Optimization of Fuzzy Economic Order Quantity using Machine Learning and Weka

Inventory management is a crucial need for businesses because it requires a significant financial and human resource investment. Machine Learning models are used by e-commerce huge companies to manage their inventory to the demand for specific products. Businesses can benefit from inventory management as a service to increase sales and forecast product demand. Inventory Management might be revolutionised by AI and ML technologies, which would allow businesses to automate repetitive processes, make data-driven choices, and instantly optimise inventory levels. By analysing vast amounts of both historical and current data, these technologies can spot inclination, forecast, and recommend, all of which increase inventory accuracy and lower the chance of stockouts or overstocking. The most efficient solution is determined using the Kuhn-Tucker Method for Nonlinear Programming, which has an impact on the monthly cost. The proposed model employs the Signed distance approach for defuzzification and the Pentagonal fuzzy number to find the lowest cost and met the demand. To evaluate the inventory concept, purchased a dataset and calculated the profit and non profit using apriori algorithm and use the Weka software to analyse the average order quantity and total cost.