- Mohammad Taleghani 1*, Mohammadreza Jabreilzadeh Sola 2
- Islamic Azad University (IAU), Rasht, Iran
This study addresses the critical challenge of production downtime in Iranian steel manufacturing, where cyberattacks and data breaches exacerbate supply chain disruptions. A novel methodology integrating digital forensic analysis with statistical modeling and optimization techniques is proposed to mitigate downtime and enhance operational flexibility. Using forensic artifacts—including network logs, intrusion detection alerts, and downtime records—survival analysis quantified disruption impacts, while machine learning (Random Forest) diagnosed root causes with 89% accuracy. A mixed-integer linear programming (MILP) model optimized resource reallocation, reducing downtime by 32% compared to reactive measures and yielding cost savings of $120,000–$250,000 per incident. Results highlight that Iranian plants face 2.8× higher risk of prolonged downtime from cyberattacks than global peers, driven by delayed responses and legacy infrastructure. The study pioneers the use of digital forensics as a predictive tool, challenging its traditional post-incident role. By contextualizing models to Iran’s geopolitical constraints, the framework offers scalable strategies for industries facing similar vulnerabilities. This research bridges gaps in supply chain resilience literature, emphasizing forensic-driven analytics to preempt disruptions and align operational metrics with global standards. Practical implications include real-time forensic monitoring, policy reforms, and workforce training.
