A Value Engineering Optimization Framework for Portfolio-Level Project Benefits Realization Using Bayesian Inference Models

Ifeoluwa Oreofe Oluwafemi

Abstract

This review explores the integration of value engineering (VE) and Bayesian inference models as a strategic optimization framework for enhancing benefits realization across portfolio-level project management. With increasing demands for delivering measurable value in complex project portfolios, organizations are turning to probabilistic and data-driven methods to align project outcomes with strategic objectives. Value engineering offers a systematic approach to optimizing functions and cost-efficiency, while Bayesian inference provides robust capabilities in uncertainty quantification, real-time learning, and predictive modeling. This paper synthesizes existing literature on VE applications in portfolio management and examines the role of Bayesian networks and decision theory in forecasting project benefit probabilities, stakeholder satisfaction, and lifecycle outcomes. Furthermore, it discusses the integration of these methodologies into benefits realization frameworks and governance models. Case studies and modeling insights are analyzed to highlight best practices, methodological advancements, and implementation barriers. The review concludes by proposing a scalable architecture for dynamic portfolio optimization using VE and Bayesian inference.

Source: semanticscholar

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