One of the most challenging tasks in project management is estimating the duration of a project. The unknowns that accompany projects, the different risks, the uniqueness of each project, and the differences between organizations’ culture and management techniques, hinder the ability to build one project duration prediction tool that can fit all types of projects and organizations. When machine learning (ML) techniques are used for project duration prediction, the challenge is even greater, as each organization has a different dataset structure, different features, and different quality of data. This hinders the ability to create one ML model that fits all types of organizations. This paper presents a new dynamic ML tool for improving the prediction accuracy of project duration. The tool is based on an artificial neural network (ANN) which is automatically adapted and optimized to different types of prediction methods and different datasets. The tool trains the ANN model multiple times with different architectures and uses a genetic algorithm to eventually choose the architecture which gives the most accurate prediction results. The validation process of the prediction accuracy is performed by using real-life project datasets supplied by two different organizations which have different project management approaches, different project types, and different project features. The results show that the proposed tool significantly improved the prediction accuracy for both organizations despite the major differences in the size, type, and structure of their datasets.

The selection of an appropriate development methodology is a critical strategic decision when managing a New Product Development (NPD) project. However, accurately estimating project duration based on the chosen methodology remains a challenge. This paper addresses the limitations of existing models and proposes a novel NPD project model that allows for testing and evaluation of different product development strategies. The model considers Waterfall, Spiral, Agile, and Hybrid methodologies and provides system engineers and project managers with decision-making tools to determine the optimal strategy and understand associated tradeoffs. The model is validated using real projects from various organizations and methodologies. It incorporates stochastic variables, risk management, and dynamic resource allocation, while addressing both Waterfall and Agile methodologies. The study contributes to the body of knowledge by offering practical tools for system engineers and project managers for choosing development methodology, improving project duration estimation, and identifying critical processes and risks in NPD projects. The research results also provide a basis for further studies and can benefit researchers interested in systems engineering methodologies. The proposed model fills a gap in the literature by providing a validated NPD model to evaluate the impact of different product development methodologies on project duration.