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Background of the Study

All construction projects are unique and have their own operating environment and sets of technical requirements. As a result, the execution of the construction project is usually subject to a number of constraints that hinder the commencement and progression of the construction operations, which consistently has enormous impact on the performance of the overall project (Richardson, 2010). By definition, constraints are any conditions that prevent the achievement of project goals, be it spatial, temporal or quality conditions. Successful management of a project largely relies on effective identification and management of the constraint through appropriate planning and short-term scheduling (Duff & Quilliam, 2010). The overall planning provides a an in-depth analysis of the project and the execution strategy while the short scheduling provides a detailed review of the operational constraints. Ideally, such detailed schedules need to reflect the actual field conditions of the project (Duff & Quilliam, 2010). The research proposal will provide an overview of the constraint analysis and propose the framework for managing such project management constraints.

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Problem Statement

The significance of developing a constraint-free and reliable work plan has for long been understood within the project management industry. Nonetheless, numerous construction projects are still being affected by the deferrals and cost overruns that can frequently be associated with ineffectual identification and treatment of constraints. When a constraint is not identified during the scheduling stage of the project, the ensuing conflicts within the project are usually unavoidable. In today’s world, construction projects are more complex and technically challenging, which is an aspect that exposes such project to even more complex constraints. Furthermore, the traditional scheduling methods, critical path methods (CPM) and bar charts are widely utilized as the foundation for constraint analysis, and greatly hinder the capability to model and resolve the constraint during future scheduling. These approaches have been impugned because of their limitations in communication and their failure to cope with time related precedence constraints. This instigates the need to better understand the constraints in project management and a new approach to identifying and modeling constraints to ensure presence of a constrain-free project plan.

Research Objectives

The long-term goal of this research is to establish a formalized constraint management system. The major objectives of this research study include:

  1. To provide a holistic review of the sources and the nature of constraints found in the construction projects.
  2. To establish a constraint classification model for easier project constraints identification and modeling.
  3. To assess the current project management industry practices and research in regard to the project management constraints modeling

Research Questions

This research will be guided by a number of research questions that will be addressed by the study. They include:

  1. What are the major constraints found in various construction projects?
  2. What is the best way to classify such constraints for easier identification and modeling within project management?
  3. What are the existing industry practices and research progress in modeling and resolution of conflicts?

Hypothesis

Project scheduling constraints do not affect the overall outcome of the project.

Literature Review

The Current Trend in Constraint Management

The importance of conducting a detailed planning and constraint analysis to issue executable project plans has gained significant recognition in the construction industry. According to Duff and Quilliam (2010), a number of constraints have been identified in a construction project that includes technological, resource, spatial and information constraints. In the modern construction project, Choo, Tommelein and Ballard (2012) note that there is increased database application as key concepts of lean construction process. The authors further note that such database permits the project manager to monitor work plans and track constraints against the crew level work packages. However, Arditi (2011) notes that though the use of the database is a major step in tracking the constraints, the application described still requires constraints to be manually identified and tracked.

Constraint Management and Constraint Classification Method

According to Arditi (2011), it is not possible to control and manage the constraints in project management without increased automation, especially given the sheer number of constraints that need to be identified and tracked in large and complex projects. The author further notes that much of information associated with constraints is normally contained across a suite of unrelated information technology (IT) applications. However, Arditi (2013) reveals that the presence of Workforce planning processes that include constraint management at the component-level offers an effective data integration and aggregation platform across different IT systems used in a construction project.

Chua, Bok and Shen (2013) indicate that failure to identify the constraint at the planning stage resulting in a conflict in the subsequent field is usually inevitable, especially as the projects become more technically complex and logically challenging. Duff and Quilliam (2010) further support the argument and note that constraints identification and classification through a structured process need to be the first step towards zero-constraints environment. Richardson (2010) has proposed a classification method combined with a template approach for the constraint instantiation. The method is further supported by an open data integration method with external information technology (IT) system to identify, track and offer the status of restraint at a detailed component level.

According to Arditi (2013), though construction projects are unique, they usually share similar types of constraints, especially at the operational level. Furthermore, the author notes that the key sources of constraints in construction project include work plans, quality inspection, safety check, equipment and tool availability, among others. In addition, Duff and Quilliam (2010) have provided three different constraint classification models that include physical, resource and information category. On the basis of such categorization, it is possible to apply pre-defined constraint template that enhances consistency, completeness and accuracy of constraint identification thus reducing chances of overlooking such constraints.

Constraint Management Strategies

Duff and Quilliam (2010) lay emphasis on the need to implement the use of information technology (IT) to permit automatic identification and tracking of constraints based on component status information in external data sources. However, the authors note that other constraints will have to be tracked and identified manually. Such sentiments have been echoed by Richardson (2010) who believes that it will offer a chance to computerize the tracking and updating of restraint status thus making constraints management more efficient.

The author further notes that constraint types that could be automated include engineering drawing and plans, material availability and pre-fabrication status. According to Chua, Bok and Shen (2013), constraints such as safety checks, crew and equipment availability can be tracked manually, especially during weekly planning meetings. In addition, it is noted that the classification of the constraints makes its reporting more efficient.

Methodology

Research Design

In this paper, the exploratory, descriptive research design will be used to inform the study. The investigation will largely focus on reviewing previous scholarly work and conceptual modeling. It will review a number of constraints on the construction projects and their fundamental characteristic. Based on this understanding, a classification method will be developed to categorize the factors for the essence of identifying the key constraints. Once the classification and modeling techniques are pinpointed, a conceptual framework for the total constraint management will be developed.

Sampling Method

A sample design refers to the roadmap or framework that serves as the foundation for the selection of the survey sample size and affects a number of other aspects of the study, as well. In a wider context, researchers are usually interested in obtaining information through the survey (Fowler, 2014). Therefore, it is essential to define the sampling frame that adequately represents the population of interest. In any research, the sampling design plays a critical role in the collection of appropriate data (Fowler, 2014). The use of appropriate sampling design ensures that the study identifies the required sample size that is adequate to inform the topic of study. However, the sampling design is influenced by the objectives of the study.

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Description of Population

Since the study aims at understanding the importance of risk and constraints in project management, it will be conducted among the project management professionals. Through the use of survey, it will be possible to obtain the opinion and perception of individuals working in construction projects. As a result, it will be possible to establish a theme of constraint identification and management in the projects. The study will include the project managers, members of the team of more than nine sectors. Such wider population will be critical in providing a divergent experience that occurs in a different construction project. The study included the team members of the project in the population of the study in order to ensure the view in regard to the fundamental origin of the project constraints is identified. The project managers will be of great significance since they will provide data about the project constraints at much higher and policy level while the team members will offer their opinions from the technical point of view.

Sampling Procedures

In this study, a non-probability sampling technique will be used to identify the sample to be used in the study. Through the technique, the sample was defined using a process that does not give the possible participant an equal chance of being selected for the study (Fowler, 2014). For the convenience purposes, the study will use convenience sampling where the sample accessible to the researcher will be collected. This will be essential since it will be easy to select and recruit the subject to be used in the study. In addition, the sampling method is easy, cheap and consumes less time.

In this study, the sampling method will be shaped by the expediency of the sample units (construction projects) and the subjects will be identified based on ease of access and availability to respond to the questions of the research. Despite using a non-probability sample, the study strived to meet the essential requirements for a multivariate analysis applying for calculation of the minimum size of the sample required to inform the data (Lohr, 2010). The use of non-probability sampling method will ensure that the study is not compromised since both operational and technical constraints experienced in the project management tend to be similar and rarely vary from one project to the other (Fowler, 2014).

As recommended for a study of such scale, the sample will be computed at a level of statistical importance of (?=5%) and the level of power required - of 95 percent. The size of effect at 15 percent will be maintained: thus, a required sample of the 411 respondents will be selected during the study. However, the respondent in the study will be defined as project members and project managers involved in the area of constraint identification and management.

Biases

In this study, two main biases are apparent; they are selection and interviewer bias

Selection Bias

Selection bias in the study is anticipated since all participants in the study will not be given an equal opportunity to take part in the research. The study used a non-probable method for convenience purpose; hence, quite a number of possible respondents were excluded from the study (Lohr, 2010). In order to reduce the effect of the selection bias in the study, the respondents of the project will be obtained from 9 different project construction sectors.

Interviewer/Question Biases

The biases usually occur due to the systematic difference between how the information is solicited, obtained, recorded or interpreted. The questionnaire design may be a source of bias depending on how the questions are structured and organized (Lohr, 2010). In order reduce/minimize the impact of the question bias, the questionnaire will be redesigned to ensure it provides standard questions that captured all required details.

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Variables

In the study, the review of literature on project risk management was essential in identifying dependent and independent variables. In addition, the independent variables constituted the technology used in the project while the dependent variables will be represented by the success factors such as customer’s satisfaction, scope, and quality (Creswell, 2014). Nonetheless, budget and duration data, as well as revenue constitute the controlling variables of the construct.

Internal and External Validity

Validity measures are essential in every study since this identifies the dependability of the study. In regard to the internal validity, the study will largely focus on the design criteria, content and construct validity in order to eliminate the issues of selection bias that tends to affect the internal validity. As an outcome, the respondents of the project will be obtained from 9 different projects (Creswell, 2014). Thus, this will ensure that respondents from different projects are incorporated into the study. In regard to content, the questionnaire will be redesigned to ensure it provides standard questions that capture all required details. Furthermore, to avoid the interpretation bias while handling open-ended questions, the universal parameters will be used to interpret and record the outcome of such questions. In order to promote external validity of the study, the process of acquiring the descriptive sample will be enhanced to achieve the necessary external validity threshold (Creswell, 2014).

Measurement Scale

Measurement is the process of analyzing and evaluating data. The measurement scale is an instrument for data assessment. In general, the measurement scales are classified in two categories – comparative scale and non-comparative scale. Non-comparative scales include Likert scale, which is commonly used as a questionnaire measurement tool since its aim is to evaluate people’s attitudes, opinions, degree of agreement or disagreement. Furthermore, the levels of measurement are represented by nominal, ordinal, interval and ratio scales. Nominal scales differentiate items by categorizing giving names and defining the quality of the objects. Ordinal scales sort the data according to their order but do not illustrate the degree of difference and the exact distance between them. In contrast, interval scales demonstrate the equal interval and difference between the points of the scale. Ratio scales possess “the properties of an interval scale together with a fixed origin or zero point” (Levels of Measurement and Scaling).

Data Analysis

In the analysis of the results of the study, the logistical regression model will be used, which is a multivariate numerical method used in predicting or explaining the relationship that influences the dependent variable. The dependent variables in the study will be analyzed using a binary logistical model in the study in accordance with the perception of success given by the respondent (Passer, 2013). Thus, the intention will be to identify the characteristic attitudes of the professionals or the respondents in construction project associated with the perception of the success of the project (Passer, 2013). Furthermore, a model will be established taking into consideration the response variables related to the perception of the success and the non-perception of the success. By these means, the modeling will enable the study to measure the dimension of the effect of the explanatory factor provided in the questionnaires.

In the study, the independent variables analyzed will include the project type, manager, revenue and factors that refer to the attitude that is essential to the project management (Azzalini & Walton, 2012). It is important to note that revenue variable will not be included in the model since it is a variation that is largely associated with the project. In regard to the logistic regression model, the parameters of estimation of the model at the input will be 5 percent significant and 10 percent significant for the output. Based on the building and the binary logistic regression model, it will be possible to verify independent variables that influence the efficiency of the timeframe (Azzalini & Walton, 2012).

In the research, coding and editing will be essential in order to enhance the clarity of the information and eliminate mistakes. It is apparent that field work produces data with mistakes, and any error on the data will affect the integrity of the project (White & McBurney, 2013). In addition, codification and categorization will be conducted in order to ensure data is easy to analyze and interpret. The data will be arranged in a systematic order, and coding of the data will be applied to qualitative data to permit the data to be separated, regrouped and relinked in order to combine the outcome of the studies. In the analysis, the estimated coefficients will be assigned the number of ease of classification such as factor1, factor 2, factor 3, among others (Azzalini & Walton, 2012). As a result, such coding will ensure that coefficients offer explicit meaning on the impacts of scheduling constraints and risks on the project success.

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