Panahi, Alireza (2025) Strategic Resource Planning in Gold Mining: Optimizing Supply Chain Management with Neural Networks-Based Gold Price Forecasting. Masters thesis, Concordia University.
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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.
| Divisions: | Concordia University > John Molson School of Business > Supply Chain and Business Technology Management |
|---|---|
| Item Type: | Thesis (Masters) |
| Authors: | Panahi, Alireza |
| Institution: | Concordia University |
| Degree Name: | M.A. |
| Program: | Business Administration (Supply Chain and Business Technology Management specialization) |
| Date: | 15 April 2025 |
| Thesis Supervisor(s): | Lahmiri, Salim |
| Keywords: | Strategic Resource Planning, Gold Price Forecasting, Neural Networks |
| ID Code: | 995566 |
| Deposited By: | Alireza Panahi |
| Deposited On: | 04 Nov 2025 17:53 |
| Last Modified: | 04 Nov 2025 17:53 |
References:
Abbasi, M., Vahidnia, M. H., & Khani, A. (2022). Multivariate time-series blood donation/demand forecasting for supply chain management. Journal of Advanced Intelligent Manufacturing, 3(1), 45–56. https://doi.org/10.1016/j.jaim.2022.100051Akiba, T., Sano, S., Yanase, T., Ohta, T., & Koyama, M. (2019). Optuna. KDD ’19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3292500.3330701
Alamdar, P. F., & Seifi, A. (2024). A deep Q-learning approach to optimize ordering and dynamic pricing decisions in the presence of strategic customers. International Journal of Production Economics, 269, 109154. https://doi.org/10.1016/j.ijpe.2024.109154
Alameer, Z., Elaziz, M. A., Ewees, A. A., Ye, H., & Jianhua, Z. (2019). Forecasting gold price fluctuations using improved multilayer perceptron neural network and whale optimization algorithm. Resources Policy, 61, 250–260. https://doi.org/10.1016/j.resourpol.2019.02.014
Aye, G., Gupta, R., Hammoudeh, S., & Kim, W. J. (2015). Forecasting the price of gold using dynamic model averaging. International Review of Financial Analysis, 41, 257–266. https://doi.org/10.1016/j.irfa.2015.03.010
Buscema, P. M., Massini, G., & Maurelli, G. (2014). Artificial Neural Networks: An Overview and their Use in the Analysis of the AMPHORA-3 Dataset. Substance Use & Misuse, 49(12), 1555–1568. https://doi.org/10.3109/10826084.2014.933009
Cho, K., Bart, V. M., Bahdanau, D., & Bengio, Y. (2014, September 3). On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. arXiv.org. https://arxiv.org/abs/1409.1259
Das, S., Sahu, T. P., & Janghel, R. R. (2022). Oil and gold price prediction using optimized fuzzy inference system-based extreme learning machine. Resources Policy, 79, 103109. https://doi.org/10.1016/j.resourpol.2022.103109
Dehnavi, E., & Niaki, S. T. A. (2025). A deep learning approach for tactical production planning. Computers & Operations Research, 155, 105116. https://doi.org/10.1016/j.cor.2025.105116
García-Gonzalo, E., García-Nieto, P. J., Valverde, G. F., Fernández, P. R., Lasheras, F. S. S., & Gómez, S. L. S. (2024). Hybrid DE-Optimized GPR and NARX/SVR Models for Forecasting gold spot Prices: A Case Study of the Global Commodities market. Mathematics, 12(7), 1039. https://doi.org/10.3390/math12071039
Géron, A. (2018). Neural networks and deep learning. O'Reilly Media, Inc. https://www.oreilly.com/library/view/neural-networks-and/9781492037354/ch04.html
Ghezavati, V. R., Shafiee, M., & Aghajani, A. (2025). A deep learning approach for vehicle routing problem with time windows. Computers & Operations Research, 155, 105115. https://doi.org/10.1016/j.cor.2025.105115
Gilbert, K. (2005). An ARIMA supply chain model. Management Science, 51(2), 305–310. https://doi.org/10.1287/mnsc.1040.0308
Grossi, E., & Buscema, M. (2007). Introduction to artificial neural networks. European Journal of Gastroenterology & Hepatology, 19(12), 1046–1054. https://doi.org/10.1097/meg.0b013e3282f198a0
Guner, M., & Sahin, G. (2022). A mixed methods approach to analyze and predict supply disruptions. European Journal of Operational Research, 300(1), 121–135. https://doi.org/10.1016/j.ejor.2022.09.005
Günther, I., Koch, S., Servaas, V. D. B., & Garrett, R. (2022). Towards responsible gold supply chains: A case study from Burkina Faso to Switzerland. Research Collection. https://doi.org/10.3929/ethz-b-000556671
Harshed. (2024, November 29). Understanding gated recurrent units in deep learning. Medium. https://medium.com/@harshedabdulla/understanding-gated-recurrent-units-grus-in-deep-learning-4404599dcefb
Hochreiter, S., & Schmidhuber, J. (1997). Long Short-Term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Khani, M. M., Vahidnia, S., & Abbasi, A. (2021). A Deep Learning-Based Method for Forecasting Gold Price with Respect to Pandemics. SN Computer Science, 2(4). https://doi.org/10.1007/s42979-021-00724-3
Kiranyaz, S., Avci, O., Abdeljaber, O., Ince, T., Gabbouj, M., & Inman, D. J. (2021). 1D convolutional neural networks and applications: A survey. Mechanical Systems and Signal Processing, 151, 107398. https://doi.org/10.1016/j.ymssp.2020.107398
Li, J., Wang, Y., Song, Y., & Su, C. W. (2023). How resistant is gold to stress? New evidence from the global supply chain. Resources Policy, 85, 103960. https://doi.org/10.1016/j.resourpol.2023.103960
MinMaxScaler. Scikit-learn. https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html
Moslehi, S., Ghasemi, P., & Aghaei, M. (2024). Deep reinforcement learning and optimization approach for multi-echelon supply chain with uncertain demands. Computers & Industrial Engineering, 183, 108122. https://doi.org/10.1016/j.cie.2023.108122
Qin, M., Su, C., Wang, Y., & Doran, N. M. (2023). COULD “DIGITAL GOLD” RESIST GLOBAL SUPPLY CHAIN PRESSURE? Technological and Economic Development of Economy, 30(1), 1–21. https://doi.org/10.3846/tede.2023.18557
Rettinger, M., Minner, S., & Birzl, J. (2024). Understand your decision rather than your model prescription: Towards explainable deep learning approaches for commodity procurement. Computers & Operations Research, 175, 106905. https://doi.org/10.1016/j.cor.2024.106905
Roslan, N. H. A., & Halim, N. A. (2024). Forecasting world gold price in year 2022 using Geometric Brownian Motion model. AIP Conference Proceedings. https://doi.org/10.1063/5.0172466
Seyedan, M., Mafakheri, F., & Wang, C. (2023). Order-up-to-level inventory optimization model using time-series demand forecasting with ensemble deep learning. Supply Chain Analytics, 3, 100024. https://doi.org/10.1016/j.sca.2023.100024
Shafiee, S., & Topal, E. (2010). An overview of global gold market and gold price forecasting. Resources Policy, 35(3), 178–189. https://doi.org/10.1016/j.resourpol.2010.05.004
Swamy, V., & Lagesh, M. (2023). Does happy Twitter forecast gold price? Resources Policy, 81, 103299. https://doi.org/10.1016/j.resourpol.2023.103299
Tadayonrad, Y., & Ndiaye, A. B. (2023). A new key performance indicator model for demand forecasting in inventory management considering supply chain reliability and seasonality. Supply Chain Analytics, 3, 100026. https://doi.org/10.1016/j.sca.2023.100026
Wang, Y., Liu, Z., & Ma, X. (2025). Deep reinforcement learning for dynamic order picking in warehouses. Computers & Operations Research, 155, 105140. https://doi.org/10.1016/j.cor.2025.105140
Zeng, X., Chen, Y., & Tao, C. (2009). Feature Selection Using Recursive Feature Elimination for Handwritten Digit Recognition. 2009 Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing. https://doi.org/10.1109/iih-msp.2009.145
Zhang, C., & Tian, Y.-X. (2023). Solving data-driven newsvendor problem with textual reviews through deep learning. Soft Computing, 27(6), 4967–4986. https://doi.org/10.1007/s00500-023-09073-0
Zhang, P., & Ci, B. (2020). Deep belief network for gold price forecasting. Resources Policy, 69, 101806. https://doi.org/10.1016/j.resourpol.2020.101806
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