This paper presents the statistical modeling for daily counting statistics of units that arrive for quality inspection at a food company. Different Poisson regression models were considered in order to analyze the data collected, with a Bayesian focus. The main objective was to forecast the daily average count based on co-variables such as days of the week. The analysis of co-variables is very often neglected by statistical packages that come with Discrete Event Simulation software. The discovery of the factors that influence these variations was essential to a more accurate modeling (the definition of simulation calendars) and enables industrial managers to make better decisions about the reallocation of people in the department, resulting in better planning of production capacity.
poisson regression; Bayesian analysis; Markov Chain Monte Carlo methods
