HOTEL AND RESTAURANT MANAGEMENT SYSTEM: LOGISTICS 2 (PROCUREMENT, VENDOR PORTAL, AUDIT MANAGEMENT) WITH PREDICTIVE ANALYTICS

Abstract

The hospitality industry, like many others, is undergoing digital transformation, requiring advanced systems to manage complex operations. In hotel and restaurant management, logistics is vital for efficient procurement, vendor communication, and audit organization. Common challenges include inefficiencies, data silos, and difficulties in demand forecasting and anomaly detection. This study proposes two logistics modules to be integrated into a Hotel and Restaurant Management System (HRMS) to enhance vendor collaboration and audit transparency by leveraging predictive analytics. The HRMS Logistics 2 system was developed using Agile methodology, with feedback incorporated at each iteration. It includes three main modules: Procurement Management, automating purchase requests, inventory-based reordering, and supplier selection; Vendor Portal, offering role-based access for vendors to view orders, manage profiles, delivery schedules, and KPIs; and Audit Management, providing automated audit trails and compliance checks. Predictive analytics supports forecasting inventory needs, anomaly detection, and vendor optimization. The Logistics 2 modules were seamlessly integrated into the HRMS and tested with simulated data from a medium-sized hotel chain. Key results included automated procurement and predictive restocking, improved vendor satisfaction, and enhanced transparency through real-time performance dashboards. The system also achieved high audit accuracy and reliable monthly demand forecasting, significantly reducing stockouts and overstocking. Predictive analytics in procurement and audit processes greatly enhances decision-making for hotel and restaurant management. The system empowers managers to act proactively by providing real-time insights and forecasts. Enhanced transparency strengthens vendor relationships, while automated tracking improves audit compliance. Future developments may incorporate machine learning-based vendor scoring, natural language processing interfaces, and blockchain technology to secure audit trails.