IJBTT

Framework for Optimized Sales and Inventory Control: A Comprehensive Approach for Intelligent Order Management Application
	© 2024 by IJCTT Journal
	Volume-72 Issue-3
	Year of Publication : 2024
	Authors : Sumit Mittal
	DOI :  10.14445/22312803/IJCTT-V72I3P109

How to Cite?

Sumit Mittal, "Framework for Optimized Sales and Inventory Control: A Comprehensive Approach for Intelligent Order Management Application," International Journal of Computer Trends and Technology, vol. 72, no. 3, pp. 61-65, 2024. Crossref, https://doi.org/10.14445/22312803/IJCTT-V72I3P109

Abstract
This research proposes a novel approach to bridging the gap between theoretical concepts and practical applications of inventory management functions within intelligent order management systems. The study introduces a robust framework specifically focused on optimizing inventory controls and provides a holistic solution for intelligent order management applications by harnessing fuzzy logic and rule-based algorithms. The proposed model streamlines the identification of demand and lead time patterns, thereby enhancing the intelligence and efficiency of existing order management systems. To validate the efficacy of this framework, it has been extensively tested within the operational context of prominent multinational B2C retailers. Results demonstrated the system's effectiveness in inventory management practices, particularly benefiting small to medium-sized enterprises. Furthermore, the scalable architecture of the system ensures further adaptability to diverse industries, including B2B distribution and manufacturing sectors, thus broadening its potential applications. This study significantly contributes to the advancements of intelligent order management offerings by optimizing inventory controls for businesses as they navigate through the complexities of modern supply chains.

Keywords
Intelligent Order Management, Inventory Optimization, Stockout Prediction, Markdown Optimization, Demand Forecasting, Lead Time Pattern Identification.

Reference

[1] K.B. Praveen et al., “Inventory Management Using Machine Learning,” International Journal of Engineering Research & Technology, vol. 9, no. 6, pp. 866-869, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Ranu Singh, and Vinod Kumar Mishra, “Machine Learning Based Fuzzy Inventory Model for Imperfect Deteriorating Products with Demand Forecast and Partial Backlogging under Green Investment Technology,” Journal of the Operational Research Society, pp. 1- 16, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Dony S. Kurian, C.R. Maneesh, and V. Madhusudanan Pillai, “Supply Chain Inventory Stockout Prediction Using Machine Learning Classifiers,” International Journal of Business and Data Analytics, vol. 1, no. 3, pp. 218-231, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Tao Zhang et al., “Improving Convection Trigger Functions in Deep Convective Parameterization Schemes Using Machine Learning,” Journal of Advances in Modeling Earth Systems, vol. 13, no. 5, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Mahmut Parlar, “EXPIM: A Knowledge-Based Expert System for Production/Inventory Modelling,” International Journal of Production Research, vol. 27, no. 1, pp. 101-118, 1989.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Antonis Loizides, “Development of a SaaS Inventory Management System,” Kemi-Tornio University of Applied Science, pp. 1-63, 2013.
[Google Scholar] [Publisher Link]