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Business performance analytics: exploring the potential for performance management systems

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journal contribution
posted on 19.10.2017 by Anna Raffoni, Franco Visani, Monica Bartolini, Riccardo Silvi
Business Performance Analytics (BPA) entails the systematic use of data and analytical methods (mathematical, econometric and statistical) for performance measurement and management. Although potentially overcoming some traditional diagnostic issues related to Performance Management Systems (PMS), such as information overload, absence of cause-effect relationships, lack of a holistic view of the organisation, research in the field is still in its infancy. A comprehensive model for operationalising analytics for diagnostic and interactive PMS is still lacking. Adopting an action research approach, this paper addresses this gap and develops a five-step framework applied to a company operating in the construction industry. The results show that in addition to encouraging dialogue, BPA can contribute to identifying critical performance variables, potential sources of risk and related interdependencies. A number of critical issues in implementing data-based approaches are also highlighted, including data quality, organisational competences and cultural shifts.

History

School

  • Business and Economics

Department

  • Business

Published in

Production Planning and Control

Citation

RAFFONI, A. ... et al, 2018. Business performance analytics: exploring the potential for performance management systems. Production Planning and Control, 29(1), pp. 51-67.

Publisher

© Taylor & Francis

Version

AM (Accepted Manuscript)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

20/07/2017

Publication date

2018

Notes

This is an Accepted Manuscript of an article published by Taylor & Francis in Production Planning and Control on 11 Oct 2017, available online: https://doi.org/10.1080/09537287.2017.138188.

ISSN

0953-7287

eISSN

1366-5871

Language

en

Exports