A demand classification scheme for spare part inventory model subject to stochastic demand and lead time

In this study, we aim to develop a demand classification methodology for classifying and controlling inventory spare parts subject to stochastic demand and lead time. Using real data, the developed models were tested and their performances were evaluated and compared. The results show that the Lapla...

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Bibliographic Details
Published inProduction planning & control Vol. 26; no. 16; pp. 1318 - 1331
Main Authors Conceição, Samuel Vieira, da Silva, Gerson Luis Caetano, Lu, Dawei, Nunes, Nilson Tadeu Ramos, Pedrosa, Guilherme Corteletti
Format Journal Article
LanguageEnglish
Published London Taylor & Francis 10.12.2015
Taylor & Francis LLC
Subjects
Classification
Component parts
Cost reduction
Demand
Inventories
inventory control
Inventory management
Laplace transforms
Lead time
MRO - maintenance, repair and operating items
Optimization
Service level agreements
Spare parts
steel industry
Stochasticity
Stockpiling
Studies
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Summary:In this study, we aim to develop a demand classification methodology for classifying and controlling inventory spare parts subject to stochastic demand and lead time. Using real data, the developed models were tested and their performances were evaluated and compared. The results show that the Laplace model provided superior performance in terms of service level, fill rate (FR) and inventory cost. Compared with the current system based on normal distribution, the proposed Laplace model yielded significant savings and good results in terms of the service level and the FR. The Laplace and Gamma optimisation models resulted in savings of 82 and 81%, respectively.
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ISSN:0953-7287
1366-5871
DOI:10.1080/09537287.2015.1033497