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

机译：餐饮精益服务项目分析的数据挖掘方法

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摘要

Applied quantile regression to explore different ways to improve the catering service so as to promote the customer's 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 customer's 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.
机译：应用分位数回归探索改善餐饮服务的不同方法，从而提高客户的服务满意度。精益服务项目旨在减少为客户提供服务以促进服务所需的材料，人工和时间成本客户的满意。本文提出了一种数据挖掘方法，以分析由大学餐厅提供的餐饮服务上的精益服务项目的有效性。我们已经设计了三个连续的服务场景阶段；每个都比前一个阶段有所改进。在这项研究中，我们首先应用灰色关联分析来确认精益服务项目的有效性。也就是说，第二阶段和第三阶段实际上比以前的相应阶段从客户那里获得了更高的服务满意度。我们进行了分位数回归分析，以探索不同因素对服务满意度的低分位数和高分位数的影响。分位数回归分析的结果为餐厅提供了提高客户服务满意度的不同方法。最后，我们建立了几个预测模型来预测服务样本的服务满意度（差或好）。实验结果表明，在八个预测模型中，FOAGRNN在敏感性，特异性，AUC和Gini性能指标方面是最好的。

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