Assessing the Profit Impact of ARIMA and Neural Network Demand Forecasts in Retail Inventory Replenishment
DOI:
https://doi.org/10.22399/ijcesen.439Keywords:
Inventory Replenishment, Sales Demand Forecasting, Integer Programming, ARIMA, Artificial Neural NetworksAbstract
This study investigates the integration of demand forecasting and inventory replenishment strategies to enhance retail profitability. A deterministic optimal replenishment model is utilized to analyze the predictive performance of various neural network architectures and ARIMA models using real sales data. The predictive accuracy and subsequent influence on optimal firm profits over a multi-period planning horizon is assessed. The Integer Programming model devised optimizes daily replenishment across multiple retail routes, taking into account sales revenue, supply costs, inventory holding, sales loss, and transportation expenses. The study is distinctive in its dual assessment: it evaluates both the accuracy of forecasting methods and their direct impact on profitability through systematic inventory decisions. Neural network architectures selected for minimizing error in product sales predictions have 6% lower mean squared error compared to Akaike Information Criterion minimizing ARIMA models. For longer horizon predictions necessary in performance gap grows larger, e.g., with %60 difference for predictions 15 days ahead. Predictions reflect as 1.6% higher profits on average, when neural network predictions and more efficient longer planning horizons of the optimization model are preferred. Planning 30 days ahead, optimizing with neural network predictions elicits 2.3% higher profits compared to profits attainable based on ARIMA predictions. Our findings illustrate how different forecasting methods can affect firm profitability by shaping inventory replenishment strategies. By merging mathematical optimization with time series forecasting, this research provides a comprehensive evaluation of how advanced predictive technologies can enhance retail inventory practices and improve profitability.
References
Mukherjee, P., & Bose, S. (2008). Does the Stock Market in India Move with Asia?: A Multivariate Cointegration-Vector Autoregression Approach. Emerging Markets Finance and Trade, 44(5), 5–22. https://doi.org/10.2753/REE1540-496X440501.
Michalski, G. M. (2013). Value-Based Inventory Management. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1081276.
Muckstadt, J. A., & Sapra, A. (2010). Principles of inventory management: when you are down to four, order more. Springer.
Wild, A. (2018). Best practice in inventory management (3 Edition). Routledge.
Silver, E. A., Pyke, D. F., Peterson, R., & Silver, E. A. (1998). Inventory management and production planning and scheduling (3. ed). Wiley.
Chandramohan, J., Asoka Chakravarthi, R. P., & Ramasamy, U. (2023). A comprehensive inventory management system for non-instantaneous deteriorating items in supplier- retailer-customer supply chains. Supply Chain Analytics, 3, 100015. https://doi.org/10.1016/j.sca.2023.100015.
Yang, H.-L. (2023). An optimal replenishment cycle and order quantity inventory model for deteriorating items with fluctuating demand. Supply Chain Analytics, 3, 100021. https://doi.org/10.1016/j.sca.2023.100021.
Rinaldi, M., Fera, M., Macchiaroli, R., & Bottani, E. (2023). A new procedure for spare parts inventory management in ETO production: a case study. Procedia Computer Science, 217, 376–385. https://doi.org/10.1016/j.procs.2022.12.233.
Gutiérrez, J., Colebrook, M., Abdul-Jalbar, B., & Sicilia, J. (2013). Effective replenishment policies for the multi-item dynamic lot-sizing problem with storage capacities. Computers & Operations Research, 40(12), Article 12. https://doi.org/10.1016/j.cor.2013.06.007.
Yang, H.-L. (2012). Two-warehouse partial backlogging inventory models with three-parameter Weibull distribution deterioration under inflation. International Journal of Production Economics, 138(1), 107–116. https://doi.org/10.1016/j.ijpe.2012.03.007.
Chiu, S. W., Wu, C.-S., & Tseng, C.-T. (2019). Incorporating an expedited rate, rework, and a multi-shipment policy into a multi-item stock refilling system. Operations Research Perspectives, 6, 100115. https://doi.org/10.1016/j.orp.2019.100115.
Kumar, S., & Mahapatra, R. P. (2021). Design of multi-warehouse inventory model for an optimal replenishment policy using a Rain Optimization Algorithm. Knowledge-Based Systems, 231, 107406. https://doi.org/10.1016/j.knosys.2021.107406.
Roozbeh Nia, A., Hemmati Far, M., & Akhavan Niaki, S. T. (2014). A fuzzy vendor managed inventory of multi-item economic order quantity model under shortage: An ant colony optimization algorithm. International Journal of Production Economics, 155, 259–271. https://doi.org/10.1016/j.ijpe.2013.07.017.
Mareeswaran, M., & Anandhi, M. (2021). Optimization of inventory in agriculture material processing industry by using multi-item deterministic model. Materials Today: Proceedings, 46, 4183–4186. https://doi.org/10.1016/j.matpr.2021.02.747.
Nobil, A. H., Nobil, E., Afshar Sedigh, A. H., Cárdenas-Barrón, L. E., Garza-Núñez, D., Treviño-Garza, G., Céspedes-Mota, A., Loera-Hernández, I. de J., & Smith, N. R. (2024). Economic production quantity models for an imperfect manufacturing system with strict inspection. Ain Shams Engineering Journal, 15(5), 102714. https://doi.org/10.1016/j.asej.2024.102714.
Alon, I., Qi, M., & Sadowski, R. J. (2001). Forecasting aggregate retail sales:: a comparison of artificial neural networks and traditional methods. Journal of Retailing and Consumer Services, 8(3), 147–156. https://doi.org/10.1016/S0969-6989(00)00011-4.
Caglayan, N., Satoglu, S. I., & Kapukaya, E. N. (2020). Sales Forecasting by Artificial Neural Networks for the Apparel Retail Chain Stores. In C. Kahraman, S. Cebi, S. Cevik Onar, B. Oztaysi, A. C. Tolga, & I. U. Sari (Eds.), Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making (pp. 451–456). Springer International Publishing. https://doi.org/10.1007/978-3-030-23756-1_56.
Das, P., & Chaudhury, S. (2007). Prediction of retail sales of footwear using feedforward and recurrent neural networks. Neural Computing and Applications, 16(4), 491–502. https://doi.org/10.1007/s00521-006-0077-3.
Penpece, D., & Elma, O. E. (2014). Predicting Sales Revenue by Using Artificial Neural Network in Grocery Retailing Industry: A Case Study in Turkey. International Journal of Trade, Economics and Finance, 5(5), 435–440. https://doi.org/10.7763/IJTEF.2014.V5.411.
Loureiro, A. L. D., Miguéis, V. L., & da Silva, L. F. M. (2018). Exploring the use of deep neural networks for sales forecasting in fashion retail. Decision Support Systems, 114, 81–93. https://doi.org/10.1016/j.dss.2018.08.010.
Deraz, N. (2023). Economic Order Quantity Predictive Model Using Supervised Machine Learning for Inventory Management of Fast-Moving Consumer Goods Distributors. Plymouth Business School Theses. https://doi.org/10.24382/2668.
Kilimci, Z. H., Akyuz, A. O., Uysal, M., Akyokus, S., Uysal, M. O., Atak Bulbul, B., & Ekmis, M. A. (2019). An Improved Demand Forecasting Model Using Deep Learning Approach and Proposed Decision Integration Strategy for Supply Chain. Complexity, 2019(1), 9067367. https://doi.org/10.1155/2019/9067367.
Borade, A. B., & Bansod, S. V. (2011). Neural networks based vendor-managed forecasting: a case study. International Journal of Integrated Supply Management. https://www.inderscienceonline.com/doi/10.1504/IJISM.2011.040713.
Jiang, S., Yang, C., Guo, J., & Ding, Z. (2018). ARIMA forecasting of China’s coal consumption, price and investment by 2030. Energy Sources, Part B: Economics, Planning, and Policy, 13(3), Article 3. https://doi.org/10.1080/15567249.2017.1423413.
Dey, B., Roy, B., Datta, S., & Ustun, T. S. (2023). Forecasting ethanol demand in India to meet future blending targets: A comparison of ARIMA and various regression models. Energy Reports, 9, 411–418. https://doi.org/10.1016/j.egyr.2022.11.038.
Chyon, F. A., Suman, M. N. H., Fahim, M. R. I., & Ahmmed, M. S. (2022). Time series analysis and predicting COVID-19 affected patients by ARIMA model using machine learning. Journal of Virological Methods, 301, 114433. https://doi.org/10.1016/j.jviromet.2021.114433.
Ediger, V. Ş., & Akar, S. (2007). ARIMA forecasting of primary energy demand by fuel in Turkey. Energy Policy, 35(3), Article 3. https://doi.org/10.1016/j.enpol.2006.05.009.
Ďurka, P., & Pastoreková, S. (2012). ARIMA vs. ARIMAX–which approach is better to analyze and forecast macroeconomic time series. Proceedings of 30th International Conference Mathematical Methods in Economics, 2, 136–140.
Siami Namini, S., & Siami Namin, A. (2018). Forecasting Economics and Financial Time Series: ARIMA vs. LSTM.
Schuster, M., & Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11), 2673–2681. IEEE Transactions on Signal Processing. https://doi.org/10.1109/78.650093.
Deng, Z., Wang, B., Xu, Y., Xu, T., Liu, C., & Zhu, Z. (2019). Multi-Scale Convolutional Neural Network With Time-Cognition for Multi-Step Short-Term Load Forecasting. IEEE Access, 7, 88058–88071. IEEE Access. https://doi.org/10.1109/ACCESS.2019.2926137.
Li, Y., Li, K., Chen, C., Zhou, X., Zeng, Z., & Li, K. (2021). Modeling Temporal Patterns with Dilated Convolutions for Time-Series Forecasting. ACM Trans. Knowl. Discov. Data, 16(1), 14:1-14:22. https://doi.org/10.1145/3453724.
Wang, J., Wang, W., Wei, S., Zeng, Y., & Luo, F. (2019). Time Series Sequences Classification with Inception and LSTM Module. 2019 IEEE International Conference on Integrated Circuits, Technologies and Applications (ICTA), 51–55. https://doi.org/10.1109/ICTA48799.2019.9012862.
Shih, S.-Y., Sun, F.-K., & Lee, H. (2019). Temporal pattern attention for multivariate time series forecasting. Machine Learning, 108(8), 1421–1441. https://doi.org/10.1007/s10994-019-05815-0.
Zhou, K., Wang, W., Hu, T., & Deng, K. (2020). Time Series Forecasting and Classification Models Based on Recurrent with Attention Mechanism and Generative Adversarial Networks. Sensors, 20(24), Article 24. https://doi.org/10.3390/s20247211.
Durdu, D. (2010). A hybrid neural network and ARIMA model for water quality time series prediction. Engineering Applications of Artificial Intelligence, 23(4), Article 4. https://doi.org/10.1016/j.engappai.2009.09.015.
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