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Digital Transformation at thyssenkrupp: Challenges, Strategies and Examples Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Enriching Decision Making with Data-Based Thresholds of Process-Related KPIs

Authors : Adela del-Río-Ortega, Félix García, Manuel Resinas, Elmar Weber, Francisco Ruiz, Antonio Ruiz-Cortés

Published in: Advanced Information Systems Engineering

Publisher: Springer International Publishing

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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.

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previous chapter Discovering Causal Factors Explaining Business Process Performance Variation
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Footnotes
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No further information can be provided about the company and its business processes due to privacy reasons.
 
2
Its identity is not revealed for confidentiality restrictions.
 
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Metadata
Title
Enriching Decision Making with Data-Based Thresholds of Process-Related KPIs
Authors
Adela del-Río-Ortega
Félix García
Manuel Resinas
Elmar Weber
Francisco Ruiz
Antonio Ruiz-Cortés
Copyright Year
2017
Book
Advanced Information Systems Engineering

Print ISBN: 978-3-319-59535-1

Electronic ISBN: 978-3-319-59536-8

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-59536-8_13

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