Machine criticality assessment for productivity improvement: Smart maintenance decision support
Journal article, 2019

Purpose 
The purpose of this paper is to increase productivity through smart maintenance planning by including productivity as one of the objectives of the maintenance organization. Therefore, the goals of the paper are to investigate existing machine criticality assessment and identify components of the criticality assessment tool to increase productivity.

Design/methodology/approach 
An embedded multiple case study research design was adopted in this paper. Six different cases were chosen from six different production sites operated by three multi-national manufacturing companies. Data collection was carried out in the form of interviews, focus groups and archival records. More than one source of data was collected in each of the cases. The cases included different production layouts such as machining, assembly and foundry, which ensured data variety.

Findings 
The main finding of the paper is a deeper understanding of how manufacturing companies assess machine criticality and plan maintenance activities. The empirical findings showed that there is a lack of trust regarding existing criticality assessment tools. As a result, necessary changes within the maintenance organizations in order to increase productivity were identified. These are technological advancements, i.e. a dynamic and data-driven approach and organizational changes, i.e. approaching with a systems perspective when performing maintenance prioritization.

Originality/value 
Machine criticality assessment studies are rare, especially empirical research. The originality of this paper lies in the empirical research conducted on smart maintenance planning for productivity improvement. In addition, identifying the components for machine criticality assessment is equally important for research and industries to efficient planning of maintenance activities.

Bottleneck

Productivity

Author

Anders Skoogh

Chalmers, Industrial and Materials Science, Production Systems

Antti Salonen

Mälardalens högskola

Maheshwaran Gopalakrishnan

Chalmers, Industrial and Materials Science, Production Systems

Martin Asp

Volvo AB

International Journal of Productivity and Performance Management

1741-0401 (ISSN)

DAIMP - Data Analytics in Maintenance Planning

VINNOVA, 2016-03-01 -- 2019-02-28.

Subject Categories

Production Engineering, Human Work Science and Ergonomics

Other Mechanical Engineering

Reliability and Maintenance

Areas of Advance

Production

DOI

10.1108/IJPPM-03-2018-0091

More information

Latest update

1/30/2019