Worker absenteeism is identified as the greatest threat to not meeting the completion date of a construction project. The purpose of this paper is to quantify the impact of employee absenteeism risk on the probabilistic lead time of a construction project. Calculations of employee absenteeism risk values were performed using data from the Central Statistical Office (Big Data). Probabilistic schedules with probability density functions (Normal, Exponential, Reyleigh, Triangle, Gamma, Cauchy) with and without calculated employee absenteeism risk were prepared. Student’s t-test and MAPE analysis of mean absolute percentage errors were performed to determine differences between groups. It was found that with respect to the probability of completing the task in the range of 75 to 95% for all functions, an unacceptable MAPE error of 32.82% to 69.23% arises. Therefore, the authors postulate that the risk of worker absenteeism should be considered in every construction process when performing probabilistic scheduling, i.e., in the Building Information Modeling BIM methodology.

Labor absenteeism is a factor that affects the good performance of organizations in any

part of the world, from the instability that is generated in the functioning of the system.

This is evident in the effects on quality, productivity, reaction time, among other aspects.

The direct causes by which it occurs are generally known and with greater reinforcement

the diseases are located, without distinguishing possible classifications. However, behind

these or other causes can be found other possible factors of incidence, such as age or sex.

This research seeks to explore, through the application of neural networks, the possible

relationship between different variables and their incidence in the levels of absenteeism. To

this end, a neural networks model is constructed from the use of a population of more than

12,000 employees, representative of various classification categories. The study allowed the

characterization of the influence of the different variables studied, supported in addition to

the performance of an ANOVA analysis that allowed to corroborate and clarify the results

of the neural network analysis.

In the management of human resources, the absences are monitored with Bradford Factor (BF) using the number and length of sick leaves. The sick leaves are also measured in health technology to assess the impact of health technologies on product loss, aka indirect cost (IC). Linking the BF and IC might promote BF as an outcome measure and facilitate the estimation of IC. We simulate a single company operation in several scenarios describing the firm's functioning and adjustments to workers' absence. We measure the BF and the IC due to absence and relate them with econometric modelling. Results show that BF and IC are associated in a non-linear way; hence, IC cannot be calculated from BF in a simple manner. The association is strongest for possibility to adjust to worker's absence, and a high elasticity of substitution between workers. Therefore, the possibility to proxy IC by BF is rather limited.