A predictive model to identify Kanban teams at risk

Ivan Shamshurin, Jeffrey S. Saltz

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)321-335
Number of pages15
JournalModel Assisted Statistics and Applications
Volume14
Issue number4
DOIs
StatePublished - Jan 1 2019

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Keywords

  • data science project management
  • Kanban
  • metrics
  • project management
  • team performance

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

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