Experience Driven Process Improvement

Abstract

The importance of process improvement and the role that best practice reference models play in the achievement of process improvement are both well recognized. Best practice reference models are generally created by experts in the domain who are external to the organization. However, best practice can also be implicitly derived from the work practices of actual workers within the organisation, especially when there is opportunity for variance within the work, i.e. there may be different approaches to achieve the same process goal. In this paper, we propose to support process improvement intrinsically by utilizing the experiences and knowledge of business process users to inform and improve the current practices. The main challenge in this regard is identifying the “best” previous practices, which are often based on multiple criteria. To this end, we propose a method based on the skyline operator, which is applied on criteria relevant data derived from business process execution logs. We will demonstrate that the proposed method is capable to generate meaningful recommendations from large data sets in an efficient way, thereby effectively facilitating organizational learning and inherent process improvement.