Construction Research Congress 2012 © ASCE 2012
Quantification of Front End Planning Input Parameters in Capital Projects Sungmin Yun1, Sung-Joon Suk2, Jiukun Dai3, and Stephen P. Mulva4 1
Construction Industry Institute, Lane, Austin, TX 78759-5316;
[email protected] 2 Construction Industry Institute, Lane, Austin, TX 78759-5316;
[email protected] 3 Construction Industry Institute, Lane, Austin, TX 78759-5316;
[email protected] 4 Construction Industry Institute, Lane, Austin, TX 78759-5316;
[email protected]
University of Texas at Austin, 3925 West Braker PH (512) 232-3051; FAX (512) 499-8101; email: University of Texas at Austin, 3925 West Braker PH (512) 232-3051; FAX (512) 499-8101; email: University of Texas at Austin, 3925 West Braker PH (512) 232-3050; FAX (512) 499-8101; email: University of Texas at Austin, 3925 West Braker PH (512) 232-3013; FAX (512) 499-8101; email:
ABSTRACT Front end planning (FEP) is recognized by both academia and industry for its potential for improving project success. Despite wide acceptance of its value, the FEP process varies in its implementation throughout the construction industry and from one project to another. Diverse circumstances will require different human and financial resources for successful implementation. The Construction Industry Institute (CII) has captured the FEP implementation efforts of leading owners and contractors since 1996 through CII’s Benchmarking and Metrics (BM&M) program. This paper quantifies several parameters for successful FEP implementation effort in terms of cost, schedule and project management (PM) team size. The paper also examines selected parameters in light of several project characteristics. Analyses show that FEP implementation efforts differ depending on project characteristics such as industry type and project nature, amongst others. The quantitative summaries presented in this paper will help practitioners plan for appropriate levels of implementation and resource utilization during front end planning. INTRODUCTION Most project practitioners recognize that effectively developing the scope of work during the front end planning phase is critical to successful project delivery (Gibson et al. 2006). Front end planning (FEP) involves the process of developing sufficient strategic information such that an owner can address risk and decide to commit resources to maximize the chance for a successful project (CII 1994; CII 1995; Gibson et al. 1995). Typically, the FEP process includes putting together the project team, selecting technology, selecting the project site, developing project scope, and developing project alternatives (Gibson et al. 1995). Many organizations struggle to effectively implement the FEP process however, due to a lack in expertise or information (Gibson et al. 2006).
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FEP is defined as the process encompassing all the tasks between project initiation and the beginning of detailed design (CII 1994). The process begins with a project concept formulated to meet a business need and ends with stated objectives that support a decision whether to proceed with project execution (CII 1995). Ideally diverse resources are brought together to work as a team in early development. This team is more responsive to customer input and can be effective at reducing overall duration and rework (Shina 1991; Turino 1991). The FEP process requires a diverse array of efforts in order to successfully scope and plan for project execution (Cho and Gibson 2000). The level of effort can be expressed quantitatively in terms of human and financial resources as inputs and it can also be qualitatively assessed based on FEP tasks and project scope completion as process outputs. The objectives of this paper are to quantify the input parameters required for successful FEP implementation with consideration for selected project characteristics. To begin, this paper describes the research methodology. The quantitative information presented in this paper will help practitioners plan for appropriate levels of implementation and resource utilization during front end planning (FEP). RESEARCH BACKGROUND Most previous FEP studies focused on FEP outputs and their relationships with project success. These studies identified the tasks needed to be done during the FEP phase to create a well-defined project scope and developed assessment tools such as the Project Definition Rating Index (PDRI) to assess scope definitions (Cho and Gibson 2000; Cho and Gibson 2001; Cho et al. 1999; Dumont et al. 1997). In addition, the relationship of FEP efforts to project success in terms of cost, schedule, and change performance were investigated. But, the FEP process varies greatly depending on characteristics such as project nature, e.g. grass roots, modernization or addition, industry type, project size, and complexity. The Construction Industry Institute (CII) has captured and measured the FEP implementation efforts of industrial projects through CII’s Benchmarking and Metrics (BM&M) program since 1996 (Hamilton and Gibson 1996). The BM&M program gathers inputs (i.e., resources) and process outputs (e.g., PDRI) related to the FEP process and other management practices. The FEP inputs measured through the BM&M program include project management (PM) team size, work-hours expended during the FEP process, and FEP phase cost and duration. The FEP process outputs include FEP practice implementation score, PDRI score for building or industrial as appropriate, and FEP phase cost and schedule growth. From these inputs and outputs, the FEP parameters can be developed. FEP input parameters The FEP input parameters used by this study are derived from the human and financial resources utilized to implement the FEP process. Typically, the human resources for the FEP process consist of engineering team discipline leads, the project manager and engineers, engineering project representatives, project sponsors, the operations manager, functional managers such as construction, procurement, safety, environmental, logistics, quality assurance/quality control (QA/QC), maintenance, and, if possible, contractors (CII 2006a; CII 2006b). The financial resource is the FEP phase cost expended in the implementation of FEP. The actual FEP input parameters
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are diverse, depending on the project objectives to be achieved, scope of work, and characteristics of the project. In general, large scale projects require more input resources for the FEP process. Therefore, the parameters need to be normalized to account for project size. Table 1 presents the four FEP input parameters and their formula. Table 1. FEP Input Parameters and Their Formula FEP Input Parameter Normalized FEP PM Team Size Normalized FEP PM Team Work-hour FEP Phase Cost Factor FEP Phase Schedule Factor
Formula = = = =
FEP PM Team Size Total Project Cost FEP PM Team Work-hours Total Project Cost FEP Phase Cost Total Project Cost FEP Phase Duration Overall Project Duration
Unit FTE/$ million work-hours/$ million % %
Normalized FEP PM Team Size indicates the number of PM team members in the FEP process as normalized to a $1 Million total project cost. The number of project management personnel is reported as the average full time equivalent (FTE) in place during the FEP process. The total project cost is the sum of the phase costs from front end planning phase through the startup phase. Normalized FEP PM Team Work-hours indicates the work-hours of PM team members in the FEP process normalized by the total project cost. FEP Phase Cost Factor indicates the proportion of FEP phase cost to total project cost. FEP Phase Schedule Factor measures the proportion of FEP phase duration to overall project duration. Project characteristics The required FEP implementation efforts may vary by project characteristics such as project nature, industry type, project size, and project complexity (CII 2009). CII BM&M classifies each project into one of three project natures: grass roots, addition, and modernization. “Grass roots,” also known as green field, is defined as a new facility from the foundation and up. A project requiring demolition of an existing facility before new construction begins is also classified as grass roots. Addition, sometimes referred to as add-on or expansion, is defined as a new addition that ties in to an existing facility, often intended to expand capacity. Modernization, which is also referred to as renovation and upgrade, is defined as a facility for which a substantial amount of the equipment, structure, or other components is replaced or modified, and which may expand capacity and/or improve the process or facility. The project’s industry type is classified as a building, infrastructure, heavy or light industrial. The project size is the total project cost and that is further divided into five cost categories: less than $5 Million dollars, $5 to $15 Million dollars, $15 to $50 Million, $50 to $100 Million, and greater than $100 million dollars. Project complexity is measured on a 7-point Likert scale from low (1) to high (7) and compared to other projects. Low complexity is defined as a project characterized by the use of well-established, proven technology, a relatively small number of process steps, a relatively small facility size or process capacity, a facility configuration or
Construction Research Congress 2012 © ASCE 2012
geometry that has been used before, and well-established, proven construction methods. High complexity is defined as a project characterized by the use of new, “unproven” technology, an unusually large number of process steps, large facility size or process capacity, new facility configuration or geometry, or new construction methods. In this study, the projects were categorized as either highly complex or low complexity on the basis of the middle point of the scale (4). RESEARCH METHODOLGY The data used in this study was extracted from the CII’s BM&M database. The CII’s BM&M program has developed questionnaires to collect project data including project performance and FEP data since the program launched in 1996. This study focuses on recent data submitted by owners since 2000 because owners have the completed data of projects. A total of 419 projects were investigated. Using the FEP input data including project team size and work-hours during the FEP phase, and FEP phase cost and duration, this study calculates the FEP input parameters listed in Table 1. It should be noted that questions regarding project team size and workhours during the FEP phase are added recently. As a result, fewer projects have those data than others, such as cost and schedule information. Total project cost was also adjusted through the RS Means historical index (RSMeans 2010) to the year of 2010. In this study, two statistical analysis methods, independent two-sample t-test and one-way analysis of variance (ANOVA), were applied to investigate how the FEP input parameters vary by the project characteristics. Specifically, the t-test was used to compare the mean score of the FEP input parameters in light of project complexity and the one-way ANOVA was used for comparing the mean scores of the parameters in terms of project nature, industry type, and project size. When an independent two-sample t-test is used, this study chooses 0.05 as the threshold of statistic significance between the two groups. The t-test is conducted based on verification of three assumptions: normality of the dependent variable, homogeneity of population variance, and independence of the observations. The ANOVA is a technique used to compare means of two or more samples using the F distribution (Stevens 2007). This technique can be used only for numerical data. The presumptions of ANOVA tests are normality of dependent variables, homogeneity of population variance, and independence of the observation (Stevens 2007). The ANOVA produces an F statistic, the ratio of the variance calculated among the means to the variance within the samples. If the groups are drawn from the same population, the variance between the group means should be lower than the variance of the samples, following central limit theorem. A higher ratio therefore implies that the samples were drawn from different populations. ANALYSIS RESULTS This study compared the mean score of the FEP input parameters by project characteristics. A quantitative summary of the FEP parameters were presented in terms such as mean, standard deviation (StDev), first quartile (25th percentile, Q1), second quartile (50th percentile or median, Q2), and third quartile (75th percentile, Q3). The FEP input parameters were examined through an independent two-sample ttest, and one-way ANOVA according to various project characteristics.
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Project nature Project nature is an inherent characteristic of the project and can be grass roots, addition or modernization. The results of ANOVA analysis of the FEP input parameters by project nature are shown in Table 2. Table 2. ANOVA results of the FEP input parameters by project nature Parameter PM Team Size/Project Cost Addition Grass Roots Modernization PM Team Work-hour/Project Cost Addition Grass Roots Modernization FEP Phase Cost Factor Addition Grass Roots Modernization FEP Phase Schedule Factor Addition Grass Roots Modernization
N
Mean
StDev
Q1
Q2
16 20 10
0.144 0.102 0.157
0.212 0.135 0.171
0.028 0.066 0.031 0.045 0.044 0.081
15 17 9
67.3 145.8 118.7
86.7 142.6 136.5
15.3 47.8 33.4
78 86 79
3.434 2.725 4.565
3.906 2.169 4.503
1.061 2.094 1.237 2.298 1.372 2.999
91 22.55 102 23.24 74 30.05
13.12 12.15 17.44
12.81 20.00 12.52 20.01 17.64 24.93
32.7 76.9 72.0
ANOVA Q3 (p-value) 0.647 0.199 0.101 0.250 0.209 57.3 240.5 161.3 0.005 3.341 (
