Predictive algorithms for supply chain management: a comprehensive approach to forecasting delivery times and managing risk

The article delves into the challenges of delivery time management in today's business landscape. The authors underscore the need for precise delivery time forecasts, a key factor in maintaining a competitive edge and meeting customer expectations. They outline various methods for estimating the time of a selected commodity based on historical data, and stress the necessity of modern tools that can adapt to the intricate web of factors influencing delivery times and facilitate swift responses to changes. The following article presents an innovative delivery time forecasting application that integrates advanced predictive algorithms with historical data, current data, and external factors affecting the delivery process. The application was developed to provide more accurate delivery time forecasts and optimize logistics processes. Through advanced technologies, it can consider even the most complex scenarios and changes, allowing companies to plan and manage their logistics operations more effectively.

REFERENCES (8)

Szymonik, A. (2014). Funkcjonowanie łańcucha dostaw w sytuacjach zagrożeń,” Logistyka, no. 6.

Google Scholar

Myszak, J. M., Sowa, M. (2016). The Risk Management in the Aspect of Supply Chain Zeszyty Naukowe Uniwersytetu Szczecińskiego Problemy Transportu i Logistyki, vol. 36, pp. 185–192, doi: 10.18276/ptl.2016.36-19.

CrossRef

Google Scholar

Jaggi, H. S., Kadam, S. S. (2016). Integration of Spark framework in Supply Chain Management, Procedia Computer Science, vol. 79, pp. 1013–1020, doi: 10.1016/j.procs.2016.03.128.

CrossRef

Google Scholar

Hastie, T., Tibshirani, R., Friedman, J. (2009). The elements of statistical learning, Springer-Verlag New York Inc.

Google Scholar

Khojasteh, Y. Ed.,(2018). Supply Chain Risk Management. Singapore: Springer Singapore,.

Google Scholar

Schonlau, M., Zou, R.Y. (2020). The random forest algorithm for statistical learning. Stata J., 20, 3–29.

Google Scholar

Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu. (2017). LightGBM: a highly efficient gradient-boosting decision tree. In Proceedings of the 31st International Conference on Neural Information Processing Systems (NIPS’17). Curran Associates Inc., Red Hook, NY, USA, 3149–3157.

Google Scholar

Tianqi Chen, and Carlos Guestrin. (2016). XGBoost: A Scalable Tree Boosting System. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ‚16). Association for Computing Machinery, New York, NY, USA, 785–794. https://doi.org/10.1145/293967....

eISSN:	2391-789X
ISSN:	1734-2031