Strategic Resource Planning in Gold Mining: Optimizing Supply Chain Management with Neural Networks-Based Gold Price Forecasting

Abstract

Strategic resource planning is crucial for optimizing supply chain management and ensuring efficient operations. This study aims to enhance strategic planning in gold mines by leveraging advanced gold price forecasting models. By predicting future gold prices accurately, mining companies can better plan their extraction, processing, and distribution activities, thereby improving overall supply chain efficiency. We employed various advanced forecasting models, including Unidirectional and Bidirectional Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), and Artificial Neural Network (ANN), to predict gold prices and analysed how these predictions can inform strategic decisions in the gold mining supply chain. Our approach includes evaluating the performance of these models using metrics such as root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute deviation (MAD). Results show that Artificial Neural Network (ANN) performed best, with the lowest (0.3514), RMSE (0.5928), and MAPE (0.34%), while Bidirectional Gated Recurrent Unit (GRU) was the poorest performer with an of 88.5474 and MAPE of 6.94%. The feature selection process, facilitated by Recursive Feature Elimination (RFE), identified critical predictors such as 'High,' 'Low,' 'Volume,' and various external market factors. Optimizing model parameters through techniques like grid search and cross-validation further improved model accuracy. Additionally, advanced forecasting models, particularly Artificial Neural Network (ANN) and Convolutional Neural Network (CNN), proved highly effective in refining gold mining companies' resource planning and supply chain management strategies, providing critical managerial implications for navigating the dynamic and volatile gold market