Data-driven decision-making in maintenance management and coordination throughout the asset life cycle: an empirical study

Abstract

Purpose

With production systems become more digitized, data-driven maintenance decisions can improve the performance of production systems. While manufacturers are introducing predictive maintenance and maintenance reporting to increase maintenance operation efficiency, operational data may also be used to improve maintenance management. Research on the value of data-driven decision support to foster increased internal integration of maintenance with related functions is less explored. This paper explores the potential for further development of solutions for cross-functional responsibilities that maintenance shares with production and logistics through data-driven approaches.

Design/methodology/approach

Fifteen maintenance experts were interviewed in semi-structured interviews. The interview questions were derived based on topics identified through a structured literature analysis of 126 papers.

Findings

The main findings show that data-driven decision-making can support maintenance, asset, production and material planning to coordinate and collaborate on cross-functional responsibilities. While solutions for maintenance planning and scheduling have been explored for various operational conditions, collaborative solutions for maintenance, production and logistics offer the potential for further development. Enablers for data-driven collaboration are the internal synchronization and central definition of goals, harmonization of information systems and information visualization for decision-making.

Originality/value

This paper outlines future research directions for data-driven decision-making in maintenance management as well as the practical requirements for implementation.

Citation

Hinrichs, M., Prifti, L. and Schneegass, S. (2024), "Data-driven decision-making in maintenance management and coordination throughout the asset life cycle: an empirical study", Journal of Quality in Maintenance Engineering, Vol. 30 No. 1, pp. 202-220. https://doi.org/10.1108/JQME-04-2023-0038