DineFlow: An Offline LAN-Based Restaurant Management System with Machine Learning-Driven Predictive Analytics

Author

Prof. Arpit Chaudhari, Arth Gundapure, Harshada Tijare, Jiva Bhujade, Vinant Rathode


Abstract

 Managing a restaurant business can be very extensive, especially when there is no organised system and everything is done manually. Even if a virtual solution is used, there are still some factors that can affect the performance, such as dependency on cloud services. That is why the purpose of this research is to propose a solution that is not only offline-first and standalone but also provides efficiency in handling different restaurant tasks in real time. DineFlow is an offline LAN-based restaurant management system that is built on Python and Flask and PostgreSQL is used as the primary relational database. Machine learning algorithms like Random Forest are used for predictive analytics over historical data and inventory intelligence to increase efficiency of the system . Therefore the goal is to provide an offline alternative to internet-based management systems for better management and execution.



Keywords

Restaurant management system, Offline-first, Local area network, Flask, PostgreSQL, Random Forest, Demand forecasting, Role-based access control



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References


  • Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
  • Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014
  • Ferraiolo, D. F., & Kuhn, D. R. (1992). Role-based access controls. In Proceedings of the 15th National Computer Security Conference(pp. 554–563).
  • Fette, I., & Melnikov, A. (2011). The WebSocket Protocol(RFC 6455). Internet Engineering Task Force. https://doi.org/10.17487/RFC6455
  • Kleppmann, M., Wiggins, A., van Hardenberg, P., & McGranaghan, M. (2019). Local-first software: You own your data, in spite of the cloud. In Proceedings of the ACM SIGPLAN International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software (Onward!)(pp. 154–178). Athens, Greece. https://doi.org/10.1145/3359591.3359737
  • Pedregosa, F., et al. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12, 2825–2830. https://doi.org/10.48550/arXiv.1201.0490
  • Schmidt, A., Kabir, M. W. U., & Hoque, T. (2022). Machine learning based restaurant sales forecasting. Machine Learning and Knowledge Extraction, 4(1), 105–130. https://doi.org/10.3390/make4010006
  • Tanizaki, T., Hoshino, T., Shimmura, T., & Takenaka, T. (2020). Restaurants store management based on demand forecasting. Procedia CIRP, 88, 580–583. https://doi.org/10.1016/j.procir.2020.05.101
  • Vinodhini, B., Abinaya, K., Roja, R., & Rajeshwari, M. (2014). Wireless two-way restaurant ordering system via touch screen. International Journal of Engineering Sciences, 3(7), 1–5.


 

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