Decision Rights and Escalation Design in Industry 4.0: A Framework for Explainable Autonomous Operations
DOI:
https://doi.org/10.71222/4zc5rm45Keywords:
industry 4.0, autonomous operations, decision rights, escalation design, explainability, human-in-the-loopAbstract
Industry 4.0 has made autonomous and semi-autonomous operations technically feasible, but many industrial studies still describe autonomy as if it were only a question of algorithms and data. In practice, industrial autonomy is a governance problem as much as a technical one. Managers need to specify who may decide, under what conditions machine recommendations become executable actions, how exceptions are escalated, and how learning is retained without weakening accountability. This paper develops a conceptual framework for decision rights and escalation design in Industry 4.0. The central argument is that industrial autonomy should be understood as a managed distribution of observation, interpretation, recommendation, authorization, execution, and learning rights rather than as a binary choice between manual and automatic operation. The paper organizes this argument through three linked ideas. First, autonomy should be calibrated by consequence severity, reversibility, and evidence quality, not by model confidence alone. Second, explainability and auditability are operational features that make autonomous actions governable rather than optional extras. Third, escalation design is the mechanism that preserves human control while still allowing fast and scalable machine-supported action. The framework is illustrated with examples from quality control, production scheduling, resource dispatching, service operations, and multi-state industrial systems. The paper contributes a governance-oriented interpretation of Industry 4.0 autonomy that is distinct from purely technical discussions and offers a practical research agenda for explainable and accountable industrial operations.References
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Copyright (c) 2026 Suzhen Huang (Author)
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