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Machine Learning based Inventory, Logistics and Prediction Application

Nithin Srihari Rangasamy*, Prabakaran Annadurai **, Prashanth Selvakumar ***, B. Vanathi ****

*-**** Department of Computer Science and Engineering, Kattankulathur, Kanchipuram, Tamil Nadu, India.

Periodicity:June - August'2020
DOI : https://doi.org/10.26634/jit.9.3.18130

Abstract

The purpose of this paper is to create an Inventory and Logistics Management software to assist inventory based businesses in placing orders and stock requests under the basis of their history of sales as datasets. By utilizing Machine learning algorithms, with user provided history of sales as datasets reliable predictions are provided to users to minimize wastage of stock and/or losses and the logistics supplier, transportation lines and methods are also determined. The software is designed to provide suggestions with regard to the management of stock to the user through user-provided information on their history of sales. It will take into account regional changes and dates to provide considerably accurate suggestions. It cannot consider consumer interest changes over time as this is incomputable data.

Keywords

Machine Learning, Logistics, Inventory management, Linear Regression, Prediction Module.

How to Cite this Article?

Rangasamy, N. S., Annadurai, P., Selvakumar, P., and Vanathi, B. (2020). Machine Learning based Inventory, Logistics and Prediction Application. i-manager's Journal on Information Technology, 9(3), 16-19. https://doi.org/10.26634/jit.9.3.18130

References

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[3]. Glöckner, M., Ludwig, A., & Franczyk, B. (2016, December). A reference architecture for the logistics service map: Structuring and composing logistics services in logistics networks. In 2016, IEEE International Conference on Computer and Information Technology (CIT) (pp. 644- 651). IEEE. https://doi.org/10.1109/CIT.2016.56

[4]. Singh, S., & Soni, U. (2019, January). Predicting order lead time for just in time production system using various machine learning algorithms: A case study. In 2019, 9^th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 422-425). IEEE. https://doi.org/10.1109/CONFLUENCE.2019.8776892

[5]. Mishra, A., & Mohapatro, M. (2020, December). Realtime RFID-based item tracking using IoT & efficient inventory management using Machine Learning. In 2020, IEEE 4^thConference on Information & Communication Technology (CICT) (pp. 1-6). IEEE. https://doi.org/10.1109/ CICT51604.2020.9312074

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