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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)
  • Received : 2021.06.14
  • Accepted : 2021.11.26
  • Published : 2021.12.31

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

Acknowledgement

I wish to convey my gratitude Thank you to all my staff in the IT Division of Pasundan University, who have helped provide the data I neededrequired for this research, especially for the network administrators Mr. Fadli Mutaqin and Mr. Doddy Ferdiansyah. Thanks are also conveyed toThis paper was also supported by Mrs. Fia Ramdhoni as a novice researcher, who has helped complete this research. Special thanks gratitude is conveyed to Ms. Rita Rijayanti for reviewing the writing of this paper's text and Prof. Mintae Hwang as the author's promoter.

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