Inventory optimization model using Artificial Neural Network method and Continuous Review (s,Q)
The medical device industry company experienced the problem of prolonged accumulation of finished goods in the warehouse, causing one of the safety box items to be defective and damaged. Therefore, this study aims to plan demand forecasting and design inventory policies that consider repair items ca...
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Universitas Mercu Buana
2025-01-01
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| Series: | Jurnal Ilmiah SINERGI |
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| Online Access: | https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/26543 |
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| author | Hanny Setyaningrum Iphov Kumala Sriwana Ilma Mufidah |
| author_facet | Hanny Setyaningrum Iphov Kumala Sriwana Ilma Mufidah |
| author_sort | Hanny Setyaningrum |
| collection | DOAJ |
| description | The medical device industry company experienced the problem of prolonged accumulation of finished goods in the warehouse, causing one of the safety box items to be defective and damaged. Therefore, this study aims to plan demand forecasting and design inventory policies that consider repair items caused during the buildup of finished goods in the warehouse to minimize total inventory costs using ANN and Continuous Review (s,Q) methods. Demand forecasting is carried out for the next 20 months, from May 2023 to December 2024, using the ANN model with a total forecasting of 17936 units of inner items and 3370 units of outer items. After that, the inventory policy calculation uses the continuous review (s,Q) method. The calculation results show a decrease in the total inventory cost on inner items by 83% and outer items by 79%. After demand forecasting, there was also a decrease in the total initial inventory cost of inner items by 81% and outer items by 80%. This research develops an inventory optimization model that considers repair items due to the accumulation of goods in the warehouse by integrating holding cost, ordering cost, and repair cost variables to develop inventory policies to be more effective and efficient and to utilize damaged products for repair and resale. The limitation of this research is that it only gets demand forecasting results for the next 20 months because the company only started operating in September 2021 and limited data access. It is hoped that future researchers can plan and design an inventory policy strategy with demand forecasting for the next 10 years, focusing on repair items caused by the accumulation of finished goods in the warehouse. |
| format | Article |
| id | doaj-art-6d7f0cb64f1f430aad999b3d46f5cc57 |
| institution | Kabale University |
| issn | 1410-2331 2460-1217 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Universitas Mercu Buana |
| record_format | Article |
| series | Jurnal Ilmiah SINERGI |
| spelling | doaj-art-6d7f0cb64f1f430aad999b3d46f5cc572025-01-13T04:38:19ZengUniversitas Mercu BuanaJurnal Ilmiah SINERGI1410-23312460-12172025-01-0129114315410.22441/sinergi.2025.1.0137879Inventory optimization model using Artificial Neural Network method and Continuous Review (s,Q)Hanny Setyaningrum0Iphov Kumala Sriwana1Ilma Mufidah2Department of Industrial Engineering, Faculty of Industrial Engineering, Universitas TelkomDepartment of Industrial Engineering, Faculty of Industrial Engineering, Universitas TelkomDepartment of Industrial Engineering, Faculty of Industrial Engineering, Universitas TelkomThe medical device industry company experienced the problem of prolonged accumulation of finished goods in the warehouse, causing one of the safety box items to be defective and damaged. Therefore, this study aims to plan demand forecasting and design inventory policies that consider repair items caused during the buildup of finished goods in the warehouse to minimize total inventory costs using ANN and Continuous Review (s,Q) methods. Demand forecasting is carried out for the next 20 months, from May 2023 to December 2024, using the ANN model with a total forecasting of 17936 units of inner items and 3370 units of outer items. After that, the inventory policy calculation uses the continuous review (s,Q) method. The calculation results show a decrease in the total inventory cost on inner items by 83% and outer items by 79%. After demand forecasting, there was also a decrease in the total initial inventory cost of inner items by 81% and outer items by 80%. This research develops an inventory optimization model that considers repair items due to the accumulation of goods in the warehouse by integrating holding cost, ordering cost, and repair cost variables to develop inventory policies to be more effective and efficient and to utilize damaged products for repair and resale. The limitation of this research is that it only gets demand forecasting results for the next 20 months because the company only started operating in September 2021 and limited data access. It is hoped that future researchers can plan and design an inventory policy strategy with demand forecasting for the next 10 years, focusing on repair items caused by the accumulation of finished goods in the warehouse.https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/26543anncontinuous review (s,q)eoqoverstock |
| spellingShingle | Hanny Setyaningrum Iphov Kumala Sriwana Ilma Mufidah Inventory optimization model using Artificial Neural Network method and Continuous Review (s,Q) Jurnal Ilmiah SINERGI ann continuous review (s,q) eoq overstock |
| title | Inventory optimization model using Artificial Neural Network method and Continuous Review (s,Q) |
| title_full | Inventory optimization model using Artificial Neural Network method and Continuous Review (s,Q) |
| title_fullStr | Inventory optimization model using Artificial Neural Network method and Continuous Review (s,Q) |
| title_full_unstemmed | Inventory optimization model using Artificial Neural Network method and Continuous Review (s,Q) |
| title_short | Inventory optimization model using Artificial Neural Network method and Continuous Review (s,Q) |
| title_sort | inventory optimization model using artificial neural network method and continuous review s q |
| topic | ann continuous review (s,q) eoq overstock |
| url | https://publikasi.mercubuana.ac.id/index.php/sinergi/article/view/26543 |
| work_keys_str_mv | AT hannysetyaningrum inventoryoptimizationmodelusingartificialneuralnetworkmethodandcontinuousreviewsq AT iphovkumalasriwana inventoryoptimizationmodelusingartificialneuralnetworkmethodandcontinuousreviewsq AT ilmamufidah inventoryoptimizationmodelusingartificialneuralnetworkmethodandcontinuousreviewsq |