Open AccessA machine learning approach to enable bulk orders of critical spare-parts in the shipping industry
Author(s) -
Fiorentia Zoi Anglou,
Stavros T. Ponis,
Athanasios C. Spanos
Publication year - 2021
Publication title -
journal of industrial engineering and management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.385
H-Index - 29
eISSN - 2013-8423
pISSN - 2013-0953
DOI - 10.3926/jiem.3446
Subject(s) - spare part , cluster analysis , purchasing , computer science , operations research , workload , process (computing) , industrial engineering , engineering , machine learning , operations management , operating system
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 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 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.