• Evidence that data quality is hardly considered in expert & intelligent systems is provided.A decision-support tool for maintenance data reporting assessment is developed.A company uses the proposed tool to rank branch offices in terms of reporting quality.Enhanced reporting quality results in enhanced business activities & decision-making. Today's largest and fastest growing companies' assets are no longer physical, but rather digital (software, algorithms...). This is all the more true in the manufacturing, and particularly in the maintenance sector where quality of enterprise maintenance services are closely linked to the quality of maintenance data reporting procedures. If quality of the reported data is too low, it can results in wrong decision-making and loss of money. Furthermore, various maintenance experts are involved and directly concerned about the quality of enterprises' daily maintenance data reporting (e.g., maintenance planners, plant managers...), each one having specific needs and responsibilities. To address this Multi-Criteria Decision Making (MCDM) problem, and since data quality is hardly considered in existing expert maintenance systems, this paper develops a maintenance reporting quality assessment (MRQA) dashboard that enables any company stakeholder to easily - and in real-time - assess/rank company branch offices in terms of maintenance reporting quality. From a theoretical standpoint, AHP is used to integrate various data quality dimensions as well as expert preferences. A use case describes how the proposed MRQA dashboard is being used by a Finnish multinational equipment manufacturer to assess and enhance reporting practices in a specific or a group of branch offices. ()
  • 5 ()
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  • 2016-07-22 ()
  • 10.1016/j.eswa.2016.06.043 ()
  • 164 ()
  • 5 ()
is ?:hasCitingEntity of
  • en ()
  • 2016-11-30 ()
  • Pergamon Press - An Imprint of Elsevier Science ()
  • 21443 ()
  • 119 ()
  • 145 ()
  • Data quality assessment of maintenance reporting procedures ()
  • 63 ()


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