Geographic Recommendation System for Supply Chain Management

Proceedings of The 3rd International Conference on Advanced Research in Supply Chain Management

Year: 2023

DOI: https://www.doi.org/10.33422/3rd.supplychainconf.2023.05.155

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Geographic Recommendation System for Supply Chain Management

Prashnim Seth, Harshita Singh, Dr. Wassnaa Al-Mawee

ABSTRACT: 

An efficient supply chain means a robust and nimble commercial enterprise. Timely and cost-effective product delivery is a crucial part of the supply chain cycle. On the flip side, late, and failed deliveries would result in the company losing its customers. To keep the efficiency of the supply chain cycle intact, this paper aims to build a recommender system that analyses the demand of a selected product, groups it into regions and suggests a suitable location for a business or a warehouse as per the product. The paper follows a content-based filtering approach by applying an unsupervised learning algorithm – K-Means Clustering to develop a system that calculates the best locations for the selected product using different order locations from the dataset to generate a location recommendation for storing the product in a closer warehouse. By analyzing product demands as per the clustered order locations, this system offers valuable insights into optimal warehouse placement, resulting in potentially reduced delivery times.

keywords: Recommender System, Clustering, K-Means Clustering, Content-Based Filtering, Warehouse, Unsupervised Learning