University of Twente Student Theses
Optimizing Emergency Medical Inventory Control Using Automated Machine Learning
Cosar, B. (2024) Optimizing Emergency Medical Inventory Control Using Automated Machine Learning.
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| Abstract: | This research is performed at the Department of Medical Logistics (DML), an emergency service department in Germany responsible for distributing medical equipment and consumables. DML has experienced frequent stockouts in medical inventory that have led to low customer satisfaction. The objective of the research is to optimize inventory control to increase the cycle service level (CSL) of products used in emergency services. To achieve this, a detailed literature review was performed to identify effective strategies for determining optimal stock levels and reorder points. Building on this, an automated machine learning model was applied to forecast future demand for products using various time series methods. Based on these demand forecasts, products were classified using ABC analysis and new inventory policies were developed. Two inventory management policies were evaluated: the Continuous Review Policy (r, Q) and the Periodic Review Policy (T, S). These policies were applied to determine the optimal order quantities and reorder points for each product. The analysis revealed that the Continuous Review Policy would result in lower costs for 90.83% of the products, while the Periodic Review Policy would be more cost-effective for the remaining 9.17%. |
| Item Type: | Essay (Bachelor) |
| Faculty: | BMS: Behavioural, Management and Social Sciences |
| Subject: | 01 general works |
| Programme: | Industrial Engineering and Management BSc (56994) |
| Link to this item: | https://purl.utwente.nl/essays/103192 |
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