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A System-Approach for Recoverable Spare Parts Management Using the Discrete Weibull Distribution

descriptionPublicationkeyboard_double_arrow_right Article , Journal , Other literature type 21 Sep 2019 Italy Publisher:MDPI AGJournal:Sustainability, volume 11, page 5,180 (eissn: 2071-1050,

Authors: Patriarca R.; Hu T.; Costantino F.; Di Gravio G.; Tronci M.;

doi: 10.3390/su11195180

handle: 11573/1333956

A System-Approach for Recoverable Spare Parts Management Using the Discrete Weibull Distribution

- Summary
- Subjects
- Metrics

Abstract

Optimal spare parts management strategies allow sustaining a system’s availability, while ensuring timely and effective maintenance. Following a systemic perspective, this paper starts from the Multi-Echelon Technique for Recoverable Item Control (METRIC) to investigate the potential use of a Weibull distribution for modelling items’ demand in case of failure. Adapting the analytic formulation of METRIC through a Discrete Weibull distribution, this study originally proposes a METRIC-based model (DW-METRIC) to be used for modelling the stochastic demand in multi-item systems, in order to ensure process sustainability. The DW-METRIC has been tested in a case study related to an industrial plant constituted by 98 items in a passive redundancy configuration. Comparing the results via a simulation model, the outcomes of the study allow defining applicability criteria for the DW-METRIC, in those settings where the DW-METRIC offers more accurate estimations than the traditional METRIC.

Country

Italy

Related Organizations

Sapienza University of Rome
Italy

Keywords

supply chain management, Discrete Weibull, Environmental effects of industries and plants, maintenance management, inventory management, TJ807-830, METRIC, TD194-195, Renewable energy sources, Environmental sciences, GE1-350, inventory control, Discrete weibull; inventory control; inventory management; maintenance management; metric; supply chain management

Impact byBIP!

	citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	8
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Average
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%