Sistema de auxílio à gestão de restaurantes industriais

Abstract

The growth of industrial hubs in remote regions drives the emergence of local businesses tailored to meet their operational demands, including the family-owned, high-volume food service sector. However, the administration of these establishments faces complex efficiency and planning bottlenecks due to the reliance on manual processes and paper-based records. In light of this, this study aims to develop a low-cost computational management support system for industrial restaurants, automating inventory control and meal registration. The adopted methodology encompassed gathering requirements from a real establishment, modeling a desktop interface using the CustomTkinter library, and utilizing computer vision algorithms with OpenCV for QR code scanning via a standard webcam, using Python and SQL Server. As results, an automated workflow was implemented to perform web scraping of electronic invoice (NFC-e) data directly from SEFAZ, along with a menu production control module featuring inventory checking and automatic stock deduction, and a decentralized Telegram bot integration for autonomous employee ID generation and shopping list delivery. The system was validated through a case study in a family restaurant located in Telêmaco Borba, proving its practical viability. It is concluded that the integration of these technologies delivers a viable, robust, and scalable solution capable of mitigating human errors and optimizing decision-making within the industrial food sector.