91
]. The residential
construction projects play a significant economic role regarding the project’s stakeholders and resources
involved in many economies [
92
]. Poor construction performance resulting from poor project planning
and control is among the most critical issues affecting project success [
91
93
]. This paper reports on
a recent study that specifically aims to prioritize head contractors’ EPC activities for better project
performance in a broader project management context. The most substantive outcome of this research
is clear confirmation that head or general contractors believe that developing engineering design
standards is the main critical factor for successful projects. In fact, the engineering phase of large-scale
residential construction projects has achieved the first rank in this study, which emphasizes that design
and planning at the beginning of the projects are crucial [
94
]. Many residential construction projects
in Iran have not been successful due to the poor aforementioned factors [
92
,
94
]. Financial benefits
generally play the most significant role in a project’s initiation in Iran’s construction industry, which is
common among all project’s stakeholders [
95
,
96
]. This issue leads to acceleration in project initiation
without adequate and precise design, estimation, and planning. Therefore, the project success is
transforming into project failure [
90
]. After engineering, construction, and procurement have achieved
second and third ranks, respectively.
Regarding indicators themselves, all participant EPC general contractors in this study also believe
that precise project planning in engineering and project control in construction should be taken to
prevent project failure. Meanwhile, quality of construction materials in the construction phase and
proper and detailed design in the engineering phase have proven to be effective tasks for improving
EPC project performance. To date, such measures have proven ineffective in high-rise building
projects and this is a main concern for engineers, project managers, clients, and other stakeholders [
62
].
Future research should seek to improve the effectiveness and efficiency of engineering standards,
and so guide building development to less hazardous locations and less vulnerable structures.
A further benefit of the results of this paper is that the critical factors for better performance in
EPC projects of different general contractors can be directly compared in project management terms.
Individual builders or developers can benchmark their project management activities against other
comparable contractors. Funding agencies can utilize the values of TOPSIS technique in prioritizing
the allocation of resources to the head contractors.
7. Conclusions
The results from this research will inform clients, planners, engineers, architects, and economists
as they develop more quantitative indicators and standards for project performance, set targets,
and make improvements over time. Clients also can use the TOPSIS indicators developed in this
paper for comparing the contractors in the tender stage to assign the job to the best contractors,
in terms of history of past performance. The TOPSIS technique provides a more realistic form
of modelling for multi-attribute group decision making because it allows for trade-offs between
engineering, procurement, and construction activities. This study has focused on the project triangle
(cost, time, and scope) due to the fact that these factors are more tangible for project’s stakeholders for
the purpose of assessing project success. However, factors such as safety, sustainability, and satisfaction
can also be discussed as project success measures.
Buildings 2019,9, 15 11 of 15
Author Contributions:
K.K. designed and performed the experiments and analysis tools. M.M. formed the
conceptual framework and analyzed the data.
Funding: This research received no external funding
Conflicts of Interest: The authors declare no conflict of interest.
References
1.
Mahmood, A.; Asghar, F.; Naoreen, B. “Success factors on research projects at university” an exploratory
study. Procedia-Soc. Behav. Sci. 2014,116, 2779–2783. [CrossRef]
2.
Martens, M.L.; Carvalho, M.M. Key factors of sustainability in project management context: A survey
exploring the project managers’ perspective. Int. J. Proj. Manag. 2017,35, 1084–1102. [CrossRef]
3.
Joslin, R.; Müller, R. The relationship between project governance and project success. Int. J. Proj. Manag.
2016,34, 613–626. [CrossRef]
4.
Larson, E.W.; Gray, C.F. A Guide to the Project Management Body of Knowledge: Pmbok (
®
) Guide; Project
Management Institute: Newtown Square, PA, USA, 2015.
5.
Kenny, C. Construction, Corruption and Developing Countries. Policy; Research Working Paper, No. WPS 4271;
World Bank: Washington, DC, USA, 2007.
6.
Lopes, J.; Oliveira, R.; Abreu, M.I.J.P.E. The sustainability of the construction industry in sub-saharan africa:
Some new evidence from recent data. Procedia Eng. 2017,172, 657–664. [CrossRef]
7.
Zavadskas, E.K.; Vaini
¯
unas, P.; Turskis, Z.; Tamošaitien
˙
e, J. Multiple criteria decision support system for
assessment of projects managers in construction. Int. J. Inf. Technol. Decis. Mak.
2012
,11, 501–520. [CrossRef]
8.
Olanrewaju, A.L.; Abdul-Aziz, A.-R. Building maintenance processes, principles, procedures, practices and
strategies; Springer: Singapore, 2015; Building Maintenance Processes and Practices; pp. 79–129. [CrossRef]
9.
Hussin, J.M.; Rahman, I.A.; Memon, A.H. The way forward in sustainable construction: Issues and challenges.
Int. J. Adv. Appl. Sci. 2013,2, 15–24. [CrossRef]
10.
Zavadskas, E.K.; Vilutien
˙
e, T.; Turskis, Z.; Šaparauskas, J. Multi-criteria analysis of projects’ performance in
construction. Arch. Civ. Mech. Eng. 2014,14, 114–121. [CrossRef]
11.
Neyestani, B.; Juanzon, J.B.P. Developing an Appropriate Performance Measurement Framework for Total Quality
Management in Construction, and Other Industries; University Library of Munich: Munich, Germany, 2016.
12.
Oakland, J.; Marosszeky, M. Total Construction Management: Lean Quality in Construction Project Delivery;
Routledge: Abington, UK, 2017.
13.
Babalola, O.; Ibem, E.O.; Ezema, I.C. Implementation of lean practices in the construction industry:
A systematic review. Build. Environ. 2018,148, 34–43. [CrossRef]
14.
Peljhan, D.; Marc, M. Total quality management and performance management systems: Team players or
lonely riders? Total Qual. Manag. Bus. Excell. 2018,29, 920–940. [CrossRef]
15.
Akinade, O.O.; Oyedele, L.O.; Ajayi, S.O.; Bilal, M.; Alaka, H.A.; Owolabi, H.A.; Bello, S.A.; Jaiyeoba, B.E.;
Kadiri, K.O. Design for deconstruction (dfd): Critical success factors for diverting end-of-life waste from
landfills. Waste Manag. 2017,60, 3–13. [CrossRef]
16.
Gudien
˙
e, N.; Banaitis, A.; Podvezko, V.; Banaitien
˙
e, N. Identification and evaluation of the critical success
factors for construction projects in lithuania: Ahp approach. J. Civ. Eng. Manag.
2014
,20, 350–359. [CrossRef]
17.
Lin, G.; Shen, G.Q.; Sun, M.; Kelly, J. Identification of key performance indicators for measuring the
performance of value management studies in construction. J. Construct. Eng. Manag.
2011
,137, 698–706.
[CrossRef]
18.
Maghsoodi, A.I.; Khalilzadeh, M. Identification and evaluation of construction projects’ critical success
factors employing fuzzy-topsis approach. KSCE J. Civ. Eng. 2018,22, 1593–1605. [CrossRef]
19.
Tripathi, K.; Jha, K. An empirical study on performance measurement factors for construction organizations.
KSCE J. Civ. Eng. 2018,22, 1–15. [CrossRef]
20.
Love, P.E.D.; Teo, P.; Morrison, J.; Grove, M. Quality and safety in construction: Creating a no-harm
environment. J. Construct. Eng. Manag. 2016,142, 05016006. [CrossRef]
21.
Ramlee, N.; Tammy, N.J.; Raja Mohd Noor, R.N.H.; Ainun Musir, A.; Abdul Karim, N.; Chan, H.B.; Mohd
Nasir, S.R. Critical success factors for construction project. In AIP Conference Proceedings; AIP Publishing:
Penang, Malaysia, 2016.
Buildings 2019,9, 15 12 of 15
22.
Sibiya, M.; Aigbavboa, C.; Thwala, W. Construction projects’ key performance indicators: A case of the South
African construction industry. In Proceedings of the 2015 International Conference on Construction and Real
Estate Management, Lulea, Sweden, 11–12 August 2015; pp. 954–960.
23.
Haslinda, A.N.; Xian, T.W.; Norfarahayu, K.; Hanafi, R.M.; Fikri, H.M. Investigation on the Factors
Influencing Construction Time and Cost Overrun for High-Rise Building Projects in Penang. J. Phys.
Conf. Ser. 2018,995, 012043. [CrossRef]
24.
Tommelein, I.D. Journey toward lean construction: Pursuing a paradigm shift in the aec industry. J. Construct.
Eng. Manag. 2015,141, 04015005. [CrossRef]
25.
Dave, B.; Kubler, S.; Främling, K.; Koskela, L. Opportunities for enhanced lean construction management
using internet of things standards. Autom. Construct. 2016,61, 86–97. [CrossRef]
26.
Shabehpour, N. An Investigation of the Implementation of Lean Philosophy within a Specialty Trade.
Master’s Thesis, University of British Columbia, Vancouver, BC, Canada, 2016.
27.
Chiarini, A.; Baccarani, C.; Mascherpa, V. Lean production, Toyota production system and kaizen philosophy:
A conceptual analysis from the perspective of zen buddhism. TQM J. 2018,30, 425–438. [CrossRef]
28.
Forbes, L.H.; Ahmed, S.M. Modern Construction: Lean Project Delivery and Integrated Practices; Crc Press: Boca
Raton, FL, USA, 2010.
29.
Goetsch, D.L.; Davis, S.B. Quality Management for Organizational Excellence; Pearson: Upper Saddle River,
NJ, USA, 2014.
30.
Hanseth, O.; Lyytinen, K. Design theory for dynamic complexity in information infrastructures: The case
of building internet. In Enacting Research Methods in Information Systems; Springer: Berlin, Germany, 2016;
pp. 104–142.
31.
Bonham, D.R.; Goodrum, P.M.; Littlejohn, R.; Albattah, M.A. Application of data mining techniques to
quantify the relative influence of design and installation characteristics on labor productivity. J. Construct.
Eng. Manag. 2017,143, 04017052. [CrossRef]
32.
Bianchi, C.; Cosenz, F.; Marinkovi´c, M. Designing dynamic performance management systems to foster sme
competitiveness according to a sustainable development perspective: Empirical evidences from a case-study.
Int. J. Bus. Perform. Manag. 2015,16, 84–108. [CrossRef]
33.
Ogunde, A.; Joshua, O.; Amusan, L.M.; Akuete, E. Project management a panacea to improving the
performance of construction project. Int. J. Civ. Eng. Technol. 2017,8, 1234–1242.
34.
Sears, S.K.; Sears, G.A.; Clough, R.H.; Rounds, J.L.; Segner, R.O. Construction Project Management; John Wiley
& Sons: Hoboken, NJ, USA, 2015.
35.
Albliwi, S.A.; Antony, J.; Arshed, N.; Ghadge, A. Implementation of lean six sigma in Saudi Arabian
organisations: Findings from a survey. Int. J. Qual. Reliab. Manag. 2017,34, 508–529. [CrossRef]
36.
Mir, F.A.; Pinnington, A.H. Exploring the value of project management: Linking project management
performance and project success. Int. J. Proj. Manag. 2014,32, 202–217. [CrossRef]
37.
González, P.; González, V.; Molenaar, K.; Orozco, F. Analysis of causes of delay and time performance in
construction projects. J. Construct. Eng. Manag. 2013,140, 04013027. [CrossRef]
38.
Ahmad Zaini, A.; Adnan, H.; Che Haron, R. Contractors’ Approaches to Risk Management at the
Construction Phase in Malaysia. In Proceedings of the International Conference on Construction Project
Management (ICCPM), Chengdu, China, 1 December 2010.
39.
Chou, J.-S.; Irawan, N.; Pham, A.-D. Project management knowledge of construction professionals:
Cross-country study of effects on project success. J. Construct. Eng. Manag. 2013,139, 04013015. [CrossRef]
40.
Demirkesen, S.; Ozorhon, B. Impact of integration management on construction project management
performance. Int. J. Proj. Manag. 2017,35, 1639–1654. [CrossRef]
41.
Meng, X. The effect of relationship management on project performance in construction. Int. J. Proj. Manag.
2012,30, 188–198. [CrossRef]
42.
Ngacho, C.; Das, D. A performance evaluation framework of development projects: An empirical study of
constituency development fund (cdf) construction projects in Kenya. Int. J. Proj. Manag.
2014
,32, 492–507.
[CrossRef]
43.
Lo, T.Y.; Fung, I.W.; Tung, K.C. Construction delays in Hong Kong civil engineering projects. J. Construct.
Eng. Manag. 2006,132, 636–649. [CrossRef]
44.
Zeng, S.; Ma, H.; Lin, H.; Zeng, R.; Tam, V.W. Social responsibility of major infrastructure projects in china.
Int. J. Proj. Manag. 2015,33, 537–548. [CrossRef]
Buildings 2019,9, 15 13 of 15
45.
Davis, K. A method to measure success dimensions relating to individual stakeholder groups. Int. J. Proj.
Manag. 2016,34, 480–493. [CrossRef]
46.
Oppong, G.D.; Chan, A.P.; Dansoh, A. A review of stakeholder management performance attributes in
construction projects. Int. J. Proj. Manag. 2017,35, 1037–1051. [CrossRef]
47.
Ogunlana, S.O. Beyond the ‘iron triangle’: Stakeholder perception of key performance indicators (kpis) for
large-scale public sector development projects. Int. J. Proj. Manag. 2010,28, 228–236.
48.
Arditi, D.; Nayak, S.; Damci, A. Effect of organizational culture on delay in construction. Int. J. Proj. Manag.
2017,35, 136–147. [CrossRef]
49.
Cheng, Y.-M. An exploration into cost-influencing factors on construction projects. Int. J. Proj. Manag.
2014
,
32, 850–860. [CrossRef]
50.
Olawale, Y.A.; Sun, M. Cost and time control of construction projects: Inhibiting factors and mitigating
measures in practice. Construct. Manag. Econ. 2010,28, 509–526. [CrossRef]
51. Mubarak, S.A. Construction Project Scheduling and Control; John Wiley & Sons: Hoboken, NJ, USA, 2015.
52.
Kerzner, H.; Kerzner, H.R. Project Management: A Systems Approach to Planning, Scheduling, and Controlling;
John Wiley & Sons: Hoboken, NJ, USA, 2017.
53.
Tonchia, S. Industrial Project Management; Springer: Berlin, Germany, 2018; Available online: https:
//link.springer.com/book/10.1007%2F978-3-662-56328-1#authorsandaffiliationsbook (accessed on 30
December 2018).
54.
Nicholas, J.M.; Steyn, H. Project Management for Engineering, Business and Technology; Routledge: Abington,
UK, 2017.
55.
Zareei, S. Project scheduling for constructing biogas plant using critical path method. Renew. Sustain. Energy
Rev. 2018,81, 756–759. [CrossRef]
56.
Darvik, L.; Larsson, J. The Impact of Material Delivery-Deviations on Costs and Performance in Construction
Projects. Master’s Thesis, Chalmers University of Technology, Göteborg, Sweden, 2010.
57.
Jollands, S.; Akroyd, C.; Sawabe, N. Core values as a management control in the construction of “sustainable
development”. Qual. Res. Account. Manag. 2015,12, 127–152. [CrossRef]
58.
Jiang, H.; Lin, P.; Qiang, M.; Fan, Q. A labor consumption measurement system based on real-time tracking
technology for dam construction site. Autom. Construct. 2015,52, 1–15. [CrossRef]
59.
Gamil, Y.; Rahman, I.A. Identification of causes and effects of poor communication in construction industry:
A theoretical review. Emerg. Sci. J. 2018,1. [CrossRef]
60.
Subramani, T.; Sruthi, P.; Kavitha, M. Causes of cost overrun in construction. IOSR J. Eng.
2014
,4, 1–7.
[CrossRef]
61.
Enshassi, A.; Arain, F.; Al-Raee, S. Causes of variation orders in construction projects in the gaza strip. J. Civ.
Eng. Manag. 2010,16, 540–551. [CrossRef]
62.
Fallahnejad, M.H. Delay causes in iran gas pipeline projects. Int. J. Proj. Manag.
2013
,31, 136–146. [CrossRef]
63.
Kazaz, A.; Ulubeyli, S.; Tuncbilekli, N.A. Causes of delays in construction projects in turkey. J. Civ. Eng.
Manag. 2012,18, 426–435. [CrossRef]
64.
Gunduz, M.; Nielsen, Y.; Ozdemir, M. Fuzzy assessment model to estimate the probability of delay in turkish
construction projects. J. Manag. Eng. 2013,31, 04014055. [CrossRef]
65.
Gündüz, M.; Nielsen, Y.; Özdemir, M. Quantification of delay factors using the relative importance index
method for construction projects in turkey. J. Manag. Eng. 2012,29, 133–139. [CrossRef]
66.
Keane, P.J.; Caletka, A.F. Delay Analysis in Construction Contracts; John Wiley & Sons: Hoboken, NJ, USA, 2015.
67.
Naoum, S.G.; Alyousif, A.-R.T.; Atkinson, A.R. Impact of national culture on the management practices of
construction projects in the united arab emirates. J. Manag. Eng. 2013,31, 04014057. [CrossRef]
68.
Zou, P.X.; Zhang, G. Managing risks in construction projects: Life cycle and stakeholder perspectives. Int. J.
Construct. Manag. 2009,9, 61–77. [CrossRef]
69.
Aziz, R.F.; Abdel-Hakam, A.A. Exploring delay causes of road construction projects in egypt. Alex. Eng. J.
2016,55, 1515–1539. [CrossRef]
70.
Jarkas, A.M.; Haupt, T.C. Major construction risk factors considered by general contractors in qatar. J. Eng.
Des. Technol. 2015,13, 165–194. [CrossRef]
71.
Oshodi Olalekan, S.; Rimaka, I. A comparative study on causes and effects of delay in nigerian and iranian
construction projects. Asian J. Bus. Manag. Sci. 2013,3, 29–36.
72. Minaie, H. Identifying Success Factor in Mass Buildings Construction; Tehran University: Tehran, Iran, 2013.
Buildings 2019,9, 15 14 of 15
73. Shokouhinia, M. Analysis of Success Factor in Aria-Petro-Gas Company; Tehran University: Tehran, Iran, 2010.
74. Piran, M. Identifying Success Factor in Oil and Gas Project; Tehran University: Tehran, Iran, 2010.
75. Abolhasani, A. Assessment of Success Factor in Construction Project; Tehran University: Tehran, Iran, 2012.
76.
Dalirpour, A. Analysis of Success Factor on the Project-Based Organization; Tehran University: Tehran, Iran,
2012.
77.
Doulabi, R.Z.; Asnaashari, E. Identifying success factors of healthcare facility construction projects in Iran.
Proc. Eng. 2016,164, 409–415. [CrossRef]
78.
Nguyen, H.T.; Hadikusumo, B. Impacts of human resource development on engineering, procurement,
and construction project success. Built Environ. Proj. Asset Manag. 2017,7, 73–85. [CrossRef]
79.
Habibi, M.; Kermanshachi, S.; Safapour, E. Engineering, procurement and construction cost and schedule
performance leading indicators: State-of-the-art review. In Proceedings of the Construction Research
Congress, ASCE, New Orleans, LA, USA, 2–4 April 2018.
80.
Pal, R.; Wang, P.; Liang, X. The critical factors in managing relationships in international engineering,
procurement, and construction (iepc) projects of chinese organizations. Int. J. Proj. Manag.
2017
,35,
1225–1237. [CrossRef]
81.
Jahantigh, F.F.; Malmir, B.; Avilaq, B.A. Engineering, S. Economic risk assessment of epc projects using fuzzy
topsis approach. Int. J. Ind. Syst. Eng. 2017,27, 161–179.
82.
Jang, W.; Hong, H.-U.; Han, S.H.; Baek, S.W. Optimal supply vendor selection model for lng plant projects
using fuzzy-topsis theory. J. Manag. Eng. 2016,33, 04016035. [CrossRef]
83.
Abbaspour, M.; Toutounchian, S.; Dana, T.; Abedi, Z.; Toutounchian, S. Environmental parametric cost model
in oil and gas epc contracts. Sustainability 2018,10, 195. [CrossRef]
84.
Safa, M.; Shahi, A.; Haas, C.T.; Hipel, K.W. Supplier selection process in an integrated construction materials
management model. Autom. Construct. 2014,48, 64–73. [CrossRef]
85.
Jato-Espino, D.; Castillo-Lopez, E.; Rodriguez-Hernandez, J.; Canteras-Jordana, J.C. A review of application
of multi-criteria decision making methods in construction. Autom. Construct. 2014,45, 151–162. [CrossRef]
86.
Zavadskas, E.K.; Turskis, Z.; Kildien
˙
e, S. State of art surveys of overviews on mcdm/madm methods. Technol.
Econ. Dev. Econ. 2014,20, 165–179. [CrossRef]
87.
Chen, C.-T. Extensions of the TOPSIS for group decision-making under fuzzy environment. Fuzzy Sets Syst.
2000,114, 1–9. [CrossRef]
88.
Pournader, M.; Tabassi, A.A.; Baloh, P. A three-step design science approach to develop a novel human
resource-planning framework in projects: The cases of construction projects in USA, Europe, and Iran. Int. J.
Proj. Manag. 2015,33, 419–434. [CrossRef]
89.
Vahdani, B.; Mousavi, S.M.; Mousakhani, M.; Hashemi, H. Time prediction using a neuro-fuzzy model for
projects in the construction industry. J. Optim. Ind. Eng. 2016,9, 97–103.
90.
Banihashemi, S.; Hosseini, M.R.; Golizadeh, H.; Sankaran, S. Critical success factors (csfs) for integration of
sustainability into construction project management practices in developing countries. Int. J. Proj. Manag.
2017,35, 1103–1119. [CrossRef]
91.
Zarei, B.; Sharifi, H.; Chaghouee, Y. Delay causes analysis in complex construction projects: A semantic
network analysis approach. Prod. Plan. Control 2018,29, 29–40. [CrossRef]
92.
Ghoddousi, P.; Hosseini, M.R. A survey of the factors affecting the productivity of construction projects in
Iran. Technol. Econ. Dev. Econ. 2012,18, 99–116. [CrossRef]
93.
De Carvalho, M.M.; Patah, L.A.; de Souza Bido, D. Project management and its effects on project success:
Cross-country and cross-industry comparisons. Int. J. Construct. Manag. 2015,33, 1509–1522. [CrossRef]
94.
Sha’ar, K.; Assaf, S.; Bambang, T.; Babsail, M.; Fattah, A.A.E. Design–construction interface problems in large
building construction projects. Int. J. Construct. Manag. 2017,17, 238–250. [CrossRef]
Buildings 2019,9, 15 15 of 15
95.
Yousefi, V.; Yakhchali, S.H.; Khanzadi, M.; Mehrabanfar, E.; Šaparauskas, J. Proposing a neural network
model to predict time and cost claims in construction projects. J. Civ. Eng. Manag.
2016
,22, 967–978.
[CrossRef]
96.
AlNasseri, H.; Aulin, R. Assessing understanding of planning and scheduling theory and practice on
construction projects. Eng. Manag. J. 2015,27, 58–72. [CrossRef]
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The intersection of Artificial Intelligence (AI) and cybersecurity offers transformative potential to address workforce development challenges, particularly for marginalized youth. This paper proposes a scalable and impactful model leveraging AI and cybersecurity to revolutionize workforce development. The model focuses on equipping marginalized youth with in-demand skills in AI, cybersecurity, and related technologies to bridge the digital divide and foster economic inclusion. By combining AI-driven personalized learning pathways with hands-on cybersecurity training, the model empowers participants with technical expertise, problem-solving skills, and industry certifications, enabling them to compete in a rapidly evolving job market. Key components of the model include AI-powered skill assessment tools, adaptive training platforms, and cybersecurity simulation environments designed to mirror real-world scenarios. These tools identify individual learning needs, recommend tailored educational pathways, and provide immersive, practical experiences. The model also incorporates mentorship programs, industry collaborations, and internship opportunities to enhance employability and foster professional growth. By integrating AI technologies, such as natural language processing and machine learning, the program ensures continuous improvement, scalability, and accessibility, even in resource-constrained environments. This study highlights case studies of successful implementation in underserved communities, showcasing measurable impacts such as increased employment rates, reduced skill gaps, and improved cybersecurity awareness. The proposed model emphasizes inclusivity, targeting marginalized youth in rural and urban areas, and provides scalable solutions to address systemic inequities in workforce development. Challenges such as ensuring data privacy, addressing biases in AI algorithms, and maintaining affordability are critically examined, along with strategies for mitigation. The paper concludes with a call for cross-sector collaboration between policymakers, industry leaders, and educational institutions to support the widespread adoption of this model. By leveraging AI and cybersecurity innovations, this approach can serve as a catalyst for empowering marginalized youth, driving economic development, and creating a resilient and inclusive workforce.
... In conclusion, while the potential benefits of a practical framework for advancing cybersecurity, AI, and technological ecosystems to support regional economic development and innovation are immense, significant challenges must be addressed to fully realize these benefits. Resource constraints, workforce skills gaps, regulatory misalignment, and technological inequities all represent substantial barriers to progress (Kabirifar & Mojtahedi, 2019, Thamrin, 2017. However, by fostering collaboration between governments, the private sector, and educational institutions, investing in digital infrastructure, and developing coherent regulatory frameworks, regions can overcome these challenges and create a secure, resilient, and innovative technological ecosystem. ...
... Furthermore, AI literacy should extend beyond technical professionals to include decision-makers, business leaders, and the general public. Promoting AI literacy among business leaders and policymakers helps them make informed decisions about how to integrate AI into their organizations and strategies (Kabirifar & Mojtahedi, 2019, Thamrin, 2017. Public awareness campaigns can help individuals understand the role of AI in their daily lives and address any concerns about its impact on jobs and society. ...
... This means crafting policies that encourage innovation while also safeguarding public interests such as data privacy, equity, and access to technology. For example, governments can promote the use of open standards for AI development to ensure interoperability between systems, making it easier for businesses and regions to collaborate (Kabirifar & Mojtahedi, 2019, Thamrin, 2017. Similarly, regional cybersecurity regulations should align with international frameworks, such as those developed by the European Union or the United States, to facilitate cross-border cooperation in addressing global cybersecurity threats. ...
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The integration of cybersecurity, artificial intelligence (AI), and advanced technological ecosystems has become a cornerstone for driving regional economic development and fostering innovation. This paper proposes a practical framework designed to enhance the synergy between these domains, aiming to create a robust foundation for regional economic growth. The framework emphasizes the role of secure digital infrastructures, AI-driven solutions, and collaborative ecosystems in addressing emerging challenges while leveraging opportunities for technological advancement. The study explores key elements required to build resilient cybersecurity systems that protect critical assets and foster trust in digital platforms. Additionally, it examines how AI-powered technologies can optimize resource allocation, improve decision-making, and support the scalability of innovation-driven initiatives. The framework also highlights the importance of interconnected technological ecosystems that enable knowledge sharing, cross-sector collaboration, and the efficient deployment of advanced technologies across industries. Through an analysis of case studies and best practices, the research identifies actionable strategies for implementing this framework, such as establishing cybersecurity hubs, promoting AI literacy, and incentivizing public-private partnerships. The findings underscore the critical need for workforce upskilling, regulatory alignment, and scalable funding models to address barriers such as resource constraints and technological gaps. Furthermore, the paper explores the role of regional policy interventions in accelerating the adoption of these strategies to promote economic resilience and technological competitiveness. This framework provides policymakers, industry leaders, and researchers with a roadmap for harnessing the transformative potential of cybersecurity, AI, and technological ecosystems. By aligning innovation strategies with regional economic priorities, this approach not only safeguards digital assets but also drives sustainable development and long-term economic benefits. The research advocates for proactive collaboration and continuous adaptation to ensure that technological advancements align with regional development goals.
... This could include family support programs, peer mentorship, and local community-based events that celebrate successes and keep participants connected to their roots. Engaging communities also fosters a sense of ownership and involvement, which can enhance the sustainability and long-term success of the program (Kabirifar & Mojtahedi, 2019, Thamrin, 2017. In terms of scalability, leveraging both public and private sector funding is essential. ...
... In conclusion, implementing a scalable and impactful model for harnessing AI and cybersecurity to empower marginalized youth requires collaboration across multiple sectors, a community-driven approach to recruitment, strategic use of funding, and careful measurement of outcomes. By leveraging the expertise of policymakers, educational institutions, and industry leaders, as well as the support of local communities, this model can provide marginalized youth with the skills and opportunities needed to succeed in the digital economy (Kabirifar & Mojtahedi, 2019, Thamrin, 2017. With successful examples from around the world demonstrating the power of such initiatives, there is significant potential for scaling these efforts to create a more inclusive and equitable workforce in the digital age. ...
... For example, AI-powered skill assessments or personalized learning pathways might unfairly favor certain demographic groups, such as those from higher socioeconomic backgrounds, while underestimating the potential of marginalized youth. Such biases could exacerbate inequalities, reinforcing the existing barriers that marginalized youth face in accessing opportunities (Kabirifar & Mojtahedi, 2019, Thamrin, 2017. Mitigating bias in AI algorithms requires a multi-faceted approach. ...
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The intersection of Artificial Intelligence (AI) and cybersecurity offers transformative potential to address workforce development challenges, particularly for marginalized youth. This paper proposes a scalable and impactful model leveraging AI and cybersecurity to revolutionize workforce development. The model focuses on equipping marginalized youth with in-demand skills in AI, cybersecurity, and related technologies to bridge the digital divide and foster economic inclusion. By combining AI-driven personalized learning pathways with hands-on cybersecurity training, the model empowers participants with technical expertise, problem-solving skills, and industry certifications, enabling them to compete in a rapidly evolving job market. Key components of the model include AI-powered skill assessment tools, adaptive training platforms, and cybersecurity simulation environments designed to mirror real-world scenarios. These tools identify individual learning needs, recommend tailored educational pathways, and provide immersive, practical experiences. The model also incorporates mentorship programs, industry collaborations, and internship opportunities to enhance employability and foster professional growth. By integrating AI technologies, such as natural language processing and machine learning, the program ensures continuous improvement, scalability, and accessibility, even in resource-constrained environments. This study highlights case studies of successful implementation in underserved communities, showcasing measurable impacts such as increased employment rates, reduced skill gaps, and improved cybersecurity awareness. The proposed model emphasizes inclusivity, targeting marginalized youth in rural and urban areas, and provides scalable solutions to address systemic inequities in workforce development. Challenges such as ensuring data privacy, addressing biases in AI algorithms, and maintaining affordability are critically examined, along with strategies for mitigation. The paper concludes with a call for cross-sector collaboration between policymakers, industry leaders, and educational institutions to support the widespread adoption of this model. By leveraging AI and cybersecurity innovations, this approach can serve as a catalyst for empowering marginalized youth, driving economic development, and creating a resilient and inclusive workforce.
... In conclusion, while the potential benefits of a practical framework for advancing cybersecurity, AI, and technological ecosystems to support regional economic development and innovation are immense, significant challenges must be addressed to fully realize these benefits. Resource constraints, workforce skills gaps, regulatory misalignment, and technological inequities all represent substantial barriers to progress (Kabirifar & Mojtahedi, 2019, Thamrin, 2017. However, by fostering collaboration between governments, the private sector, and educational institutions, investing in digital infrastructure, and developing coherent regulatory frameworks, regions can overcome these challenges and create a secure, resilient, and innovative technological ecosystem. ...
... Furthermore, AI literacy should extend beyond technical professionals to include decision-makers, business leaders, and the general public. Promoting AI literacy among business leaders and policymakers helps them make informed decisions about how to integrate AI into their organizations and strategies (Kabirifar & Mojtahedi, 2019, Thamrin, 2017. Public awareness campaigns can help individuals understand the role of AI in their daily lives and address any concerns about its impact on jobs and society. ...
... This means crafting policies that encourage innovation while also safeguarding public interests such as data privacy, equity, and access to technology. For example, governments can promote the use of open standards for AI development to ensure interoperability between systems, making it easier for businesses and regions to collaborate (Kabirifar & Mojtahedi, 2019, Thamrin, 2017. Similarly, regional cybersecurity regulations should align with international frameworks, such as those developed by the European Union or the United States, to facilitate cross-border cooperation in addressing global cybersecurity threats. ...
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The integration of cybersecurity, artificial intelligence (AI), and advanced technological ecosystems has become a cornerstone for driving regional economic development and fostering innovation. This paper proposes a practical framework designed to enhance the synergy between these domains, aiming to create a robust foundation for regional economic growth. The framework emphasizes the role of secure digital infrastructures, AI-driven solutions, and collaborative ecosystems in addressing emerging challenges while leveraging opportunities for technological advancement. The study explores key elements required to build resilient cybersecurity systems that protect critical assets and foster trust in digital platforms. Additionally, it examines how AI-powered technologies can optimize resource allocation, improve decision-making, and support the scalability of innovation-driven initiatives. The framework also highlights the importance of interconnected technological ecosystems that enable knowledge sharing, cross-sector collaboration, and the efficient deployment of advanced technologies across industries. Through an analysis of case studies and best practices, the research identifies actionable strategies for implementing this framework, such as establishing cybersecurity hubs, promoting AI literacy, and incentivizing public-private partnerships. The findings underscore the critical need for workforce upskilling, regulatory alignment, and scalable funding models to address barriers such as resource constraints and technological gaps. Furthermore, the paper explores the role of regional policy interventions in accelerating the adoption of these strategies to promote economic resilience and technological competitiveness. This framework provides policymakers, industry leaders, and researchers with a roadmap for harnessing the transformative potential of cybersecurity, AI, and technological ecosystems. By aligning innovation strategies with regional economic priorities, this approach not only safeguards digital assets but also drives sustainable development and long-term economic benefits. The research advocates for proactive collaboration and continuous adaptation to ensure that technological advancements align with regional development goals.
... This could include family support programs, peer mentorship, and local community-based events that celebrate successes and keep participants connected to their roots. Engaging communities also fosters a sense of ownership and involvement, which can enhance the sustainability and long-term success of the program (Kabirifar & Mojtahedi, 2019, Thamrin, 2017 [44] . In terms of scalability, leveraging both public and private sector funding is essential. ...
... This could include family support programs, peer mentorship, and local community-based events that celebrate successes and keep participants connected to their roots. Engaging communities also fosters a sense of ownership and involvement, which can enhance the sustainability and long-term success of the program (Kabirifar & Mojtahedi, 2019, Thamrin, 2017 [44] . In terms of scalability, leveraging both public and private sector funding is essential. ...
... In conclusion, implementing a scalable and impactful model for harnessing AI and cybersecurity to empower marginalized youth requires collaboration across multiple sectors, a community-driven approach to recruitment, strategic use of funding, and careful measurement of outcomes. By leveraging the expertise of policymakers, educational institutions, and industry leaders, as well as the support of local communities, this model can provide marginalized youth with the skills and opportunities needed to succeed in the digital economy (Kabirifar & Mojtahedi, 2019, Thamrin, 2017 [44] . With successful examples from around the world demonstrating the power of such initiatives, there is significant potential for scaling these efforts to create a more inclusive and equitable workforce in the digital age. ...
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The intersection of Artificial Intelligence (AI) and cybersecurity offers transformative potential to address workforce development challenges, particularly for marginalized youth. This paper proposes a scalable and impactful model leveraging AI and cybersecurity to revolutionize workforce development. The model focuses on equipping marginalized youth with in-demand skills in AI, cybersecurity, and related technologies to bridge the digital divide and foster economic inclusion. By combining AI-driven personalized learning pathways with hands-on cybersecurity training, the model empowers participants with technical expertise, problem-solving skills, and industry certifications, enabling them to compete in a rapidly evolving job market. Key components of the model include AI-powered skill assessment tools, adaptive training platforms, and cybersecurity simulation environments designed to mirror real-world scenarios. These tools identify individual learning needs, recommend tailored educational pathways, and provide immersive, practical experiences. The model also incorporates mentorship programs, industry collaborations, and internship opportunities to enhance employability and foster professional growth. By integrating AI technologies, such as natural language processing and machine learning, the program ensures continuous improvement, scalability, and accessibility, even in resource-constrained environments. This study highlights case studies of successful implementation in underserved communities, showcasing measurable impacts such as increased employment rates, reduced skill gaps, and improved cybersecurity awareness. The proposed model emphasizes inclusivity, targeting marginalized youth in rural and urban areas, and provides scalable solutions to address systemic inequities in workforce development. Challenges such as ensuring data privacy, addressing biases in AI algorithms, and maintaining affordability are critically examined, along with strategies for mitigation. The paper concludes with a call for cross-sector collaboration between policymakers, industry leaders, and educational institutions to support the widespread adoption of this model. By leveraging AI and cybersecurity innovations, this approach can serve as a catalyst for empowering marginalized youth, driving economic development, and creating a resilient and inclusive workforce.
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The increasing prevalence of cyber threats in the digital age underscores the urgent need for innovative and data-driven approaches to cybersecurity. This study proposes a high-impact decision-making model designed to integrate cutting-edge cybersecurity strategies into public policy, governance, and organizational frameworks. The model emphasizes leveraging big data, artificial intelligence (AI), and advanced analytics to inform policy design, risk assessment, and strategic planning in diverse institutional contexts. Key components of the model include real-time data aggregation, predictive analytics, and machine learning algorithms to identify and mitigate cyber risks proactively. By incorporating advanced threat intelligence and risk quantification, the model enables stakeholders to prioritize vulnerabilities, allocate resources effectively, and enhance resilience against evolving cyber threats. The framework also integrates multi-stakeholder collaboration, ensuring alignment between public and private sector efforts in addressing cybersecurity challenges. This model is adaptable across various governance levels and organizational structures, providing actionable insights to policymakers, regulators, and organizational leaders. It aligns with global cybersecurity standards and emphasizes compliance with frameworks such as the NIST Cybersecurity Framework, GDPR, and ISO/IEC 27001. The research highlights the importance of embedding cybersecurity into governance processes and organizational strategies to foster a culture of security and accountability. Pilot studies demonstrate the model's applicability in enhancing decision-making processes, reducing response times, and improving risk mitigation outcomes. Case studies from public and private sectors reveal the model's capacity to drive more informed and adaptive policy frameworks while promoting operational efficiency and trust among stakeholders. This study contributes to the evolving field of cybersecurity by providing a scalable and replicable approach for integrating data-driven strategies into policy and governance. By bridging the gap between technological innovation and institutional readiness, the proposed model equips policymakers and organizations to navigate complex cyber landscapes effectively, ensuring the protection of critical infrastructure, data, and digital assets.
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Small and Medium-sized Enterprises (SMEs) in Nigeria play a critical role in economic growth, employment generation, and poverty alleviation. However, these businesses are highly vulnerable to economic disruptions such as currency devaluation, inflation, policy instability, and global shocks like the COVID-19 pandemic. This study proposes a framework for developing resilient business models that enhance the adaptability and sustainability of Nigerian SMEs in the face of economic uncertainties. The framework integrates strategic agility, digital transformation, financial literacy, and risk management to mitigate vulnerabilities and foster long-term growth. The research employs a mixed-methods approach, combining qualitative case studies and quantitative surveys to assess the resilience levels of SMEs across various sectors. The findings indicate that most Nigerian SMEs lack structured risk mitigation strategies, relying instead on reactive measures that often lead to business failure. To address these challenges, this study develops a resilience framework incorporating proactive financial planning, adaptive business strategies, and digital integration. The framework emphasizes the role of digital transformation, including e-commerce adoption, cloud computing, and data analytics, in enhancing operational efficiency and market reach. Furthermore, financial literacy and risk management training are proposed as essential components to help SMEs navigate economic disruptions effectively. The proposed framework also highlights the importance of government policies and private sector support in fostering a resilient SME ecosystem. Recommendations include targeted financial interventions, regulatory reforms, and capacity-building programs to improve SME competitiveness. By leveraging a structured resilience framework, Nigerian SMEs can transition from reactive survival strategies to proactive business continuity planning. The study contributes to the existing literature by providing a contextualized model for enhancing SME resilience in developing economies. Future research should explore sector-specific resilience strategies and the role of artificial intelligence in SME risk management.
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The increasing prevalence of cyber threats in the digital age underscores the urgent need for innovative and data-driven approaches to cybersecurity. This study proposes a high-impact decision-making model designed to integrate cutting-edge cybersecurity strategies into public policy, governance, and organizational frameworks. The model emphasizes leveraging big data, artificial intelligence (AI), and advanced analytics to inform policy design, risk assessment, and strategic planning in diverse institutional contexts. Key components of the model include real-time data aggregation, predictive analytics, and machine learning algorithms to identify and mitigate cyber risks proactively. By incorporating advanced threat intelligence and risk quantification, the model enables stakeholders to prioritize vulnerabilities, allocate resources effectively, and enhance resilience against evolving cyber threats. The framework also integrates multi-stakeholder collaboration, ensuring alignment between public and private sector efforts in addressing cybersecurity challenges. This model is adaptable across various governance levels and organizational structures, providing actionable insights to policymakers, regulators, and organizational leaders. It aligns with global cybersecurity standards and emphasizes compliance with frameworks such as the NIST Cybersecurity Framework, GDPR, and ISO/IEC 27001. The research highlights the importance of embedding cybersecurity into governance processes and organizational strategies to foster a culture of security and accountability. Pilot studies demonstrate the model's applicability in enhancing decision-making processes, reducing response times, and improving risk mitigation outcomes. Case studies from public and private sectors reveal the model’s capacity to drive more informed and adaptive policy frameworks while promoting operational efficiency and trust among stakeholders. This study contributes to the evolving field of cybersecurity by providing a scalable and replicable approach for integrating data-driven strategies into policy and governance. By bridging the gap between technological innovation and institutional readiness, the proposed model equips policymakers and organizations to navigate complex cyber landscapes effectively, ensuring the protection of critical infrastructure, data, and digital assets.
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Time and cost overruns have become one prominent issue for most construction projects around the world. Project costing and timeframe extension had been causing a lot of wastage and loss of opportunity for many parties involved. Therefore, this research was carried out to investigate the factors influencing time and cost overruns for high-rise construction projects in Penang, Malaysia. A set of questionnaires survey was distributed to the project managers who had been or currently involved in the high-rise building projects in Penang to get their input and perceptions for each factor identified as well as its frequency of occurrence. In order to rank all the factors gathered, the mean index of the most distinguishing factors and its frequency of occurrence were multiplied to get the severity index. The results revealed that for time overrun, the most predominant causes were due to design changes, inadequate planning and scheduling and poor labor productivity. Meanwhile, the predominant causes of cost overrun were poor pre-construction budget and material cost planning, inaccurate quantity take-off and materials cost increased by inflation. The significance of establishing the issues related to time and cost overruns for the high-rise building construction project is to provide a greater insight and understanding on the causes of delays, particularly among the main project players: contractors, client, and consultants.
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Construction industry is characterized in nature as complex, fragmented, dynamic and involves many parties therefore effective communication is essential to overcome these challenges. Many researchers found that the industry faces major challenge to ensure effective and successful communication throughout the lifecycle of the project which therefore resulted to project failure. Poor communication in construction industry had been addressed in previous research studies; however, this paper presents and examines the identification of causes and effects which lead to poor communication. Further investigations on previous literature were conducted to extract the causes and effects which contributed to poor communication in construction industry. Similarity technique was applied to avoid duplications in the identified causes and effect of poor communication. Using the frequency technique, from the 33 causes of poor communication it was found that the most dominant cause is lack of effective communication. Whereas, out of 21 effects from poor communication, it was found that highly repeated effect is time overrun. These findings will serve a good platform for further investigation on the relevancy of causes and effects to the local construction practitioners.
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This study aims at identifying the parameters that govern the environmental costs in oil and gas projects. An initial conceptual model was proposed. Next, the costs of environmental management work packages were estimated, separately and were applied in project control tools (WBS/CBS). Then, an environmental parametric cost model was designed to determine the environmental costs and relevant weighting factors. The suggested model can be considered as an innovative approach to designate the environmental indicators in oil and gas projects. The validity of variables was investigated based on Delphi method. The results indicated that the project environmental management’s weighting factor is 0.87% of total project’s weighting factor.
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The implementation of lean principles and approaches is gaining grounds in the construction industry globally. However, there is no clear understanding of the number and categories of lean practices implemented and the benefits associated with it in the planning, design and construction of building and infrastructure projects. This paper relied on a systematic review of published literature in Scopus, Science Direct and Google Scholar to identify and categorize the different lean practices implemented in the construction industry and the benefits derivable from them. Totally, 102 documents published between 1996 and 2018 were reviewed and their contents analyzed using descriptive statistics and content analysis. A total of 32 different lean practices categorised into design and engineering; planning and control; construction and site management; and health and safety management were identified. The review also found that the last planner system and just-in-time were the top two most implemented lean practices and about 20 different economic, social and environmental benefits were linked to the implementation of lean practices in the construction industry. This review is instructive that lean practices have good prospects for enhancing the productivity of the construction industry and achieving sustainable built environment, but a critical mass uptake and sustained implementation are required to attain these goals.
Book
The most significant unanticipated costs on many construction projects are the financial impacts associated with delay and disruption to the works. Assessing these, and establishing a causal link from each delay event to its effect, contractual liability and the damages experienced as a direct result of each event, can be difficult and complex. This book is a practical guide to the process of delay analysis and includes an in-depth review of the primary methods of delay analysis, together with the assumptions that underlie the precise calculations required in any quantitative delay analysis. The techniques discussed can be used on projects of any size, under all forms of construction contract, both domestic and international. The authors discuss not only delay analysis techniques, but also their appropriateness under given circumstances, demonstrating how combined approaches may be applied where necessary. They also consider problematic issues including 'who owns the float', concurrent delay, early completion programmes, and disruption. The book, which is well illustrated, features practical worked examples and case studies demonstrating the techniques commonly used by experienced practitioners. This is an invaluable resource to contractors, architects, engineers, surveyors, programmers and delay analysts, and will also be of interest to clients' professional advisors managing extension of time or delay claims, as well as construction lawyers who require a better understanding of the underlying assumptions on which many quantitative delay analyses are based.
Book
During the past several decades, the manufacturing and service industries significantly increased their levels of productivity, quality, and profitability through the application of process improvement techniques and information technology. Unfortunately, the construction industry lags far behind in the application of performance improvement and optimization techniques, as well as its overall competitiveness. Written by Lincoln H. Forbes and Syed M. Ahmed, both highly regarded for leadership and innovation, Modern Construction: Lean Project Delivery and Integrated Practices offers cutting-edge lean tools and other productive strategies for the management of people and processes in the construction industry. Drs. Forbes and Ahmed focus mainly on lean construction methodologies, such as The Last Planner(R) System, The Lean Project Delivery System (TM), and Integrated Project Delivery(TM). The tools and strategies offered draw on the success of the world-renowned Toyota Production System (TPS) adapted to the construction environment by construction professionals and researchers involved in developing and advocating lean construction methods. The book also discusses why true lean construction can best occur when all the construction stakeholders, owners, designers, constructors, and material suppliers are committed to the concept of optimizing the flow of activities holistically while de-emphasizing their self-interest. The authors also reintroduce process improvement approaches such as TQM and Six Sigma as a foundation for the adoption of lean methodologies, and demonstrate how these methods can improve projects in a so-called traditional environment. The book integrates these methods with emerging interest in "green construction" and the use of information technology and Building Information Modeling (BIM), while recognizing the human element in relation to motivation, safety, and environmental stresses. Written specifically for professionals in an industry that desperately needs to play catch up, the book delineates cutting-edge approaches with the benefit of successful cases and explains how their deployment can improve construction performance and competitiveness.
Book
The most significant unanticipated costs on many construction projects are the financial impacts associated with delay and disruption to the works. Assessing these, and establishing a causal link from each delay event to its effect, contractual liability and the damages experienced as a direct result of each event, can be difficult and complex. This book is a practical guide to the process of delay analysis and includes an in-depth review of the primary methods of delay analysis, together with the assumptions that underlie the precise calculations required in any quantitative delay analysis. The techniques discussed can be used on projects of any size, under all forms of construction contract, both domestic and international. The authors discuss not only delay analysis techniques, but also their appropriateness under given circumstances, demonstrating how combined approaches may be applied where necessary. They also consider problematic issues including 'who owns the float', concurrent delay, early completion programmes, and disruption. The book has been brought fully up to date, including references to the latest publications from the CIOB, AACEI and SCL, as well as current case law. Broad in scope, the book discusses the different delay analysis approaches likely to be encountered on national and international projects, and features practical worked examples and case studies demonstrating the techniques commonly used by experienced practitioners. This is an invaluable resource to programmers and schedulers, delay analysts, contractors, architects, engineers and surveyors. It will also be of interest to clients' professional advisors managing extension of time or delay claims, as well as construction lawyers who require a better understanding of the underlying assumptions on which many quantitative delay analyses are based. Reviews of First Edition. "John Keane and Anthony Caletka are pukka analysts in that tricky area of delays, programming and extension of time. I highly recommend their book Delay Analysis in Construction Contracts. Buy the book." (Building Magazine, February 2009). "The book's stated purpose is to provide a practical guide for those interested in schedule delay analysis. It provides a good in-depth review of the most common delay analysis techniques.... An excellent book, full of practical tips for the reader and very timely in its publication. It is well worth the cost and a good read for anyone involved in schedule delay analysis." (Cost Engineering, February 2009). It achieves in spades its stated aim of being a practical guide for contractors, contract administrators, programmers and delay analysts, as well as construction lawyers who require a better understanding of the underlying assumptions on which many quantitative delay analyses are based. (Construction Law Journal, 2009).
Book
THE #1 PROJECT MANAGEMENT CASE STUDIES BOOK NOW FEATURING NEW CASES FROM DISNEY, THE OLYMPICS, AIRBUS, BOEING, AND MORE. After on-the-job experience, case studies are the most important part of every project manager's training. This Fifth Edition of Project Management Case Studies features more than one hundred case studies that detail projects at high-profile companies around the world. These cases offer you a unique opportunity to experience, first-hand, project management in action within a variety of contexts and up against some of the most challenging conditions any project manager will likely face. New to this edition are case studies focusing on agile and scrum methodologies. Contains 100-plus case studies from companies that illustrate both successful and not-so-successful project management. Represents an array of industries, including medical and pharmaceutical, aerospace, entertainment, sports, manufacturing, finance, telecommunications, and more. Features 18 new case studies, including high-profile cases from Disney, the Olympics, Boeing 787 Dreamliner, and Airbus 380Follows and supports preparation for the Project Management Professional (PMP)® Certification Exam. Experienced PMs, project managers in training, and students alike will find this book to be an indispensable resource whether used as a standalone or combined with the bestselling Project Management: A Systems Approach to Planning, Scheduling, and Controlling, 12th Edition. PMI, CAPM, PMBOK, PMP and Project Management Professional are registered marks of the Project Management Institute, Inc.
Preprint
Total quality management (TQM) is a set of opinions and ideas that widely called “management philosophy”. This management technique is able to assist the construction firms in improving continuously the organization's performance, in order to satisfy customers and survive in the market. The success of TQM implementation is tightly dependent on identifying and selecting the appropriate critical success factors (CSFs), quality tools, and performance measures (KPIs) within TQM framework. Doubtlessly, a set of suitable performance measures (indicators) has a significant role to verify and ensure that TQM implementation can successfully achieve its aims in the organization. Thus, the main purpose of this paper was to develop a suitable framework to assess the effects of TQM implementation on organization's performance in construction industry. For this aim, the study was conducted a comprehensive literature review to specify the existing key performance indicators (KPIs) from 26 TQM frameworks that formulated by scholars. In data analysis, the only 20 KPIs were found of 26 TQM studies. The extracted KPIs (20) classified based on four dimensions of balanced scorecard (BSC), and then arranged from highest to lowest frequency for each perspective of BSC. Finally, a total of ten KPIs were determined and presented within BSC system as an appropriate performance measures framework, which enables evaluating critical areas that very vital to the success of TQM implementation in construction firms at project and enterprise levels.
Article
Purpose The purpose of this paper is to compare principles from the original Toyota Production System (TPS), the Toyota Way 2001 and Kaizen philosophy with principles derived from Japanese Zen Buddhism. The paper would also like to enlarge the debate concerning some lessons learnt from Japanese culture in order to avoid Lean implementation failures. Design/methodology/approach The original English version of Taiichi Ohno’s book dedicated to the TPS, the Toyota Way 2001 and other relevant papers regarding Kaizen were reviewed and analyzed. The principles that emerged from the review of this literature were then compared with similar philosophical principles from Japanese Soto Zen Buddhism. The literature concerning Zen philosophy was methodically analyzed and categorized using the content analysis. Findings The results of this research show many theoretical parallelisms as well as lessons for practitioners, in particular referring to principles such as Jidoka, just-in-time, waste identification and elimination, challenge, Kaizen, Genchi Genbutsu, respect for people and teamwork. Research limitations/implications Analysis and results are mainly based on the literature that was found, reviewed and categorized, along with the knowledge of authors on Zen philosophy. Results could differ depending on the literature reviewed and categorized. Practical implications The results of this research bring food for thought to practitioners in terms of lessons learnt from Japanese culture, Toyota principles and management style in order to avoid Lean implementation failures. Originality/value This is one of the first papers which compares Lean-TPS and Kaizen principles with the Zen philosophy to try to learn lessons for succeeding in Lean implementation.