IDEAS home Printed from https://ideas.repec.org/a/ids/ijisen/v34y2020i3p321-341.html
   My bibliography  Save this article

The value of fleet information: a cost-benefit model

Author

Listed:
  • Sini-Kaisu Kinnunen
  • Salla Marttonen-Arola
  • Timo Kärri

Abstract

Internet of things (IoT) technologies enable the collection of wide-ranging data related to industrial assets which can be used as a support of decision making in asset management, varying from operative maintenance decisions concerning one asset to the management of asset fleets. Technologies and data-refining processes need to be invested in to create knowledge from the massive amounts of data. However, it is not clear that the investments in technologies will pay back, as the data analysis and modelling processes need to be developed as well and the potential benefits must be considerable. This paper contributes to this field by modelling the costs and benefits of IoT investments. As a result, we develop a model that evaluates the value of fleet information in the maintenance context by applying the cost-benefit approach. The costs consist of hardware, software and data processing – related work costs, while the benefits comprise savings in maintenance and quality costs, as well as other savings or increased revenues. Testing the model with a descriptive case demonstrates that the realised cost savings and other benefits need to be considerable for the investment in IoT technologies to be profitable. The results emphasise the importance of data utilisation in decision making in order to gain benefits and to create value from data.

Suggested Citation

  • Sini-Kaisu Kinnunen & Salla Marttonen-Arola & Timo Kärri, 2020. "The value of fleet information: a cost-benefit model," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 34(3), pages 321-341.
  • Handle: RePEc:ids:ijisen:v:34:y:2020:i:3:p:321-341
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=105734
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ids:ijisen:v:34:y:2020:i:3:p:321-341. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=188 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.