Predictive maintenance: strategic use of IT in manufacturing organizations

Salvatore T. March () and Gary D. Scudder ()
Additional contact information
Salvatore T. March: Vanderbilt University
Gary D. Scudder: Vanderbilt University

Information Systems Frontiers, 2019, vol. 21, issue 2, No 5, 327-341

Abstract: Abstract A combination of big data and predictive analytics orchestrated through the Internet of Things (IoT) offers many opportunities for researchers in Information Systems, Operations Management and Strategy to look at old problems in new ways, and to identify completely new research areas. While there is much hype, little research has been conducted that informs companies about how to profitably integrate the IoT with strategic or operational processes. This paper views the IoT through the lens of predictive maintenance -- the use of real-time data and predictive analytics algorithms to dynamically manage preventive maintenance policies. These are being used by numerous manufacturing organizations to transition from product-oriented to service-oriented business models. In particular, we analyze optimal preventive maintenance policies in an environment where equipment is subject to a deterioration, which shifts it from its initial, fully-productive state, having a specified, age-dependent failure rate to a less-productive or deteriorated state, having a different, presumably higher, age-dependent failure rate. The deterioration, itself, is a random process.

Keywords: Internet of things; Strategic information systems; Preventive maintenance; Predictive maintenance (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://link.springer.com/10.1007/s10796-017-9749-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:infosf:v:21:y:2019:i:2:d:10.1007_s10796-017-9749-z

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10796

DOI: 10.1007/s10796-017-9749-z

Access Statistics for this article

Information Systems Frontiers is currently edited by Ram Ramesh and Raghav Rao

More articles in Information Systems Frontiers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().