The framework for data-driven maintenance planning and problem solving in maintenance communities

Pasi Valkokari, Toni Ahonen, Helena Kortelainen, Jesse Tervo

Research output: Contribution to journalArticle in a proceedings journalScientificpeer-review

Abstract

In capital-intensive industries, the management and optimization of the work in daily maintenance and turnaround maintenance is a significant area requiring attention. Especially the increasing collaboration with external companies call for better understanding on how information should be exploited in daily maintenance management and in shutdown planning, and what challenges the companies encounter. In this paper, we propose a framework for data-driven maintenance planning and problem solving, and integrate reliability and maintenance management processes in this framework. We also apply the framework to practical examples and illustrate the benefits expected to arise from using the proposed framework in empirical case studies. The concept of a maintenance community as a form of advanced collaboration sets a reference for current practices. Exchange of information and collaborative processes are in the core of such communities.
Original languageEnglish
Pages (from-to)175-180
Number of pages6
JournalIFAC-PapersOnLine
Volume55
Issue number19
DOIs
Publication statusPublished - 28 Sep 2022
MoE publication typeA4 Article in a conference publication
Event5th IFAC Workshop on Advanced Maintenance Engineering, Services and Technologies, AMEST 2022 - Bogotá, Colombia
Duration: 26 Jul 202229 Jul 2022

Keywords

  • Information management
  • Maintenance community
  • Maintenance management
  • Shutdown management
  • Tacit knowledge
  • Turnaround management

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