Smart Maintenance Decision Support Systems (SMDSS) based on corporate big data analytics. (30th December 2017)

• Record Type: Journal Article
• Authors: Bumblauskas, Daniel; Gemmill, Douglas; Igou, Amy; Anzengruber, Johanna
• Is Part Of: Expert systems with applications. Volume 90(2017)
• Page Start: 303
• Page End: 317
• Publication Date: 2017-12-30
• Subjects: Big data -- Asset management -- Maintenance -- Decision Support Systems -- Analytical modeling; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2017.08.025
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: A framework for smart maintenance decision support system is provided. Applications using big data analytics and a specific case study for the electrical utility industry are detailed. An integrated expert system making use of Markov Decision Process and Analytical Hierarchy Process Models is developed. 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.