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Multivariate Six Sigma: A Case Study in Industry 4.0

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Multivariate Six Sigma: A Case Study in Industry 4.0

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dc.contributor.author Palací-López, Daniel es_ES
dc.contributor.author Borràs-Ferrís, Joan es_ES
dc.contributor.author da Silva de Oliveria, Larissa Thaise es_ES
dc.contributor.author Ferrer, Alberto es_ES
dc.date.accessioned 2021-02-19T04:33:48Z
dc.date.available 2021-02-19T04:33:48Z
dc.date.issued 2020-09 es_ES
dc.identifier.uri http://hdl.handle.net/10251/161851
dc.description.abstract [EN] The complex data characteristics collected in Industry 4.0 cannot be efficiently handled by classical Six Sigma statistical toolkit based mainly in least squares techniques. This may refrain people from using Six Sigma in these contexts. The incorporation of latent variables-based multivariate statistical techniques such as principal component analysis and partial least squares into the Six Sigma statistical toolkit can help to overcome this problem yielding the Multivariate Six Sigma: a powerful process improvement methodology for Industry 4.0. A multivariate Six Sigma case study based on the batch production of one of the star products at a chemical plant is presented. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Processes es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Six Sigma es_ES
dc.subject Industry 4.0 es_ES
dc.subject Multivariate data analysis es_ES
dc.subject Latent variables models es_ES
dc.subject PCA es_ES
dc.subject PLS es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title Multivariate Six Sigma: A Case Study in Industry 4.0 es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/pr8091119 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-82896-C2-1-R/ES/DISEÑO, CARACTERIZACION Y AJUSTE OPTIMO DE BIOCIRCUITOS SINTETICOS PARA BIOPRODUCCION CON CONTROL DE CARGA METABOLICA/
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat es_ES
dc.description.bibliographicCitation Palací-López, D.; Borràs-Ferrís, J.; Da Silva De Oliveria, LT.; Ferrer, A. (2020). Multivariate Six Sigma: A Case Study in Industry 4.0. Processes. 8(9):1-20. https://doi.org/10.3390/pr8091119 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/pr8091119 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 20 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 8 es_ES
dc.description.issue 9 es_ES
dc.identifier.eissn 2227-9717 es_ES
dc.relation.pasarela S\424362 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.description.references Linderman, K., Schroeder, R. G., Zaheer, S., & Choo, A. S. (2002). Six Sigma: a goal-theoretic perspective. Journal of Operations Management, 21(2), 193-203. doi:10.1016/s0272-6963(02)00087-6 es_ES
dc.description.references Grima, P., Marco-Almagro, L., Santiago, S., & Tort-Martorell, X. (2013). Six Sigma: hints from practice to overcome difficulties. Total Quality Management & Business Excellence, 25(3-4), 198-208. doi:10.1080/14783363.2013.825101 es_ES
dc.description.references Reis, M., & Gins, G. (2017). Industrial Process Monitoring in the Big Data/Industry 4.0 Era: from Detection, to Diagnosis, to Prognosis. Processes, 5(4), 35. doi:10.3390/pr5030035 es_ES
dc.description.references Ferrer, A. (2007). Multivariate Statistical Process Control Based on Principal Component Analysis (MSPC-PCA): Some Reflections and a Case Study in an Autobody Assembly Process. Quality Engineering, 19(4), 311-325. doi:10.1080/08982110701621304 es_ES
dc.description.references Peruchi, R. S., Rotela Junior, P., Brito, T. G., Paiva, A. P., Balestrassi, P. P., & Mendes Araujo, L. M. (2020). Integrating Multivariate Statistical Analysis Into Six Sigma DMAIC Projects: A Case Study on AISI 52100 Hardened Steel Turning. IEEE Access, 8, 34246-34255. doi:10.1109/access.2020.2973172 es_ES
dc.description.references Ismail, A., Bahri Mohamed, S., Juahir, H., Ekhwan Toriman, M., Md. Kassim, A., Md Zain, S., … Yang, C. (2018). DMAIC Six Sigma Methodology in Petroleum Hydrocarbon Oil Classification. International Journal of Engineering & Technology, 7(3.14), 98. doi:10.14419/ijet.v7i3.14.16868 es_ES
dc.description.references Jaeckle, C. M., & Macgregor, J. F. (1998). Product design through multivariate statistical analysis of process data. AIChE Journal, 44(5), 1105-1118. doi:10.1002/aic.690440509 es_ES
dc.description.references Höskuldsson, A. (1988). PLS regression methods. Journal of Chemometrics, 2(3), 211-228. doi:10.1002/cem.1180020306 es_ES
dc.description.references Wold, S., Sjöström, M., & Eriksson, L. (2001). PLS-regression: a basic tool of chemometrics. Chemometrics and Intelligent Laboratory Systems, 58(2), 109-130. doi:10.1016/s0169-7439(01)00155-1 es_ES
dc.description.references De Mast, J., & Lokkerbol, J. (2012). An analysis of the Six Sigma DMAIC method from the perspective of problem solving. International Journal of Production Economics, 139(2), 604-614. doi:10.1016/j.ijpe.2012.05.035 es_ES
dc.description.references Tomba, E., Facco, P., Bezzo, F., & Barolo, M. (2013). Latent variable modeling to assist the implementation of Quality-by-Design paradigms in pharmaceutical development and manufacturing: A review. International Journal of Pharmaceutics, 457(1), 283-297. doi:10.1016/j.ijpharm.2013.08.074 es_ES
dc.description.references Wold, S., Esbensen, K., & Geladi, P. (1987). Principal component analysis. Chemometrics and Intelligent Laboratory Systems, 2(1-3), 37-52. doi:10.1016/0169-7439(87)80084-9 es_ES
dc.description.references Abdi, H., & Williams, L. J. (2010). Principal component analysis. Wiley Interdisciplinary Reviews: Computational Statistics, 2(4), 433-459. doi:10.1002/wics.101 es_ES
dc.description.references Bro, R., & Smilde, A. K. (2014). Principal component analysis. Anal. Methods, 6(9), 2812-2831. doi:10.1039/c3ay41907j es_ES
dc.description.references Tomba, E., Barolo, M., & García-Muñoz, S. (2012). General Framework for Latent Variable Model Inversion for the Design and Manufacturing of New Products. Industrial & Engineering Chemistry Research, 51(39), 12886-12900. doi:10.1021/ie301214c es_ES
dc.description.references Kourti, T., & MacGregor, J. F. (1996). Multivariate SPC Methods for Process and Product Monitoring. Journal of Quality Technology, 28(4), 409-428. doi:10.1080/00224065.1996.11979699 es_ES
dc.description.references Geladi, P., & Kowalski, B. R. (1986). Partial least-squares regression: a tutorial. Analytica Chimica Acta, 185, 1-17. doi:10.1016/0003-2670(86)80028-9 es_ES
dc.description.references Barker, M., & Rayens, W. (2003). Partial least squares for discrimination. Journal of Chemometrics, 17(3), 166-173. doi:10.1002/cem.785 es_ES
dc.description.references Wold, S., Kettaneh-Wold, N., MacGregor, J. F., & Dunn, K. G. (2009). Batch Process Modeling and MSPC. Comprehensive Chemometrics, 163-197. doi:10.1016/b978-044452701-1.00108-3 es_ES
dc.description.references Nomikos, P., & MacGregor, J. F. (1995). Multivariate SPC Charts for Monitoring Batch Processes. Technometrics, 37(1), 41-59. doi:10.1080/00401706.1995.10485888 es_ES
dc.description.references Wold, S., Kettaneh, N., Fridén, H., & Holmberg, A. (1998). Modelling and diagnostics of batch processes and analogous kinetic experiments. Chemometrics and Intelligent Laboratory Systems, 44(1-2), 331-340. doi:10.1016/s0169-7439(98)00162-2 es_ES
dc.description.references Kourti, T. (2003). Abnormal situation detection, three-way data and projection methods; robust data archiving and modeling for industrial applications. Annual Reviews in Control, 27(2), 131-139. doi:10.1016/j.arcontrol.2003.10.004 es_ES
dc.description.references González-Martínez, J. M., de Noord, O. E., & Ferrer, A. (2014). Multisynchro: a novel approach for batch synchronization in scenarios of multiple asynchronisms. Journal of Chemometrics, 28(5), 462-475. doi:10.1002/cem.2620 es_ES
dc.description.references Kassidas, A., MacGregor, J. F., & Taylor, P. A. (1998). Synchronization of batch trajectories using dynamic time warping. AIChE Journal, 44(4), 864-875. doi:10.1002/aic.690440412 es_ES
dc.description.references Camacho, J., Pérez-Villegas, A., Rodríguez-Gómez, R. A., & Jiménez-Mañas, E. (2015). Multivariate Exploratory Data Analysis (MEDA) Toolbox for Matlab. Chemometrics and Intelligent Laboratory Systems, 143, 49-57. doi:10.1016/j.chemolab.2015.02.016 es_ES
dc.description.references González-Martínez, J. M., Camacho, J., & Ferrer, A. (2018). MVBatch: A matlab toolbox for batch process modeling and monitoring. Chemometrics and Intelligent Laboratory Systems, 183, 122-133. doi:10.1016/j.chemolab.2018.11.001 es_ES


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