Summary of Enhancing Project Performance Forecasting Using Machine Learning Techniques, by Soheila Sadeghi
Enhancing Project Performance Forecasting using Machine Learning Techniques
by Soheila Sadeghi
First submitted to arxiv on: 26 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Applications (stat.AP)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed machine learning-based approach utilizes time series forecasting techniques, including ARIMA and LSTM networks, to predict project performance metrics such as cost variance and earned value. The model incorporates historical data and project progress, as well as external factors like weather patterns and resource availability, to enhance forecast accuracy. This enables proactive identification of potential deviations from the baseline plan, allowing for timely corrective actions by project managers. The research aims to validate the effectiveness of this approach using a case study of an urban road reconstruction project, comparing forecasts with actual project performance data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a machine learning model to predict project performance metrics in urban road reconstruction projects. It uses historical data and project progress, along with external factors like weather and resources, to make predictions. This helps project managers catch any problems early and fix them before they get worse. The researchers tested their approach on an actual construction project to see how well it worked. |
Keywords
» Artificial intelligence » Lstm » Machine learning » Time series
