An Acuity-Oriented Framework for Explainable and Actionable Operational Dashboards in Production and Logistics

Industrial production and logistics dashboards increasingly aggregate real-time data but remain monitoring tools with limited support for action prioritization, explainable recommendations, adaptive response, and organizational learning. This study proposes the Intelligent Acuity-Oriented Operations Dashboard (IAOOD), a conceptual, design-oriented framework that repositions operational dashboards toward active decision support. An exploratory user-preference study (10 interviews followed by a questionnaire with 41 professionals) provides preliminary evidence of the perceived relevance of the advanced capabilities to be added to the framework. Another evidence of relevance is a feature-availability comparison against selected industrial and academic dashboard approaches. This comparison is intended to assess conceptual coverage of key dashboard capabilities, not to demonstrate real-world performance improvement or operational superiority. The leading scientific contribution is a coherent integration of several recent key capabilities into a single operational design logic. These capabilities are: (1) closed-loop prediction–response–learning, (2) adaptive interfaces, (3) unified hard and soft metrics, (4) explainable AI, and (5) acuity classification. The second contribution is the empirical assessment of dashboard user preferences and the acuity dashboard hierarchy and its four-level acuity classification (urgent, acute, warning, weak link). A classification that organizes events by severity, urgency, confidence, impact, and transparent justification, enabling prioritized and governance-aligned interventions. Methodologically, IAOOD is developed through a structured conceptual framework-development process and illustrated via dashboard mock-ups of the proposed hierarchical structure. The study contributes architectural elements, evaluation dimensions, and six testable propositions linking framework mechanisms to expected outcomes (response timeliness, decision trust, recurrence reduction, strategic alignment, cognitive effectiveness, situational awareness). IAOOD is positioned as an integrative benchmark whose scientific value depends on subsequent empirical testing through industrial pilots, controlled user studies, and longitudinal KPI analysis.