Improving Production Time Forecasting: A Comparison of Machine Learning Approahces

The thesis aims to develop a machine learning (ML) pipeline for building production order regression models. Accurate and precise estimates of job durations are crucial to optimize processes and improve efficiency, considering the rising costs of energy resources. By leveraging ML algorithms, this project aims to analyze the historical data and provide reliable predictions for future production orders, contributing to creating better planning.