Smart Maintenance Decision Support Systems (SMDSS) based on corporate big data analytics

Daniel Bumblauskas, Douglas Gemmill, Amy Igoua, Johanna Anzengruber

Research output: Contribution to journalArticlepeer-review

44 Citations (Scopus)

Abstract

The purpose of this article is to outline the architectural design and the conceptual framework for a Smart Maintenance Decision Support System (SMDSS) based on corporate data from a Fortune 500 company. Motivated by the rapidly transforming landscape for big data analytics and predictive maintenance decision making, we have created a system capable of providing end users with recommendations to improve asset lifecycles. Methodologically, a cost minimization algorithm is used to analyze a large industry service and warranty data sets and two analytical decision models were developed and applied to a case study for an electrical circuit breaker maintenance problem. Some of these techniques can be applied to other industries, such as jet engine maintenance, and can be expanded to others with implications for robust decision analysis. The SMDSS provides a predictive analytical model that can be applied in manufacturing and service based industries. Our findings and results show that existing solution algorithms and optimization models can be applied to large data sets to lay out executable decisions for managers.

Original languageEnglish
Pages (from-to)303-317
Number of pages15
JournalExpert Systems with Applications
Volume90
DOIs
Publication statusPublished - 30 Dec 2017

Keywords

  • Analytical modeling
  • Asset management
  • Big data
  • Decision Support Systems
  • Maintenance

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