Abstract
Nowadays, supply chains have recently shown to be more prone than ever to disruption. Predicting possible future risks has thus become necessary. In this context, artificial intelligence as a pillar of the Industry 4.0 paradigm has proven to deal well with supply chain-related problems. In particular, supervised machine learning tools have shown good predictive capabilities, but they require structured data to work well. While several data models have been proposed in the literature, no data model for supply chain risk management has been found. This paper thus aims to propose a new data model to support supply chain risk-related predictions and evaluate this data model's contribution to enhancing models prediction performance. Following a dimensional fact model formalism (DFM), a conceptual model has been first developed and then has been translated to its respective logic version. Once the data model has been built, the problem of predicting the transport risk for a mechanical component delivered to an automotive OEM has been investigated. The predictive performance of a naïve forecasting model and of two long short-term memory (LSTM) models trained with different data have been compared. Results have shown the better performance of LSTM models against the traditional ones. In addition, the LSTM model built with the support of our data model has shown greater forecasting capabilities compared to the ones relying only on past observation of the variable to predict.
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Gabellini, M., Civolani, L., Regattieri, A., Calabrese, F. (2023). A Data Model for Predictive Supply Chain Risk Management. In: Galizia, F.G., Bortolini, M. (eds) Production Processes and Product Evolution in the Age of Disruption. CARV 2023. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-34821-1_40
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DOI: https://doi.org/10.1007/978-3-031-34821-1_40
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