Inventory management optimization with data analytics for a trading company

Khan, Shayan Omer; Hasan, Raza; Hussain, Saqib; Malik, Mazhar Hussain; Mahmood, Salman

Abstract

Distributors, manufacturers, and suppliers face the daunting challenge of inventory control. Each supply management problem that arises has ramifications. To satisfy supply and demand, inventory optimization will ensure that the correct commodity is available in the right amounts, at the right price, and in the right places. Furthermore, companies that optimize their inventory can reduce stock levels and, as a result, prevent bearing expenses and obsolescence write-downs. Data analytics helps suppliers and marketers assess their stocking goals and whether any upstream or downstream problems need to be resolved, which is critical in resource control and optimization processes. This study aims to explore how inventory management optimization, supported by data analytics, would be beneficial for a trading company operating in Oman. Currently, trading companies can only solve inventory management problems by either hiring expensive offshore software or using open-source software with little to no knowledge on how to adapt that software to suit specific needs. An online inventory management system is developed using the Java language and MySQL as the database server. Optimization is performed using the Orange data mining tool. The methodology chosen for application development is the Dynamic Systems Development Method. An interview has been conducted with a trading company employee for data collection purposes and the testing was done to ensure optimal performance. Data analytics was performed on the data collected from the online system and data mining was applied by applying feature reduction methods to optimize the results. The study showed a promising result to provide insights on the latest business trends and access the inventory effectively and efficiently.

• Journal Article Type: Article
• Acceptance Date: Nov 10, 2022
• Publication Date: Feb 1, 2023
• Deposit Date: Mar 15, 2023
• Publicly Available Date: Mar 15, 2023
• Journal: Journal of Big Data & Smart City
• Print ISSN: 2706-7912
• Electronic ISSN: 2788-4112
• Peer Reviewed: Peer Reviewed
• Keywords: Classification; Data Analytics; Inventory Management; Optimization; Random Forest
• Public URL: https://uwe-repository.worktribe.com/output/10553109
• Publisher URL: https://www.mjbdsc.org/wp-content/uploads/2023/02/JBDSC-Vol-2-Issue-2-1.pdf