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A Data Mining Approach to the Analysis of a Catering Lean Service Project


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Abstract

Applied quantile regression to explore different ways to improve the catering service so as to promote the customer2019s service satisfaction.A lean service project aims to reduce the cost of material, labor and time required in providing a service to a customer so as to promote the service satisfaction from the customer. This paper presents a data mining approach to analyze the effectiveness of a lean service project on a catering service provided by a university restaurant. We have designed three consecutive stages of service scenarios; each represents an improvement over its previous stage. In this study, we first applied the grey relational analysis to confirm the effectiveness of the lean service project. That is, stage two and three actually obtained higher service satisfaction from customers than their corresponding previous stages did. We have performed a quantile regression analysis to explore the effect of different factors on low and high quantiles of service satisfaction. The result of the quantile regression analysis provides different ways for the restaurant to improve its customer2019s service satisfaction. Finally, we have built several prediction models to forecast the service satisfaction (Poor or Good) of a service sample. The experimental result showed that among the eight prediction models, FOAGRNN is the best in terms of the sensitivity, specificity, AUC and Gini performance measures.


Keywords


Pages

Total Pages: 8
Pages: 243-250

DOI
10.1080/10798587.2016.1203564


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Published

Volume: 23
Issue: 2
Year: 2016

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References

Bradley, Andrew P. "The Use of the Area Under the ROC Curve in the Evaluation of Machine Learning Algorithms." Pattern Recognition 30.7 (1997): 1145-1159. Crossref. Web. https://doi.org/10.1016/S0031-3203(96)00142-2

Chang Y.W. Journal of City and Planning

Chen, Peng-Wen et al. "Using Fruit Fly Optimization Algorithm Optimized Grey Model Neural Network to Perform Satisfaction Analysis for E-Business Service." Applied Mathematics & Information Sciences 7.2L (2013): 459-465. Crossref. Web. https://doi.org/10.12785/amis/072L12

Chiang, Thomas, and Jiandong Li. "Stock Returns and Risk: Evidence from Quantile." Journal of Risk and Financial Management 5.1 (2012): 20-58. Crossref. Web. https://doi.org/10.3390/jrfm5010020

Ju-Long, Deng. "Control Problems of Grey Systems." Systems & Control Letters 1.5 (1982): 288-294. Crossref. Web. https://doi.org/10.1016/S0167-6911(82)80025-X

Hand, David J., and Robert J. Till. Machine Learning 45.2 (2001): 171-186. Crossref. Web. https://doi.org/10.1023/A:1010920819831

Hsieh W.-J. Journal of International and Global Economic Studies

Koenker, Roger, and Gilbert Bassett. "Regression Quantiles." Econometrica 46.1 (1978): 33. Crossref. Web. https://doi.org/10.2307/1913643

Koenker, Roger, and Gilbert Bassett. "Robust Tests for Heteroscedasticity Based on Regression Quantiles." Econometrica 50.1 (1982): 43. Crossref. Web. https://doi.org/10.2307/1912528

Koenker, Roger, and Kevin F Hallock. "Quantile Regression." Journal of Economic Perspectives 15.4 (2001): 143-156. Crossref. Web. https://doi.org/10.1257/jep.15.4.143

Kuan C.M. An introduction to quantile regression, lecture notes

Lee J.S. Review of Securities and Futures Markets

Li X.L. System Engineering Theory and Practice

Li, Hong-ze et al. "A Hybrid Annual Power Load Forecasting Model Based on Generalized Regression Neural Network with Fruit Fly Optimization Algorithm." Knowledge-Based Systems 37 (2013): 378-387. Crossref. Web. https://doi.org/10.1016/j.knosys.2012.08.015

Pan, Wen-Tsao. "A New Fruit Fly Optimization Algorithm: Taking the Financial Distress Model as an Example." Knowledge-Based Systems 26 (2012): 69-74. Crossref. Web. https://doi.org/10.1016/j.knosys.2011.07.001

Raghuraman S. International Journal of Innovative Research in Science, Engineering and Technology

Rai K.B. IMPACT: International Journal of Research in Engineering & Technology

Shi, Zhi Biao, and Ying Miao. "Prediction Research on the Failure of Steam Turbine Based on Fruit Fly Optimization Algorithm Support Vector Regression." Advanced Materials Research 614-615 (2012): 409-413. Crossref. Web. https://doi.org/10.4028/www.scientific.net/AMR.614-615.409

Specht, D.F. "A General Regression Neural Network." IEEE Transactions on Neural Networks 2.6 (1991): 568-576. Crossref. Web. https://doi.org/10.1109/72.97934

Wen K.L. Apply MATLAB in grey system theory

Womack J.P. Lean thinking

JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




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