Process Mining IoT-Enriched Tyre Lifecycles for Predictive Maintenance Across Fleet Operators

Abstract

Tyre maintenance remains a cost-critical process for trailer fleets, yet the industry’s tread-depth rule often prompts premature replacements. We propose an IoT-aware process-mining bridge that fuses build records, workshop events, and 15-min telematics into 7 826 tyre lifecycles (> 60 000 events). Gradient-boosted trees trained on 25 lifecycle-level stress features reach R² = 0.60 and MAE ≈ 7 900 km on a 2024/25 hold-out set. Inverse-power learning curves from 10 %, 20 %, … to 100 % training slices (422 → 4 226 lifecycles) reveal a sharp elbow: accuracy plateaus after ≈ 2 500 lifecycles (60 %), and the remaining data trim MAE by only 22 km (< 0.3 %). The study quantifies data-efficiency thresholds and offers actionable benchmarks for process-technology adoption in predictive tyre maintenance. It also demonstrates how IoT-enriched event logs can be leveraged within business-process-technology frameworks to support continuous improvement and regulatory-compliance assurance.