Integrated demand forecasting and planning model for repairable spare part: an empirical investigation

Babaveisi, Vahid, Teimoury, Ebrahim, Gholamian, Mohammad Reza and Rostami-Tabar, Bahman ORCID: https://orcid.org/0000-0002-3730-0045 2023. Integrated demand forecasting and planning model for repairable spare part: an empirical investigation. International Journal of Production Research 61 (20) , pp. 6791-6807. 10.1080/00207543.2022.2137596

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Abstract

Efficient resource management methods are essential for spare parts used in the maintenance and repair of equipment. Forecasting plays a critical role in planning, especially under demand uncertainty. Existing works regarding spare parts with intermittent demand focus on the mere forecasting model while integrating the planning and forecasting models are not sufficiently investigated. We examine the interaction between two models to optimise planning and forecasting decisions and prevent sub- optimality. This paper presents two mathematical models, including a planning model that determines stock level, spare part order assignment to suppliers, equipment repair assignment, and the number of intervals over the planning horizon. The second model is the forecasting model by Support Vector Machine (SVM). Considering uncertainty, demand estimation is performed by piecewise linearization considering the optimal number of intervals in the planning model used in forecasting. An interactive procedure is developed to optimise models. We use an empirical investigation from an oil company providing the spare part supply chain data. The analyses show that demand estimation by piecewise method and integrating the decisions optimises the cost, improves the forecasting accuracy, and planning performance. Moreover, we offer several insights to practitioners that shed light on spare part planning and forecasting decisions.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Business (Including Economics)
Subjects:	H Social Sciences > H Social Sciences (General)
Publisher:	Taylor and Francis Group
ISSN:	0020-7543
Date of First Compliant Deposit:	18 October 2022
Date of Acceptance:	15 October 2022
Last Modified:	17 Nov 2024 11:15
URI:	https://orca.cardiff.ac.uk/id/eprint/153444

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