Bus Spare Parts Demand Forecasting via Holt Winter’s Method and Support Vector Regression Algorithm

Abstract

Demand forecasting and spare parts availability are crucial in managing spare parts for public transport systems. The condition of public transport buses is critical for service quality. Regular maintenance and repair of buses can increase public transport usage. This case study focuses on forecasting spare parts demand for a public transport company with more than 1700 buses and five workshops. Considering the variety of bus models and wide range of parts effective demand forecasting is essential to avoid unnecessary holding costs or service interruptions. This study aims to apply two different demand forecasting methods Winter’s Method and Support Vector Regression to a public transport company that makes forecast demands based on experience and to interpret the results. While applying the Support Vector Regression method demand dates and bus ages were used as factors. Parameter optimization and demand forecasting methods were applied to the demand data using Weka software. Data were collected from the company's ERP software between 2019 and 2024. Spare parts were determined from five spare parts from different workshop units of the company. As a result of the study MAPE values were calculated. According to the calculated MAPE values it was determined that both methods gave similar results and successfully captured the demand structure. © 2025 Elsevier B.V. All rights reserved.