An Evaluation of Public Construction Contracting Methods for the Public Building Sector in Oregon using Data Envelopment Analysis

Abstract

When applied to parking structure projects CM/GC PDS results in projects that outperform DBB PDS projects. H A3. 7 : When applied to building remodel projects CM/GC PDS results in projects that outperform DBB PDS projects. H A3. a : When applied to school projects CM/GC PDS results in projects that outperform DBB PDS projects. HA3.9: When applied to sports facility projects CM/GC PDS results in projects that outperform DBB PDS projects. H03: CM/GC PDS does not result in projects that outperform DBB PDS on similar types of projects. (Note the sub-hypotheses of the null hypothesis H03 are omitted for brevity.) 3 1.2 Research Design and Data Collection How is "outperformed" determined? Or, more generally, how is performance determined, what measures are used, and where does the required information come from? Clearly, performance can be defined in a number ofways and use any number ofmetrics or measures. Sanvido [182] Chua [47, 48] and others have laid some of the groundwork by providing critical project success factors, while Knuf, [123] Harma, [104] Russell, [177] and others have contributed benchmarking metrics. These can be thought of as inputs and outputs to a construction project management model. However, the number of metrics and measures provided in the literature is quite large, and therefore only the most important measures must be separated out from those oflesser importance for this analysis. The process undertaken in this research started with assembling a panel of construction industry experts (referred to hereafter as the Expert Panel), which is detailed in Chapter 3. Metrics were elicited from the Expert Panel consistent with methods described by Ayyub [13], Chua [48] and Kocaoglu [124]. Data was obtained on a 407 public projects constructed in Oregon from 1986 to 2002 (the "Oregon database"). One hundred and ninety-two of the projects either did not meet the criteria for the analysis or had significant missing data, which made them unusable in the model. Two hundred and fifteen projects were used in the final DEA model and analysis. These were nearly equally distributed between competitive lunlP sum Design-Bid-Build (DBB) and negotiated Construction Manager/General Contractor

Project Delivery Method Performance for Public School Construction: Design-Bid-Build versus CM at Risk

“…Findings of the Konchar (1997) and Konchar and Sanvido (1998) works were challenged by Williams (2003) based on the statistical significance of the results and wide variations of the comparative project types included in their study, which led to gross disparities of overall project costs, square foot sizes, and complexity. The Williams (2003) research was directed at the comparative performance of 215 Oregon public schools constructed with the CM/GC (CM at Risk) and DBB methods using statistical analysis and data envelope analysis (DEA). Results of the study indicated that statistically significant differences did not exist in terms of cost or schedule, although observable evidence revealed that schools constructed using CM at Risk had a higher per-square-foot cost than DBB schools.…”

Benchmarking Engineering, Procurement and Construction (EPC) Power Plant Projects by Means of Series Two-Stage DEA

“…There are a few empirical studies on the relative efficiency of construction projects using DEA. This literature includes the works [11,[13][14][15][26][27][28][29].…”

“…Williams [26] investigates different PDSs using data from building projects in Oregon employing radial DEA models. Farris et al [14] carry out a DEA case study for evaluating the relative performance of engineering design projects.…”