SAGAR GUPTA
ERP Implementation Leader
ERP consultant
ERP consultant
My Scholar Articles
Research Paper : Critical Success Factors for ERP Implementation
Enterprise Resource Planning (ERP) systems have become essential tools for organizations seeking to integrate and streamline business functions such as finance, human resources, sales, and manufacturing. However, ERP implementation remains a complex, multi- phase process characterized by both technical and organizational challenges. This study systematically reviews the critical success factors (CSFs) that influence successful ERP implementations, drawing insights from extensive literature and case studies. Key factors identified include effective change management, robust data management, strong management commitment, comprehensive project planning, proactive risk assessment, and strategic vendor partnerships. These elements play a pivotal role in addressing challenges such as resistance to change, system integration issues, and process reengineering complexities. By focusing on these CSFs, organizations can enhance operational efficiency, improve decision-making, and ensure a positive return on investment….
Research Paper : AI Cancers: Systemic Limitations Threatening the Integrity of Artificial Intelligence
As Artificial Intelligence (AI) systems permeate critical sectors like healthcare, finance, and governance, deep-rooted limitations have begun to surface—often referred to metaphorically as “AI cancers.” These include systemic issues such as algorithmic bias, hallucination, goal misalignment, data poisoning, overfitting, and lack of explainability. Like cancer in a biological organism, these flaws can spread undetected, undermining trust, accuracy, and societal safety. This paper explores the nature, origin, and consequences of these “AI cancers,” while outlining the emerging strategies to detect, contain, and remediate them….
Research Paper : ERP Post-implementation Challenges and Solutions
Enterprise Resource Planning (ERP) systems have become essential tools for organizations seeking to integrate and streamline business functions such as finance, human resources, sales, and manufacturing. However, ERP implementation remains a complex, multi-phase process characterized by both technical and organizational challenges. This study systematically reviews the critical success factors (CSFs) that influence successful ERP implementations, drawing insights from extensive literature and case studies. Key factors identified include effective change management, robust data management, strong management commitment, comprehensive project planning, proactive risk assessment, and strategic vendor partnerships. These elements play a pivotal role in addressing challenges such as resistance to change, system integration issues, and process reengineering complexities. By focusing on these CSFs, organizations can enhance operational efficiency, improve decision-making, and ensure a positive return on investment. This review provides valuable guidance for practitioners and scholars, offering a consolidated perspective on achieving successful ERP deployment in today’s competitive business landscape….
Research Paper : Essential Strategic Factors for Ensuring a Successful ERP Implementation
Enterprise Resource Planning (ERP) systems have become integral to modern organizations, enabling the seamless integration and automation of core business processes, including finance, human resources, sales, and manufacturing. However, ERP implementation remains a complex, multi-phase endeavor characterized by significant technical and organizational challenges. This study presents a systematic analysis of the critical success factors (CSFs) influencing successful ERP deployments, synthesizing insights from extensive literature and real-world case studies. Key technical factors identified include comprehensive system architecture design, effective data migration and validation strategies, rigorous system testing and quality assurance, and scalable infrastructure deployment. Organizational factors such as strong executive sponsorship, effective stakeholder engagement, structured change management frameworks, and strategic vendor alignment are also highlighted as pivotal to success. Addressing technical issues such as data consistency, system interoperability, and process automation requires meticulous project planning and continuous performance monitoring. By aligning technical execution with strategic business objectives, organizations can mitigate implementation risks, enhance system reliability, and optimize long-term return on investment. This review provides a consolidated technical and strategic framework to guide practitioners and researchers in achieving successful ERP implementation….
Research Paper : Retrieval- Augmented Generation and Hallucination in Large Language Models: A Scholarly Overview
Large Language Models (LLMs) have revolutionized natural language processing tasks, yet they often suffer from “hallucination” the confident generation of factually incorrect information. Retrieval-Augmented Generation (RAG) has emerged as a promising technique to mitigate hallucinations by grounding model responses in external documents. This article explores the underlying causes of hallucinations in LLMs, the mechanisms and architectures of RAG systems, their effectiveness in reducing hallucinations, and ongoing challenges. We conclude with a discussion of future directions for integrating retrieval mechanisms more seamlessly into generative architecture. Keywords: Large Language Models (LLMs), Hallucination, Retrieval-Augmented Generation (RAG), Factual Inaccuracy, External Document Retrieval….
Research Paper : Artificial Intelligence in ERP Inventory Management: Transforming Supply Chain Agility and Efficiency
The integration of artificial intelligence (AI) into enterprise resource planning (ERP) systems represents a transformative advancement in inventory management. This paper explores AI-enabled ERP modules’ roles in demand forecasting, stock optimization, and supply chain responsiveness, with reference to case studies from Zara and Amazon, as well as insights from healthcare institutions. Using a conceptual research framework and enriched scholarly citations, this study highlights efficiency gains, operational challenges, and organizational implications of AI adoption in ERP. Findings suggest AIdriven techniques enhance decision accuracy, cost efficiency, and operational agility, though technical, cultural, and ethical challenges persist. Future research directions include deep and reinforcement learning, IoT integration, and responsible AI governance.
Research Paper : Comparative Analysis of Generic and Specialized Natural Language Processing Models Using Prompt Engineering
Recent advances in Natural Language Processing (NLP) have been driven by the widespread adoption of large-scale pretrained language models (LMs). While generic NLP models such as GPT, BERT, and T5 exhibit strong zero-shot and fewshot performance across diverse tasks, specialized NLP models (e.g., BioBERT, FinBERT, SciBERT) are fine-tuned on domainspecific corpora to achieve superior performance in targeted applications. With the emergence of prompt engineering as a method to guide large language models (LLMs), a new research challenge arises: can prompt engineering narrow the performance gap between generic and specialized models, or does domain-specific pretraining remain necessary? This paper provides a comparative analysis of generic and specialized NLP models under different prompt-engineering strategies, focusing on domains such as finance, healthcare, and legal text processing. Experimental findings indicate that while prompt engineering enhances the adaptability of generic LMs, specialized models continue to outperform in precision-critical tasks. The study underscores the complementary role of prompt design and domain-specific adaptation in the next generation of NLP systems.
Research Paper : Recurrent Neural Networks in Complex Finance Applications
The financial domain is inherently dynamic, stochastic, and complex, making it one of the most fertile grounds for the application of advanced machine learning techniques. Among these, Recurrent Neural Networks (RNNs) have emerged as particularly well-suited for modeling sequential and temporal dependencies in financial data. This paper explores the role of RNNs in complex finance applications, tracing their evolution from basic time-series forecasting to modern variants such as Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs). The discussion highlights applications in algorithmic trading, credit risk assessment, fraud detection, portfolio optimization, and regulatory compliance. Case studies are presented to illustrate both the potential and the limitations of RNNs in finance. The paper concludes with a critical discussion of challenges such as interpretability, overfitting, adversarial risks, and future research directions, including hybrid neurosymbolic architectures and transformer-RNN hybrids for financial intelligence.
Research Paper : Evolution of a Neural Network in ERP Implementations
Enterprise Resource Planning (ERP) systems have long served as the backbone of organizational information systems, integrating finance, operations, human resources, supply chains, and customer-facing processes into unified platforms. Traditionally, ERP implementations relied on rule-based configurations and deterministic workflows. However, the evolution of neural networks has introduced adaptive, data-driven intelligence into ERP ecosystems. Neural architectures are increasingly being deployed to enhance demand forecasting, anomaly detection, process optimization, and user personalization within ERP systems. This paper traces the evolution of neural networks in ERP implementations, from early adoption in predictive analytics to contemporary applications in autonomous process automation and decision intelligence. It also explores case studies, challenges, and future research directions, highlighting the transformative potential of neural networks in reshaping the ERP landscape.
Research Paper : Crowdsource Activity as Applications of Neural Networks
Crowdsourcing has emerged as a powerful mechanism for harnessing distributed human intelligence at scale, enabling diverse applications such as data annotation, collective problem solving, and decision-making across domains. With the advent of neural networks, crowdsourced activity has been both a source of critical training data and an arena for deploying advanced artificial intelligence systems to optimize participation, reliability, and outcome quality. This paper explores the intersection between crowdsourced activity and neural networks, emphasizing how neural architectures are applied to classify, validate, and enhance crowd contributions. The discussion spans natural language processing, computer vision, recommendation systems, quality assurance, and hybrid human–AI collaboration frameworks. The review concludes with challenges in scalability, bias mitigation, and ethical considerations, highlighting emerging opportunities for integrating neural networks to reshape crowdsourced ecosystems.
