Abstract
This systematic literature review examines Human-in-the-Loop (HITL) systems within dark factory environments through the lens of Complex Adaptive Systems, investigating how these systems bridge full automation with human expertise to maintain adaptability in highly automated manufacturing settings that operate as complex systems characterized by multiplicity, interdependence, and dynamic interactions where identical conditions can yield different outcomes. Dark factories represent strategic inflection points creating tenfold shifts in manufacturing operations, yet fully automated systems face inherent brittleness when confronting rare events, unintended consequences, and contextual ambiguities that require human cognitive capabilities. Following PRISMA 2020 guidelines, this review analyzed 134 peer-reviewed publications from 2020-2025 through systematic database searches, quality assessment, and thematic analysis to identify patterns, gaps, and emerging trends in HITL implementation. The research reveals that HITL systems have evolved from episodic interventions into strategic design approaches permeating all manufacturing stages, fundamentally transforming human roles from manual operators to cognitive supervisors, exception handlers, and innovation catalysts. Workforce composition shifted from 85% human participation in 2020 to a balanced 40% human-60% automation ratio by 2025, with HITL systems now accounting for 42% of operations. The study proposes a three-layer HITL collaboration framework spanning operational, tactical, and strategic levels to ensure continuous adaptive human-AI interaction. Critical research gaps identified include the absence of dynamic trust calibration models, manufacturing-specific cognitive load frameworks, and standardized performance metrics. This research contributes original insights into how HITL systems preserve human relevance in Industry 5.0 by creating "bright factories" that optimize both productivity and human well-being, offering sustainable pathways to mitigate job displacement through discovery-driven learning architectures while maintaining manufacturing competitiveness amid accelerating automation.
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