Introducing Economic Order Quantity Model for Inventory Control in Web based Point of Sale Applications and Comparative Analysis of Techniques for Demand Forecasting in Inventory Management
|
10.5120/18856-7385 |
Komal Nain Sukhia, Aliya Ashraf Khan and Mukhtiar Bano. Article: Introducing Economic Order Quantity Model for Inventory Control in Web based Point of Sale Applications and Comparative Analysis of Techniques for Demand Forecasting in Inventory Management. International Journal of Computer Applications 107(19):1-8, December 2014. Full text available. BibTeX
@article{key:article,
author = {Komal Nain Sukhia and Aliya Ashraf Khan and Mukhtiar Bano},
title = {Article: Introducing Economic Order Quantity Model for Inventory Control in Web based Point of Sale Applications and Comparative Analysis of Techniques for Demand Forecasting in Inventory Management},
journal = {International Journal of Computer Applications},
year = {2014},
volume = {107},
number = {19},
pages = {1-8},
month = {December},
note = {Full text available}
}
Abstract
This paper has primary focus on the aspect of inventory management in web based point of sale applications for supermarkets. The major research focus include selection of efficient technique for demand forecasting in retail industry, the introduction of Economic Order Quantity model to reduce the overall inventory related costs and stock-out, analyzing customer transactions to improve sales, determining product shelving and supplier selection. For this purpose, Economic Order Quantity model is applied on the forecasted demands using simple moving average, linear regression, back propagation algorithm and afterwards a comparative analysis is conducted on the basis of costs generated by each demand forecasting technique. The comparison shows that back propagation algorithm is more efficient for demand forecasting and the overall inventory costs after applying Economic Order Quantity model are found to be lowest for back propagation algorithm as compared to the Linear Regression and Simple Moving Average.
References
- Karthik Sundararaman, Jinka Parthasarathi, G Subrahmanya VRK Rao, S Nandha Kumar," Baseline Prediction of Point of Sales data for Trade Promotion Optimization", The 2nd International Conference on Communications and Information Technology (ICCIT): Digital Information Management, Hammamet, IEEE 978-1-4673-1950 8/12/$31. 00, 2012.
- Numera Tahir, Muhammad Abbas Choudhary," Development of a Decision Support System for Inventory Analysis and Control", IEEE 978-1-61284-952-2/11/$26. 00, 2011.
- Yunkang Yue, Tiebao Zhang, Xingbing Xu," Analysis on the Effect of Data-mining to Supermarket", The 5th International Conference on Computer Science & Education Hefei, China. August 24–27, 2010 IEEE.
- Qizhi Wu, Aihua Fang, He Gao," A Study on Inventory Cost Reduction Based on Economic Order Quantity Model", The International Conference on Logistics Systems and Intelligent Management, 2010 IEEE.
- Pradip Kumar Bala," Decision Tree Based Demand Forecasts for Improving Inventory Performance", International Conference on Industrial Engineering and Engineering Management (IEEM), 2010 IEEE.
- Goswami D. N. , Chaturvedi Anshu. , Raghuvanshi C. S. ," An Algorithm for Frequent Pattern Mining Based On Apriori", International Journal on Computer Science and Engineering, Vol. 02, No. 04, 2010, 942-947.