Optimization of inventory management through computer vision and machine learning technologies
This study presents implementing and evaluating a computer vision platform to optimize warehouse inventory management. Integrating machine learning and computer vision technologies, this solution addresses critical challenges in inventory accuracy and operational efficiency, overcoming the limitatio...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Intelligent Systems with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305324001121 |
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| _version_ | 1846124995676209152 |
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| author | William Villegas-Ch Alexandra Maldonado Navarro Santiago Sanchez-Viteri |
| author_facet | William Villegas-Ch Alexandra Maldonado Navarro Santiago Sanchez-Viteri |
| author_sort | William Villegas-Ch |
| collection | DOAJ |
| description | This study presents implementing and evaluating a computer vision platform to optimize warehouse inventory management. Integrating machine learning and computer vision technologies, this solution addresses critical challenges in inventory accuracy and operational efficiency, overcoming the limitations of traditional methods and pre-existing automated systems. The platform uses convolutional neural networks and open-source libraries such as TensorFlow and PyTorch to recognize and accurately classify products from images captured in real time. Practical implementation in a natural warehouse environment allowed the proposed platform to be compared with traditional systems, highlighting significant improvements, such as a 45% reduction in the time required for inventory counting and a 9% increase in inventory accuracy. Despite facing challenges such as staff resistance to change and technical limitations on image quality, these difficulties were overcome through effective change management strategies and algorithm improvements. The findings of this study identify the potential for computer vision technology to transform warehouse operations, offering a practical and adaptable solution for inventory management. |
| format | Article |
| id | doaj-art-0399e0ae6ca645e8a2484e1dc29f4366 |
| institution | Kabale University |
| issn | 2667-3053 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj-art-0399e0ae6ca645e8a2484e1dc29f43662024-12-13T11:07:24ZengElsevierIntelligent Systems with Applications2667-30532024-12-0124200438Optimization of inventory management through computer vision and machine learning technologiesWilliam Villegas-Ch0Alexandra Maldonado Navarro1Santiago Sanchez-Viteri2Escuela de Ingeniería en Ciberseguridad, Facultad de Ingenierías y Ciencias Aplicadas, Universidad de Las Américas, Redondel del Ciclista, Antigua Via a Nayon., Quito, 170125, Pichincha, Ecuador; Corresponding author.Maestría en Seguridad Digital, Universidad de Las Américas, Redondel del Ciclista, Antigua Via a Nayon., Quito, 170125, Pichincha, EcuadorDepartamento de Sistemas, Universidad Internacional del Ecuador, Av. Simón Bolívar y Av. Jorge Fernández., Quito, 170411, Pichincha, EcuadorThis study presents implementing and evaluating a computer vision platform to optimize warehouse inventory management. Integrating machine learning and computer vision technologies, this solution addresses critical challenges in inventory accuracy and operational efficiency, overcoming the limitations of traditional methods and pre-existing automated systems. The platform uses convolutional neural networks and open-source libraries such as TensorFlow and PyTorch to recognize and accurately classify products from images captured in real time. Practical implementation in a natural warehouse environment allowed the proposed platform to be compared with traditional systems, highlighting significant improvements, such as a 45% reduction in the time required for inventory counting and a 9% increase in inventory accuracy. Despite facing challenges such as staff resistance to change and technical limitations on image quality, these difficulties were overcome through effective change management strategies and algorithm improvements. The findings of this study identify the potential for computer vision technology to transform warehouse operations, offering a practical and adaptable solution for inventory management.http://www.sciencedirect.com/science/article/pii/S2667305324001121Deep learningIndustrial process optimizationSensor data fusionPredictive maintenance |
| spellingShingle | William Villegas-Ch Alexandra Maldonado Navarro Santiago Sanchez-Viteri Optimization of inventory management through computer vision and machine learning technologies Intelligent Systems with Applications Deep learning Industrial process optimization Sensor data fusion Predictive maintenance |
| title | Optimization of inventory management through computer vision and machine learning technologies |
| title_full | Optimization of inventory management through computer vision and machine learning technologies |
| title_fullStr | Optimization of inventory management through computer vision and machine learning technologies |
| title_full_unstemmed | Optimization of inventory management through computer vision and machine learning technologies |
| title_short | Optimization of inventory management through computer vision and machine learning technologies |
| title_sort | optimization of inventory management through computer vision and machine learning technologies |
| topic | Deep learning Industrial process optimization Sensor data fusion Predictive maintenance |
| url | http://www.sciencedirect.com/science/article/pii/S2667305324001121 |
| work_keys_str_mv | AT williamvillegasch optimizationofinventorymanagementthroughcomputervisionandmachinelearningtechnologies AT alexandramaldonadonavarro optimizationofinventorymanagementthroughcomputervisionandmachinelearningtechnologies AT santiagosanchezviteri optimizationofinventorymanagementthroughcomputervisionandmachinelearningtechnologies |