Multivariate Statistical Analysis Approach to Cluster Construction Workers based on Labor Productivity Performance
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Abstract
In the construction industry, the direct workforce is one of the most important drivers of the work process. Identifying and quantifying labor productivity impact factors allows the diagnosis of recurring problems during the construction phase. Understanding how these factors influence the productive and the nonproductive states according to the characteristics of workers or group of workers is an essential tool to boost productivity. This paper introduces a multivariate statistical analysis approach to cluster workers based on the characteristics of the actions that are performed during the daily construction tasks. This study analyzed the data from a field experiment based on human observation of actions of 10 welders during a week in a pipe-shop. The case study conducted step by step presented in this work indicates retention of 50% and 40% of the total sample in segmented workers clusters.
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