Research article
● Open access
Self-Healing Machine Learning Systems for Supply Chain Operations: A Systematic Review of Drift-Aware Continual Learning and MLOps Architectures
Abstract
Background: Machine learning systems deployed in supply chain operations operate in inherently non-stationary environments where data distributions, label definitions, and operational constraints continuously evolve. Promotions, assortment changes, supplier transitions, external shocks, and sensor drift routinely destabilize model assumptions, leading to degradation in predictive accuracy, service levels, and decision latency. Traditional static models fail to maintain performance under such conditions, necessitating adaptive approaches.
Objective: This systematic review examines the landscape of drift-aware, continual learning pipelines for supply chain operations and proposes a governed, self-healing MLOps architecture that enables reliable, auditable, and resilient deployments under real-world non-stationarity.
Methods: A structured literature search was conducted across Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and INFORMS PubSOnline for publications between 2015 and 2025. Studies were included if they addressed supply chain operations using operational tabular or time-series data with business or reliability metrics, implemented adaptive mechanisms, and reported empirical results. Thematic synthesis was performed across three analytical axes: drift landscape and adaptation mechanisms, operational effectiveness and risk, and self-healing MLOps design.
Results: Fifteen applied studies spanning retail, e-commerce, logistics, manufacturing, pharmaceutical, and cold-chain contexts met inclusion criteria. Evidence demonstrates that drift-aware, self-healing approaches reduce operational costs and prediction errors, maintain service stability under changing conditions, enable fairness-aware dispatch, improve ETA and promise-date accuracy, and reduce false alerts in anomaly detection systems. However, explicit drift detectors, reliability key performance indicators (time-to-detect, time-to-recover), and governance artifacts were inconsistently reported across studies, limiting cross-study comparability and reproducibility.
Conclusion: Self-healing machine learning systems that integrate continuous monitoring, drift detection, automated attribution, controlled adaptation, and gated rollout can materially improve supply chain decisions under non-stationarity. We propose a reference architecture codifying data validation, versioned storage, observability, trigger thresholds, and controlled rollout paths, coupled with a standardized benchmarking and reporting protocol. Future research should prioritize reliability metric standardization, temporal benchmark development, comparative evaluation of composite monitors, and extension to federated, privacy-preserving settings with energy accounting.
Keywords
Self-healing machine learning; concept drift; MLOps; supply chain analytics; continual learning; drift detection; operational reliability
References
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Jangam, S. K. (2022). Role of AI and ML in enhancing self-healing capabilities, including predictive analysis and automated recovery. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(4), 47-56.
Jiang, L., Wang, S., Guo, B., Wang, H., Zhang, D., & Wang, G. (2023). FairCod: A fairness-aware concurrent dispatch system for large-scale instant delivery services. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 4229-4238). ACM.
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Kreuzberger, D., Kühl, N., & Hirschl, S. (2023). Machine learning operations (MLOps): Overview, definition, and architecture. IEEE Access, 11, 31866-31879. https://doi.org/10.1109/ACCESS.2023.3262138
Lim, B., Arik, S. Ö., Loeff, N., & Pfister, T. (2021). Temporal fusion transformers for interpretable multi-horizon time series forecasting. International Journal of Forecasting, 37(4), 1748-1764. https://doi.org/10.1016/j.ijforecast.2021.03.012
Lin, H., Lin, J., & Wang, F. (2022). An innovative machine learning model for supply chain management. Journal of Innovation & Knowledge, 7(4), 100276.
Liu, Q., Boniol, P., Palpanas, T., & Paparrizos, J. (2024). Time-series anomaly detection: Overview and new trends. Proceedings of the VLDB Endowment, 17(12), 4229-4232. https://doi.org/10.14778/3685800.3685842
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Montiel, J., Read, J., Bifet, A., & Abdessalem, T. (2018). Scikit-Multiflow: A multi-output streaming framework. Journal of Machine Learning Research, 19(72), 1-5.
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., ... & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
Paleyes, A., Urma, R. G., & Lawrence, N. D. (2022). Challenges in deploying machine learning: A survey of case studies. ACM Computing Surveys, 55(6), 1-29. https://doi.org/10.1145/3533378
Phumchusri, N., Chewcharat, T., & Kanokpongsakorn, S. (2024). Price promotion optimization model for multiperiod planning: A case study of beauty category products sold in a convenience store chain. Journal of Revenue and Pricing Management, 23(2), 164-178. https://doi.org/10.1057/s41272-023-00438-6
Polyzotis, N., Zinkevich, M., Roy, S., Breck, E., & Whang, S. (2019). Data validation for machine learning. Proceedings of Machine Learning and Systems, 1, 334-347.
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