Ishikawa diagrams and Bayesian belief networks for continuous improvement applications

Abstract

Purpose In continuous improvement (CI) projects, cause-and-effect diagrams are used to qualitatively express the relationship between a given problem and its root causes. However, when data collection activities are limited, and advanced statistical analyses are not possible, practitioners need to understand causal relationships. The paper aims to discuss these issues. Design/methodology/approach In this research, the authors present a framework that combines cause-and-effect diagrams with Bayesian belief networks (BBNs) to estimate causal relationships in instances where formal data collection/analysis activities are too costly or impractical. Specifically, the authors use cause-and-effect diagrams to create causal networks, and leverage elicitation methods to estimate the likelihood of risk scenarios by means of computer-based simulation. Findings This framework enables CI practitioners to leverage qualitative data and expertise to conduct in-depth statistical analysis in the event that data collection activities cannot be fully executed. Furthermore, this allows CI practitioners to identify critical root causes of a given problem under investigation before generating solutions. Originality/value This is the first framework that translates qualitative insights from a cause-and-effect diagram into a closed-form relationship between inputs and outputs by means of BBN models, simulation and regression.

Analysis of IT Project Schedule Inaccuracy

“…Quantitative parts are shaped like marginal priori and probability conditionals to quantify the dependence between connected nodes. This probability calculation has been used in various studies in various fields including manufacturing [10], machinery [1], supply chain [3].…”

“…Research relating to the causes and consequences of project delays is still limited. Previous research has focused on the differences between Intentional Attacks and Uncommon Technical Failures [1], quality and reliability in statistic [2], the application of continuous improvement [3].…”

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