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 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.

Automated summary

Efficient resource management methods are crucial for spare parts in equipment maintenance and repair. Forecasting plays a critical role in planning, especially when there is uncertainty in demand. However, most existing studies focus solely on forecasting models for spare parts with intermittent demand, without sufficient investigation on integrating planning and forecasting models. This paper examines the interaction between two models to optimize planning and forecasting decisions, preventing sub-optimality. The proposed mathematical models include a planning model and a forecasting model using Support Vector Machine (SVM). Empirical investigations from an oil company show that integrating decisions and performing demand estimation using the piecewise method optimizes cost, improves forecasting accuracy, and enhances planning performance.