Category Archives: Uncategorized

Authors: Ritvik Nandesh

Abstract: Predictive maintenance has emerged as a strategic capability for industrial and enterprise environments seeking to reduce unplanned downtime, optimize asset performance, and improve operational efficiency. Traditional SAP-based maintenance systems primarily rely on historical data and scheduled maintenance plans, limiting their ability to respond to real-time equipment conditions. The integration of Internet of Things technologies and artificial intelligence enables a data-driven approach that transforms maintenance operations from reactive to predictive. This article presents a unified AI and IoT architecture integrated with SAP platforms to support intelligent predictive maintenance operations. The proposed architecture leverages IoT sensors and edge computing for real-time data acquisition, AI models for failure prediction and anomaly detection, and SAP systems for orchestrating maintenance workflows and enterprise processes. Key architectural components, data flows, and predictive maintenance workflows are discussed, along with security, governance, and compliance considerations. The article also examines performance evaluation metrics, business impact, and implementation challenges. Finally, emerging trends such as edge AI, digital twins, and autonomous maintenance systems are explored, highlighting their potential to further enhance SAP-based predictive maintenance solutions. The insights provided aim to guide organizations in designing scalable, secure, and intelligent maintenance architectures aligned with enterprise objectives.

DOI: https://doi.org/10.5281/zenodo.18162331

Published by: vikaspatanker

Authors: Anvesha Tilvani

Abstract: Enterprises are increasingly adopting intelligent technologies to remain competitive in rapidly evolving digital environments. SAP ecosystems play a central role in this transformation by integrating core business processes with advanced technologies such as artificial intelligence, cloud computing, and the Internet of Things. The convergence of these technologies enables real-time data processing, predictive analytics, and intelligent automation, transforming traditional SAP systems into adaptive and insight-driven enterprise platforms. This article explores the evolution of SAP ecosystems toward future-ready architectures that support intelligent enterprise capabilities. It examines cloud foundations, AI-enabled enterprise applications, and IoT integration within SAP environments, highlighting how their convergence enhances operational efficiency, scalability, and decision-making. Key use cases across manufacturing, supply chain management, finance, and customer experience are discussed to illustrate practical business value. The article also addresses security, privacy, and governance considerations, as well as implementation challenges related to integration complexity, data quality, and organizational readiness. Finally, emerging trends such as autonomous enterprise systems, generative AI, edge intelligence, and sustainable SAP ecosystems are explored, emphasizing their role in shaping the next generation of intelligent enterprises. The insights presented aim to guide organizations in designing resilient, scalable, and future-ready SAP ecosystems aligned with strategic business objectives.

DOI: https://doi.org/10.5281/zenodo.18162235

Published by: vikaspatanker

Authors: Nishka Vardhan

Abstract: Regulatory auditing and compliance in SAP financial systems are critical for ensuring organizational accountability, risk management, and adherence to standards such as SOX, IFRS, and GDPR. Traditional audit approaches, often manual and rule-based, struggle with real-time monitoring and predictive insights, creating gaps in efficiency and transparency. This article investigates the role of Explainable Artificial Intelligence (XAI) in enhancing SAP financial auditing and compliance processes. It presents a structured XAI framework that integrates SAP ERP and S/4HANA data sources, anomaly detection, risk scoring, and human-interpretable explanations to support auditors and compliance teams. Use cases, including explainable fraud detection, continuous compliance monitoring, and audit decision assistance, are analyzed. An experimental evaluation demonstrates that XAI models achieve competitive predictive accuracy while significantly improving transparency, traceability, and auditor trust compared to black-box models. The discussion addresses trade-offs between interpretability and performance, adoption challenges in SAP environments, and ethical considerations. Overall, the study highlights XAI’s potential to transform financial auditing by providing actionable, explainable insights that align with regulatory requirements and foster a more transparent, accountable, and efficient audit ecosystem.

DOI: http://doi.org/10.5281/zenodo.18162206

Published by: vikaspatanker

Authors: Ira Somketu

Abstract: The healthcare industry generates vast amounts of sensitive patient data daily, creating both opportunities and challenges for healthcare providers. Secure management and intelligent analysis of this data are critical for improving patient outcomes, operational efficiency, and regulatory compliance. SAP systems, widely used in healthcare, provide robust platforms for data storage, integration, and management; however, traditional implementations often struggle with unstructured data analysis, predictive insights, and advanced security requirements. The integration of Artificial Intelligence (AI) with SAP systems addresses these gaps by enabling real-time analytics, predictive modeling, anomaly detection, and automated security monitoring. This article explores the convergence of AI and SAP in healthcare, focusing on secure patient data management, AI-driven intelligence, implementation strategies, and emerging trends. Through AI-enhanced SAP systems, healthcare organizations can transform complex datasets into actionable insights while maintaining patient privacy, regulatory compliance, and operational excellence. The article also examines future directions, including real-time analytics, ethical AI, and the integration of emerging technologies such as blockchain and federated learning, highlighting the strategic importance of AI-powered patient data intelligence in modern healthcare ecosystems.

DOI: https://doi.org/10.5281/zenodo.18162131

 

Published by: vikaspatanker

Authors: Drupad Gowda N, GS Rajdeep, Puneeth Kumar GJ, Vignesh Shenoy R

Abstract: Videos are now used everywhere — in education, smart cameras, video calls, hospitals, industries, and home security. But whenever the internet becomes slow or the wireless signal becomes weak, the normal video transmission systems fail and the video quality becomes very poor. The MDVSC (Model Division Video Semantic Communication) technique solves this by transmitting only the meaningful information from video frames instead of sending the entire frame pixel by pixel. It carefully separates the information that stays the same across frames (like background or static objects) and the information that changes (like moving objects). Then, it sends only the important features first. Because of this, MDVSC provides better video clarity, uses less data, and continues working even when network strength is low.

Published by: vikaspatanker

Authors: Aarvik Bhatnagar

Abstract: Effective financial governance is critical for ensuring accuracy, transparency, compliance, and risk mitigation in enterprise resource planning (ERP) systems. SAP ERP provides robust financial and control functionalities; however, traditional governance mechanisms largely depend on static rule-based controls and manual audits, which are increasingly insufficient in handling high-volume, complex, and dynamic financial transactions. This paper proposes an intelligent financial governance approach for SAP ERP systems using hybrid machine learning models that combine rule-based logic, statistical methods, and advanced machine learning techniques. The proposed framework integrates seamlessly with SAP financial modules to enable real-time monitoring, anomaly detection, predictive risk assessment, and continuous compliance management. By leveraging hybrid model architectures, the approach balances adaptability and learning capability with transparency and regulatory interpretability. Practical use cases, including fraud detection, compliance monitoring, and predictive financial controls, demonstrate the effectiveness of the proposed solution. Experimental evaluation highlights the superiority of hybrid models over traditional rule-based and standalone machine learning approaches in terms of detection accuracy, false-positive reduction, and operational scalability. The findings indicate that hybrid machine learning models can transform financial governance in SAP ERP from a reactive control function into a proactive, intelligent, and strategic capability.

DOI: http://doi.org/10.5281/zenodo.18159759

Published by: vikaspatanker

Authors: Mehar Bediya

Abstract: The rapid expansion of the Internet of Things (IoT) and the increasing reliance on wireless connectivity have driven the integration of cloud computing into large-scale IoT ecosystems. While cloud-based solutions offer elastic resources and centralized management, the growing number of connected devices, heterogeneous workloads, and dynamic wireless conditions pose significant challenges in terms of scalability and adaptability. Traditional Cloud–IoT architectures often struggle to efficiently accommodate massive device connectivity, fluctuating data rates, and varying quality-of-service requirements. Consequently, there is a growing need for architectural designs that can dynamically scale resources and adapt system behavior in response to changing network and application conditions. This review paper provides a comprehensive analysis of scalable and adaptive Cloud–IoT ecosystem design for wireless networks. It examines foundational architectural models, wireless communication technologies, and cloud computing paradigms that support IoT deployments. The paper further investigates scalability challenges related to device density, data volume, network capacity, and resource provisioning, as well as adaptability mechanisms that enable context-aware, autonomous, and mobility-aware system operation. Key architectural approaches, including edge and fog computing, microservices, containerization, and serverless computing, are reviewed and compared. In addition, the paper discusses resource management, orchestration strategies, and critical considerations related to security, privacy, and reliability. Through a comparative analysis of existing solutions and an exploration of application domains, the review identifies current limitations, trade-offs, and open research challenges. The paper aims to guide researchers and practitioners in designing resilient, efficient, and future-ready Cloud–IoT ecosystems for dynamic wireless environments.

DOI: http://doi.org/10.5281/zenodo.18159755

Published by: vikaspatanker

Authors: Ira Chaturvedi

Abstract: The rapid digitization of corporate finance and the increasing complexity of global regulatory frameworks have necessitated a shift from manual oversight to intelligent automation. This review article investigates the integration of Machine Learning algorithms within the SAP S/4HANA ecosystem to enhance financial compliance and reporting efficiency. By leveraging the SAP Business Technology Platform, organizations can move beyond traditional rule-based systems to implement real-time anomaly detection, automated intercompany reconciliations, and predictive financial closing processes. The analysis explores the technical architecture required to bridge the gap between transactional data and autonomous governance, highlighting the role of the Universal Journal as a single source of truth. Furthermore, the article addresses the strategic challenges of data orchestration, the necessity of Explainable AI for auditability, and the emerging role of Natural Language Processing in interpreting unstructured regulatory documents. As financial reporting transitions toward a continuous monitoring model, the synergy between ERP robustness and machine intelligence becomes a critical factor in reducing operational risk and ensuring transparency. The findings suggest that while intelligent automation significantly reduces the manual burden of compliance, a human-in-the-loop approach remains essential for maintaining ethical oversight and professional judgment. Ultimately, this review provides a comprehensive framework for organizations seeking to leverage SAP and machine learning to transform the finance function into a proactive strategic asset.

DOI: http://doi.org/10.5281/zenodo.18159753

Published by: vikaspatanker

Authors: Kabir Sehgal

Abstract: The seamless integration of the Internet of Things (IoT) with Cloud Computing has revolutionized data-driven ecosystems, yet it has simultaneously birthed a sophisticated class of "Invisible Risks." Unlike traditional cyber threats that target known software vulnerabilities or hardware weaknesses, invisible risks emerge from the systemic complexity, algorithmic opacity, and "gray-zone" interactions inherent in distributed architectures. These risks including data shadowing, logic flaws in cross-protocol interoperability, and the silent propagation of algorithmic bias—often bypass conventional signature-based detection systems, remaining latent until they manifest as catastrophic failures. This review article proposes a comprehensive Conceptual Framework for Managing Invisible Risks by synthesizing multi-disciplinary research across cybersecurity, system engineering, and cognitive psychology. We categorize these risks across a four-tier architecture: the Perception, Network, Cloud, and Application layers. Each layer is analyzed to identify the "invisibility triggers" that obscure threat vectors from administrative oversight. Furthermore, the paper evaluates contemporary risk assessment methodologies, advocating for a transition from static monitoring to dynamic observability through the use of Bayesian Networks, Digital Twins, and Chaos Engineering. We propose a proactive management strategy anchored by three pillars: Zero Trust Architecture (ZTA), AI-driven Automated Governance, and Edge Intelligence. The framework aims to bridge the "transparency gap" in Cloud-IoT environments, providing researchers and practitioners with a structured roadmap to identify, quantify, and mitigate hidden threats. Finally, the article discusses future directions, including the role of blockchain for provenance and quantum-resistant cryptography, emphasizing that the future of Cloud-IoT security depends on our ability to make the invisible visible.

DOI: http://doi.org/10.5281/zenodo.18159648

Published by: vikaspatanker

Authors: Navya Kulshreshtha

Abstract: The rapid evolution of Industry 4.0 has necessitated a transition from traditional administrative Enterprise Resource Planning (ERP) to "Intelligent ERP" systems that leverage real-time operational data. This review article investigates the optimization of ERP performance through the integration of Machine Learning (ML)–based Predictive Maintenance (PdM) models. While traditional maintenance strategies within ERP namely reactive and preventive often lead to unplanned downtime or resource wastage, ML-based PdM offers a data-driven alternative that predicts equipment failure before it occurs. This study synthesizes current literature regarding the architectural integration of Industrial Internet of Things (IIoT) sensors with ERP modules, such as Asset Management, Production Planning, and Materials Management. We categorize the predominant ML methodologies including Supervised Learning for fault classification, Deep Learning (LSTM and GRU) for Remaining Useful Life (RUL) estimation, and Unsupervised Anomaly Detection evaluating their specific contributions to enterprise-level efficiency. The review highlights how PdM-driven insights directly optimize ERP Key Performance Indicators (KPIs) by reducing maintenance costs, streamlining spare parts inventory through Just-in-Time (JIT) procurement, and enhancing Overall Equipment Effectiveness (OEE). Furthermore, the article addresses critical implementation challenges, such as data silos, scalability, and the "black box" nature of AI models. By analyzing the synergy between predictive analytics and resource orchestration, this review provides a roadmap for researchers and practitioners to build resilient, self-optimizing industrial ecosystems. The findings suggest that the integration of ML-PdM is no longer a peripheral technical upgrade but a core strategic necessity for modern enterprise resource management, enabling a shift from descriptive reporting to prescriptive action.

DOI: http://doi.org/10.5281/zenodo.18159637

Published by: vikaspatanker