research_article

Self-Healing Machine Learning Systems for Supply Chain Operations: A Systematic Review of Drift-Aware Continual Learning and MLOps Architectures

O. Akinyemi
Volume1
Issue1
Pages22-34
DOI#
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 learningconcept driftMLOpssupply chain analyticscontinual learningdrift detectionoperational reliability
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