International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 11 Issue: 05 | May 2024
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p-ISSN: 2395-0072
Medical Inventory Optimization using Customer Data for Reducing Bounce Rate Yashraj Mishra1, Ankita Jaiswal2 , Dr. Goldi Soni 3 1Student , Amity University Chhattisgarh 2Student , Amity University Chhattisgarh 3Assistant Professor, Amity University Chhattisgarh
----------------------------------------------------------------***------------------------------------------------------------Abstract- Healthcare establishments all over the world need to handle their medical inventories effectively. Patient care may suffer because of overstocking, stockouts, and higher expenses brought on by inaccurate medication demand predictions. This research proposes an advanced machine learning model-based data-driven approach for medical inventory management optimization. The suggested solution seeks to decrease bounce rates by 30%, increase customer happiness, decrease wasteful inventory costs, and improve product availability. The solution's effectiveness is shown by outcome analysis, where the Gradient Boost model performs better than the others. This method has a very low MAPE (Mean Absolute Percentage Error) score (less than 5 percent). Hospital scan work together and embrace best practices more readily because of the solution's scalability, utility, and capacity to be used across several facilities and specialties. The initiative seeks to offer insightful information on supplier selection for higher operational performance, better customer service, and cost efficiency. In this project there are two methodologies used: Python programming libraries and Structured Query Language programming which is used for data preprocessing.
Fig – 1: Python Programming mind map For manufacturing businesses to operate effectively, product transportation is essential. Reducing transportation expenses is crucial for sand production enterprises to preserve their profitability and competitive edge. This research study focuses on maximizing the amount of sand transported utilizing various logistics to minimize transportation costs from various warehouses to various demand destinations. A solver-based strategy that makes use of mathematical modeling and optimization approaches is used to accomplish this goal.
Keywords: Python Programming, SQL (Structured Query Language), Exploratory Data Analysis, Machine Learning, Data Preprocessing, Data Visualization, Transportation Cost Reduction
1.Introduction
Effective medical inventory management is essential to delivering high-quality patient care in healthcare facilities across the globe. Medical supplies and necessary pharmaceuticals must always be available, but keeping the right amount of inventory on hand while reducing waste and expenses can be difficult. To solve this problem, healthcare facilities are using more and more data-driven strategies, utilizing cutting-edge machine learning methods to maximize medical inventory management. Moreover, CRISP-ML (Q)- Cross Industry Standard Process of Machine Learning with Quality Assurance. points say:
This research study's goal is to minimize the transportation cost in the supply chain and logistics management system using operational research, Python programming and PostgreSQL Server. This study and development approach's Python programming portion is built on the mind map that contains a certain part as shown in [Fig. 1]. This mind map for Python programming serves as a useful tool for comprehending and effectively implementing Python code by providing a visual representation of the essential ideas and elements of the language.
1.1. Business Problem: Bounce rate is increasing significantly leading to patient dissatisfaction.
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