Table 2. Scenario-based bounded...

Table 2.

Scenario-based bounded estimates of ΔHEP for smart technologies mapped to HFACS error tiers

Smart technology	HFACS tier targeted	Key error types addressed	Dominant PSFs affected	ΔHEP (% relative reduction)*	Evidence basis
AR	Unsafe acts	Step omission, procedural deviation, documentation slips	Procedural complexity, memory dependence, checklist discipline, time pressure	20%–35%	Documentary corpus + expert feedback + PSF pre/post scoring (subsection 5.5)
AI	Preconditions for unsafe acts (primary); unsafe acts (secondary)	Fatigue-mediated lapses, attention degradation, workload-driven slips	Fatigue exposure, workload variability, vigilance/attention	20%–35% (Preconditions); 5%–10% (Unsafe Acts)	Documentary corpus + expert feedback + PSF pre/post scoring (subsection 5.5)
RFID tool tracking	Unsafe supervision (primary); organizational influences (secondary)	Tool misplacement/FOD exposure, traceability gaps, accountability lapses	Supervision adequacy, control routines, documentation integrity	20%–35% (Unsafe Supervision); 10%–20% (Organizational)	Documentary corpus + expert feedback + PSF pre/post scoring (subsection 5.5)
Digital twins	Organizational influences	Planning/resource allocation errors, latent systemic conditions, deferred-risk accumulation	Planning quality, coordination, data/visibility, resource allocation	20%–35%	Documentary corpus + expert feedback + PSF pre/post scoring (subsection 5.5)
Wearables/sensors	Preconditions for unsafe acts	Fatigue/stress exposure signals, degraded alertness	Fatigue/stress detection, workload tolerance, physiological strain	10%–20%	Documentary corpus + expert feedback + PSF pre/post scoring (subsection 5.5)

Smart technology	HFACS tier targeted	Key error types addressed	Dominant PSFs affected	ΔHEP (% relative reduction)*	Evidence basis
AR	Unsafe acts	Step omission, procedural deviation, documentation slips	Procedural complexity, memory dependence, checklist discipline, time pressure	20%–35%	Documentary corpus + expert feedback + PSF pre/post scoring (subsection 5.5)
AI	Preconditions for unsafe acts (primary); unsafe acts (secondary)	Fatigue-mediated lapses, attention degradation, workload-driven slips	Fatigue exposure, workload variability, vigilance/attention	20%–35% (Preconditions); 5%–10% (Unsafe Acts)	Documentary corpus + expert feedback + PSF pre/post scoring (subsection 5.5)
RFID tool tracking	Unsafe supervision (primary); organizational influences (secondary)	Tool misplacement/FOD exposure, traceability gaps, accountability lapses	Supervision adequacy, control routines, documentation integrity	20%–35% (Unsafe Supervision); 10%–20% (Organizational)	Documentary corpus + expert feedback + PSF pre/post scoring (subsection 5.5)
Digital twins	Organizational influences	Planning/resource allocation errors, latent systemic conditions, deferred-risk accumulation	Planning quality, coordination, data/visibility, resource allocation	20%–35%	Documentary corpus + expert feedback + PSF pre/post scoring (subsection 5.5)
Wearables/sensors	Preconditions for unsafe acts	Fatigue/stress exposure signals, degraded alertness	Fatigue/stress detection, workload tolerance, physiological strain	10%–20%	Documentary corpus + expert feedback + PSF pre/post scoring (subsection 5.5)

Note(s):

ΔHEP values are analytic, PSF-based bounded estimates derived from documentary evidence, expert clarification and scenario-based pre/post scoring described in subsection 5.5. They are not directly measured empirical effects from experiments, surveys or operational intervention trials

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