Please use this identifier to cite or link to this item: https://doi.org/10.1080/00207540802431326
Title: Shift detection and source identification in multivariate autocorrelated processes
Authors: Hwarng, H.B. 
Wang, Yu.
Keywords: Forecasting
Logistics
Neural networks
Simulation
Simulation applications
SPC
Supply chain management
Issue Date: 2010
Source: Hwarng, H.B., Wang, Yu. (2010). Shift detection and source identification in multivariate autocorrelated processes. International Journal of Production Research 48 (3) : 835-859. ScholarBank@NUS Repository. https://doi.org/10.1080/00207540802431326
Abstract: Motivated by the challenges of identifying the true source of shift in multivariate processes, we propose a neural-network-based identifier (NNI) for multivariate autocorrelated processes. A rather extensive performance evaluation of the proposed scheme is carried out, benchmarking it against three statistical control charts, namely the Hotelling T 2 control chart, the MEWMA chart, and the Z chart. The comparative study shows the strengths and weaknesses of each control scheme. The proposed NNI is most effective in detecting small-to-moderate shifts and has the most stable run-length property. Designing to identify the source of the shift, the NNI performs more stably than the Z chart under high autocorrelation. The NNI's source identification property can be further improved with the devised alternative decision heuristics. A pair-wise modular approach is also proposed to extend the NNI for multivariate processes.
Source Title: International Journal of Production Research
URI: http://scholarbank.nus.edu.sg/handle/10635/44040
ISSN: 00207543
DOI: 10.1080/00207540802431326
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