Abstract
High mix industrial systems operate under uncertainty from machine failures, product variation, demand fluctuations, material delays, maintenance requirements, and limited production capacity. Conventional reliability centered maintenance methods often evaluate equipment separately and provide limited representation of how failures affect interconnected production schedules. This study proposes a reliability centered lifecycle resilience framework that integrates asset failure probability, MTBF, MTTR, production dependency, dynamic Severity of Failure, buffer conditions, maintenance decisions, and recovery performance. A discrete event simulation model represents a synthetic high mix job shop with machine failures, capacity losses, demand changes, material delays, and maintenance disruptions. The framework links failure probability with schedule impact to update machine criticality according to current production conditions. A decision layer selects maintenance, task reassignment, processing rate adjustment, and capacity redistribution actions under operational constraints. Performance is assessed through throughput, makespan deviation, downtime, OEE, recovery time, lifecycle maintenance cost, and resilience indicators representing anticipation, absorption, adaptation, and restoration. The results show that dynamic criticality provides a stronger representation of system level failure consequences than static asset ranking. Buffer capacity and adaptive decisions also influence production recovery and resilience. The proposed framework provides an analytical basis for integrating reliability assessment, production control, maintenance planning, and lifecycle resilience within high mix industrial systems.