Ir al contenido (pulsa Retorno)

Universitat Politècnica de Catalunya

    • Català
    • Castellano
    • English
    • LoginRegisterLog in (no UPC users)
  • mailContact Us
  • world English 
    • Català
    • Castellano
    • English
  • userLogin   
      LoginRegisterLog in (no UPC users)

UPCommons. Global access to UPC knowledge

Banner header
13.660 Articles in journals published by the UPC
You are here:
View Item 
  •   DSpace Home
  • Revistes
  • JIEM: Journal of Industrial Engineering and Management
  • 2021, vol. 14, núm. 3
  • View Item
  •   DSpace Home
  • Revistes
  • JIEM: Journal of Industrial Engineering and Management
  • 2021, vol. 14, núm. 3
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

A machine learning approach to enable bulk orders of critical spare-parts in the shipping industry

Thumbnail
View/Open
Anglou, F.Z, A Machine Learning Approach to Enable Bulk Orders of Critical Spare_Parts in the Shipping Industry.pdf (897,4Kb)
 
10.3926/jiem.3446
 
  View Usage Statistics
  LA Referencia / Recolecta stats
Cita com:
hdl:2117/356858

Show full item record
Anglou, Fiorentia Zoi
Ponis, Stavros
Spanos, Athanasios
Document typeArticle
Defense date2021
PublisherOmniaScience
Rights accessOpen Access
Attribution-NonCommercial 4.0 International
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial 4.0 International
Abstract
Purpose: The main purpose of this paper is to propose a methodological approach and a decision support tool, based on prescriptive analytics, to enable bulk ordering of spare parts for shipping companies operating fleets of vessels. The developed tool utilises Machine Learning (ML) and operations research algorithms, to forecast and optimize bulk spare parts orders needed to cover planned maintenance requirements on an annual basis and optimize the company’s purchasing decisions. Design/methodology/approach: The proposed approach consists of three discrete methodological steps, each one supported by a decision support tool based on clustering and Machine Learning (ML) algorithms. In the first step, clustering is applied in order to identify high interest items. Next, a forecasting tool is developed for estimating the expected needs of the fleet and to test whether the needed quantity is influenced by the source of purchase. Finally, the selected items are cost-effectively allocated to a group of vendors. The performance of the tool is assessed by running a simulation of a bulk order process on a mixed fleet totaling 75 vessels. Findings: The overall findings and approach are quite promising Indicatively, shifting demand planning focus to critical spares, via clustering, can reduce administrative workload. Furthermore, the proposed forecasting approach results in a Mean Absolute Percentage Error of 10% for specific components, with a potential for further reduction, as data availability increases. Finally, the cost optimizer can prescribe spare part acquisition scenarios that yield a 9% overall cost reduction over the span of two years. Originality/value: By adopting the proposed approach, shipping companies have the potential to produce meaningful results ranging from soft benefits, such as the rationalization of the workload of the purchasing department and its third party collaborators to hard, quantitative benefits, such as reducing the cost of the bulk ordering process, directly affecting a company’s bottom line
CitationAnglou, F.Z.; Ponis, S.; Spanos, A. A machine learning approach to enable bulk orders of critical spare-parts in the shipping industry. "Journal of Industrial Engineering and Management", 2021, vol. 14, núm. 3, p. 604-621. 
URIhttp://hdl.handle.net/2117/356858
DOI10.3926/jiem.3446
DLB-28744-2008
ISSN2013-0953
Collections
  • JIEM: Journal of Industrial Engineering and Management - 2021, vol. 14, núm. 3 [14]
  View Usage Statistics

Show full item record

FilesDescriptionSizeFormatView
Anglou, F.Z, A ... the Shipping Industry.pdf897,4KbPDFView/Open

Browse

This CollectionBy Issue DateAuthorsOther contributionsTitlesSubjectsThis repositoryCommunities & CollectionsBy Issue DateAuthorsOther contributionsTitlesSubjects

© UPC Obrir en finestra nova . Servei de Biblioteques, Publicacions i Arxius

info.biblioteques@upc.edu

  • About This Repository
  • Contact Us
  • Send Feedback
  • Privacy Settings
  • Inici de la pàgina