INFORMATION SUPPORT MODEL FOR RAW MATERIAL QUALITY CONTROL IN RESTAURANTS

Abstract

This article substantiates an information support model for quality control of raw materials in restaurant establishments, oriented to auditability, traceability and compliance with HACCP-based food safety requirements. The model is designed as a tool-agnostic description of what data must be recorded, where, and by whom at key control moments, so that a restaurant can demonstrate due diligence and promptly localize nonconformities. Three operational control points are defined: (OCP1) incoming control during receiving; (OCP2) control of storage conditions and batch integrity during storage; and (OCP3) control immediately before use in production. For each control point, the required data fields, evidence artefacts and decision outcomes are specified. A critical requirement is a unique internal batch identifier that links receiving records, storage monitoring, nonconformity classification, corrective actions and product release/withdrawal decisions across all subsequent processes. The article details the core entities and relationships of the data model (supplier, batch, product item, check event, measurement, nonconformity, corrective action, disposition), proposes a compact classifier of typical nonconformities with codes, and outlines typical corrective actions supporting rapid decision-making (isolation, re-check, return, disposal, process adjustment). Particular attention is paid to storage monitoring: the proposed OCP2 is optional for establishments without prolonged storage, but becomes mandatory where raw materials are held in cold rooms or warehouses and the “cold chain” must be evidenced. The model can be scaled from small venues to multi-unit networks and may be implemented with paper forms, spreadsheets or digital systems, while preserving a consistent set of records required for internal control and external audits. Methodologically, the study relies on a synthesis of Codex and EU traceability principles, HACCP documentation logic, and practical requirements of restaurant operations. Scientific novelty lies in separating “information support” from concrete information systems and in formalizing a minimal yet sufficient set of records for end-to-end batch tracking within HoReCa. The proposed structure supports performance analytics (recurring issues by supplier, temperature deviations, rejection rates) and can be used as a basis for designing digital checklists, sensor integration or ERP interfaces.