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João P. Martins, Ramos, F.R., Pereira, M.T., Oliveira, M.J. & Ferreira, F.A. (2025). Development of a Decision Support System for Freight Forecasting. In Innovations in Industrial Engineering IV. (pp. 152-164).: Springer Nature.

J. P. Martins et al.,  "Development of a Decision Support System for Freight Forecasting", in Innovations in Industrial Engineering IV, Springer Nature, 2025, pp. 152-164

@incollection{martins2025_1766371051848,
	author = "João P. Martins and Ramos, F.R. and Pereira, M.T. and Oliveira, M.J. and Ferreira, F.A.",
	title = "Development of a Decision Support System for Freight Forecasting",
	chapter = "",
	booktitle = "Innovations in Industrial Engineering IV",
	year = "2025",
	volume = "",
	series = "",
	edition = "",
	pages = "152-152",
	publisher = "Springer Nature",
	address = "",
	url = "https://doi.org/10.1007/978-3-031-94484-0_13"
}

TY  - CHAP
TI  - Development of a Decision Support System for Freight Forecasting
T2  - Innovations in Industrial Engineering IV
AU  - João P. Martins
AU  - Ramos, F.R.
AU  - Pereira, M.T.
AU  - Oliveira, M.J.
AU  - Ferreira, F.A.
PY  - 2025
SP  - 152-164
DO  - 10.1007/978-3-031-94484-0_13
UR  - https://doi.org/10.1007/978-3-031-94484-0_13
AB  - The transportation of goods is critical to supply chains, directly influencing efficiency, cost management, and competitiveness. Accurate freight cost forecasting is essential for decision-making, enabling businesses to allocate resources effectively, reduce financial uncertainties, and ensure timely deliveries. This study, conducted in the After-Sales department of a global company, aimed to analyse freight costs per shipment and develop a predictive system based on predefined parameters. Historical data were examined using analytical techniques and time series metrics to identify suitable forecasting methodologies. Specific algorithms, including classical methodologies (exponential smoothing models) and hybrid deep learning models (BJ-DNN model), were tested to evaluate predictive accuracy. Results showed prediction errors ranging from 17% to 56% for exponential smoothing models and from 5% to 27% for BJ-DNN models, demonstrating the superior performance of hybrid approaches. These findings emphasize the potential of predictive models to enhance freight cost forecasting, minimizing error margins and optimizing resource allocation. This research provides a foundation for refining these methodologies, contributing to improved freight cost management and operational efficiency.
ER  -