PREDICTIVE FRAMEWORK FOR DOMINANT DELAY RISKS IN METALLURGICAL PROJECTS

Abstract: Indian steel and aluminium megaprojects frequently experience multi-year schedule overruns despite detailed planning and risk registers. This paper presents a predictive delay-risk framework that learns from 20 completed projects using contextual similarity and interaction-aware modelling of 39 delay events. Projects are encoded through execution context, while events are represented using normalised delay impacts and a structured dependency network derived from empirical co-occurrence and expert judgement. A prototype tool generates probabilistic completion envelopes and ranked, interaction-aware risk lists for new projects. Initial results align with observed completion behaviour and highlight a small set of high-leverage risks for targeted mitigation.