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Profiling effects in industrial data mining by non-parametric DOE methods: an application on screening checkweighing systems in packaging operations. (English) Zbl 1253.90226
Summary: There is a growing interest in applying robust techniques for profiling complex processes in industry. We present an approach for analyzing fractional-factorial data by building distribution-free models suitable for dealing with replicated trials in search of non-linear effects. The technique outlined in this article is synthesized by implementing four key elements: (1) the data collection efficiency of non-linear fractional factorial designs, (2) the data compression capabilities of rank-sums for repetitive sampling schemes, (3) the rank-ordering as a means to transform data, and (4) the non-parametric screening for prominent effects where the normality and sparsity assumptions are waived. The technique is tested on four controlling factors for profiling the packaging weighing operations of a pharmaceutical enterprise. The robust data mining of repeated trials based on an \(L_{9}(3^{4})\) orthogonal array scheme with embedded uncontrolled noise is discussed extensively. The technique has been subjected to quality control as it is tested with well-defined artificial data. Concluding remarks involve contrasting this new technique with mainstream competing schemes.

90C90 Applications of mathematical programming
62P30 Applications of statistics in engineering and industry; control charts
62G99 Nonparametric inference
62K25 Robust parameter designs
Full Text: DOI
[1] Adenso-Diaz, B.; Laguna, M., Fine-tuning of algorithms using fractional experimental designs and local search, Operations research, 54, 1, 99-114, (2006) · Zbl 1167.90654
[2] Antony, J., Design of experiments for engineers and scientists, (2003), Butterworth-Heinemann Burlington, MA, USA
[3] Allenberg, B., Requirements on continuous weighers in a quality assurance system, Bulk solids handling, 13, 2, 316-318, (1993)
[4] Alonso, M.C.; Bousbaine, A.; Llovet, J.; Malpica, J.A., Obtaining industrial experimental designs using a heuristic technique, Expert systems with applications, 38, 8, 10094-10098, (2011)
[5] Besseris, G.J., Analysis of an unreplicated fractional-factorial design using nonparametric tests, Quality engineering, 20, 1, 96-112, (2008)
[6] Besseris, G.J., Product screening to multi-customer preferences: multi-response unreplicated nested super-ranking, International journal of quality, statistics and reliability, 2008, 1-25, (2008)
[7] Besseris, G.J., Order statistics for a two-level eight-run saturated-unreplicated fractional-factorial screening, Quality engineering, 21, 416-431, (2009)
[8] Besseris, G.J., Prioritised multi-response product screening using fractional factorial designs and order statistics, Journal of manufacturing technology management, 20, 513-532, (2009)
[9] Besseris, G.J., A methodology for product reliability enhancement via saturated-unreplicated fractional factorial designs, Reliability engineering & systems safety, 95, 742-749, (2010)
[10] Besseris, G.J., Quality screening in an information technology process, The TQM journal, 22, 159-174, (2010)
[11] Besseris, G.J., Non-linear nonparametric quality screening in low sampling testing, International journal quality and reliability management, 27, 893-915, (2010)
[12] Bhote, K.R., Bhote, A.K., 2000. World class quality: Using design of experiments to make it happen. In: AMACOM/American Management Association, second ed.
[13] Box, G.E.P., Signal-to-noise ratios, performance criteria and transformations, Technometrics, 30, 1-17, (1988)
[14] Box, G.E.P.; Hunter, J.S.; Hunter, W.G., Statistics for experimenters: design, Innovation and discovery, (2005), Wiley-Interscience New Jersey, NJ, USA · Zbl 1082.62063
[15] Chang, H.-H., A data mining approach to dynamic multiple responses in Taguchi experimental design, Expert systems with applications, 35, 3, 1095-1103, (2008)
[16] Chen, L.H., Designing robust products with multiple quality characteristics, Computers and operations research, 24, 10, 937-944, (1997) · Zbl 0893.90074
[17] Chiang, T.-L.; Su, C.-T., Optimization of TQFP molding process using neuro-fuzzy-GA approach, European journal of operational research, 147, 1, 156-164, (2003) · Zbl 1011.90042
[18] Co, H.C., Confirmation of the Taguchi methods by artificial neural-networks simulation, International journal of production research, 46, 17, 1-15, (2008) · Zbl 1159.90364
[19] Conover, W.J., 1998. Practical nonparametric statistics, Wiley Series in Probability and Statistics, third ed., New York, NY, USA.
[20] Costa, N.; Pereira, Z.L., Decision-making in the analysis of unreplicated factorial designs, Quality engineering, 19, 215-225, (2007)
[21] Dellino, G.; Kleijnen, J.P.C.; Meloni, C., Robust optimization in simulation: Taguchi and response surface methodology, International journal of production economics, 125, 1, 52-59, (2010)
[22] Donis, V.K.; Rachkovskii, A.E.; Gudovskaya, N.Y., Methods of verifying continuous automatic belt weighers: state and prospects, Measurement techniques, 46, 9, 851-856, (2003)
[23] Huber, P.J., 2003. Robust statistics, Wiley Series in Probability and Statistics, Wiley-Interscience, first ed., New York, NY, USA.
[24] Ilzarbe, L.; Alvarez, M.J.; Viles, E.; Tanco, M., Practical applications of design of experiments in the fields of engineering: A bibliographical review, Quality and reliability engineering international, 24, 417-428, (2008)
[25] Kallgren, H.; Pendrill, L., Requirements of weighing in legal metrology, Metrologia, 40, 6, 316-323, (2003)
[26] Lauwaars, H.; Kallgren, M.; Magnusson, B.; Pendrill, L.; Taylor, P., Role of measurement uncertainty in conforming assessment in legal metrology and trade, Accreditation and quality assurance, 8, 12, 541-547, (2003)
[27] Kleijnen, J.P.C.; Pierreval, H.; Zhang, J., Methodology for determining the acceptability of system designs in uncertain environments, European journal of operational research, 209, 2, 176-183, (2010) · Zbl 1208.90088
[28] Karasozen, B.; Rubinov, A.; Weber, G.-W., Optimization in data mining, European journal of operational research, 173, 3, 701-704, (2006)
[29] Lehmann, E.L., Nonparametrics: statistical methods based on ranks, (1975), Holden-Day San Francisco, CA, USA · Zbl 0354.62038
[30] Meisel, S.; Mattfeld, D., Synergies of operations research and data mining, European journal of operational research, 206, 1, 1-10, (2010) · Zbl 1188.90001
[31] Montgomery, D.C., Design and analysis of experiments, (2008), Wiley New Jersey, NJ, USA
[32] Olafsson, S.; Li, X.; Wu, S., Operations research and data mining, European journal of operational research, 187, 3, 1429-1448, (2008) · Zbl 1137.90776
[33] Park, G.-J.; Lee, T.-H.; Lee, K.-H.; Hwang, K.-H., Robust design: an overview, AIAA journal, 44, 1, 181-191, (2006)
[34] Park, S.H.; Antony, J., Robust design for quality engineering and six sigma, (2009), World Scientific Company Singapore
[35] Pavan, M.; Todeschini, R., Scientific data ranking methods-theory and applications: data handling in science and technology, vol. 27, (2008), Elsevier, Amsterdam The Netherlands
[36] Piratelli-Filho, A.; DiGiacomo, B., CMM uncertainty analysis with factorial design, Precision engineering, 27, 3, 283-288, (2003)
[37] Roemer, T.A.; Ahmadi, R., Models for concurrent product and process design, European journal of operational research, 203, 3, 601-613, (2009) · Zbl 1177.90132
[38] Rousseeuw, P.J.; Croux, C., Alternatives to the Median absolute deviation, Journal of American statistical association, 88, 1273-1283, (1993) · Zbl 0792.62025
[39] Shen, H.; Wan, H., Controlled sequential factorial design for simulation factor screening, European journal of operational research, 198, 2, 511-519, (2009) · Zbl 1163.90637
[40] Taguchi, G.; Chowdhury, S.; Taguchi, S., Robust engineering-learn how to boost quality while reducing costs and time to market, (2000), McGraw-Hill New York, NY, USA
[41] Tan, K.K.; Tang, K.Z., Vehicle dispatching system based on Taguchi-tuned fuzzy rules, European journal of operational research, 128, 545-557, (2001) · Zbl 0983.90024
[42] Tanco, M.; Viles, E.; Ilzarbe, L.; Alvarez, M.J., Implementation of design of experiments projects in industry, Applied stochastic models in business industry, 25, 478-505, (2009) · Zbl 1224.62154
[43] Tatakis, A., 2010. Dynamic weighing optimization using the Taguchi method, MSc Thesis, Quality Management Department, The University of the West of Scotland Paisley, Scotland, UK.
[44] Tiwari, M.K.; Raghavendra, N.; Agrawal, S.; Goyal, S.K., A hybrid-Taguchi-immune approach to optimize an integrated supply chain design problem with multiple shipping, European journal of operational research, 203, 1, 95-106, (2010) · Zbl 1176.90077
[45] Tsai, H.; Moskowitz, H.; Lee, L., Human resource selection for software development projects using taguchi’s parameter design, European journal of operational research, 151, 1, 167-180, (2003) · Zbl 1033.90056
[46] Vieira, H.; Sanchez, S.; Kienitz, K.H.; Belderrain, M.C.N., Generating and improving orthogonal designs by using mixed integer programming, European journal of operational research, 215, 3, 629-638, (2011) · Zbl 1238.90145
[47] Wan, H.; Ankenman, B.E.; Nelson, B.L., Controlled sequential bifurcation: A new factor-screening method for discrete-event simulation, Operations research, 54, 4, 743-755, (2006)
[48] Wang, T.-Y.; Huang, C.-Y., Improving forecasting performance by employing the Taguchi method, European journal of operational research, 176, 2, 1052-1065, (2007) · Zbl 1110.90034
[49] Zecchin, P., A guide dynamic weighing for industry, Measurement and control, 38, 6, 173-174, (2005)
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