Agile methodology to performance measure and identification of impact factors in the labour productivity of industrial workers

Abstract

The objective of this work is to adapt and test an agile methodology based on human observation that waives the data collection based on the timing of time in activity. Aiming to evaluate the productivity and non-productivity of workers in a factory during the pipe welding process for use in the construction of industrial plants. Through human observation the data was gathered by registering punctually the activities of the welders during a week. The results obtained by the Monte Carlo simulation were validated by comparing the results obtained by the probability and statistically analysis of the complete sample. After the simulation validation the Sensibility Analysis test was conducted in order to evaluate the variables of higher impact in the performance of the welders. The average of Labour Rating Factor and Idleness Rating Factor obtained by Monte Carlo simulation were respectively, 0.5529 and 0.4549 and by the sampling chart Labour Rating Factor 0.5552 and Idleness Rating Factor 0.4448. The methodology identified 9(nine) actions in the productive state, where the welding activity presents the greatest impact on the output of the mean of the Labour Rating Factor. In addition, 8(eight) actions were considered non-productive, where displacements and human conditions activities have the greatest impact on the Idleness Rating Factor. Furthermore, the results were compared with the work of other Authors. The research results shows the feasibility for industry to use this proposal of an agile methodology for evaluating the workforce performance, spending less resource compared with traditional ones.