@inproceedings{269c23921cd54a82b54084ea47207016,
title = "An Approach for Automatically Deriving Key Performance Indicators from Ontological Enterprise Models",
abstract = "Organizations use Key Performance Indicators (KPIs) to monitor whether they attain their goals. Software vendors that supply generic software provide predefined KPIs in their software products for these organizations. However, each organization wants KPIs to be tailored to its specific goals.Th erefore, software vendors spend significant efforts on tailoring KPIs to organizations. That tailoring process is time-consuming and costly due to differences in the real-world phenomena of these organizations. In this context, we present our novel Automated KPI Derivation Approach. To automate the derivation of KPIs, our approach obtains the exact meaning of the terms in the real-world phenomena of an organization that is modeled in the form Ontological Enterprise Models (OEMs). As a proof-of-concept we implemented our approach. We demonstrate its use in a real-life setting and present preliminary results. ",
keywords = "Enterprise Resource Planning, Key Performance Indicators, Ontological Enterprise Modeling",
author = "U.A. Aksu and D.M.M. Schunselaar and H.A. Reijers",
year = "2017",
month = dec,
day = "8",
language = "English",
volume = "2016",
series = "CEUR Workshop Proceedings",
publisher = "CEUR",
pages = "38--53",
editor = "Paolo Ceravolo and {van Keulen}, Maurice and Kilian Stoffel",
booktitle = "SIMPDA 2017 Data-driven Process Discovery and Analysis",
}