Prediction of perishable goods deliveries by GRU neural networks for reduction of logistics costs (CROSBI ID 716160)
Prilog sa skupa u zborniku | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Ivana Bašljan ; Naomi Frida Munitić, Nikica Perić, Vinko Lešić
engleski
Prediction of perishable goods deliveries by GRU neural networks for reduction of logistics costs
Forecasting of demands of perishable goods is crucial in planning production schedules to satisfy customer needs on time, and to lower the profit losses of over or under stocks. Collaboration with one of local supermarket chains provided a reasonable foundation for this academic study to optimize the forecasting of deliveries of perishable goods for food supply chains. By carefully analyzing its logistics operations and real-time data of short-shelf life product deliveries, it is discovered that the current supply management of the stores is solely based on prior managerial experiences, taking into consideration the spoilage, stock-out rates, and holiday seasons. Sudden change in demand causes problems to managers who struggle with keeping up with unpredictable frequency, type, and quantity of goods delivered to a particular place from an assigned warehouse. The paper presents a methodology for reliable planning and scheduling of orders of perishable goods, enabling planners to construct delivery schedules having a low expected total cost. This study aims to implement artificial intelligence where the demand for perishable goods can be predicted a few days in advance, independently of any sudden changes. For that, it is used the Gated Recurrent Unit recurrent neural networks, which results in 81.3% average accuracy for observed 10 delivery points. Accurate prediction of demand results in delivering fresher products, which translates into economic benefits in terms of a higher product price.
neural networks, prediction, perishable goods, logistics, deliveries, supply management
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o prilogu
1-6.
2021.
objavljeno
10.1109/ICTMOD52902.2021.9739498
Podaci o matičnoj publikaciji
Proceedings of the IEEE International Conference on Technology Management, Operations and Decisions (IEEE ICTMOD 2021)
Podaci o skupu
IEEE International Conference on Technology Management, Operations and Decisions (IEEE ICTMOD 2021)
predavanje
24.11.2021-26.11.2021
Marakeš, Maroko
Povezanost rada
Elektrotehnika, Informacijske i komunikacijske znanosti, Računarstvo