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Framework for a Predictive Progress Model - case of infrastructure projects
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AI
DOI：10.1080/17509653.2022.2042749.png)
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摘要
En 中文
Project progress is an apprehension for every project, as it indicates how the project is likely to meet the associated milestones. Utilizing historical data from archived projects can assist managers in predicting project progress. By leveraging the power of data analytics, this research attempts to highlight data trends based on data collected from 279 infrastructure projects in the UAE. Specifically, this research rigorously analyses the relationships between project budget, duration, and progress using K-means clustering techniques and hypothesis testing. We then provide predictive models using Autoregressive Integrated Moving Average - ARIMuA and Multivariate regression models that allow managers to predict with a 99.15% accuracy the monthly progress of an infrastructure project over the next three months. This research provides project managers with a comprehensive framework that combines data analytics techniques with agility practices to predict short-term project progress to take proactive measures on different influencing factors.
Keyword:
Data analytics
project progress
project management
hypothesis testing
predictive models
clustering
trend
infrastructure
trend
ARIMA
time series
clusters
framework
regression
期刊
IF:
2.6
论文数:
243
被引数:
739
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