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http://dx.doi.org/10.6109/jicce.2021.19.4.221

Early Warning System for Inventory Management using Prediction Model and EOQ Algorithm  

Majapahit, Sali Alas (Department of Informatics Engineering, Pasundan University)
Hwang, Mintae (Department of Information and Communication Engineering, Changwon National University)
Publication Information
Journal of information and communication convergence engineering / v.19, no.4, 2021 , pp. 221-227 More about this Journal
Abstract
An early warning system was developed to help identify stock status as early as possible. For performance to improve, there needs to be a feature to predict the amount of stock that must be provided and a feature to estimate when to buy goods. This research was conducted to improve the inventory early warning system and optimize the Reminder Block's performance in minimum stock settings. The models used in this study are the single exponential smoothing (SES) method for prediction and the economic order quantity (EOQ) model for determining the quantity. The research was conducted by analyzing the Reminder Block in the early warning system, identifying data needs, and implementing the SES and EOQ mathematical models into the Reminder Block. This research proposes a new Reminder Block that has been added to the SES and EOQ models. It is hoped that this study will help in obtaining accurate information about the time and quantity of repurchases for efficient inventory management.
Keywords
Early warning system; Inventory management; Economic order quantity (EOQ); Prediction model; Single exponential smoothing (SES);
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