• artificial intelligence
• business operations
• machine learning
• planning
• statistics

The integration of Artificial Intelligence (AI), particularly Machine Learning (ML), into project management is a significant advancement in order to improve the operational efficiency and reduce risks. In particular, this thesis explores the application of ML and statistical techniques to historical project data from Baker Hughes srl, focusing on identifying patterns and predicting delivery delays. This analysis considers three critical phases of the production process (item order placement, item procurement, and manufacturing cycles) so that the underlying factors that influence project timelines and contribute to possible delays can be detected and properly handled.
The outcomes of this analysis will help planners make better and more informed scheduling decisions. By predicting potential delays early in a project, project managers can adopt specific strategies to prevent or minimize these delays. This proactive approach aims to improve the company's On-Time Delivery (OTD) rate. Improving OTD rates also leads to lower costs. Specifically, this would reduce the Cost of Quality (COQ) and the financial penalties, known as liquidated damages (LDs), that result from late deliveries.
This thesis uses several methods, including statistical analysis, clustering, and time series forecasting with ML algorithms. Different approaches will be tested to determine which provides the most accurate predictions. Clustering analysis groups similar items together to better understand the common causes of delays. Time series forecasting considers trends and patterns over time to predict future project performance.
In conclusion, this thesis highlights how using statistics and machine learning in project management can significantly improve planning accuracy, reduce risks, save costs, and enhance organizational performance. The approaches and methods developed in this research can serve as a valuable foundation for innovation in project management practices at Baker Hughes srl.