What is the Return on Investment of Digital Engineering for Complex Systems Development? Findings from a Mixed-Methods Study on the Post-production Design Change Process of Navy Assets

TL;DR

This study investigates the potential ROI of Digital Engineering in complex systems development, specifically in Navy asset sustainment, showing it could significantly reduce project durations and improve schedule predictability.

Contribution

It identifies inefficiency modes causing delays, quantifies schedule slips, and estimates potential schedule improvements through digital transformation in Navy projects.

Findings

Digital Engineering could reduce median project duration by 50.1%.

Schedule variability could decrease by 41.5%.

Gains vary across different task categories.

Abstract

Complex engineered systems routinely face schedule and cost overruns, along with poor post-deployment performance. Championed by both INCOSE and the U.S. Department of Defense (DoD), the systems engineering (SE) community has increasingly looked to Digital Engineering (DE) as a potential remedy. Despite this growing advocacy, most of DE's purported benefits remain anecdotal, and its return on investment (ROI) remains poorly understood. This research presents findings from a case study on a Navy SE team responsible for the preliminary design phase of post-production design change projects for Navy assets. Using a mixed-methods approach, we document why complex system sustainment projects are routinely late, where and to what extent schedule slips arise, and how a DE transformation could improve schedule adherence. This study makes three contributions. First, it identifies four archetypical inefficiency modes that drive schedule overruns and explains how these mechanisms unfold in their organizational context. Second, it quantifies the magnitude and variation of schedule slips. Third, it creates a hypothetical digitally transformed version of the current process, aligned with DoD DE policy, and compares it to the current state to estimate potential schedule gains. Our findings suggest that a DE transformation could reduce the median project duration by 50.1% and reduce the standard deviation by 41.5%, leading to faster and more predictable timelines. However, the observed gains are not uniform across task categories. Overall, this study provides initial quantitative evidence of DE's potential ROI and its value in improving the efficiency and predictability of complex system sustainment projects.