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Data Driven Maintenance: A Promising Way of Action for Future Industrial Services Management
Linnaeus University, Sweden.
Linnaeus University, Sweden.
Chalmers University of Technology, Sweden.
Chalmers University of Technology, Sweden.
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2022 (English)In: Lecture Notes in Mechanical Engineering, Springer Science and Business Media Deutschland GmbH , 2022, p. 212-223Conference paper, Published paper (Refereed)
Abstract [en]

Maintenance and services of products as well as processes are pivotal for achieving high availability and avoiding catastrophic and costly failures. At the same time, maintenance is routinely performed more frequently than necessary, replacing possibly functional components, which has negative economic impact on the maintenance. New processes and products need to fulfil increased environmental demands, while customers put increasing demands on customization and coordination. Hence, improved maintenance processes possess very high potentials, economically as well as environmentally. The shifting demands on product development and production processes have led to the emergency of new digital solutions as well as new business models, such as integrated product-service offerings. Still, the general maintenance problem of how to perform the right service at the right time, taking available information and given limitations is valid. The project Future Industrial Services Management (FUSE) project was a step in a long-term effort for catalysing the evolution of maintenance and production in the current digital era. In this paper, several aspects of the general maintenance problem are discussed from a data driven perspective, spanning from technology solutions and organizational requirements to new business opportunities and how to create optimal maintenance plans. One of the main results of the project, in the form of a simulation tool for strategy selection, is also described.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2022. p. 212-223
Keywords [en]
Data driven maintenance, Maintenance planning, Service-related business models, Simulation tool
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:ri:diva-59768DOI: 10.1007/978-3-030-93639-6_18Scopus ID: 2-s2.0-85125283057ISBN: 9783030936389 (print)OAI: oai:DiVA.org:ri-59768DiVA, id: diva2:1681817
Conference
International Congress and Workshop on Industrial AI, IAI 2021, Virtual, Online, 6 October 2021 through 7 October 2021
Available from: 2022-07-07 Created: 2022-07-07 Last updated: 2023-05-09Bibliographically approved

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Ekman, JanHolst, AndersRudström, Åsa

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