Utilizing Evidence in Asset Management in the Era of Industry 4.0 and Artificial Intelligence

Jesse Tervo (Corresponding author), Helena Kortelainen, Anu Purhonen

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsScientificpeer-review

1 Citation (Scopus)


The main motivation of this paper is to present solutions that gather data and knowledge and provide evidence for asset-related decision-making in various industrial maintenance tasks. In our research, identification of user needs and analyzing how their requirements could be fulfilled is in the core. We develop a proof of concept (POC), which is a practical MS Excel-solution that is based on the concept of evidence-based asset management, an approach that focuses on broad utilization of evidence from multiple information sources. Requirements for the POC were drawn from end user interviews and the results of the subsequent evaluation interviews, in which a previous version of the POC was tested. The requirements included, for instance, implementation of visual elements and the utilization of artificial intelligence. In this paper, we discuss the means to fulfill such requirements and propose a software solution. In addition, we will study the feasibility of the solution in asset management organizations from a data management perspective.

Original languageEnglish
Title of host publicationAdvances in Systems Engineering
Subtitle of host publicationProceedings of the 28th International Conference on Systems Engineering, ICSEng 2021, December 14-16, Wrocław, Poland
Number of pages10
ISBN (Electronic)978-3-030-92604-5
ISBN (Print)978-3-030-92603-8
Publication statusPublished - 2022
MoE publication typeA4 Article in a conference publication
Event28th International Conference On Systems Engineering, ICSEng 2021: Online - Wrocław, Poland
Duration: 14 Dec 202116 Dec 2021

Publication series

SeriesLecture Notes in Networks and Systems


Conference28th International Conference On Systems Engineering, ICSEng 2021


  • Artificial intelligence
  • Asset management
  • Data management
  • Evidence


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