Framework adaptativo para rastreabilidade de processos industriais numa abordagem modular e agnóstica

Abstract

An adaptive framework for the traceability of industrial processes is proposed, based on a modular and agnostic architecture designed for high-density manufacturing. The central objective is to overcome the rigidity of current systems, which operate in technological silos and fail to record operational parameters of shop floor processes, remaining limited to superficial logistical data. An intelligent data orchestrator is established to capture real-time process variables, providing a robust baseline for information sovereignty and industrial transparency without technological lock-in. The methodology encompasses a Systematic Literature Review (SLR) and the development of an integrated experimental ecosystem. Native integration of Automatic Identification and Data Capture (AIDC) technologies into manufacturing execution systems (MES) and Enterprise Resource Planning (ERP) is applied. Architectural flexibility stands out by allowing data orchestration on-premise or off-chain, treating the implementation of smart contracts on a blockchain as an optional feature, triggered according to audit requirements or stipulated quality parameters. Quantitative results obtained through experimental validation on a PCB assembly line confirm that the framework guarantees the immutability and provenance of manufactured items without introducing operational bottlenecks, consolidating a solution adaptable to the cost and latency demands of heterogeneous production chains and converting traceability into a technical value-adding factor for the final product.