Inventory management in any business aims to ensure optimum stock levels are maintained by reducing operation costs while satisfying customer demand. Traditional means of inventory control are usually based on manual tracking of stock levels with the possibility of large errors. This could be replaced by an AI-powered inventory management system that automates the processes by giving accurate forecasts of demand, updating real-time stock status, and automatically reordering. This project focuses on developing such a system, using AI and machine learning to increase efficiency in inventory management.
Week 1-2
Initial Planning and Requirement Analysis
Define scope, objectives, and high-level requirements clearly.
Gather necessary data and resources.
Week 3-4
Data Collection and Preprocessing Phase
Gather historical inventory and sales data to preprocess for the training and test of the model.
Feature Extraction and Cleaning of Data.
Week 5-6
Model Development Phase
Develop and train Machine learning models about demand prediction and inventory optimization.
Validate model accuracy and performance using test data.
Week 7-8
System Implementation Phase.
Develop demand prediction and auto-reordering features.
Integrate the developed models into available inventory management software to incorporate real-time updates.
System testing and refinement based on defined performance metrics and user feedback.
Week 9-10
System Integration and Testing Phase
All features will go through extensive tests to ensure that inventory manages, and demand is perfectly predicted.
Performed system performance observation and user interaction.
Improvement based on analytics and user feedback implementation
Week 11-12
Final Evaluation and Reporting phase
Final evaluation and validation of the AI inventory management system.
Compilation of the final project report and its documentation
Presentations by students of individual reports
