Bayesian Cost-to-Complete Forecasting for Architecture and Engineering Design
Open AccessEarned Value Management (EVM) is a widely adopted project management methodology that integrates scope, schedule, and cost to analyze and predict performance. This research builds on the current body of knowledge and previous research efforts to improve project analysis and forecasting for work based on level-of-effort (LOE) tasks, specifically as it relates to architecture and engineering (AE) design and management of AE projects within the architecture, engineering, and construction (AEC) industry. LOE tasks are the primary building blocks currently used by industry to define planning, design, and management tasks. However, EVM calculations using LOE tasks are problematic for predictive analysis. While the assumption that each LOE unit, such as direct hours on a project, is equivalent remains necessary to implement EVM, the reality is that one hour of effort does not necessarily equate to any other hour when performing planning, design, and management tasks. The issues inherent to LOE task definition in the AE space do not reduce the need for accurate project analysis and forecasting. One benefit of effective project management and the EVM method is the ability to forecast cost-to-complete. Understanding the cost-to-complete is critical to leadership decisions and potentially critical interventions, when necessary, related to the performance of work. This Praxis research looks to test and validate using a Bayesian method for cost-to-complete forecasting as a more accurate alternative to traditional EVM cost-to-complete methods when dealing with LOE-defined AE design work.
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