Journal
INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS
Volume 208, Issue -, Pages 483-499Publisher
ELSEVIER
DOI: 10.1016/j.ijpe.2018.12.022
Keywords
Internet of Things; Preventive maintenance; Warranty policies; Remanufacturing; Extending product life-cycle
Ask authors/readers for more resources
The Internet of Things plays a critical role as a facilitator of concrete planning for both warranty and maintenance procedures for remanufactured products by significantly reducing or nearly eliminating ambiguity about the existence, conditions, and types as well as the remaining lives of individual constituents of an End-of-Use product. Since remaining useful life serves as a consistent measure of reliability, an immediate determination of a product's remaining useful life facilitates an optimal decision on recovery by meeting the minimum quality requirement defined in the underlying system criteria. This paper studies and scrutinizes the potential effect by offering one-dimensional renewing/non-renewing warranties on remanufactured products. The study deliberates on one type of product recovery system: The Advanced Remanufacturing-To-Order (ARTO) system. A discrete-event simulation model was developed from the view of remanufacturer for remanufactured items sold with one-dimensional warranty, in which, an End-Of-Life product is subjected to preventive maintenance action when the product's remaining life reaches a pre-determined value in order to maximize expected profit for the remanufacturer. Experiments were designed using Taguchi's Orthogonal Arrays to embody the full recovery system and observe its performance under various experimental parameters. In order to assess the effect of both warranty and preventive maintenance on remanufacturer total cost, pairwise tests were conducted as well as one-way analyses of variance (ANOVA) and Tukey pairwise comparisons test for each performance measure of the ARTO system.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available