With the rapid advancement of hardware and software technologies, machine learning has been pushed to the forefront of business value generating technologies. More and more businesses start to invest in machine learning to keep up with those that have already benefited from it. A local paper processing business is looking to improve upon the estimation of each order's runtime on the machines by leveraging the machine learning technologies. Traditionally, the predictions are done by experienced planners, but the actual runtimes do not always match the predictions. This thesis conducted an investigation about whether a machine learning model could be built to produce better estimations on behalf of the local business. By following a well-defined machine learning workflow in combination with Microsoft's AutoML model builder and data processing techniques, the result shows that predictions made by the machine learning model are able to perform better than the human made ones within an accepted margin.