Dineflow ERP: An Advanced Industry-Grade Solution for Optimised Restaurant Management

Managing a food-service establishment involves constant negotiation between perishable inventory, fluctuating customer demand, and narrow profit margins. Despite these pressures, a substantial fraction of independent restaurants in India continue to rely on isolated point-of-sale terminals that provide no decision-support for procurement, workload forecasting, or shift scheduling. This paper introduces DineFlow ERP, a cloud-native, microservice-based enterprise resource planning platform engineered exclusively for restaurant environments. The system unifies the complete order lifecycle, kitchen-order-ticket (KOT) dispatch, table and floor coordination, live inventory tracking, payroll processing, and contactless QR-based guest ordering within a coherent three-tier architecture. A dedicated Predictive Intelligence layer integrates Ridge Regression and Random Forest for short-horizon demand forecasting; a Collaborative Filtering engine combining Singular Value Decomposition (SVD) with the Apriori association-rule algorithm for personalised menu recommendations; a Log-Log Ordinary Least Squares (OLS) dynamic pricing module; and a Heuristic Waste Predictor aligned with UN SDG Target 12.3. Identity and access management is enforced through Auth0, RS256-signed JSON Web Tokens, and AES-256-CBC client-side encryption distributed across five role-based access control (RBAC) personas. A live production deployment recorded 98 % module completion, a 7.6 % MAPE on stable SKUs via Random Forest, a 23.4 % reduction in procurement over-ordering, and zero critical OWASP vulnerabilities.

Restaurant ERP; Demand Forecasting; Random Forest; Ridge Regression; Collab- orative Filtering; SVD; Apriori; Cloud-Native; FastAPI; SDG 12.3; Auth0; JWT; QR Ordering; Dynamic Pricing; Waste Prediction.

1. R. Shriwas et al., “Touchscreen based ordering system for restaurants,” in Proc. 2014 Int. Conf. Communication and Signal Processing, IEEE, pp. 1021–1024. [Google Scholar] [Crossref]

2. D. Albuquerque et al., “Enhancing sustainable customer dining experience through QR code and geo-fencing,” in Proc. ICCAKM 2020, IEEE, pp. 190–196. [Google Scholar] [Crossref]

3. L. Intal et al., “Restaurant information system with QR code to improve service operations,” in Proc. IEEE ICIEA 2020, pp. 1054–1059. [Google Scholar] [Crossref]

4. S. Raykar et al., “Demand sensing for restaurants using forecasting methods,” IARJSET, vol. 10, no. 5, 2023. [Google Scholar] [Crossref]

5. D. Preil and M. Krapp, “Artificial intelligence-based inventory management: a Monte Carlo tree search approach,” Annals of Operations Research, pp. 1–25, 2022. [Google Scholar] [Crossref]

6. D. Li, “Intelligent inventory management system for restaurants,” IJRPR, 2024. [Google Scholar] [Crossref]

7. N. Raghavendra and S. Amalanathan, “Role of AI in inventory management of agri-fresh produce at HOPCOMS,” in Advances in ML Algorithms for Complex Financial Applications, IGI Global, pp. 115–131, 2023. [Google Scholar] [Crossref]

8. M. Sharma et al., “Scope of cloud computing for SMEs in India,” Journal of Computing, vol. 2, no. 5, 2010. [Google Scholar] [Crossref]

9. C. H. Tekawade et al., “Automated restaurant management system,” IJRPR, 2023. [Google Scholar] [Crossref]

10. V. Galabi et al., “Smart restaurant management system,” IJERT, vol. 11, no. 5, pp. 124–128, 2023. [Google Scholar] [Crossref]

11. L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001. [Google Scholar] [Crossref]

12. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2nd ed., Springer, 2009. [Google Scholar] [Crossref]

13. National Restaurant Association of India, “State of the restaurant industry report,” 2023 [Google Scholar] [Crossref]

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