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Benchmarking and Metrics Summary Report for 2001

Publication No
BMM2002-3
Type
Research & Development Product
Publication Date
Feb 01, 2002
Pages
38
Research Team
BMM-Summary
DOCUMENT DETAILS
Abstract
Key Findings
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Abstract
Activities during the past year mark the evolution of the CII Benchmarking and Metrics program into a Web-based system of data collection, performance reporting, and industry analysis. CII believes that it is appropriate to broaden the Benchmarking an
Key Findings

CII started collecting project data with a paper-based questionnaire in 1996. By the time the 2002 summary report was published, the database had grown to hold 1,037 projects with a total installed value of $54.2 billion and had begun accepting project data through a web-based questionnaire. There was an almost even split between owner and contractor submitted projects as shown in Figure 1. After consistent decreases in average project cost for both owners and contractors, the trend had begun to level with small projects being dominant. As the trend toward small projects continues, changes in aggregate performance and practice use metrics may also become apparent (BMM2013-1, p. 3).

Several years ago, CII recognized that automating the entire CII Benchmarking process using the Web was essential to making the system work in a lean and efficient manner to meet participant needs and resource constraints. Significant strides have been made in this regard, and members are currently reaping the benefits. Data are now collected exclusively via a Web-based questionnaire and reports are now returned in a similar manner. By 2002, data could be submitted during project execution when it was most convenient to the project team. (An interim report, the Progress Key Report, was already available on-line even before a project has been finally submitted. BMM2002-3, p. 23)
Filters & Tags
Knowledge Area
Best Practice
Research Topic
Benchmarking & Metrics Summary Reports
Keywords
Benchmarking, Metrics, Performance Assessment, PAL, PAS, Project performance, Data mining, Best practices