A Case Study on Estimating Optimal Labor Productivity

Abstract

The purpose of this study is to investigate the feasibility of a two-prong strategy for estimating optimal labor productivity in a labor-intensive operation where workers perform sequential and parallel tasks. The first prong represents a top-down approach that estimates optimal productivity by introducing system inefficiencies into the productivity frontier. A qualitative factor model (QFM) is used to determine the impact of those inefficiencies. This top-down approach yields the upper level estimation of optimal productivity. The second prong is a bottom-up approach that determines optimal productivity by removing non-contributory work from actual productivity in a discrete event simulation (DES). This bottom-up approach generates the lower level estimation of optimal productivity. An average of the upper and lower limits reveals the best estimate for optimal productivity. Optimal productivity is useful as an absolute benchmark for gauging efficiency and is a radically different approach from traditional methods of comparing actual against historical productivity, which only provides relative measures of efficiency. A case study was conducted at a sheet metal duct fabrication activity. The activity included eight tasks and forty-two actions. Using QFM, the loss in productivity due to system inefficiencies was found to be 0.39 ducts per crew-hour. Using DES, the loss in productivity due to operational inefficiencies was found to be 0.50 ducts per crewhour. The productivity frontier computed for the activity was 2.83 ducts per crewhour. Finally, the estimate of optimal productivity was determined to be 2.07 ducts per crew-hour. Given that actual productivity in the field was 1.20 ducts per crewhour, the work was only 58% efficient as compared to optimal productivity. This study contributes to the body of knowledge by providing a methodology to estimate optimal productivity in a labor-intensive operation.