At Los Lorentes Residences, housekeeping hours and cleaning activities were documented on paper and transferred manually. This led to high administrative effort, increased errors, and delayed or incomplete data. Management lacks a reliable basis to compare planned time budgets with actual effort across rooms, teams, or days, while staff had no simple digital workflow. The project therefore aimed to define and prototype a secure, practical MVP to digitalize time tracking without disrupting daily operations. In accordance with the applicable Collective Bargaining Agreement (L-GAV/CCNT)

We worked agile with scrum across five sprints and used Jira for backlog management, sprint planning, and progress tracking. Requirements were translated into epics and user stories and refined through client feedback. We evaluated Power Apps to validate feasibility but identified limitations for our needs. Therefore, we built an MVP prototype as a web app with Next.js, Supabase (PostgreSQL + authorization) and Vercel for deployment. Protel PMS integration was assessed but de-scoped due to missing API access to protect scope and timeline.

The project outcome is a coherent, documented MVP prototype that demonstrates how Los Lorentes Recidences can replace paper-based time sheets with a secure digital workflow. The prototype supports user login and role-based access for housekeeping staff and management, team and group structures, and the recording of work sessions per room/task. It also enables visibility of time budgets versus actual effort and provides exportable datasets for controlling and administrative follow-up. Even though the solution was not deployed as a productive system during the project, the MVP makes the target process tangible, validates the concept with stakeholders, and reduces uncertainty for the next implementation step. The chosen architecture offers a maintainable foundation for further development, testing, and scaling. In a planned bachelor thesis, the MVP will be expanded into a fully implemented solution. This creates a clear roadmap to cut manual transcription, improve data quality, and enable faster, data-driven staffing and process decisions company-wide.