A novel performance evaluation model for MRO management indicators of high-end equipment

Abstract

High-end equipment oriented maintenance, repair and operation (MRO) management is crucial for asset intensive industries. The existing works mainly focus on providing the best possible joint optimisation for production and maintenance management without aiming at the complicated relationships among them. In the intelligence-connected era, the rapid development of Internet of things and big data technologies enables us to access, collect, and store the industrial big data, which is especially necessary for MRO management indicator evaluation, and so we try to apply big data analysis to visualise the system structure of complicated relationships among MRO indicators at different management levels. In this paper, the decision-making trial and evaluation laboratory (DEMATEL) and improved analytical network process (ANP) are applied to build the performance evaluation model for MRO management indicators, in which DEMATEL is utilised to quantify the system structure of different management levels, and the improved ANP is introduced to calculate relative weights of corresponding indicators. The results point out to managers which indicators should deserve more attention in MRO management decision-making as well as joint optimisation for production and maintenance management. A case study illustrates the feasibility and practicality of the proposed model.

Keywords:

• MRO management indicators
• high-end equipment
• evaluation model
• interdependence
• innovation management
• engineering management

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Ling Li http://orcid.org/0000-0001-6365-5221

Min Liu http://orcid.org/0000-0002-8902-5460

Weiming Shen http://orcid.org/0000-0001-5204-7992

Additional information

Funding

Supported from the National Natural Science Foundation of China (No. 71690234, 61573257, 71661016) and Natural Science Foundation of Jiangxi Province (No. 20171BAA208005, 20171BAA218005) for this research is gratefully acknowledged.