Modelling and analysing the uncertainty in business processes: pMeta-BPMN based on probability theory

Purpose The purpose of this paper is to contribute to the extant literature about the uncertain process modelling as well as integrated qualitative and quantitative analysis by proposing the pMeta-BPMN method to address the uncertainty in BPM from both workflow and information flow perspectives. Design/methodology/approach Motivated by the strong uncertainty of VUCA world, the probability theory is introduced in the combination of BPMN and Meta graph to mathematically characterize and quantify uncertainty, which gives rise to a novel method by integrating qualitative modelling (via BPMN's symbolic notation) and quantitative analytics (via Meta graph's algebraic framework). Findings The pMeta-BPMN integrates the technical frameworks of BPMN and Meta graph for business process modelling, which combining the respective advantages of the two in activity logic modelling and information element analytics. The proposed pMeta-BPMN method availability helps to model uncertain business processes and analyse the dependency of activities and information involved in order to enhance the process analysis towards uncertainty. A logistic case pilots pMeta-BPMN to illustrate the usability and evaluate the advantage. Originality/value Despite some research that tried to analyse process uncertainty, the extant process analysis methods focus heavily on the qualitative perspective such as pre-defined rules. A holistic method for modelling and analysis of process uncertainty considering both workflow and dataflow is still insufficient for presenting the specific quantitative results of adopting mathematical probability. This paper fills in this gap.