Determining the invoicing dates for raw material order and finish product dispatch using neural networks under exchange rate volatility

Weerasingha, Janith Piyumal; Bandara, Yapa Mahinda; Edirisinghe, Pasan Manuranga

Abstract

The gains from international supply chains are highly affected by the exchange rate fluctuations in the foreign exchange market. Traditional forecasting methods have not been very useful, and as a result, business firms tend to use hedging or forward contracts to mitigate the exchange rate risk. This research focuses on using machine learning models to forecast the exchange rate for future decision-making in business. This paper uses both time-series data and the categorical data with the LSTM (Long Short-Term Memory) Neural Network Model to tackle both linear and non-linear data on monetary fundamentals and derives the best dates for invoicing in the international transaction using data of a manufacturing firm. Results show that using the predictions of the LSTM model to decide the invoicing dates for international transactions delivers foreign exchange gain with a better success rate than selecting random dates for both import and export.

Citation

Weerasingha, J. P., Bandara, Y. M., & Edirisinghe, P. M. (2023). Determining the invoicing dates for raw material order and finish product dispatch using neural networks under exchange rate volatility. International Journal of Logistics Research and Applications, 26(2), 211-231. https://doi.org/10.1080/13675567.2021.1945018

• Journal Article Type: Article
• Acceptance Date: Jun 15, 2021
• Online Publication Date: Jul 3, 2021
• Publication Date: 2023
• Deposit Date: Dec 4, 2021
• Journal: International Journal of Logistics Research and Applications
• Print ISSN: 1367-5567
• Electronic ISSN: 1469-848X
• Publisher: Routledge
• Peer Reviewed: Peer Reviewed
• Volume: 26
• Issue: 2
• Pages: 211-231
• DOI: https://doi.org/10.1080/13675567.2021.1945018
• Keywords: Import and export invoicing dates, exchange rate forecasting, LSTM Neural networks, VAR forecasting, news effects
• Public URL: http://researchrepository.napier.ac.uk/Output/2824378