Managing Informational Uncertainty in Traceability Systems: A Structural Entropy-Based Model for Complex Production Systems

The digital transformation of shrimp aquaculture in Ecuador has accelerated the adoption of traceability technologies to improve transparency, regulatory compliance, and supply chain coordination. However, persistent structural limitations—such as technological fragmentation, low interoperability, and inconsistent data quality—continue to constrain their effectiveness. This study develops an approach to managing informational uncertainty in traceability systems, grounded in information theory and operationalized through the Technological Management Model for Shrimp Production (TMMT-SP). Methodologically, the research follows an abductive systemic modeling approach, integrating a systematic literature review, structural analysis of the production system, and ontological modeling to identify and classify interdomain gaps. The findings show that informational uncertainty emerges as a structural property of the system, resulting from misalignments across informational, technological, and governance dimensions. These misalignments limit the coherence, reliability, and integration of traceability processes. In response, the study proposes a structural entropy-based framework (understood as an operational representation of systemic informational dispersion) to diagnose, prioritize, and address these gaps, shifting the focus from isolated technological adoption toward systemic coherence. This approach provides a conceptual and methodological basis for designing technology management strategies to reduce uncertainty in complex production systems.