An open innovative inventory management based demand forecasting approach for the steel industry

Abstract

This research focuses on accurately forecasting steel wire mesh demand to ensure timely order fulfillment. Various univariate time series forecasting methods were employed, including Prophet, Support Vector Regression (SVR), 1-Dimensional Convolutional Neural Network (1D-CNN), Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). These RNN-based models were implemented to enhance the dynamics of open innovation. The study also employed the split test method to assess the impact of data size on model performance. Following a comprehensive comparative analysis, LSTM outperformed other models at 80:20 data split ratio, achieving the lowest RMSE of 51,473 and MAPE of 1.43. The robust performance of LSTM underscores its ability to expertly predict complex demand patterns. Moreover, forecast accuracy was enhanced using ensemble models, with RNN-LSTM delivering the best results (RMSE of 45,931 and MAPE of 1.20) at the same data split ratio. This accurate forecasting supports steel wire mesh companies in making informed decisions regarding inventory management. The study additionally demonstrated the integration of forecasting results into inventory strategies to improve decision-making for cost optimization.

Keywords

• Ensemble model
• Demand prediction
• Open innovation
• Machine learning
• Steel wire mesh
• Inventory optimization