TY - JOUR
T1 - Smart Maintenance Decision Support Systems (SMDSS) based on corporate big data analytics
AU - Bumblauskas, Daniel
AU - Gemmill, Douglas
AU - Igoua, Amy
AU - Anzengruber, Johanna
PY - 2017/12/30
Y1 - 2017/12/30
N2 - 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.
AB - 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.
KW - Analytical modeling
KW - Asset management
KW - Big data
KW - Decision Support Systems
KW - Maintenance
UR - http://www.scopus.com/inward/record.url?scp=85027974417&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2017.08.025
DO - 10.1016/j.eswa.2017.08.025
M3 - Article
VL - 90
SP - 303
EP - 317
JO - Expert Systems with Applications
JF - Expert Systems with Applications
ER -