A framework to measure the value of IoT in spare parts logistics networks

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dc.contributor.advisorKutanoglu, Erhan
dc.creatorRekapalli, Krishna Teja
dc.creator.orcid0000-0001-8796-5224
dc.date.accessioned2018-08-09T19:38:07Z
dc.date.available2018-08-09T19:38:07Z
dc.date.created2017-12
dc.date.issued2017-12-08
dc.date.submittedDecember 2017
dc.date.updated2018-08-09T19:38:08Z
dc.description.abstractWith increasing connectivity and declining data processing costs day-by-day, industrial systems hold a promising future in the wake of technologies like Internet of Things (IoT). Spare-parts logistics networks can leverage continuous sensor data from machines to provide better service to their customers. This work introduces a framework to evaluate the impact of Internet of Things on a multi-echelon spare parts logistics network. A discrete event simulation of a stylized system is developed and numerical experiments are used to study the system-wide effects of different factors like inspection interval and replacement policy. The simulations are used to evaluate the costs under different key factor settings and decision plots are derived to identify the cost settings under which the IoT is beneficial. The results suggest that continuous data collection about the part health can enable early replacement policies which result in reduced total cost. The study also found that in the systems with high holding cost, making inventory and replacement policy decisions jointly can be more beneficial.
dc.description.departmentOperations Research and Industrial Engineering
dc.format.mimetypeapplication/pdf
dc.identifierdoi:10.15781/T2D795V6S
dc.identifier.urihttp://hdl.handle.net/2152/67270
dc.language.isoen
dc.subjectInternet of Things
dc.subjectSpare parts logistics
dc.subjectPredictive maintenance
dc.subjectSmart maintenance
dc.titleA framework to measure the value of IoT in spare parts logistics networks
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentOperations Research and Industrial Engineering
thesis.degree.disciplineOperations Research & Industrial Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Engineering

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