Design of Economic Order Quantity on Polyester Yarn Raw Material Based on Artificial Neural Network Forecasting - Fathurrohman, Muhammad Salman; Nurhasanah, Nunung; Tripiawan, Wawan (Universitas Al Azhar Indonesia, 2025)
Polyester yarn is a key raw material in textile manufacturing due to its durability and affordability. PT ABC relies on external suppliers for polyester yarn, making inventory management crucial for production efficiency. However, the company's current ordering approach has led to occasional stock shortages, impacting operations. This study develops an inventory control model using the Economic Order Quantity (EOQ) method, incorporating safety stock and reorder point calculations to minimize stockouts and reduce inventory costs. Additionally, Artificial Neural Networks (ANN) are used to forecast demand for 2022, improving estimation accuracy. Based on historical demand data from 2019 to 2021, the EOQ method lowers inventory costs compared to the company’s approach, achieving efficiency gains of 19%, 12%, and 29%, saving IDR45,745,000, IDR23,735,000, and IDR98,020,000, for each respective year. The ANN model utilizing the TrainLM training function achieves the lowest Mean Squared Error (MSE) of 0.063528 and forecasts a total raw material requirement of 2,510,628 kg for 2022. The EOQ value for 2022 is set at 44,817 kg, with safety stock and reorder point levels of 8,438 kg and 29,360 kg, respectively.Keywords – Artificial Neural Network, Economic Order Quantity, Inventory, Reorder Point, Safety Stock.