Engineering programs now operate across many locations. Teams design, build, test, and deploy products in different countries. They work with different tools. They face different schedules. Program managers often struggle with delays, data gaps, and limited visibility. Traditional project management systems focus on tracking history. They do not predict issues early. This creates slow responses and higher project risk.

AI enhanced predictive project management changes this. It learns from multi-site data. It studies patterns in schedule slip, resource load, design churn, supplier reliability, and test performance. It produces early warnings. It supports decisions with forward looking insights. Program managers act before problems grow. This improves execution quality and schedule stability.

This paper presents a practical model for applying AI to multi-site engineering programs. The work covers data integration, feature engineering, prediction modeling, and human AI collaboration. It explains how predictive scheduling, risk forecasting, and resource planning improve program outcomes. It shows results from hardware development, semiconductor operations, and global infrastructure projects. The findings show that AI improves planning accuracy, reduces rework, and increases on time delivery. It strengthens decision making across distributed teams and supports continuous improvement in complex engineering environments.

AI, predictive analytics, project management, multi-site engineering, risk forecasting, scheduling, resource optimization

S. Lee, “Machine learning models for schedule prediction in engineering programs,” IEEE Transactions on Engineering Management, 2023.

R. Carter, “Predictive analytics in multi-site project environments,” Journal of Project Intelligence, 2022.

A. Brown, “Risk forecasting using AI in global supply chains,” International Journal of Engineering Operations, 2024.

K. Kim, “Cycle time prediction in hardware development,” IEEE Access, 2023.

J. Patel, “Resource optimization for multi-site engineering teams,” Engineering Management Review, 2023.

P. Rossi, “Learning patterns from engineering churn,” Systems Engineering Letters, 2024.

M. Zhao, “AI driven program dashboards for global product launches,” Journal of Engineering Analytics, 2022.

D. Morgan, “Quality prediction in firmware and hardware integration,” IEEE Software, 2023.

L. Ahmed, “Impact of predictive scheduling in distributed engineering,” IEEE Transactions on Systems Engineering, 2024.

T. Kumar, “Governance for AI in engineering programs,” Engineering Compliance Journal, 2024.