ERP / CRM Implementation

Mid-sized enterprises are increasingly challenged to make rapid, accurate, and resource-efficient decisions in a competitive data-driven environment. While useful for descriptive reporting, Traditional Business Intelligence (BI) systems often lack predictive power, require manual interpretation, and limit accessibility for non-technical staff. This study develops and evaluates an augmented analytics framework that integrates machine learning (ML), natural language processing (NLP), and automation into BI platforms to improve decision-making efficiency in small and medium-sized enterprises (SMEs). The framework was validated through three finance, retail, and manufacturing case studies. Results demonstrate consistent performance gains: decision latency reduced by 38-40%, forecasting accuracy improved by 21-23%, and user satisfaction increased by +1.3 to +1.5 points on a five-point Likert scale. Analytical methods showed that LSTM models outperformed ARIMA in forecasting nonlinear and seasonal trends. In contrast, XGBoost outperformed Random Forest, delivering up to 9% higher classification accuracy in credit risk, demand forecasting, and fault detection tasks. NLP components achieved over 90% intent recognition accuracy, enabling natural-language queries, while explainability tools (SHAP, LIME) improved user trust by clarifying the drivers of predictions. The findings highlight the potential of augmented analytics to democratize BI, reduce reliance on technical specialists, and enhance organizational agility. Challenges remain in user trust, data governance, change management, and infrastructure scalability. The study contributes a validated engineering framework for SME adoption and a practical roadmap for implementation. Future research should extend validation to realtime streaming data, unstructured sources, and larger cross-industry samples.

Education provides students with knowledge, hones their skills, and teaches them values. With this, active class
participation is important for students’ academic journey. Student attendance is a crucial factor in determining
student academic performance, as it allows them to participate in class, gain proper knowledge, and have access
to learning materials. However, traditional attendance methods have become inefficient, as it takes a lot of time
to take and is prone to human error and fraudulent activities, including proxy signing. To address this problem,
the proponents propose an attendance system that is integrated with radio-frequency identification (RFID) and
fingerprint authentication, ensuring that only the students can verify their own attendance. With the use of
Arduino Uno and the NodeMCU module, the system will be able to scan and compare attendance records stored
in a central database. With the help of artificial intelligence (AI), the proposed attendance system is able to
feature an alert system that monitors student attendance and notifies the educators when students reach a
specified number of absences that makes them a candidate to drop out of school. The proposed system will be
tested in Doña Teodora Alonzo High School to determine its effectiveness and readiness to be implemented in
educational institutions.

Implementation of Website-Based Academic Information System with RFID on Pesantren Peradaban Dunia Jagat Arsy Pesantren Peradaban Dunia Jagat Arsy is an educational institution that has traditionally used manual attendance systems to record the presence of teachers and students. This manual process has led to several issues, including human error in data entry, loss of Attendance forms, and delays in processing attendance records. To address these problems, a web-based academic information system has been designed using Radio Frequency Identification (RFID) technology. The system enables teachers and students to check in by tapping their RFID cards on a reader, allowing attendance data to be automatically recorded and stored in a MySQL database. This system is developed using the PHP programming language and involves stages such as requirement analysis, system design, testing, and user training. The primary goal of this software is to improve the efficiency and accuracy Of the attendance process while minimizing input errors. The system is restricted to use by administrators, teachers, and students, and is accessible only via a web-based platform. Initial testing is conducted within the environment of Pesantren Peradaban Dunia Jagat Arsy to ensure readiness for full implementation.

Driving profitability in hospitality goes beyond filling rooms and hinges on strategic revenue management. By combining accurate demand forecasting, dynamic pricing, and effective inventory control, hotels can optimize RevPAR. Advanced automation tools now empower hoteliers to make data-driven decisions, react swiftly to market shifts, improve efficiency, and deliver better guest experiences that foster loyalty.

The biomanufacturing industries are based on the differences in systems, which include Laboratory Information Management Systems (LIMS), Manufacturing Execution Systems (MES), and Quality Management Systems (QMS) causing compromises with transparency, delays in decision-making process, and operational inefficiency in Chemistry, Manufacturing, and Controls (CMC) processes. This paper introduces an interoperability architecture that would cut across these silos and create a decision fabric that incorporates real-time shop-floor signals in high-level portfolio decision support. With the help of Data Fabric and Data Mesh concepts, the suggested solution will allow the data flow to be seamlessly organized, keeping data of LIMS, MES, and QMS systems structured and unstructured to be aligned. The architecture application transports shop-floor sensor information, quality control reports and manufacturing records into a centralized decision layer, enabling analytics-informed insights. The framework is focused on scalability, data governance and security to comply with the industry requirements. The complex data models and metadata-driven methods guarantee the contextual data knowledge and provenance. A case study illustrates how the architecture enhances speed of transparency, less data redundancy and predictive decision-making in CMC operations. Findings suggest that there were immense gains in operational visibility, quicker issue resolution and higher strategic decisions at the portfolio level. The given strategy of integration will lead to the transition of the disjointed data management to the single decision support fabric and enable the stakeholders, giving them access to the timely, correct and practical data. The architecture can be scaled to upcoming digital twin and AI-based optimization plans.

Artificial Intelligence (AI) is transforming global economies and societies, reshaping labor markets, education, and governance. For Bangladesh, a developing country with a labor-intensive economy, AI offers both opportunities and risks. This study identifies the socioeconomic challenges Bangladesh may face due to AI adoption, such as unemployment, income inequality, digital divides, and ethical concerns. It also explores mitigation strategies including workforce reskilling, inclusive digital infrastructure, regulatory policies, and innovation promotion. The findings suggest that timely policy interventions are essential to ensure that AI contributes positively to sustainable development without exacerbating inequality.

The traditional Intelligence Cycle—requirements, planning, collection, processing, analysis, and
dissemination—is criticised in this paper for being out of step with the threat dynamics of the twenty-first
century. It makes the case that the linear model from the Cold War era overlooks operational delays and
cognitive blind spots to handle terrorism, disinformation, hybrid threats, and fast cyberwarfare. Cross-agency
integration and timely all-source analysis are impeded by structural pathologies, particularly bureaucratic
stovepipes and an uneven emphasis on TECHINT versus HUMINT. The study emphasises how demand-driven
analysis and the "wrong puzzle" syndrome can cause cognitive errors that result in missed or incorrect
conclusions. In order to realign culture, technology, and processes towards non-linear, recursive, and
hypothesis-driven intelligence appropriate for 21st-century security challenges, it recommends implementing the
Adaptive Intelligence Systems framework.

This is to certify that the thesis entitled A STUDY ON CUSTOMER RELATIONSHIP MANAGEMENT IN INSURANCE SECTOR WITH SPECIAL REFERENCE TO TAMILNADU submitted by Mr.V.RAMESH KUMAR, Part-Time Ph.D., Scholar, Research Department of Business Administration, H.H. RAJAH'S COLLEGE (Autonomous), Pudukkottai -622 001, in partial fulfillment of the requirements for the award of the degree of DOCTOR OF PHILOSOPHY IN MANAGEMENT, is a bonafide record of research work done by him under my guidance and supervision as a Part-Time research scholar in Management and that the thesis has not previously formed the basis for the award to the candidate of any degree, diploma, associateship, fellowship or any other similar titles at any university. I further certify that this thesis represents the independent work on the part of the candidate.

Lead qualification is a critical process in sales and marketing, determining which leads have the highest potential for conversion into customers. This paper explores the integration of Robotic Process Automation (RPA) with Salesforce Customer Relationship Management (CRM) to automate lead qualification processes. By leveraging the capabilities of RPA bots to extract, process, and analyze data from Salesforce, businesses can achieve faster and more accurate lead qualification, leading to increased sales efficiency and revenue generation. This paper discusses the benefits, challenges, and implementation considerations of this integration, supported by real-world examples from companies that have successfully implemented automated lead qualification using RPA and Salesforce.

At its core, generative AI is changing creative industries by enveloping, readdressing, and promoting how ideas are
formulated, shaped, and channeled toward art, music, design, film, gaming, advertising, and publishing. Generative
systems fabricate assets for the commercial world using foundation model breakthroughs and advances in diffusion
architectures from ideation to prototyping; asset generation, post-production, audience-testing, and adaptive
personalization at scale. The article attempts to lay together evidence bearing on productivity, costs, and enabling
conditions for new creativity, with the risks surrounding authorship, labor displacement, data governance, privacy,
and cultural homogenization. The authors differentiate between impacts at the model, tool, workflow, and ecosystem
level, outline nascent patterns for human-AI co-creation, and teach response governance waves as to provenance
standards, licensing regimes, and transparency mechanisms. In that sense, we empirically map capability frontiers in
text, image, audio, video, coding, and multimodal generation while aligning these new capabilities to role
reconfiguration along the creative supply chains. Finally, we propose research and policy priorities for responsible
deployment focusing on human agency, fair compensation, safety, and inclusivity for generative AI to amplify human
creativity instead of replacing it.

The competition for postgraduate admissions has intensified due to a rise in the number of applicants. Many students struggle to understand specific admission criteria and rely on costly and biased consultancy services. To address this, a machine learning-based system is proposed to predict admission chances based on individual profiles. The system uses a historical dataset with features like GRE scores, GPA, TOEFL Scores, Statement of purpose, Letter of recommendation, research experience, and professional background. Three models are developed and compared: Linear Regression, Decision Tree Regression, and Logistic Regression. Linear and Decision Tree models explore linear and non-linear patterns, respectively. Logistic Regression, designed for binary classification, predicts admission probabilities effectively. Logistic Regression shows superior accuracy and minimal error, making it the best choice. The system provides a scalable and data-driven alternative to help students apply strategically.

The trillion-dollar invisible catastrophe of silent customer defects is that 42% of disgruntled customers never complain and leave, costing organizations 15% of yearly revenue in unnecessary churn. Traditional reactive complaint handling service recovery strategies typically arrive too late to save these relationships. This pioneering study shows how artificial intelligence can detect pre-complaint dissatisfaction signals-micro-shifts in language tone, escalating frustration markers in emails, or hesitation patterns in chat interactions-before they become formal grievances. We used AI sentiment analysis tools to monitor realtime communications in a rigorous field experiment spanning 10,000 customer interactions in banking, telecommunications, and retail. We randomly assigned participants to AI-monitored intervention or service control groups. The machine detected linguistic biomarkers such as rapid adjective shifts from "fine" to "unworkable," recurrent problem statements, and passive-aggressive phrasing with 78% accuracy, compared to 31% for humans. Preventive recovery activities, such as fast technical support for tech issues or targeted discounts for delivery annoyance, reduced formal complaints by 43% and increased 90-day retention by 19%. More importantly, clients who received unsolicited aid before complaining were 22% happier than those who had flawless transactions, proving the "preemptive recovery paradox." The Preemptive Recovery Framework identifies five high-probability linguistic triggers that predict silent churn with 89% certainty, allowing organizations to target interventions. This research makes AI-driven sentiment analysis a strategic priority, turning latent unhappiness into loyalty-building moments and redefining service excellence in the algorithmic age.

In today's digital landscape, customer reviews are pivotal in shaping public perception and guiding purchasing decisions. The vast volume of textual feedback generated online presents both an opportunity and a challenge for businesses aiming to understand consumer sentiment. This project focuses on leveraging machine learning techniques to perform sentiment analysis on customer reviews, with the goal of automatically classifying the expressed sentiments as positive, negative, or neutral. By systematically analyzing textual data, the project seeks to provide businesses with actionable insights to enhance services, improve customer satisfaction, and make informed, data-driven decisions. The methodology begins with the collection of customer reviews from relevant platforms, followed by preprocessing to clean and standardize the text. This involves removing noise such as punctuation, stop words, and irrelevant characters, as well as applying techniques like tokenization and lemmatization to ensure consistency. Feature extraction is then performed using Term Frequency-Inverse Document Frequency (TF-IDF), which converts the preprocessed text into numerical features suitable for machine learning algorithms. To classify sentiments, several supervised learning models are employed, including Logistic Regression, Naive Bayes, and Support Vector Machines (SVM). These models are trained on labeled datasets and evaluated using key performance metrics such as accuracy, precision, recall, and F1-score. The comparative analysis of these models aims to identify the most effective approach for sentiment classification, balancing computational efficiency with predictive accuracy. The primary objective of this project is to develop a reliable and scalable sentiment analysis model that can process large volumes of customer reviews in real time. Such a model enables businesses to gain a deeper understanding of customer opinions, identify areas for improvement, and respond proactively to feedback. By automating sentiment analysis, the project contributes to more efficient feedback processing, ultimately fostering enhanced user experiences and strategic decision-making. I.

Navigating the labyrinth of modern career options presents a formidable challenge for students and early-career professionals, often leading to decision paralysis amidst the rapid evolution of industry demands and escalating competition in the global job market. This research project introduces a "Career Guidance Platform," an integrated digital ecosystem powered by a symbiotic pairing of a conversational AI chatbot and an automated resume generator. The core of the platform is an intelligent chatbot that engages users in a nuanced, conversational interface to deeply understand their academic background, co-curricular interests, innate skills, and long-term professional aspirations. Based on a comprehensive analysis of this interaction, the system leverages advanced language models to provide highly personalized career suggestions, skill-gap analyses, and actionable guidance. In parallel, the integrated resume generator empowers users to instantly translate this guidance into a professional, well-formatted resume, tailored to their unique profile and career objectives. This unified solution aims to fundamentally transform career planning by making it more accessible, interactive, data-driven, and efficient, serving as a virtual mentor for students and professionals navigating the complexities of their career journeys.

Increasing need for efficient and unbiased hiring practices has led to a growing interest in automating personality
assessment methods. This paper presents a system that predicts an individual’s personality traits based on their
resume using machine learning algorithms implemented in Python. The system focuses on analyzing textual data
extracted from resumes and utilizes Natural Language Processing (NLP) techniques to determine the applicant’s
alignment with the Big Five personality dimensions: Openness, Conscientiousness, Extraversion, Agreeableness,
and Neuroticism. This approach reduces human bias, speeds up candidate evaluation, and enhances decisionmaking processes in human resource management. The results demonstrate that machine learning models can
accurately classify personality types with significant reliability

Jerawat adalah suatu keadaan di mana pori-pori kulit tersumbat sehingga menimbulkan kantung nanah yang meradang. Kemungkinan besar perubahan hormonal yang merangsang kelenjar minyak di kulit yang menjadi penyebab timbulnya jerawat. Jerawat merupakan penyakit kulit yang cukup besar jumlah penderitanya. Seringkali orang awam mengalami kesulitan dalam melakukan proses identifikasi antara penyakit jerawat dengan penyakit yang mirip dengan jerawat, karena gejala yang ditimbulkan dari penyakit hampir sama. Berdasarkan permasalahan tersebut, perlu adanya pengembangan sistem pakar untuk identifikasi dan tindakan perawatan jerawat wajah menggunakan metode forward chaining. Sistem yang dirancang dan diimplementasikan berbasis Dekstop, menggunakan bahasa pemrograman Visual Basic, dan basis data XAMPP, dengan berisikan 7 data penyakit, 32 data gejala penyakit dan 18 data tindakan pengobatan penyakit. Sistem ini juga telah diuji coba dan divalidasi oleh pakar serta beberapa user. Hasilnya didapa...

Customer comments are a major element influencing how people view and decide on trustworthy businesses in the world of Internet shopping. Reviewers on the other hand may diminish the legitimacy of the site by being untruthful or deceptive. The study offers a machine learning-based solution that helps to find and remove false product reviews. Using algorithms like logistic regression, random forest, and XGBoost, natural language processing techniques are used to turn text into main characteristics for categorization. Among the assessed models, Logistic Regression is picked for its accuracy and speed. Managers can control flagged reviews via the system's Flask-based web interface. Further security measures including IP monitoring and request limits help to increase the system's resilience. The main goals are to promote openness and trust in online marketplaces and to lower fake review activity.

This last years European Higher Education Area (EHEA) has special importance with the adjustment of the curricula and the non-presential hours of the ECTS (European credits). Web services are considered an important tool to develop the new education, even out of universities, and to fit in with the new european project of education. We present in this paper an educational use of web services even with commercial applications suc as Mathemnatica.

A potent strategy of corporate growth through M&A almost always includes a financial manipulation of quantity or
value elements to confuse decision-making. The paper analyses a strategic notion of earnings management (EM) visà-vis investment behavior in M&A-based economies with the idea that acquirers and targets will manipulate earnings
to communicate performance misperception, target attractiveness, and the optimization of transactional outcomes. The
study reviews recent data based on empirical findings internationally (and in GCC nations and the U.K.) about the
kind of manipulation with real earnings management (REM) or accrual-based mechanisms that distressed and non
distressed firms engage in the pre-acquisition period. It further explores the selective acquisition of target firms with
high leverage or earnings opacity and the way acquirers, especially strategic as opposed to financial buyers, recognize
manipulated signals within their models for valuation. This paper, providing further conceptualization linking EM,
investment timing, and target selection, also discusses governance factors such as gender-diverse boards and the
efficiency of capital allocation. Findings from recent studies in the extant literature show that in many ways, EM may
be a concern for short-term objectives but may be seriously damaging for value creation post-merger, especially when
acquirers do not look beyond the manipulated metrics. Thus, this review aims to enhance the knowledge base of how
strategic manipulation governs M&A pathways in emerging markets with useful insights for regulators, investors, and
corporate strategists.

Digital platforms have revolutionized the promotion of nutritious food products, particularly Nutri-cereals, by enhancing consumer awareness, accessibility, and market demand. This study explores the role of digital platforms, including social media, e-commerce, mobile applications, and digital marketing, in promoting the nutritional and health benefits of Nutri-cereal products. Findings reveal that digital platforms effectively increase awareness, with social media campaigns and online advertisements playing a significant role in influencing consumer knowledge and purchasing decisions. However, challenges such as misinformation, lack of interactive content, and difficulties in distinguishing genuine advertisements remain barriers to consumer trust. The study highlights the need for more engaging, transparent, and educational digital strategies to maximize the potential of digital platforms in encouraging Nutri-cereal consumption.

The integration of Artificial Intelligence (AI) into financial technology (FinTech) has significantly reshaped the landscape of investment management, offering personalized, data-driven portfolio optimization solutions that adapt to real-time market conditions and investor risk preferences. Traditional portfolio strategies-such as Mean-Variance Optimization (MVO) and Buy-and-Holdoften lack the agility and predictive accuracy required in today's volatile, data-rich financial environments. This study presents an AI-driven investment portfolio optimization framework that combines deep learning, sentiment analysis, and reinforcement learning to enhance asset allocation decisions. The proposed modular pipeline includes five key components: data collection and preprocessing, hybrid market prediction using LSTM and NLP-based sentiment analysis, dynamic investor risk profiling via unsupervised learning, portfolio optimization through reinforcement learning agents, and real-time rebalancing supported by performance feedback. A proof-of-concept was tested on a five-year historical dataset (2018-2023) covering 50 diversified assets across equities, ETFs, and cryptocurrencies. Comparative analysis with traditional strategiesincluding MVO, Equal-Weighted, Black-Litterman, and Buy-and-Hold-demonstrates that the AI model achieved superior outcomes. The reinforcement learning engine produced the highest annual return (14.5%) and Sharpe ratio (1.38) while maintaining the lowest volatility (11.3%) and maximum drawdown (-12.1%). Notably, the inclusion of sentiment data improved signal precision by over 12%, and the model showed robust adaptability during periods of market stress, such as the COVID-19 pandemic. This study not only confirms the potential of AI in optimizing financial portfolios but also addresses challenges related to transparency, turnover control, and regulatory compliance. The results establish a foundation for next-generation FinTech platforms that are intelligent, ethical, and investorcentric.

This paper presents a programming-enhanced educational framework that combines C# code, mathematical formulations, and visual descriptions to teach machine learning foundations. We present sixteen core mathematical domains through this approach, demonstrating how programming implementation enhances mathematical understanding. The framework addresses the gap between theoretical mathematics and practical machine learning applications, providing instructors with a transferable methodology for undergraduate education. Our approach shows how code implementation can serve as a bridge between abstract mathematical concepts and real-world applications, making complex topics more accessible to students.

This article presents a comprehensive examination of a transformative project that reimagined marketplace payment onboarding through the implementation of a unified global payment framework. The initiative addressed critical challenges faced by marketplace platforms operating across borders, including fragmented user experiences, technical debt accumulation, inconsistent security controls, operational inefficiencies, and limited scalability. Through a carefully architected solution built on React JS, Spring Boot microservices, and Kubernetes orchestration, the article implemented key innovations including universal payment provider integration, enhanced multi-factor authentication, and dynamic compliance management. The phased deployment strategy minimized disruption while validating system performance, resulting in dramatic improvements in seller onboarding time, support requirements, completion rates, and fraud prevention. The article examines technical challenges overcome during implementation, including integration complexity, data migration, and scalability under load, while detailing the multi-layered security approach that protected sensitive financial information through encryption, tokenization, behavioral analysis, and automated compliance controls. This work advances industry understanding of how unified payment frameworks can enable marketplace scalability in an increasingly global commercial landscape.

This introductory chapter presents an overview of the Foundations and Applications of Operational and Business Support Systems in Retail and Manufacturing. Operational and Business Support Systems, complimentary to Informatics Applications, play a strategic role in the success of Retail and Manufacturing companies. While specialized Retailers and Manufacturers are using increasingly different Development Platforms and Technological Solutions, design processes are converging and share a common -and ever growing -body of knowledge. Operational and Business Support Systems deal mainly with the processing of huge volumes of business data and correspondingly to the optimization of Operational and Business Decision Processes. This optimization is essential for Big Data Analytics, Business Intelligence and Meta-business Models. Business Models define how companies derive their Revenues and Profit. Meta-business Models define how companies derive their Revenues and Profit collaborating with other companies, Suppliers and Clients. Meta-business Models concern Supply Chain or Ecosystem Partnerships and use ICT Systems to support their operational collaboration. The optimization addresses also the meta-processes oriented to the definition of the Business Models and Meta-business Models and concern different decision areas, different levels of complexity and different time horizons

This paper explores the optimization of healthcare management systems using Artificial Intelligence (AI) and Machine Learning (ML). As the complexity of healthcare systems continues to grow, AI and ML have emerged as key tools to improve decision-making, resource allocation, and patient care. This paper provides a detailed discussion on four AI algorithms, namely Logistic Regression, Random Forest, Support Vector Machine (SVM), and Neural Networks, and their application in the prediction of patient outcomes, including postoperative LOS and disease diagnosis. Experimental results indicate that the accuracy of the Neural Network model was 91.5%, outperforming other algorithms. The precision of the Random Forest model was 87.3%, while the recall of SVM was 82.4%. Apart from the above point, the current research has noted AI application use for the reduction of healthcare-related cost optimization via predicting financial risk and improving a management strategy pertaining to patient data. Machine learning implementation in an edge computing facility showcased a drop in patient wait time by up to 20% and achieved 15% increase in overall efficiency. Promising results and huge challenges exist side by side with model interpretation and data protection issues. This study highlights the requirement for future AI transparency and ethical data management in order to achieve the full potential of AI in healthcare.

Prezentowana książka powstała na skutek tego, że Uniwersytet Ekonomiczny w Krakowie podjął w 2010 roku na Wydziale Zarządzanie nauczanie w ramach nowego kierunku "Informatyka Stosowana". Dla studentów tego nowego kierunku studiów potrzebne były podręczniki, a na rynku takich nie  było. W związku z tym wykładowcy prowadzący zajęcia na tym nowym kierunku zostali zobligowani do napisania odpowiadających tym wykładom rozdziałów w książce przeznaczonej dla owych studentów. W ten sposób powstała ta prezentowana tu książka i kilka innych, także przywołanych w serwisie Academia.edu.

In the current competitive business world, monitoring and enhancing sales performance is key to organizational growth and sustainability. Based on quantitative analysis, the study explores how Salesforce maximizes sales team productivity by automating mundane tasks and enhancing lead management. Findings indicate impressive gains in core performance indicators such as a 29% decrease in the sales cycle length and 75% decrease in time spent on manual data entry. This study suggests creating a Sales Performance Analysis on Salesforce for improved visibility of sales activity and facilitating data-driven decision-making. The design also capitalizes on the richness of Salesforce features like automated tasks, custom objects, dynamic reports, and real-time dashboards to track such key performance indicators as revenue targets, opportunity wins, sales cycle performance, and lead conversions. Authorized stakeholders can have authority to access real-time sales trend insights, productivity of the teams, and customer interactions using easy-to-consume dashboards and detailed reports. Automated alerts and reminders ensure timely follow-up and proactive sales actions. Even integration of Salesforce analytics capabilities offers predictive analysis, where organizations can forecast sales performance and also determine areas for improvement. This study illustrates how effective deployment of Salesforce CRM tools can greatly ease sales activities, promote responsibility, and enhance overall sales performance, ultimately leading to smarter and more strategic business expansion. These increases in productivity brought about a 30% increase in monthly revenue, as well as increased levels of customer retention.

The rapid digitization of financial services has transformed banking systems worldwide, offering innovative solutions to address financial exclusion in rural economies. This review paper explores the critical role of digital banking in enhancing financial inclusion by providing underserved rural populations with accessible, affordable, and user-friendly financial services. It examines the integration of mobile banking, digital wallets, and internet-based platforms to overcome traditional barriers such as geographical isolation, limited banking infrastructure, and high transaction costs. The study highlights the potential of digital banking technologies to empower rural communities by enabling secure transactions, microcredit access, savings mobilization, and insurance services. Additionally, the role of government policies, financial literacy initiatives, and public-private partnerships in fostering the adoption of digital banking solutions is critically analyzed. Despite these advancements, challenges such as limited internet penetration, lack of digital literacy, cybersecurity threats, and trust issues persist, hindering the widespread adoption of digital banking in rural regions. The paper further emphasizes the importance of tailored strategies, including the development of localized digital tools and infrastructure, to bridge the digital divide and ensure equitable financial access. By synthesizing insights from case studies, policy reviews, and empirical research, this study underscores the transformative impact of digital banking on rural financial inclusion while identifying key areas for improvement. Ultimately, this paper contributes to the discourse on sustainable economic development by advocating for collaborative efforts among stakeholders to harness the full potential of digital banking technologies in addressing financial exclusion in rural economies.

The integration of distributed ledger technologies (DLTs) into multi-cloud environments presents a transformative approach to addressing data integrity and transactional security challenges in modern digital infrastructures. This review comprehensively examines the intersection of multi-cloud computing and distributed ledger systems, highlighting their potential to provide decentralized, tamper-proof, and transparent data management solutions across diverse cloud platforms. The paper explores key architectural frameworks, consensus mechanisms, interoperability protocols, and cryptographic models that enable seamless integration while ensuring scalability, reliability, and enhanced security. Furthermore, it analyzes current use cases, such as supply chain management, financial services, and healthcare, where multi-cloud DLT integration mitigates risks of single points of failure, data breaches, and unauthorized access. By identifying emerging trends, technological limitations, and research gaps, this review offers valuable insights into optimizing multicloud DLT deployments for robust data integrity and secure transactional processes. The study underscores the growing importance of cross-cloud blockchain interoperability and regulatory compliance in advancing secure and resilient multicloud ecosystems.

Enterprise resource planning (ERP) is designed to provide seamless integration of processes across functional areas with improved workflow, standardisation of business practice, and access to real-time, up-to-date data. As a consequence, ERP systems are complex and implementing them can be a challenging, complex, time consuming and expensive project for any company (Yusuf et al. 2004, Koh and. Although there are multitude of installed ERP systems, ERP projects continue to be considered risky to implement in business enterprises as they fail for a variety of closely interconnected organisational and technical factors ). In fact, the introduction of any large-scale integrated information system (i.e. ERP) can lead to significant changes in processes, tasks and people-related issues . The particular risks associated with ERP projects make it necessary for organisations to deploy risk management approaches throughout the project lifecycle ). The need for risk management approaches arises due to the lack of effective guidance on the implementation of ERP . The reluctance of companies to communicate about implementation failures (Hakim and Hakim 2010) does not make it easy for researchers to propose and test efficient frameworks. So far, the literature on ERP projects Hall 2005, Bakker et al. 2009) reveals that (1) knowledge of the risks alone is not sufficient for companies deploying a risk management approach and (2) the deployment of systematic and scientific approaches by managers is uncommon. Based on publications from 1997 to 2009, Baker et al. ( ) also state that the current studies are largely based on how risk management is assumed to work instead of how it is actually used in project practice. The main purpose of this article is to develop a new risk assessment framework (RAF), which enables better management of those risks. The study distinguishes itself from the existing literature on ERP risk management by adopting a more balanced and integrative framework as it demonstrates a practical and holistic approach to identifying and managing risk in ERP implementation. It integrates risk identification, analysis and control by classifying risk hierarchically (external engagement, programme, work stream and work package levels) across technical, schedule, operational, business and organisational categories. This not only helps develop responses to mitigate risks but also facilitates risk control through the organisational hierarchy. This article first reviews the literature in order to identify various risk factors and challenges of ERP implementation, along with any available risk management frameworks. Then using a case study it demonstrates the application of the new RAF for managing ERP implementation.

УДК 004.415.5 А.О. Волоха, Л.П. Дмитроца, канд. техн. наук Тернопільський національний технічний університет імені Івана Пулюя, Україна МОНІТОРИНГ ТА АВТОМАТИЗАЦІЯ КЕРУВАННЯ СЕРВЕРАМИ В ВИСОКОНАВАНТАЖЕНИХ СИСТЕМАХ A.O. Volokha, L.P. Dmytrotsa, Ph.D MONITORING AND AUTOMATION OF SERVER CONTROL IN HIGHLY LOADED SYSTEMS Під час роботи серверів у компаніях щодня створюється величезна кількість файлів журналів. Оскільки програмне забезпечення для збору інформації все ще перебуває в стадії постійного вдосконалення, журнали не тільки корисні для діагностики несправностей, виявлених під час роботи, але також необхідні для відображення довгострокового аналізу продуктивності та забезпечують швидкий огляд поведінки системи. Файли журналів зберігаються у форматі звичайного рядку й зберігаються у файловій системі серверу. Розробники та інженери служби підтримкинайпоширеніші користувачі цих журналів щодня. Залежно від проблеми діагностики, певні групи файлів завантажуються та перевіряються в текстовому редакторі за бажанням користувачів, що не дозволяє ефективно фільтрувати чи шукати інформацію. Є можливість покращити зручність використання і, крім того, використати цінну інформацію, доступну у файлах журналів. Розмір звичайної бази даних журналів вимірюється в терабайтах, що вимагає гнучкої інфраструктури, здатної обробляти великі дані, щоб відповісти на запитання різних зацікавлених сторін. Основною задачею системи моніторингу та автоматизації керування є збереження актуальної інформації в кластері Elastic, далі ELK, бази даних та подальша обробка за допомогою машинного навчання на предмет аномалій в журналах. При виявленні аномалій буде виконана одна з 3-х дій: 1. Автоматичне відправлення повідомлення відповідальній людині у випадку, якщо проблема типу Warning. 2. Виклик мікросервіса, який виведе сервер з кластера, повідомить відповідальну людину та вимкне його у випадку, якщо проблема типу Fatal або Error. 3. Повідомлення відповідальну людину, якщо нетипово багато, але не критичних записів одного типу Info та Debug. Також в кластері ELK запропоновано будувати візуалізацію з більше ніж 6TB даних та отримувати аналітику використання та навантаження серверів, що дозволяє економити на закупках обладнання та покращити взаємодію користувача з сервісом.

Salesforce, one of the leading Customer Relationship Management (CRM) platforms, has evolved by integrating cutting-edge technologies, notably Artificial Intelligence (AI) and Automation, to help organizations optimize their customer engagement and business processes. This research explores how AI and automation have been implemented within Salesforce, focusing on the resulting innovations that enhance operational efficiency, decision-making, and customer satisfaction. By utilizing AI technologies such as machine learning, natural language processing (NLP), and predictive analytics, Salesforce has revolutionized CRM functions, offering features such as predictive sales forecasting, automated lead scoring, and smart customer service interactions. Automation further streamlines workflows, reduces manual effort, and ensures consistency in handling customer queries and sales processes. This study examines the benefits and challenges of integrating AI and automation into Salesforce, highlighting real-world applications and case studies. It also discusses the future potential of AI and automation within Salesforce, suggesting avenues for further innovation in CRM systems. The research methodology includes a mix of qualitative and quantitative approaches, using case studies, expert interviews, and performance metrics to evaluate the impact of these technologies. Findings from the research underscore that while the integration of AI and automation into Salesforce delivers tangible benefits, organizations face challenges related to data quality, integration complexity, and employee adaptation. The paper concludes by providing recommendations for businesses to maximize the potential of these technologies within Salesforce while mitigating the associated challenges.

The emergence of Artificial Intelligence (AI) is transforming lockbox and treasury operations, improving financial efficiency, accuracy, and security. Traditional treasury management processes often struggle with manual processing, reconciliation delays, and fraud. In fact, AI-powered innovations such as intelligent automation, machine learning, predictive analytics, and natural language processing are revolutionizing these functions by enabling the automation of payment processing, improving cash flow forecasting, and reducing fraud detection mechanisms. This paper discusses how AI-based solutions can help process data in real time, facilitate better decision-making using advanced analytics, and lower operational costs for financial institutions and corporate treasury organizations. AI enables organizations to optimize working capital management, enhance liquidity visibility, and support strategic financial planning. Lockbox with AI starts processing payment within hours, thereby reducing small batches of payment, minimizing or eliminating errors, and providing insight through an automated extraction and classification of the data. The future of treasury operations will be characterized by enhanced efficiency, agility, and security as financial institutions and businesses more widely embrace AI-driven solutions. This research, in its nature, provides a state-of-the-art overview of the most critical AI use cases, bottlenecks, and opportunities in lockbox and treasury operations, serving as a roadmap for organizations looking to remain competitive in a changing financial landscape.

ERP systems are the backbone of global supply chain, while their success and failure determines the fate of the business. With enormous competition and ever increasing challenges in boundless trading, the IT linkages and E-Business involve extensive customization. There are many researches on the implementation facilitators and barriers in all types of organizations throughout the world but limited literature can be found in work specific to Pakistan. This study brings out the critical factors that drive a successful ERP system in Pakistan and also discusses the pitfalls to be avoided in order to prevent a disaster. Through this study, the critical success factors and the main challenges for implementation of ERP in Pakistani organizations have been recognized. In this way, suggested to localize for ERP implementation in Pakistani organizations.

ERP systems are evolving vastly with the implementation of Artificial Intelligence (AI) and cloud computing. In this article, the future ERP is discussed through the examination of AI-optimized optimization platforms that ensure maximum system efficiency, security, and decision-making. Increasingly, companies are looking towards cloud-based models of ERP with the utilization of AI to automate processes, identify anomalies, and deliver realtime insights that enhance overall responsiveness in volatile markets. The research encompasses key concerns such as data protection, interoperability, and scalability of the AI model, with advantages of AI-based ERP in predictive analytics, process automation, and better management of resources. AI-based ERP solutions allow organizations to attain increased operational efficiency, lower costs, and higher system resilience against cyberattacks. The study also investigates case studies that illustrate the actual impact of AI in ERP systems in sectors such as finance, healthcare, and supply chain management. Further, the article presents the significance of highspeed storage and multi-cloud strategies in facilitating AI-based ERP transformations. By tapping the potential of AI, contemporary enterprises can realize unprecedented customization, automation, and strategic decisionmaking.

Underinvestment in training during ERPII implementations is having a significant impact upon benefits realisation. There is a lack of research covering the training requirements of customer facing organisations implementing ERPII. Literature shows that training attracts the smallest proportion of the implementation budget. However, the current research highlights the critical role training plays in these systems and that pre-and post-implementation training should not be underestimated for the full benefits of the system to be realised. This paper presents nine CSFs required for ERPII implementation in customer facing organisations and outlines recommendations for tackling each. A case study was undertaken to identify resource allocation during the implementation lifecycle and training guidelines were prepared following in-depth interviews with client and supplier consultant practitioners.

This paper presents a combination model which sets out a critical pathway for ERPII implementation for customer facing organisations (CFOs). The model incorporates a resource based view of implementation and the implementation lifecycle (from conception to completion), allowing identified critical success factors (CSFs) to be considered at each stage. A case study analysis was undertaken to identify potential CSFs. These were subsequently appraised by four senior management members within the project team two years post go-live and cross referenced for validation against the views of seven highly experienced consultants within supplier organisations. Analysis of the validated CSFs using the combination model identified that CFOs require specific CSFs at particular stages of the implementation lifecycle. This type of organisation should invest heavily in people-related CSFs, with a particular emphasis upon these in the exploitation phase.

PurposeDelivering benefits from ERP II is challenging and the purpose of this paper is to investigate the allocation of resources contributing towards benefits realisation.Design/methodology/approachA case‐study investigation was based on a five‐month placement within the project team of an organisation implementing ERP II. A critical success factors (CSF) phasing model was developed in order to categorise invested resources. Participant observations formed the first stage of the research and two years post implementation the observations were presented back to four key members of the project team to identify which invested resources contributed towards benefits realisation. Subsequently, the findings were presented to seven supplier organisations to validate the CSFs required to deliver benefits realisation from ERP II implementations.FindingsThis research has identified 19 CSFs for ERP II, contributing an additional six to those already outlined in literature. A critical pathway f...

The document presents strategies for small and medium-sized businesses to implement effective Customer Relationship Management systems despite limited resources. It covers various aspects including platform selection criteria, implementation techniques, sales and marketing alignment, data management practices, utilization maximization, and scaling options. The content emphasizes cost-effective approaches that deliver substantial business benefits without requiring extensive technical expertise or large budgets. While extensive research exists on enterprise CRM implementation, there is a notable gap in practical guidance for resource-constrained SMBs. This study fills this gap by synthesizing best practices specifically tailored to SMBs with limited budgets and technical expertise, drawing on recent empirical evidence from successful implementations across diverse industry sectors.

The emerging role of Artificial Intelligence (AI) in Enterprise Resource Planning (ERP) Cloud and Oracle Integration Cloud (OIC) is revolutionizing functional procedures across various industries. Automation by AI enhances operational effectiveness with speeded-up workflows, business pattern forecasts, and enabling real-time decision-making. The current article explores how AI is incorporated into ERP Cloud platforms to maximize data management, predictive analytics, and business intelligence optimization. It also explores how companies are confronted with the implementation of AI-powered cloud solutions, such as data security issues, system compatibility and adjustment of employees. AI-powered improvements in OIC facilitate seamless integration of enterprise applications, automation of day-today tasks, and innovation. Through machine learning algorithms, businesses can be more agile, scalable, and cost-efficient. But to adopt AI-enabled ERP Cloud, there is a need for change through strategic planning, highperformance infrastructure, and trained human capital to unlock its maximum potential. This articleinvestigates the future of AI within ERP Cloud focusing on its contribution towards enterprise digital transformation, automation, and strategic decision-making.

Resumo: Com a quantidade de indicadores estudados dentro do projeto de consultoria em segurança do trabalho, fica evidente a necessidade de controlar todo avanço existente no desenvolvimento, para se manter nos prazos, estáveis na elaboração e foco nos pontos para atuação. O presente trabalho teve como objetivo a elaboração de um dashboard para acompanhamento de um projeto de consultoria em segurança do trabalho em uma indústria de mineração, pautado na abordagem qualitativa. Destacamos que, com o uso dessa ferramenta, foi possível acompanhar o projeto e seus níveis de criticidade, bem como, futuramente, as multas a serem acarretadas em caso de não cumprimento das ações sugeridas, além da contagem de itens exigidos por cada norma e quais eram atendidos ou não pela realidade da indústria, para rastreamento e facilidade ao se pensar no plano de ação que seria proposto pela empresa prestadora de consultoria.

Customer Relationship Management (CRM) recruitment tools powered by Machine Learning (ML) are transforming talent acquisition by automating and optimizing the hiring process. This system leverages ML algorithms for resume parsing, candidate screening, and job matching, ensuring efficient and data-driven decision-making. By analysing historical hiring patterns, candidate behaviour, and job descriptions, the tool enhances recruitment accuracy and reduces time-to-hire. Natural Language Processing (NLP) enables sentiment analysis of candidate interactions, improving engagement and retention. In addition to, predictive analytics help forecast hiring needs, minimizing workforce gaps. This ML-driven CRM recruitment tool enhances efficiency, diversity, and overall hiring quality, making the recruitment process more intelligent and scalable.

This research aims to identify specific management practices that can help to improve the process of IS/business alignment and the design of ITG architecture that supports those processes. To this end the authors propose a framework that is used as a reference to classify relevant management practices in the process of IS/business alignment and design of ITG architectures. The analysis was carried out in a large leading international food and beverage company. Findings show that at operational and tactical levels, improving the coordination of the IT investment management process and enabling structures that strengthen the connection of budgetary controls are core structural capabilities that can positively improve the process of IS/business alignment and impact significantly the design of ITG architectures.

ERP or Enterprise Resource Planning can enable companies to optimize their business processes and allows for necessary management .Thus, ERP can be said as system software that can integrate several activities in a project & deliver a unison result for bettering performance & increase profits. A construction ERP system provides Cost optimization, incorporate design changes, Consistent quality conformance, Reliable, Faster and on time delivery, Incorporates value engineering, Collaborative work environment, team tracking facility. The case study relating to ERP implementation by firms operating in the Infrastructure construction industry is investigated. It is found that to ensure efficient implementation firms must first have a good reason why ERP should be implemented, determine the tradeoffs, choose an appropriate re-engineering process, identify and mitigate risks that may arise. Based on the findings, strategies for managing the implementation of ERP in the construction industry...

This paper proposes an integrated framework for enhancing sales enablement through the adoption of advanced Customer Relationship Management (CRM) and analytics solutions. In the face of rapidly changing market conditions and increasingly sophisticated customer expectations, organizations need to empower their sales teams with the right tools to drive growth and customer satisfaction. This framework integrates cutting-edge CRM systems with powerful analytics tools to optimize sales processes, improve decision-making, and enhance customer engagement. The first component of the framework focuses on the implementation of advanced CRM platforms that centralize customer data, streamline communication, and enable personalized interactions. By consolidating data from multiple touchpoints, CRM solutions provide sales teams with real-time insights into customer behavior, preferences, and needs, allowing for more targeted and effective sales strategies. The integration of CRM with artificial intelligence (AI) and machine learning further enhances predictive capabilities, enabling sales teams to identify high-value opportunities and automate repetitive tasks. Analytics solutions play a crucial role in the framework by offering deep insights into sales performance, customer trends, and market dynamics. With advanced data analytics, sales

The purpose of this research study is to investigate the impact of critical success factors (CSFs) on performance in enterprise resource planning (ERP) system implementations. A model was developed and a survey instrument used to gather data from a cross-section of production firms to test the model relationship. The data were analyzed using standard linear regression analyses. The findings suggest that CSFs contribute differentially to performance changes in ERP system implementations.

The integration of AI-driven virtual assistants in healthcare is transforming administrative operations and enhancing patient engagement, presenting a significant shift in the sector's approach to efficiency and patient-centered care. By automating routine administrative tasks, such as appointment scheduling, billing inquiries, and patient data management, AI virtual assistant free healthcare professionals from time-consuming duties, allowing them to concentrate on direct patient care. This shift not only improves operational efficiency but also minimizes the administrative burden on healthcare staff, contributing to a more streamlined workflow. Moreover, AI virtual assistants play a crucial role in fostering patient engagement, providing personalized health reminders, assisting with medical queries, and offering continual support outside clinical settings. Such intelligent systems enhance accessibility, enabling patients to access vital health information and support seamlessly, thus bridging the communication gap between patients and providers. As the healthcare industry faces increased demand for accessible, high-quality care, AI virtual assistants emerge as a vital tool, promoting an environment that prioritizes patient needs while enhancing healthcare delivery. This article explores the dual impact of AI virtual assistants on operational productivity and patient engagement, underscoring their potential to reshape healthcare systems and optimize patient outcomes.