Designing a Multi-Level Support Automation Framework for Predictive Fault Detection and IT Process Improvement

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@article{1713057,
      author = {Odunayo Mercy Babatope, Taiwo Oyewole, Jolly I. Ogbole, Taiwo Oyewole},
      title = {Designing a Multi-Level Support Automation Framework for Predictive Fault Detection and IT Process Improvement},
      journal = {Iconic Research And Engineering Journals},
      year = {2018},
      volume = {1},
      number = {10},
      pages = {336-354},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1713057.pdf},
      abstract = {The increasing complexity of modern IT environments?characterized by distributed architectures, hybrid cloud systems, and rapidly evolving service demands?has intensified the need for intelligent, automated support frameworks capable of proactively detecting faults and optimizing operational workflows. This review explores the design, implementation, and performance implications of a Multi-Level Support Automation Framework (MLSAF) that integrates predictive analytics, machine learning?based anomaly detection, and automated incident resolution across Tier 0 to Tier 3 support layers. The paper synthesizes state-of-the-art methods in predictive fault detection, event correlation, knowledge-driven automation, and IT service management (ITSM) orchestration, examining how multi-level automation improves system reliability, reduces mean time to detect (MTTD) and mean time to resolve (MTTR), and enhances IT process maturity. Furthermore, the review analyzes enabling technologies such as AIOps, digital twins, intelligent workflow engines, and real-time telemetry pipelines, highlighting their contributions to scalable automation ecosystems. Key challenges?including data quality limitations, model drift, legacy system integration, governance, and human?automation collaboration?are also discussed. The study concludes by proposing a conceptual MLSAF architecture and outlining future directions for adaptive, self-healing IT operations.},
      keywords = {Predictive Fault Detection, IT Process Improvement, AIOps, Multi-Level Support Automation, Anomaly Detection, IT Service Management (ITSM).},
      month = {April},
      doi = {https://doi.org/10.64388/IREV1I10-1713057}
  }