Determining Severity and Recommendations in Process Non-Conformance Instances

We have seen a variety of frameworks and methodologies aimed at dealing with non-conformance in processes presented in the literature. These methodologies seek to find discrepancies between process reference models and data returned from instances of process enactments. These range from methodologies aimed at preventing deviations and inconsistencies involved in workflow and process support systems to the mining and comparison of observed and recorded process data. What has not been presented in the literature thus far is a methodology for explicitly discerning the severity of instances of non-conformance once they are detected. Knowing how severe an instance of non-conformance might be, and therefore an awareness of the possible consequences this may have on the process outcome can be helpful in maintaining and protecting the process quality. Subsequently, a mechanism for using this information to provide some kind of recommendation or suggested remedial actions relating to the nonconformance for process improvement has also not been explored. In this paper we present a framework to address both these issues. A case study is also presented to evaluate the feasibility of this framework.