3 Reasons Why Loss of Efficiency Claims Are So Hard to Prove

In part 1 of this blog series, we highlighted the difficulties facing the construction market due to chronic labor shortages and in part 2, we linked the labor shortage with a rise in disputes, particularly over claims for lost labor efficiency. In a market where loss of efficiency claims are increasingly common, it’s worth noting some hard truths about these claims: they are often very hard to prove.

Why is it so hard to measure, demonstrate, and ultimately prove a loss of labor productivity or efficiency? The reasons include:

  1. Bad data. The typical metric for construction effort is labor hours, often coupled with some measure of quantities of work accomplished. In addition, there should be initial estimate data to refer back to. The problem? Daily work records often include no specifics about the interruptions or problems that led to inefficiency and frequently lack basic data about the quantity of work accomplished or not accomplished. Those reviewing data after the fact struggle to define what happened quantitatively, much less provide insight into what affected labor productivity.
  2. Unclear causes. Labor production efficiency losses are often attributed to a myriad of factors—design changes, congestion, weather, unclear direction. The contributions of the various factors are difficult to segregate. Rarely are the various factors affecting productivity documented in real time, as production forces are preoccupied with getting the project built. Moreover, a construction project is a dynamic environment, making it virtually impossible to isolate various factors that affect productivity.
  3. Limited tools. Among construction managers and forensic analysts, the “gold standard” for demonstrating a loss of efficiency is a “measured mile” analysis. Under this concept, the analyst compares productivity for a given work operation subject to an adverse impact with the same operation performed absent that impact. They then attribute the difference in productivity to the adverse impact. This approach fails, however, when the unimpacted period—or “measured mile”—does not exist, as is often the case.

Other approaches are often found wanting. For example, comparing productivity to an initial estimate relies an assumption that the estimate itself was accurate and detailed enough to render a comparison. In the absence of an estimate, analysts may turn to generic studies and references that possess a veneer of reliability and authority, such as Mechanical Contractors Association of America labor inefficiency factors, overlooking that these references are based on very limited actual data or general underlying assumptions that might be inapplicable to the particular situation.

Not surprisingly, disputes over lost labor efficiency are often very contentious. Many analysts are not principled or careful in their presentations of data. Attractive charts or graphs that illustrate a correlation with the cited issue may be persuasive or generate sympathy in a negotiation, but when parties view the root cause of inefficiency differently, the underlying analysis is often too insufficient to be persuasive.

For example, a highway contractor may cite a change in work schedule for road paving, from day to night shift, as having affected productivity and therefore lost efficiency due to poor visibility and crew fatigue working at night. However, if no similar work was ever performed in the daytime (providing a measured mile) or if the change in schedule was offered as a potential benefit to the contractor due to reduced traffic (unclear causes), the issues may be difficult to resolve.

When faced with presenting a loss of efficiency analysis or reviewing and assessing one, careful analysis and expertise is needed. The parties involved should draw from the best data available in the project record and tie into real-time observations from those who were there. These steps offer the best chance for satisfactory resolution.

With that said, however, it is clear that the industry needs better tools to measure efficiency and some potential solutions will be shared in the next blog post.


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