arXiv subjects

Shunmukha Sagar Puppala

Publications and source records attributed to Shunmukha Sagar Puppala.

4 recordsLinked to original sources

arXiv2026

Enterprise Process Control Tower for IT-Led Improvement

Enterprise integration traffic in large Enterprise Resource Planning (ERP) systems move through Electronic Data Interchange (EDI) and the Intermediate Document (IDoc), and the operational tools that surround that traffic remain fragmented. Status records are written for a technical audience, exception handling is performed one document at a time, and process health is read manually from long lists. This work proposes an enterprise process control tower that lets an Information Technology (IT) team observe, diagnose, and improve business workflows from a single operational layer. The design links process health observation, semantic translation of technical status text, machine-learning diagnosis of exceptions with model explainability, grouped recovery actions, and key performance indicators inside one governance framework. The proposed framework is evaluated on a synthetic IDoc event dataset of 84,000 records spanning twelve months, of which 15,120 error records are used to train a five-class remediation-routing model that maps each exception to a standard recovery action. A rule-based router that reproduces current production heuristics serves as the primary baseline. The proposed gradient-boosted diagnosis engine reaches a macro-averaged F1 score of 0.883 on the held-out test partition, against 0.742 for the rule-based router, while Shapely-based attributions expose the drivers of each routing decision to operators. Results are derived from synthetic data and are presented as an internal-consistency demonstration rather than real-world validation. The contribution is an integrated tool design that places diagnosis and governance on the same plane as observation.

cs.CE↗

arXiv2026

Zero-Trust Federated Learning for Connected Aftermarket Devices

Connected aftermarket devices extend vehicle diagnostics, repair workflows, and over-the-air software maintenance beyond original equipment manufacturer boundaries, yet their heterogeneous ownership and long service life complicate conventional perimeter security. This paper develops Zero Trust Federated Learning for Connected Aftermarket Devices (ZT FL CADE), an edge-learning architecture that combines device-level access control, privacy-preserving federated learning, and adversarial validation for over-the-air update and predictive maintenance decisions. The evaluation uses a single synthetic dataset of 144,000 telemetry windows from 240 devices, 12 vendor domains, and 180 days of operation. Features include bus entropy, update latency, attestation age, signature retries, environmental signals, fault-code rates, packet loss, drift, mileage, and trust score, with targets for maintenance risk, update intrusion, and access action. ZT FL CADE trains local temporal models, aggregates privacy-bounded updates, scores each device against behavioral and update integrity evidence, and routes update requests to allow, challenge, or quarantine actions. Synthetic experiments improve maintenance risk F1 from 0.837 for Fed Avg to 0.883, improve intrusion F1 from 0.856 to 0.897, and preserve 0.842 intrusion recall when 20 percent of selected clients are adversarial. Mean access-decision latency remains 44 MS, below the 100 MS operational budget used in the simulation. The results do not establish field validation, but they indicate that zero-trust policy enforcement and federated learning can be evaluated jointly rather than as separate aftermarket security controls. Index Terms Zero trust architecture, federated learning, connected aftermarket devices, over-the-air updates, adversarial machine learning, edge artificial intelligence, predictive maintenance, automotive cybersecurity

cs.CR↗

arXiv2026

Federated Ensemble Forecasting Under Supply-Chain Market Volatility

Supply chain forecasting systems increasingly operate under market shocks, non-identically distributed regional demand, and limited willingness to centralize commercial data. This work proposes Federated Ensemble Forecasting with Negative-Correlation Learning (FEF NCL), a distributed method that trains specialized forecasting experts across client nodes while discouraging redundant model errors. The framework combines temporal feature encoders, client level drift scoring, reliability-weighted aggregation, and an explain ability layer that exposes the market and supplier variables most responsible for each forecast. A single synthetic dataset is used to evaluate the design. It contains 124,800 weekly SKU region observations from ten regional client nodes, 60 product families, 40 suppliers, five commodity groups, and a 2021-2024 volatility profile with explicit price-shock regimes. Because the dataset is synthetic, the reported results should be interpreted as controlled evidence of internal consistency rather than real-world validation. Across the synthetic test split, FEF NCL reduces weighted mean absolute percentage error from 13.9% for the best federated baseline to 12.4%, improves delay-risk macro-F1 from 0.755 to 0.801, and lowers the high volatility quintile error by 2.1 percentage points relative to SCAFFOLD. The analysis suggests that negative-correlation specialization is useful when clients face different supplier, freight, and commodity conditions, although deployment would require stronger privacy analysis, live drift monitoring, and operational calibration. Index Terms federated learning, ensemble learning, negative correlation learning, supply chain forecasting, market volatility, data drift, demand planning, risk governance

cs.LG↗

arXiv2026

Enterprise Integration Modernization with SAP BTP

Hybrid and multi cloud adoption has made enterprise integration a strategic architecture problem rather than a narrow middleware concern. Large organizations frequently operate SAP S4HANA, SAP ECC, SaaS business applications, partner portals, data lakes, and hyper scaler services across heterogeneous environments. Point-to-point interfaces and legacy enterprise service bus patterns often create tight coupling, duplicated mappings, weak observability, and security gaps. This paper proposes a Hybrid Multi Cloud Integration Modernization Framework, abbreviated as HMC-IMF, for SAP Business Technology Platform centered enterprise integration modernization. The framework combines SAP Integration Suite, API management, event driven integration, clean core extensions, runtime placement, security governance, and observability into a unified modernization model. A synthetic enterprise integration dataset is defined with 186 applications, 2,850 interfaces, 1,120 APIs, 740 event topics, 320 batch flows, five cloud environments, and 24 months of operational logs. Experimental analysis compares a legacy point-to-point baseline with the proposed BTP centered architecture. Results show a 38 percent reduction in interface complexity, a decrease in average integration latency from 420 MS to 245 MS, a reduction in mean time to recovery from 155 minutes to 54 minutes, and a decline in failed message rate from 3.8 percent to 1.1 percent. The findings suggest that SAP BTP can serve as a governed integration modernization layer when paired with explicit pattern selection, policy controls, and operational telemetry. Index Terms SAP BTP, enterprise integration, hybrid cloud, multi-cloud architecture, SAP Integration Suite, API management, event-driven architecture, clean core

cs.CE↗