Data-driven machine criticality assessment – maintenance decision support for increased productivity
Journal article, 2022
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.
bottleneck
Productivity
maintenance prioritization
criticality assessment
data-driven decision-making
Author
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 1-19DAIMP - 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
Production
DOI
10.1080/09537287.2020.1817601