Affiliations: School of Information Studies, Syracuse University, Hinds Hall, Syracuse, NY 13210, USA
Correspondence:
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Corresponding author: Ivan Shamshurin, School of Information Studies, Syracuse University, Hinds Hall, Syracuse, NY 13210, USA. %****␣mas-14-mas190471_temp.tex␣Line␣25␣**** E-mail: [email protected].
Abstract: Kanban, which is an agile process methodology as well as a means to implement lean principles, has been growing as a project management framework across a range of domains, including manufacturing, software development and data science. This paper explores, for teams using Kanban, the ability to predict low team performance. The prediction is based on an analytical model that uses specific project metrics that can be collected via the team’s visual Kanban board. Specifically, data from 80 teams was used to build and test machine learning models that predict teams at risk for delivering low quality results. The model developed was significantly better than the baseline situation of thinking that all teams were at risk. While this analysis was done within a data science project context, the results are likely applicable across a range of information system projects.
Keywords: Kanban, metrics, project management, team performance, data science project management
