Cross-organisational processes represent similar processes from different organisations providing similar business capabilities. Cross-organisational process benchmarking has immense potential to empower organisations to learn from each other and unearth opportunities for significant productivity gains. Regrettably, this potential is not fully realised yet as existing benchmarking approaches are mostly focused on the performance aspect of process behaviour (e.g., how long does a process instance take to complete?) rather than on how the process is completed or on the resources involved.
Our goal is to design, implement, and empirically validate innovative cross-organisational process mining and process improvement techniques. These techniques will make use of different process-related information including process models, process context data and historical records of process executions. Process similarity metrics that take into account the degrees of similarity in terms of process context, behaviour and performance will be designed. These metrics will then be used to identify ‘best’ or ‘better’ practices among different organisations. The proposed framework will quantify and explain the differences between cross-organisational processes by making use of observed behaviours of these processes in relevant contexts; and recommend process improvement actions.
The key question that we attempt to address in this research domain is as follows: using multiple event logs, how can we automate the discovery of the (dis)similarity of processes across organisations that provide similar services but with different environmental contexts?
We conducted a preliminary case study in the area of cross-organisational process mining within the context of comparing the patient flows of four South Australian hospitals. The techniques employed in this case study were mainly based on traditional process mining techniques based on single event log. For more details about this case study, please refer to the following page. More details about the work can be found in the following publication.
Partington, Andrew, Wynn, Moe T., Suriadi, Suriadi, Ouyang, Chun, & Karnon, Jonathan (2015) Process mining for clinical processes: A comparative analysis of four Australian hospitals. ACM Transactions on Management Information Systems, 5(4), 19:1-19:18.