@article {5356,
	title = {Dynamic Var Model-Based Control Charts for Batch Process Monitoring},
	journal = {European Journal of Operational Research (EJOR)},
	volume = {285},
	year = {2020},
	pages = {296-305},
	abstract = {<p>In the field of Statistical Process Control (SPC) there are several different approaches to deal with monitoring of batch processes. Such processes present a three-way data structure (batches {\texttimes} variables {\texttimes} time-instants), so that for each batch a multivariate time series is available. Traditional approaches do not take into account the time series nature of the data. They deal with this kind of data by applying multivariate techniques in a reduced two-way data structure, in order to capture variables dynamics in some way. Recent developments in SPC have proposed the use of the Vector Autoregressive (VAR) time series model considering the original three-way structure. However, they are restricted to control approaches focused on VAR residuals. This paper proposes a new approach to deal with batch processes using the VAR model, but focusing on coefficients instead of residuals. Through a simulated batch process, we illustrate the better performance of our approach over the residual-based control charts in both offline and online context.</p>
},
	url = {https://www.sciencedirect.com/science/article/pii/S0377221719310781},
	author = {Marcondes, D. F. and Valk, M.}
}
