Visani_Raffoni_Costa_The quest for business value drivers_machine learning_performance management_PPC_2022_1.pdf (936.72 kB)
The quest for business value drivers: applying machine learning to performance management
journal contributionposted on 2023-03-03, 15:39 authored by Franco Visani, Anna RaffoniAnna Raffoni, Emanuele Costa
The paper explores the potential role of Machine learning (ML) in supporting the development of a company’s Performance Management System (PMS). In more details, it investigates the capability of ML to moderate the complexity related to the identification of the business value drivers (methodological complexity) and the related measures (analytical complexity). A second objective is the analysis of the main issues arising in applying ML to performance management. The research, developed through an action research design, shows that ML can moderate complexity by (1) reducing the subjectivity in the identification of the business value drivers; (2) accounting for cause-effect relationships between business value drivers and performance; (3) balancing managerial interpretability vs. predictivity of the approach. It also shows that the realisation of such benefits requires a combined understanding of the ML techniques and of the performance management model of the company to frame and validate the algorithm in light of the context in which the organisation operates. The paper contributes to the literature analysing the role of business analytics in the field of performance management and it provides new insights into the potential benefits of introducing an ML-based PMS and the issues to consider to increase its effectiveness.
- Business and Economics
Published inProduction Planning and Control
- AM (Accepted Manuscript)
Rights holder© Informa UK Limited
Publisher statementThis is an Accepted Manuscript version of the following article, accepted for publication in Production Planning and Control. Franco Visani, Anna Raffoni & Emanuele Costa (2023) The quest for business value drivers: applying machine learning to performance management, Production Planning & Control, DOI: 10.1080/09537287.2022.2157776. It is deposited under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
DepositorDr Anna Raffoni. Deposit date: 2 March 2023