Abstract
Process performance indicators (PPIs) allow the quantitative evaluation of business processes, providing essential information for decision making. It is common practice today that business processes and PPIs are usually modelled separately using graphical notations for the former and natural language for the latter. This approach makes PPI definitions simple to read and write, but it hinders maintenance consistency between business processes and PPIs. It also requires their manual translation into lower-level implementation languages for their operationalisation, which is a time-consuming, error-prone task because of the ambiguities inherent to natural language definitions. In this article, Visual ppinot, a graphical notation for defining PPIs together with business process models, is presented. Its underlying formal metamodel allows the automated processing of PPIs. Furthermore, it improves current state-of-the-art proposals in terms of expressiveness and in terms of providing an explicit visualisation of the link between PPIs and business processes, which avoids inconsistencies and promotes their co-evolution. The reference implementation, developed as a complete tool suite, has allowed its validation in a multiple-case study, in which five dimensions of Visual ppinot were studied: expressiveness, precision, automation, understandability, and traceability.
Highlights
Collecting and analysing process-related key performance indicators (KPIs) are the first prerequisites for holistic process management and form the basis for consistent and continuous process optimisation (Kronz 2006)
The first improvement was related to the distinction between linear and cyclic time measures, identified during the definition of Process performance indicators (PPIs)-3 in the AHS, which implied measuring an average duration located within a loop
The second was the inclusion of the isGroupedBy connector, used to define different target values according to a certain attribute value of a data object, as required by PPI-9 and PPI-10 from the AHS with respect to Request for Change (RFC) objects
Summary
Collecting and analysing process-related key performance indicators (KPIs) are the first prerequisites for holistic process management and form the basis for consistent and continuous process optimisation (Kronz 2006) These process-related KPIs are known as process performance indicators (PPIs) and are a key asset in evaluating the performance of business processes (Andrikopoulos et al 2008). The PPINOT metamodel was first introduced in del RıoOrtega et al (2013) and serves as a foundation for VISUAL PPINOT It was developed following an iterative and incremental process that included the following three steps (Brambilla et al 2012): modelling domain analysis, which involved defining the metamodel’s purpose and identifying the modelling concepts and their properties; modelling language design, which involved formalising these models; and modelling language validation, which involved instantiating the metamodel with more examples to validate its completeness and correctness. The validation involved its application to a number of real scenarios
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