Data-driven machine criticality assessment – maintenance decision support for increased productivity
Journal article, 2022

Data-driven decision support for maintenance management is necessary for modern digitalized production systems. The data-driven approach enables analyzing the dynamic production system in realtime. Common problems within maintenance management are that maintenance decisions are experience-driven, narrow-focussed and static. Specifically, machine criticality assessment is a tool that is used in manufacturing companies to plan and prioritize maintenance activities. The maintenance problems are well exemplified by this tool in industrial practice. The tool is not trustworthy, seldom
updated and focuses on individual machines. Therefore, this paper aims at the development and validation of a framework for a data-driven machine criticality assessment tool. The tool supports prioritization and planning of maintenance decisions with a clear goal of increasing productivity. Four empirical cases were studied by employing a multiple case study methodology. The framework provides guidelines for maintenance decision-making by combining the Manufacturing Execution System (MES) and Computerized Maintenance Management System (CMMS) data with a systems perspective. The results show that by employing data-driven decision support within the maintenance organization, it can truly enable modern digitalized production systems to achieve higher levels of productivity.



maintenance prioritization

criticality assessment

data-driven decision-making


Maheshwaran Gopalakrishnan

Chalmers, Industrial and Materials Science, Production Systems

Mukund Subramaniyan

Chalmers, Industrial and Materials Science, Production Systems

Anders Skoogh

Chalmers, Industrial and Materials Science, Production Systems

Production Planning and Control

0953-7287 (ISSN) 1366-5871 (eISSN)

Vol. 33 1 1-19

DAIMP - Data Analytics in Maintenance Planning

VINNOVA (2015-06887), 2016-03-01 -- 2019-02-28.

Subject Categories

Production Engineering, Human Work Science and Ergonomics

Transport Systems and Logistics

Areas of Advance




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4/5/2022 5