Optimal Labor Productivity: A Case Study in a Sheet Metal Bending

Traditional practice of estimating labor productivity in construction industries compares actual versus historical productivity. This comparison exhibits relative efficiency rather than absolute efficiency. Accurate estimation of optimal productivity would allow project managers to determine the efficiency of their labor-intensive construction operations by comparing actual versus optimal rather than actual versus historical productivity because optimal productivity is the highest sustainable production level per unit of time achievable in the field under good management and typical field conditions. This study advances the feasibility of a novel two-prong approach, a radically different approach from traditional methods, by applying into a crew performing repetitive sheet metal bending task. The first prong estimates optimal productivity by minimizing system inefficiencies using qualitative factor model. This yields the upper limit estimation of optimal productivity. The second prong estimates optimal productivity by reducing operational inefficiencies using discrete event simulation. The result of this prong is the lower limit estimation of optimal productivity. An average of these two limits is the best estimate of optimal labor productivity. A case study was conducted on a sheet metal roll bending task which was labor-intensive. Actual productivity observed was 32.76 sheets per crew-hour. After minimizing loss due to system inefficiencies, the first prong estimated 60.88 sheets per crew-hour. Similarly, after reducing loss due to operational inefficiencies, the second prong estimated 38.58 sheets per crew-hour. Finally, the estimate of optimal productivity, average of two limits, was determined to be 49.73 sheets per crew-hour. The analysis showed that the work was only 51.8% efficient as compared to the optimal productivity. Therefore, this study contributes to the current body of knowledge by presenting a rationality of the two-prong strategy for estimating optimal labor productivity in labor-intensive operations and helps project managers to determine unbiased efficiency.