Automobile spare-parts forecasting: A comparative study of time series methods

Abstract

In Mexico, the automotive industry is considered to be strategic in the industrial and economic development of the country because it generates production, employment and foreign exchange. Good demand forecasts are needed for better manufacturing management. The time series modelling tools applied to the monthly demand forecasting of automobile spare parts in Mexico are assessed, for the case of a transnational enterprise, considering affordability. The classic methods of moving averages, final value and exponential smoothing, the prestigious autoregressive integrated models of moving averages (ARIMA), the rarely implemented artificial neural networks (ANNs) and the very little explored ARIMA-ANNs hybrid models are compared. A good performance of the models involving ANNs is observed, but they were not as steady as the ARIMA models in the post-sample periods. The mean absolute percentage error (MAPE) was reduced from an original 57% to 32.65%. The obtained results could help demonstrate the importance of improving industrial forecasting methodologies for better planning.

Forecasting Spare Parts Demand of Military Aircraft: Comparisons of Data Mining Techniques and Managerial Features from the Case of South Korea

“…Table 1 shows the previous works, searched initially by us to investigate spare parts demand forecasting in various industries. Though the applications of spare parts demand forecasting have been made in different industries such as electronics [34], automobile [35], mining [11], maintenance/repair [36,37], consumer goods [38], aviation [39][40][41], energies [42], etc., those can be grouped into three according to their demand forecasting methods as shown in Table 1. Table 1, we could confirm that the prior works on spare parts demand forecasting have applied different methods to solve their specific problems in their industries.…”

Planificación y Gestión de Inventarios en la cadena de Suministro del sector Post Venta Automotriz

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Deep Learning and Statistical Models for Forecasting Transportation Demand: A Case Study of Multiple Distribution Centers

“…One significant factor influencing transportation demands is the order cycle [4], as each delivery depends on the customer's demand [5]. Additionally, uncertainty plays a significant role in transportation demand in areas such as sales [1], manufacturing [3], inventory [6], organizational risks, lead times [7], and the economy [8], making it challenging to make predictions. However, improving the accuracy of transportation demand prediction could significantly improve the decision-making processes of different agents in the supply chain.…”

“…The work by [10] evaluated the use of deep neural networks in this domain, identifying their potential applications in real-world scenarios. The works by [6,8,12,13] explored different aspects of using machine learning methods to predict transportation demands. There seems to be a consensus in the literature that using machine learning methods, especially deep learning, may improve the accuracy of transportation demand prediction.…”