Leveraging hybrid ANN–AHP to optimize cement industry average inventory levels
In recent years, inventory has been critical due to the production cost and overstock risk related to the expiration date and the fluctuation price risk. This study's minimization of overstock and price fluctuation in the warehouse used a hybridized artificial neural network (ANN) and analytica...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Ijain
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/41317/1/Leveraging%20hybrid%20ANN%E2%80%93AHP%20to%20optimize%20cement%20industry%20average%20inventory%20levels.pdf |
| _version_ | 1811138400817774592 |
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| author | Edy, Fradinata Noor, M. M. Zurnila Marli, Kesuma Sakesun, Suthummanon Didi, Asmadi |
| author_facet | Edy, Fradinata Noor, M. M. Zurnila Marli, Kesuma Sakesun, Suthummanon Didi, Asmadi |
| author_sort | Edy, Fradinata |
| collection | UMP |
| description | In recent years, inventory has been critical due to the production cost and overstock risk related to the expiration date and the fluctuation price risk. This study's minimization of overstock and price fluctuation in the warehouse used a hybridized artificial neural network (ANN) and analytical hierarchy process (AHP) to produce an optimum model. The variables, such as average demand, reorder point, order quantity, factor service level, safety stock, and average inventory level, were used to obtain the optimal condition of the average inventory levels to maximize the profit. Then, the type of inventory system that guarantees the minimum risks in managing the inventory would be selected. The result shows that the data has a mean of 39.2 units, and the standard deviation (SD) was 12.9. This means that the order quantity is 20.2 units, the average inventory level is 57.3, and the average demand is 39. These conditions used the factor z, which is 97% service level. This study concludes that the optimum average inventory level is 91 units, the order quantity is 11 units with the maximum average profit is $1098, and the peak fluctuation condition maximum profit is $1463 when the average inventory level is 7.3, and the inventory policy system used to minimize the risk is the continuous review policy type. The study could be beneficial to reduce production costs and enhance overall profitability and operational efficiency in the sector by mitigating the risks associated with excessive inventory and price volatility while also minimizing the potential for expired inventory. |
| first_indexed | 2024-09-25T03:49:36Z |
| format | Article |
| id | UMPir41317 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-09-25T03:49:36Z |
| publishDate | 2024 |
| publisher | Ijain |
| record_format | dspace |
| spelling | UMPir413172024-06-10T03:20:30Z http://umpir.ump.edu.my/id/eprint/41317/ Leveraging hybrid ANN–AHP to optimize cement industry average inventory levels Edy, Fradinata Noor, M. M. Zurnila Marli, Kesuma Sakesun, Suthummanon Didi, Asmadi T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics In recent years, inventory has been critical due to the production cost and overstock risk related to the expiration date and the fluctuation price risk. This study's minimization of overstock and price fluctuation in the warehouse used a hybridized artificial neural network (ANN) and analytical hierarchy process (AHP) to produce an optimum model. The variables, such as average demand, reorder point, order quantity, factor service level, safety stock, and average inventory level, were used to obtain the optimal condition of the average inventory levels to maximize the profit. Then, the type of inventory system that guarantees the minimum risks in managing the inventory would be selected. The result shows that the data has a mean of 39.2 units, and the standard deviation (SD) was 12.9. This means that the order quantity is 20.2 units, the average inventory level is 57.3, and the average demand is 39. These conditions used the factor z, which is 97% service level. This study concludes that the optimum average inventory level is 91 units, the order quantity is 11 units with the maximum average profit is $1098, and the peak fluctuation condition maximum profit is $1463 when the average inventory level is 7.3, and the inventory policy system used to minimize the risk is the continuous review policy type. The study could be beneficial to reduce production costs and enhance overall profitability and operational efficiency in the sector by mitigating the risks associated with excessive inventory and price volatility while also minimizing the potential for expired inventory. Ijain 2024-02 Article PeerReviewed pdf en cc_by_sa_4 http://umpir.ump.edu.my/id/eprint/41317/1/Leveraging%20hybrid%20ANN%E2%80%93AHP%20to%20optimize%20cement%20industry%20average%20inventory%20levels.pdf Edy, Fradinata and Noor, M. M. and Zurnila Marli, Kesuma and Sakesun, Suthummanon and Didi, Asmadi (2024) Leveraging hybrid ANN–AHP to optimize cement industry average inventory levels. International Journal of Advances in Intelligent Informatics, 10 (1). pp. 159-170. ISSN 2442-6571. (Published) https://doi.org/10.26555/ijain.v10i1.631 https://doi.org/10.26555/ijain.v10i1.631 |
| spellingShingle | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics Edy, Fradinata Noor, M. M. Zurnila Marli, Kesuma Sakesun, Suthummanon Didi, Asmadi Leveraging hybrid ANN–AHP to optimize cement industry average inventory levels |
| title | Leveraging hybrid ANN–AHP to optimize cement industry average inventory levels |
| title_full | Leveraging hybrid ANN–AHP to optimize cement industry average inventory levels |
| title_fullStr | Leveraging hybrid ANN–AHP to optimize cement industry average inventory levels |
| title_full_unstemmed | Leveraging hybrid ANN–AHP to optimize cement industry average inventory levels |
| title_short | Leveraging hybrid ANN–AHP to optimize cement industry average inventory levels |
| title_sort | leveraging hybrid ann ahp to optimize cement industry average inventory levels |
| topic | T Technology (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery TL Motor vehicles. Aeronautics. Astronautics |
| url | http://umpir.ump.edu.my/id/eprint/41317/1/Leveraging%20hybrid%20ANN%E2%80%93AHP%20to%20optimize%20cement%20industry%20average%20inventory%20levels.pdf |
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