Artificial Intelligence-Based Supply Chain Resilience for Strengthening Manufacturing Performance During Global Operational Disruptions Effectively

Nurlaela Kumala Dewi (Universitas Logistik dan Bisnis Internasional)
Afferdhy Ariffien (Universitas Logistik dan Bisnis Internasional)
Santi Susanti (Universitas Negeri Jakarta)
Arlis Dewi Kuraesin (Universitas Teknologi Muhammadiyah Jakarta)

Journal of Industrial Engineering and Management
Vol 1, No 3 (2023)

Supply-chain resilience requires an organization to detect disruptions, coordinate a response, and restore service while preserving decision traceability. This study descriptively analyzes academic consumer-goods manufacturing records containing 12 quarterly supply-chain-performance observations from 2022 to 2024 and eight paired risk assessments. The performance series compares four conventional-period observations with eight artificial-intelligence-enhanced observations; the risk dataset compares probability, impact, and risk-priority-number values before and after an AI-enhanced mitigation label. Mean supply-chain-performance-index score increased from 62.77 to 85.06. Mean disruption count decreased from 7.50 to 3.38 per quarter, and mean recovery time decreased from 12.22 to 4.88 days. Mean fill rate increased from 84.98% to 93.02%, while mean perfect-order rate increased from 76.43% to 90.05%. Inventory days declined from 39.25 to 18.50, and supplier lead-time variance declined from 6.10 to 1.62 days. Total risk-priority number across eight categories decreased from 351 to 124. The two datasets are analyzed separately because the records do not identify a common intervention protocol, algorithm, control group, or matched operational conditions. The findings therefore describe differences associated with the supplied period and mitigation labels rather than a causal estimate of artificial-intelligence impact. The study contributes a reproducible reporting format that links quarterly resilience indicators, paired risk-priority analysis, and a practical review matrix for supply-chain governance