A dynamic artificial neural network model for forecasting nonlinear processes
Computers and Industrial Engineering
Simultaneous process mean and variance monitoring using artificial neural networks
Computers and Industrial Engineering
System analysis approach for the identification of factors driving crude oil prices
Computers and Industrial Engineering
Reliability evaluation for a manufacturing network with multiple production lines
Computers and Industrial Engineering
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The crescent demand for customized products has challenged industries with reduced lot sizes. As a result, frequent product model changing and short series of observable variables decreased the performance of many traditional tools used in process control. This paper proposes the use of endogenous variables in predictive models aimed at overcoming the multiple setup and short production runs problems found in customized manufacturing systems. The endogenous variables describe the type/model of manufactured products, while the response variable predicts a product quality characteristic. Three robust predictive models, ARIMA, structural model with stochastic parameters fitted by Kalman filter, and Partial Least Squares (PLS) regression, are tested in univariate time series relying on endogenous variables. The PLS modeling yielded better predictions in real manufacturing data, while the structural model led to more robust results in simulated data.