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Effective Production Rate Estimation and Activity Sequencing Logics Using Daily Work Report Data

Project Details
STATUS

Completed

START DATE

01/09/17

END DATE

01/31/19

RESEARCH CENTERS InTrans, CMAT
SPONSORS

Montana Department of Transportation

Researchers
Principal Investigator
Hyung Seok "David" Jeong

Affiliate Researcher

About the research

Accurate and practical production rate estimates are crucial for an accurate forecast of contract completion time. As costs of highway projects increase with time, the importance of estimating highway construction contract time has increased significantly, thereby emphasizing the need for effective production rates due to the interrelatedness between the two. By reviewing the literature, various aspects of production rate estimation were identified including factors that influence production rates, production rate adjustment factors, and statistical methods, and current practices of the Montana Department of Transportation (MDT). The purpose of this research was to develop historical data-driven estimates of production rates using daily work report (DWR) data in order to enhance current contract time determination practices.

The research team analyzed the MDT’s DWR data along with bid data and GIS data to estimate realistic production rates. Descriptive analysis, regression analysis, and Monte Carlo simulation were deployed to offer insights into historical projects’ characteristics and production rates of 31 controlling activities of MDT. The major findings of the descriptive analysis were statistical measures (i.e., mean, first quartile, median, and third quartile) of 31 controlling activities, which provide more practical, detailed, and updated estimates in comparison with the current published values. In addition, variations of production rates in terms of different seasons of work, districts, area types (urban/rural), and budget types were explored. The study also developed regression equations to estimate production rates of 27 out of 31 controlling activities. For each activity, factors that had a significant effect on production rate were included in the regression model as predictor variables. Besides, a production rate-based method was proposed to evaluate contractor’s performances, and a Microsoft Excel based Production Rate Estimation Tool (PRET) was developed to assist MDT practitioners.

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