AI-Powered Multimodal Data Integration in ERP Systems for Holistic Enterprise Analytics
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I2P112Keywords:
ERP Analytics, Knowledge Graph, Semantic Layer, Transformers, Process Mining, MLOps, Explainable Ai, Federated LearningAbstract
Enterprise Resource Planning (ERP) systems capture high-value signals in finance, procurement, manufacturing, and order management but a significant portion of the insight is confined in modality silos organized tables, non-structured contracts and emails, scanned invoices and images, IoT telemetry and system logs. The following paper suggests an AI-driven multimodal data integration layout that integrates these disparate sources into a controlled semantic layer to be used in comprehensive enterprise analytics. The strategy standardizes ingestion (batch, CDC, and streaming), uses modality-specific encoders (time-series models of ledgers and telemetry, layout-aware document/vision models of PDFs and images, and NLP of text), and integrates outputs through an enterprise ontology and knowledge graph. Fusion engine is a combination of early, late, cross-modal attention that aligns entities and events in digital threads at the process level (e.g. PO-GRN-Invoice-Payment). Basing on it, retrieval-augmented analytics allow evidence-linked natural-language queries, process mining and anomaly detection can be used to improve compliance and fraud detection, and forecasting/optimization assists in making prescriptive demand, lead time, and working capital decisions. Governance-by-design (lineage, access policies, privacy) and MLOps (drift/bias monitoring, canarying, rollback) guarantee reliability and auditability in controlled environments. A prototype through representative ERP workflows would show its previous detection of anomalies, more comprehensive root-cause and more precise KPI results than unimodal baselines. Provide a reference architecture, single semantic schema, and reproducible evaluation protocol, and present future directions in federated learning, edge-AI, explainable AI, and blockchain-secured intercompany integrity
References
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