Enriching decision making with data-based thresholds of process-related KPIs

Abstract

The continuous performance improvement of business processes usually involves the definition of a set of process performance indicators (PPIs) with their target values. These PPIs can be classified into lag PPIs, which establish a goal that the organization is trying to achieve, though are not directly influenceable by process performers, and lead PPIs, which are influenceable by process performers and have a predictable impact on the lag indicator. Determining thresholds for lead PPIs that enable the fulfillment of the related lag PPI is a key task, which is usually done based on the experience and intuition of the process owners. However, the amount and nature of currently available data make it possible for data-driven decisions to be made in this regard. This paper proposes a method that applies statistical techniques for thresholds determination successfully employed in other domains. Its applicability has been evaluated in a real case study, where data from more than a thousand process executions was used.