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Yadav, Abhishek Yogesh (2024) Real-Time Inventory Optimization Using AWS Lambda and Amazon Kinesis. Masters thesis, Dublin, National College of Ireland.
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Abstract
In the needs of discussing the importance of developing cost-effective, scalable, and fast systems that handle variation of data in real-time and across various inventory environments. A comprehensive analysis using a variety of AWS cloud services was used, to realize the significance of inventory optimization, whereby Amazon Kinesis was used to stream data like stock information in real-time, AWS Lambda was leveraged to compute the event-driven application in a serverless environment, and finally Amazon SageMaker to deploy and train the machine learning model, which was found to be of paramount significance to realize the stock optimization along with Amazon S3 and DynamoDB for seamless data management and access. This research provides a detailed monthly spend analysis across various AWS services to evaluate cost efficiency, scalability, and resource utilization. This research incorporated AWS CloudWatch, which demonstrates graphical views of resource usage helping decision-makers to make proactive choices for cost and performance improvement. Moreover, this work notes practical implementations and solutions for challenges such as over-provisioning cabin, idle time of services such as SageMaker and Kinesis. The results demonstrate the benefits of using cloud services for real-time inventory management delivering the dynamic needs for data processing without sacrificing financial efficiency. The outcomes of this research satisfy the objectives laid out for them and open avenues for future evolution in the fields of predictive analytics, automation, and wider integration of datasets for effective inventory optimization across industries.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Jaswal, Shivani UNSPECIFIED |
| Uncontrolled Keywords: | Inventory; AWS Lambda; Kinesis; SageMaker; CloudWatch; DynamoDB; S3 Bucket; QuickSight |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
| Divisions: | School of Computing > Master of Science in Cloud Computing |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 17 Jul 2025 13:11 |
| Last Modified: | 17 Jul 2025 13:11 |
| URI: | https://norma.ncirl.ie/id/eprint/8165 |
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