Gestão de processos e conhecimento organizacional na qualidade de uma plataforma analítica voltada à PD&I farmacêutica

Introduction: The pharmaceutical sector is highly regulated to ensure the quality, safety, and efficacy of medicines throughout their entire lifecycle. Quality-oriented Research, Development & Innovation (R&D&I) is crucial for achieving valid, traceable, and reproducible results. Analytical methods play a fundamental role in the control strategy that drives the technological maturity of these products. Organizational Knowledge is an essential resource for achieving institutional objectives. Objective: To demonstrate how a process-based approach, integrated with Knowledge Management practices, can contribute to the development of a sustainable Quality Management System (QMS). Method: Descriptive and exploratory in nature, this study reports the experience of the technological platform of the Department of Analytical Methods (DMA) at Farmanguinhos, the pharmaceutical laboratory of the Brazilian Ministry of Health and a technicalscientific unit of the Oswaldo Cruz Foundation (Fiocruz). Critical processes were identified and prioritized according to their impact on deliverables, costs, and regulatory compliance. These processes were then consolidated into a SIPOC matrix and modeled using the Bizagi Process Modeler®. Bottlenecks were identified and improvement proposals developed through brainstorming sessions. Results: Five processes were implemented, one of them being a core process, and twelve supporting documents were established, including standard operating procedures, recording forms, and control spreadsheets. Cloud-based platforms were incorporated to support team collaboration and Electronic Document Management (EDM). This systematization established a foundation of Explicit Knowledge and processes capable of promoting operational efficiency, strengthening the QMS, and driving continuous improvement. Conclusions: The DMA’s model is knowledge-centered for process restructuring. Its flexibility supports application across other platforms within the Fiocruz and research laboratories, with potential contributions to future studies.