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A loss prevention methodology for catering industry based on operation data analysis

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Abstract

In recent years, the competitions among the catering enterprises are becoming aggressive, which makes managers require for a higher standard to the operating ability and profitability. The company operation will not be significantly improved simply via the expanding of business volumes. Company inner control mechanism can be an effective means of strengthening competitiveness. Normative service system and management mode can effectively reduce the unnecessary coast and profit loss. In this paper, we propose a loss prevention method for the catering enterprises based on machine learning algorithm. With large amount of real data from a famous restaurant in China, we can obtain solid features related to the restaurant operations. And based on the Bayesian method the exceptional events during the operation process and the service process can be identified. The experiment results show that our method can effectively identify the exceptional events which could cause losses to the restaurant.

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Acknowledgements

The research presented in this paper is supported in part by the National Natural Science Foundation (No.: 61571360), Shaanxi Science & Technology Co-ordination & Innovation Project (No.: 2016KTZDGY05-09), and the Innovation Project of Shaanxi Provincial Department of Education (No.: 17JF019).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Xi’an University of Technology, Xi’an, 710048, China

    Qindong Sun, Yimin Qiao, Hongyi Zhou & Nan Wang

  2. Shaanxi Key Laboratory of Network Computing and Security, Xi’an University of Technology, Xi’an, 710048, China

    Qindong Sun

  3. School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, 710048, China

    Jiamin Wang

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  1. Qindong Sun
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  2. Yimin Qiao
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Correspondence to Qindong Sun.

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Sun, Q., Qiao, Y., Zhou, H. et al. A loss prevention methodology for catering industry based on operation data analysis. Cluster Comput 21, 923–931 (2018). https://doi.org/10.1007/s10586-017-0961-x

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  • DOI: https://doi.org/10.1007/s10586-017-0961-x

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