Cognitive-Technical Scheduling: A Framework for Addressing DoD Weapon System Acquisition Failures

Abstract

Defense acquisition programs continue to face schedule overruns due to cognitive biases, overly optimistic planning assumptions, and limited visibility into evolving program behavior. This paper proposes a cognitive-technical scheduling system that combines three key capabilities: Defense Acquisition Visibility Environment’s (DAVE’s) and other DoD databases’ empirical performance data, AI-enabled forecasting, and system dynamics modeling. DAVE offers the historical data needed to address base-rate neglect and anchor estimates in proven outcomes. AI systems trained on this data could produce probabilistic forecasts, identify emerging deviations, and explore alternative execution paths, thereby offsetting human limitations in perception and memory. System dynamics models illustrate feedback loops, delays, and accumulations that influence schedule behavior, aligning mental models across the PMO and IPTs and enabling the development of schedule-level digital twins. Collectively, these components create a continuously updated simulation environment that forecasts results, visualizes secondary and tertiary effects, and supports proactive interventions. The paper presents a conceptual model where Cognitive Technical Integration enhances schedule performance both directly and through improved situational awareness, bias reduction, and adaptive learning. This framework provides a way to achieve more realistic scheduling, stronger governance, and greater strategic flexibility across the defense acquisition system.