Data-driven machine criticality assessment – maintenance decision support for increased productivity
Artikel i vetenskaplig tidskrift, 2020

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

data-driven maintenance planning

maintenance prioritization

bottleneck

criticality assessment

increase productivity

Författare

Maheshwaran Gopalakrishnan

Chalmers, Industri- och materialvetenskap, Produktionssystem

Mukund Subramaniyan

Chalmers, Industri- och materialvetenskap, Produktionssystem

Anders Skoogh

Chalmers, Industri- och materialvetenskap, Produktionssystem

Production Planning and Control

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

Vol. In Press

DAIMP - Dataanalys inom underhållsplanering

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

Ämneskategorier

Produktionsteknik, arbetsvetenskap och ergonomi

Transportteknik och logistik

Styrkeområden

Produktion

DOI

10.1080/09537287.2020.1817601

Mer information

Senast uppdaterat

2020-10-13