Visual ppinot: A Graphical Notation for Process Performance Indicators

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.

Measuring Performance in Knowledge-intensive Processes

“…From these definitions, the computation of these PPIs and measures can be carried out in different ways. If an event log of the information system that supports the KIP is available, it is possible to use the KiPPINOT definition to directly automate its computation [del Río-Ortega et al 2017b]. A proof-of-concept of this automation is available at the PPINOT Tool Suite 1 .…”

“…The use of general approaches for the definition of performance measures also reduces the risk of introducing differences into the operationalization of the performance information, thus avoiding erroneous analyses during decision-making tasks and inconsistencies during information exchange. In addition, these approaches also allow the automation of PPI calculation as well as easier maintenance, which are time-consuming and error-prone tasks in their absence [del Río-Ortega et al 2017b].…”

The ProcessPAIR Method for Automated Software Process Performance Analysis

“…Control charts: In Statistical Process Control (SPC), control charts are a kind of run charts used to graphically represent the behavior over time of variables that characterize process performance (i.e., process performance indicators [15] [16]) and help to assess process stability and capability. Stability has to do with the level of variability in the variable under analysis, and is assessed with the help of lower and upper control limits.…”

A Method for Modeling Process Performance Indicators Variability Integrated to Customizable Processes Models

“…PPIs are quantifiable metrics that allow an evaluation of the efficiency and effectiveness of business processes. PPIs can be measured directly by generating data through the process flow [5]. Decision-makers identify PPIs to get the necessary information to compare current process performances with a required objective and thus determine fundamental actions to reach proposed goals [6].…”