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

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 w...

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Bibliographic Details
Published in:International journal of production research 2023-10, Vol.61 (20), p.6791-6807
Main Authors: Babaveisi, Vahid, Teimoury, Ebrahim, Gholamian, Mohammad Reza, Rostami-Tabar, Bahman
Format: Article
Language:English
Subjects:
Decisions
Demand
Empirical analysis
Forecasting
Intervals
inventory management
Management methods
Mathematical models
Optimization
planning
Repair
Resource management
spare part
Spare parts
Supply chains
support vector machine
Support vector machines
Uncertainty
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Summary: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 linearisation 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.
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2022.2137596