Implementation of Isolation Forest and Rule-Based Prioritization  in the Automation of Warehouse Inventory Monitoring  Using a Near Real-Time Dashboard

Muhammad Windri; Ida Nurhaida

doi:10.30871/jaic.v10i4.13218

Authors

Muhammad Windri Universitas Pembangunan Jaya
Ida Nurhaida Universitas Pembangunan Jaya

DOI:

https://doi.org/10.30871/jaic.v10i4.13218

Keywords:

Anomaly Detection, Inventory Monitoring, Isolation Forest, Rule-Based Prioritization, Near Real-Time Dashboard

Abstract

This research implements an automated warehouse inventory monitoring system using a hybrid approach that combines Isolation Forest and rule-based prioritization for anomaly detection, while Long Short-Term Memory (LSTM) is used for demand forecasting. The system automatically detects three types of anomalies: spike (quantity surge), drop (quantity decline), and pattern shift (changes in transaction patterns). The method utilizes 341,879 inventory records over two and a half years, combining Isolation Forest for outlier detection, rule-based prioritization for spike and drop detection, and LSTM for time series forecasting. The system is equipped with a near real-time dashboard (periodic auto-refresh), API service, and a business validation mechanism involving warehouse users. The system generated 211,450 detection candidates, consisting of 187,537 operational anomaly alerts and 23,913 INFO-level records, with accuracy of 93.0%, precision of 88.7%, recall of 100%, and F1-score of 94.0%. The INFO category is not displayed on the dashboard due to low confidence scores. The near real-time dashboard displays HIGH, MEDIUM, and LOW priority alerts with auto-refresh. Business validation achieved an 84% confirmation rate. The system also provides LSTM forecast features for predicting inventory needs for 7, 14, and 30 days ahead. This research shows that AI implementation for warehouse inventory monitoring can automate anomaly detection and improve inventory issue identification. The system shows potential for medium to large-scale warehouses requiring near real-time monitoring.

Downloads

Download data is not yet available.

References

[1] A. Kumar et al., "Revolutionising anomaly detection: a hybrid framework for anomaly detection integrating isolation forest, autoencoder, and Conv. LSTM," Knowl. Inf. Syst., vol. 67, pp. 11903–11953, 2025. doi: 10.1007/s10115-025-02580-6.

[2] S. Tripathi, R. Misra, and T. N. Singh, "Anomaly-aware quantum convolutional neural network for carbon-efficient job arrival rate prediction in cloud computing," Future Gener. Comput. Syst., 2025. doi: 10.1016/j.future.2025.107789.

[3] C. Jiang and W. Wu, "Anomaly detection in distributed systems based on Spatio-Temporal Causal Inference," Eng. Appl. Artif. Intell., vol. 147, p. 109855, 2025. doi: 10.1016/j.engappai.2025.109855.

[4] A. B. Bereketoglu, "Hybrid Meta-Learning Framework for Anomaly Forecasting in Nonlinear Dynamical Systems via Physics-Inspired Simulation and Deep Ensembles," arXiv:2506.13828, 2025.

[5] "Multi-model predictive framework for unsupervised anomaly detection in univariate time series data from hydraulic turbine units," Energy Rep., vol. 14, pp. 4701-4709, 2025. doi: 10.1016/j.energy.2025.123456.

[6] "HIPER-CHAD: Hybrid Integrated Prediction-Error Reconstruction-Based Anomaly Detection for Multivariate Indoor Environmental Time-Series Data," Sensors, vol. 26, no. 1, p. 171, 2026. doi: 10.3390/s26010171.

[7] "A Multi-Working States Sensor Anomaly Detection Method Using Deep Learning Algorithms," Sensors, vol. 25, no. 18, p. 5686, 2025. doi: 10.3390/s25185686.

[8] "Unsupervised Machine Learning Methods for Anomaly Detection in Network Packets," Electronics, vol. 14, no. 14, p. 2779, 2025. doi: 10.3390/electronics14142779.

[9] "Enhancing Critical Infrastructure Security: Unsupervised Learning Approaches for Anomaly Detection," Int. J. Comput. Intell. Syst., vol. 17, p. 236, 2024. doi: 10.1007/s44196-024-00644-z.

[10] F. Antonicoli, F. R. Galeppi, and M. Piccirilli, "Advanced Time Series Forecasting and Anomaly Detection in Transactional Data," GitHub Repository, 2025. [Online]. Available: https://github.com/francescagaleppi/anomaly-detection-time-series

[11] F. T. Liu, K. M. Ting, and Z. H. Zhou, "Isolation Forest," in Proc. 8th IEEE Int. Conf. Data Mining, Pisa, Italy, 2008, pp. 413-422. doi: 10.1109/ICDM.2008.17.

[12] S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Comput., vol. 9, no. 8, pp. 1735-1780, 1997. doi: 10.1162/neco.1997.9.8.1735.

[13] C. Xiaoyang, "Explainable dual LSTM-autoencoders with exogenous features for anomaly detection and supply chain forecasting," Sci. Rep., vol. 15, p. 42371, 2025. doi: 10.1038/s41598-025-26449-4.

[14] S. Selvakumar et al., "Enhanced Anomaly Detection in Wireless Sensor Networks Using Isolation Forest, LSTM, and LSTM Autoencoder," in Proc. 3rd Int. Conf. Inventive Comput. Inform. (ICICI), 2025, pp. 1528-1534. doi: 10.1109/ICICI65870.2025.11069875.

[15] W. Villegas-Ch et al., "Integrating Explainable Artificial Intelligence in Anomaly Detection for Threat Management in E-Commerce Platforms," IEEE Access, vol. 13, pp. 29830-29846, 2025. doi: 10.1109/ACCESS.2025.3541979.

[16] "AI-based Hybrid Approach for Real-Time Anomaly Detection and Fault Management in Digital Services," in Proc. Int. Conf. Electron., Commun. Aerosp. Technol. (ICECA), 2025. doi: 10.1109/ICECA66444.2025.11382729.

[17] T. Abu Musa, A. Bouras, and A. Belhi, "Anomaly Detection in Supply Chain Business Processes: A Deep Learning Approach," in Product Lifecycle Management. Leveraging AI, Digital Twins, and Smart Technologies, Springer, 2025, pp. 35-48. doi: 10.1007/978-3-031-93323-3_3.

[18] J. Cheng et al., "Real-Time Warehouse Monitoring with Ceiling Cameras and Digital Twin for Asset Tracking and Scene Analysis," Logistics, vol. 9, no. 4, p. 153, 2025. doi: 10.3390/logistics9040153.

[19] "Detecting Anomalies in Container Resource Traces From Production Datacenter," in IEEE Conf. Proc., 2025. [Online]. Available: https://ieeexplore.ieee.org/document/11072954

[20] M. Hosseini, S. Chalil Madathil, and M. T. Khasawneh, "Reinforcement learning-based simulation optimization for an integrated manufacturing-warehouse system: a two-stage approach,"Expert Syst. Appl., 2025. doi: 10.1016/j.eswa.2025.125123.

[21] A. Iqbal and R. Amin, "Time series forecasting and anomaly detection using explainable AI," J. Supercomput., vol. 81, no. 4, p. 523, 2025. doi: 10.1007/s11227-025-07040-0.

[22] M. I. Sayyaf, P. Pascacio, N. Zhu, and V. Renaudin, "Time-Series Anomaly Detection for Sensor Data: Models, Metrics, and Methodologies - A Review," IEEE Sensors J., vol. 25, no. 24, pp. 43603-43619, 2025. doi: 10.1109/JSEN.2025.3616395.

[23] J. Blázquez-García, A. Conde, U. Mori, and J. A. Lozano, "A review on outlier/anomaly detection in time series data," ACM Comput. Surv., vol. 54, no. 3, pp. 1-33, 2021. doi: 10.1145/3444690.

[24] K. Shaukat et al., "A review of time-series anomaly detection techniques: A step to future perspectives," in Adv. Intell. Syst. Comput., Springer, 2021. doi: 10.1007/978-3-030-73100-7_60.