An improved methodology for selecting similar working days for measured mile analysis

Abstract Quantifying economic losses on construction projects caused by labor inefficiencies is often a difficult and tedious task. A widely accepted way to quantify such losses is by using the “measured mile” approach. This technique compares the productivity achieved during an unimpacted or minimally impacted time period with productivity realized during an impacted period. The dependability of the periods that are chosen is vital and plays a key role in the determination of merit, liability and quantum. The work performed during the measured mile period should be substantially similar to the work that was affected. As currently practiced though, choosing the periods for measured mile analysis is usually made in a largely subjective manner. The objective of this article is to introduce and illustrate the statistical clustering method as a tool for selecting the similar working periods. This new approach is advocated because it determines similarity of work condition using objective criteria. The method is agile and can be easily applied in practice by project managers or construction consultants. In this paper the factors that affect the similarity of work are identified, and the clustering procedure is developed. An example is also included to show how the method works in practice.