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Using an Artificial Neural Network for Improving the Prediction of Project Duration

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  • Itai Lishner

    (Faculty of Industrial Engineering and Management, Technion—Israel Institute of Technology, Haifa 320003, Israel)

  • Avraham Shtub

    (Faculty of Industrial Engineering and Management, Technion—Israel Institute of Technology, Haifa 320003, Israel)

Abstract

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.

Suggested Citation

  • Itai Lishner & Avraham Shtub, 2022. "Using an Artificial Neural Network for Improving the Prediction of Project Duration," Mathematics, MDPI, vol. 10(22), pages 1-16, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4189-:d:967819
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    References listed on IDEAS

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