Abstract :

Inventory management plays a pivotal role in the coconut farming business, directly influencing sales and income. An essential component of this management is warehousing, which not only affect revenue but also involves suppliers in the coconut storage process. Warehousing management and technology are two elements that can help companies operate more effectively and efficiently. This research focuses on efforts to improve warehouse management efficiency in the agricultural sector, particularly at Banio Lahewa, a company that operates as a coconut supplier in a small village with limited resources. Currently, the company still records data manually and lacks a real-time system to monitor demand patterns, stock rotation, and restocking frequency in the warehouse. This situation is caused by uncertainty about the products entering the warehouse, leading to the company’s focus being more limited to daily operational issues rather than future planning. To address this challenge, this research uses future event prediction methods, specifically forecasting by applying two neural network models: the Feed Forward Neural Network and the Long Short Term Memory. The implementation of this system is expected to provide new insights to the company, enabling them to be more adaptive in efficiently managing warehouse systems. With an understanding of patterns and predictions of future events, it is expected that the company can be more prepared and responsive to changes in customer demand and able to expand products more quickly. The results of this research are expected to make a positive contribution to the company, helping them optimize warehouse management and become more adaptive to market dynamics.

---

Keywords :

Data Analytics, Forecasting, Neural Network, Warehousing

---

References :

1. Bartodziej, C. J. (2017). The Concept Industry 4.0. Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-16502-4
2. Choi, Cho, & Kim. (2020). Power Demand Forecasting using Long Short-Term Memory (LSTM) Deep-Learning Model for Monitoring Energy Sustainability. Sustainability, 12(3), 1109. https://doi.org/10.3390/su12031109
3. Danese, P., & Kalchschmidt, M. (2011). The role of the forecasting process in improving forecast accuracy and operational performance. International Journal of Production Economics, 131(1), 204–214. https://doi.org/10.1016/j.ijpe.2010.09.006
4. Davies, N. (2023). How Descriptive Analytics Leverages Data for Better Decisions.
5. Edgell, A. (2021, September). Feedforward Neural Networks. https://www.datasciencecentral.com/feedforward-neural-networks/
6. Edwards, S. (2021). Predictive Analytics 101: Leverage Prediction to Get Business Results.
7. Fahim, A., Tan, Q., Mazzi, M., Sahabuddin, M., Naz, B., & Ullah Bazai, S. (2021). Hybrid LSTM Self-Attention Mechanism Model for Forecasting the Reform of Scientific Research in Morocco. Computational Intelligence and Neuroscience, 2021, 1–14. https://doi.org/10.1155/2021/6689204
8. Flynn, S. (2023, October). What Is Predictive Analytics?
9. Frankenfield, J., & Kelly, R. (2023). Descriptive Analytics: What They Are and Related Terms.
10. Gammelli, D., Wang, Y., Prak, D., Rodrigues, F., Minner, S., & Pereira, F. C. (2022). Predictive and prescriptive performance of bike-sharing demand forecasts for inventory management. Transportation Research Part C: Emerging Technologies, 138, 103571. https://doi.org/10.1016/j.trc.2022.103571
11. Jomaa, D., Monteiro, T., & Besombes, B. (2013). Design and Development of a Forecasting Module: Case of a Warehouse Management System (Vol. 46). IFAC Proceedings Volumes.
12. Lu, W., Mcfarlane, D., Giannikas, V., & Zhang, Q. (2015). An algorithm for dynamic order-picking in warehouse operations. European Journal of Operational Research.
13. Morris, A. (2021). Descriptive Analytics Defined: Benefits & Examples.
14. Namir, K., Labriji, H., & Ben Lahmar, E. H. (2022). Decision Support Tool for Dynamic Inventory Management using Machine Learning, Time Series and Combinatorial Optimization. Procedia Computer Science, 198, 423–428. https://doi.org/10.1016/j.procs.2021.12.264
15. Park, Y.-S., & Lek, S. (2016). Artificial Neural Networks. In Developments in Environmental Modelling (Vol. 28, pp. 123–140). Elsevier. https://doi.org/10.1016/B978-0-444-63623-2.00007-4
16. Praveen, U., Farnaz, G., & Hatim, G. (2019). Inventory management and cost reduction of supply chain processes using AI based time-series forecasting and ANN modeling. Procedia Manufacturing, 38, 256–263. https://doi.org/10.1016/j.promfg.2020.01.034

17. Qiu, J., Wang, B., & Zhou, C. (2020). Forecasting stock prices with long-short term memory neural network based on attention mechanism. PLOS ONE, 15(1), e0227222. https://doi.org/10.1371/journal.pone.0227222
18. Silva, J., Mojica Herazo, J. C., Rojas Millán, R. H., Pineda Lezama, O. B., Gamero, W. B. M., & Varela, N. (2019). Early warning method for the commodity prices based on artificial neural networks: SMEs case. Procedia Computer Science, 151, 1243–1248. https://doi.org/10.1016/j.procs.2019.04.179
19. Sivakumar, D. (2023). Introduction to Feed Forward Neural Network. https://www.scaler.com/topics/deep-learning/introduction-to-feed-forward-neuralnetwork/
20. (2023). Production of coconut in Indonesia from 2013 to 2022(in million metric tons). https://www.statista.com/statistics/706968/coconutproduction-in-indonesia/

21. Su’i, M. (2010). PERUBAHAN FISIOLOGIS BUAH KELAPA SELAMA GERMINASI PHYSIOLOGICAL CHANGE COCONUT DURING GERMINATION. Agrika, 4, 60–66.

22. Tanizaki, T., Hoshino, T., Shimmura, T., & Takenaka, T. (2020). Restaurants store management based on demand forecasting. Procedia CIRP, 88, 580– 583. https://doi.org/10.1016/j.procir.2020.05.101
23. Thivakaran, T. K., & Ramesh, M. (2022). Exploratory Data analysis and sales forecasting of bigmart dataset using supervised and ANN algorithms. Measurement: Sensors, 23, 100388. https://doi.org/10.1016/j.measen.2022.100388
24. Trevisan, V. (2022, January). Comparing Robustness of MAE, MSE and RMSE.https://towardsdatascience.com/comparing-robustness-of-mae-mse-andrmse-6d69da870828
25. Tuovila, A., Anderson, S., & Reeves, M. (2023, May 27). Forecasting: What It Is, How It’s Used in Business and Investing.https://www.investopedia.com/terms/f/forecasting.asp