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Data-driven performance management of business units using process mining and DEA: case study of an Iranian chain store

Data-driven performance management of business units using process mining and DEA: case study of... The underlying purpose of this paper is to propose a comprehensive framework evaluating the performance of business units of an organization with a process perspective, identifying the most influential performance indicators, enabling managers to make more informed decisions based on data recording every day in their operational information systems.Design/methodology/approachFor proposing the conceptual framework of performance evaluation a synchronized analysis of selected process' data, obtained from an integrated information system of an Iranian chain store, was performed.FindingsThe superiority of the proposed framework results is demonstrated in comparison to applying the process mining solely; principal component analysis was identified as an efficient link between process mining and data envelopment analysis. Also, based on the final data analytics, the units' throughput times and the variety of brands and suppliers had the most impact on their performances.Research limitations/implicationsThe data of abundant business units and performance indicators, which would have allowed adding data prediction and other data analytics techniques for more insight, was not able to be accessed.Practical implicationsOrganizations' managers can use the framework to evaluate their business units' current status and then prioritize their resources based on the most influential performance indicators for overall improvement.Originality/valueThe study contributes to the research on performance management and process mining by presenting a comprehensive framework with two levels of data analytics. It stresses discovering what is happening in business units, and how to prioritize their improvement opportunities learning the significant correlations between performance indicators and units' performance. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Productivity and Performance Management Emerald Publishing

Data-driven performance management of business units using process mining and DEA: case study of an Iranian chain store

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References (33)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1741-0401
DOI
10.1108/ijppm-10-2020-0562
Publisher site
See Article on Publisher Site

Abstract

The underlying purpose of this paper is to propose a comprehensive framework evaluating the performance of business units of an organization with a process perspective, identifying the most influential performance indicators, enabling managers to make more informed decisions based on data recording every day in their operational information systems.Design/methodology/approachFor proposing the conceptual framework of performance evaluation a synchronized analysis of selected process' data, obtained from an integrated information system of an Iranian chain store, was performed.FindingsThe superiority of the proposed framework results is demonstrated in comparison to applying the process mining solely; principal component analysis was identified as an efficient link between process mining and data envelopment analysis. Also, based on the final data analytics, the units' throughput times and the variety of brands and suppliers had the most impact on their performances.Research limitations/implicationsThe data of abundant business units and performance indicators, which would have allowed adding data prediction and other data analytics techniques for more insight, was not able to be accessed.Practical implicationsOrganizations' managers can use the framework to evaluate their business units' current status and then prioritize their resources based on the most influential performance indicators for overall improvement.Originality/valueThe study contributes to the research on performance management and process mining by presenting a comprehensive framework with two levels of data analytics. It stresses discovering what is happening in business units, and how to prioritize their improvement opportunities learning the significant correlations between performance indicators and units' performance.

Journal

International Journal of Productivity and Performance Management – Emerald Publishing

Published: Jan 30, 2023

Keywords: Performance management; Process mining; Data analysis; Business process management; Supply chain management

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