Abstract
This paper presents a discrete events simulation tool developed to support undergraduate students in their Statistics and Data Analysis course. Although the use of modern smart technologies in the industry contributes to a profusion of data, very few enterprise datasets are freely available, resulting in a serious lack of open real-world data for research and education. To overcome this difficulty, we designed a tool that simulates scheduling scenarios in a manufacturing environment. The generated data may be used to put statistical concepts and methods into practice to design cost-effective strategies for optimizing key performance indicators, such as reducing production time, improving quality, eliminating wastes, and maximizing profits. Keywords: industrial datasets, teaching statistics, discrete events simulation
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
