Robotic Process Research Articles

Proposal for a Sustainable Model for Integrating Robotic Process Automation and Machine Learning in Failure Prediction and Operational Efficiency in Predictive Maintenance

This paper proposes a sustainable model for integrating robotic process automation (RPA) and machine learning (ML) in predictive maintenance to enhance operational efficiency and failure prediction accuracy. The research identified a key gap in the literature, namely the limited integration of RPA, ML, and sustainability in predictive manufacturing, which led to the development of this model. Using the PICO methodology (Population, Intervention, Comparison, Outcome), the study evaluated the implementation of these technologies in Alpha Company, comparing results before and after the model’s adoption. The intervention integrated RPA and ML to improve failure prediction accuracy and optimize maintenance operations. Results showed a 100% increase in mean time between failures (MTBF), a 67% reduction in mean time to repair (MTTR), a 37.5% decrease in maintenance costs, and a 71.4% reduction in unplanned downtime costs. Challenges such as initial implementation costs and the need for continuous training were also noted. Future research could explore integrating big data and AI to further improve prediction accuracy. This model demonstrates that integrating RPA and ML leads to operational improvements, cost reductions, and environmental benefits, contributing to the sustainability of industrial operations.

Customer Support Automation of Ticket Creation (RPA) using UiPath

In the dynamic landscape of customer support, manual ticket creation from customer emails is a labor-intensive and error-prone process that impedes operational efficiency. This project proposes an automated system utilizing Robotic Process Automation (RPA) powered by UiPath to streamline the ticket creation process. The solution automates the extraction of critical data from emails, including customer IDs, issue descriptions, and priorities, to create tickets in Customer Relationship Management (CRM) systems. Missing information prompts automated follow-ups, ensuring query resolution. Additionally, the system recognizes structured templates to send predefined responses to frequently asked questions, reduces response time, and handles bounced emails by notifying support teams. Scalability is achieved through parallel bot deployments, enabling efficiency, reduces manual high-volume processing with accuracy and speed. This automation framework enhances operational workload, and improves customer satisfaction, offering a scalable model adaptable across industries.

Intelligent Automation for Retail: Solving Inventory Management Challenges with RPA

Intelligent Robotic Process Automation (IRPA) integrates Artificial Intelligence (AI) with Robotic Process Automation (RPA) to automate complex, unstructured tasks, enhance decision-making, and adapt to evolving scenarios. This paper explores how IRPA can address the challenges of retail inventory management by ensuring real-time stock updates, improving replenishment accuracy, and optimizing processes. A framework for IRPA adoption was developed through extensive research and expert consultations, highlighting key factors such as accuracy, human involvement, and task standardization as primary drivers for successful implementation. Secondary considerations, including digital data input, system integration, and cost implications, were also analyzed. The study leverages Relative Importance Index (RII) analysis and Causal-Loop Diagrams (CLD) to provide actionable insights for organizations. This framework serves as a practical guide for leveraging IRPA to transform inventory management, improve operational efficiency, and enhance value creation. Keywords: Retail, Inventory Management, Robotic Process Automation (RPA), Intelligent Automation, Stock Replenishment, Real-Time Updates, Optimization, Digital Transformation, Process Analysis Framework.

Customer Support Automation of Ticket Creation

It has become a critical research area and innovation in itself to meet the efficiency and scale in handling customer inquiries because automation in customer support is critical. This paper comes as a robust solution toward the automatic creation of customer support tickets by using UiPath with Robotic Process Automation, or RPA. In such an approach, the proposed system will leverage the more advanced capability of UiPath automation so that all incoming queries from customers through different sources, including emails, even chatbots or social media, could be extracted, classified, and prioritized. The proposed system thus encompasses along with ZOHO application so that a good system is made ensuring it helps in extracting intent as well as related data while creating tickets seamlessly. Automated workflow eliminates human intervention, thereby cutting the time taken for processing quite appreciably and minimizing error. The automated workflow eliminates manual intervention, which cuts down the processing time quite significantly and minimizes errors. The key features are data validation, dynamic template mapping, and real-time updates to the CRM system. Experimental results show improvement in operational efficiency with 40% reduction in ticket handling time and 95% accuracy in query classification. This work opens up the possibility of scalability and flexibility of RPA-based solutions to enhance customer support processes in the future. KEYWORD: Email Parsing and Data Extraction, Integration with Ticketing Systems, Zoho application, UI path, RPA (robotic process automation systems), ticket creation, excel automation.

A Review of Integration of Robotic Process Automation and Artificial Intelligence: Advancements, Applications and Challenges

A Review of Integration of Robotic Process Automation and Artificial Intelligence: Advancements, Applications and Challenges

Evaluating the Impact of Robotic Process Automation on Earnings Management

Evaluating the Impact of Robotic Process Automation on Earnings Management

A robotic process automation model for order-handling optimization in supply chain management

A robotic process automation model for order-handling optimization in supply chain management

Force–Position Coordinated Compliance Control in the Adhesion/Detachment Process of Space Climbing Robot

Adhesion-based space climbing robots, with their flexibility and multi-functional capabilities, are seen as a promising candidate for in-orbit maintenance. However, challenges such as uncertain adhesion establishment, unexpected detachment, and body motion unsteadiness in microgravity environments persist. To address these issues, this paper proposes a coordinated force–position compliance control method that integrates novel adhesion establishment and rotational detachment strategies, integrated into the gait schedule for a space climbing robot. By monitoring the foot-end reaction forces in real time, the proposed method establishes adhesion without risking damaging the spacecraft exterior, and smooth detachment is achieved by rotating the foot joint instead of direct pulling. These strategies are dedicated to reducing unnecessary control actions and, accordingly, the required adhesion forces in all feet, reducing the possibility of unexpected detachment. Climbing experiments have been conducted in a suspension-based gravity compensation system to examine the merits of the proposed method. The experimental results demonstrate that the proposed rotational detaching method decreases the required pulling force by 65.5% compared to direct pulling, thus greatly reducing the disturbance introduced to the robot body and other supporting legs. When stepping on an obstacle, the compliant control method is shown to reduce unnecessarily aggressive control actions and result in a reduction in relevant normal and shear adhesion forces in the supporting legs by 44.8% and 35.1%, respectively, compared to a PID controller.

Future Relevance of Innovative Information Technology in External Auditing and the Gap Between the Expertise in Sri Lanka

This study addresses a critical empirical gap by investigating the perceptions of external auditors in Sri Lanka regarding their current expertise and the future relevance such expertise in terms of innovative information technology (IT) in external auditing. The research also assesses whether discrepancies exist between auditors’ current IT capabilities and the expected expertise of emerging IT technologies. Additionally, it explores how specific auditor characteristics influence IT competence and perceptions of future relevance. A structured questionnaire was administered to a sample of 200 external auditors from both Big Four and non-Big Four firms, and the data were analyzed using descriptive and inferential statistical techniques. The findings indicate that while auditors acknowledge the growing importance of IT, a significant gap persists between their existing expertise and the skills required to adopt future IT tools effectively. This gap is most pronounced for advanced technologies such as machine learning, process mining, and robotic process automation. On the other hand, regression analysis highlights that work experience and hierarchical position of external auditors significantly influence IT knowledge and perceptions of relevance. Conversely, demographic factors such as age, gender, education, and firm size did not show a significant impact. The study underscores the need for targeted professional development, particularly for senior auditors, to bridge the technological gap. The results suggest that regulatory and educational bodies should implement strategic initiatives to ensure external auditors are equipped to meet the evolving demands of the audit profession in a rapidly changing technological landscape.

Leveraging RPA for enhancing audit efficiency: Insights and challenges in the audit landscape

The adoption of Robotic Process Automation (RPA) in auditing has emerged as a transformative trend, offering significant potential to enhance efficiency, accuracy, and overall audit effectiveness. This study explores the integration of RPA in auditing processes, identifying key areas where automation can streamline workflows and reduce human errors. Drawing from a qualitative research approach, semi-structured interviews were conducted with 15 professionals across diverse audit sectors, uncovering insights into the benefits, challenges, and strategic implications of RPA adoption. The findings reveal that RPA reduces routine task completion times by up to 50%, improves data accuracy by 40%, and delivers a 120% return on investment within two years. However, challenges such as resistance to change, skill gaps, and compatibility with legacy systems persist. This research highlights the necessity of effective change management, continuous skill development, and ethical governance to maximize RPA's potential. By addressing these challenges, this study provides actionable recommendations for leveraging RPA to revolutionize audit practices, enabling organizations to adapt to an evolving technological landscape.

Best practices for seamless integration of Robotic Process Automation (RPA) and cloud technologies

Transformative technology such as Robotic Process Automation (RPA) and Cloud Integration is the platform to achieve streamlining of operations, enhance employee productivity, and drive innovation for your business. While RPA automates repetitive, rule based work, cloud computing is the scalable, flexible and decentralized infrastructure for storing and processing of data, and collaboration. With RPA integrated with cloud platforms, organizations gain several advantages including accessibility, low cost and real time collaboration. Choosing the right cloud provider that supports RPA workloads, ensuring data flow seamlessly between RPA bots and cloud systems, and using cloud based RPA tools that can scale and that add flexibility, are the best practices for RPA and Cloud integration. It is also important to enforce robust security protocols, data governance as well as comply with risks. Integration is successful when RPA links with cloud native apps and services with as little disruption to work as possible and as much automation effect as possible. It is therefore recommended that performance monitoring, testing and continuous improvement of automation process should be given priority. Through the implementation of these best practices, organizations can attain near full potential RPA and cloud integration for operation efficiency, better customer experience and faster digital transformation. In this paper we explore the key strategies, challenges and solutions to ensure success with the integration of RPA into cloud technologies to help businesses looking to optimize the use of automation and cloud capabilities in unison.

Leveraging technology in internal audit processes for streamlined management and risk oversight

The integration of advanced technologies into internal audit processes has revolutionized organizational management and risk oversight, enabling streamlined, data-driven decision-making. As businesses face increasingly complex regulatory environments and operational risks, traditional audit methods often struggle to keep pace. This paper explores the transformative role of emerging technologies, including data analytics, artificial intelligence (AI), robotic process automation (RPA), and blockchain, in enhancing internal audit efficiency, accuracy, and transparency. By automating repetitive tasks, RPA reduces audit cycle times and minimizes human errors, allowing auditors to focus on value-adding activities. AI and machine learning algorithms enable predictive risk analysis, anomaly detection, and real-time monitoring, strengthening an organization’s ability to identify and mitigate risks proactively. Additionally, data analytics tools facilitate comprehensive assessments of large datasets, uncovering patterns and insights that would otherwise go undetected in manual audits. The paper also examines the role of blockchain technology in fostering transparency and accountability through secure, tamper-proof audit trails. Despite the benefits, the adoption of these technologies presents challenges, including integration costs, skill gaps, and data privacy concerns. Addressing these challenges requires strategic investment, workforce upskilling, and collaboration between audit professionals and IT specialists. By leveraging technology, internal audit functions can evolve into proactive, agile frameworks that align with organizational goals, providing robust risk oversight and contributing to long-term sustainability.

FORMATION OF AN ENTERPRISE MANAGEMENT SYSTEM IN THE CONDITIONS OF THE DIGITAL ECONOMY

The purpose of the article is to study the features of the formation of an enterprise management system in the conditions of digital transformation of the economy. Attention is focused on the importance of building an enterprise management system in the conditions of digitalization. Key changes that the transition to a digital economy leads to are identified. Factors that contribute to the development of the digital economy are highlighted (development of the information and communication technologies industry, strong educational institutions, competitiveness of innovations). The stages of digital transformation that an enterprise must go through are characterized. The conceptual principles of the enterprise management system in the conditions of digital transformation of the economy are defined. The goal of enterprise management in the conditions of digital economy is established. Management tasks are defined, the implementation of which ensures the achievement of the goal. The principles (innovation, adaptability, digitalization, automation, customer-orientation, flexibility, transparency, accountability) and functions (planning, organization, motivation, control, communicative, adaptive) of management in the conditions of digital transformation of the economy are revealed. The principle of omnichannel is proposed for use, which involves the integration of various communication channels, which will ensure the efficiency and quality of management decisions. The list of functions is supplemented with communicative and adaptive functions. Management methods in the digital economy are characterized, which are based on the use of digital technologies and allow for the development of effective management solutions (management method based on digital transformations, data-driven management method, management method based on the use of Agille principles, management using cloud technologies, management using robotics and process automation (Robotic Process Automation (RPA)), management based on multichannel, management using digital platforms). The main directions of development of the management system in the context of digitalization are determined.

Reducing administration? Examining the alignment of robotic process automation and social assistance in Swedish Personal Social Services

ABSTRACT In line with the global trend towards digitalization, digital automation has become a politically endorsed strategy to enhance efficiency and transparency in public service delivery. One such example is the adoption of Robotic Process Automation (RPA) in the context of social assistance (SA) in Sweden. While SA is a municipally organized means-tested subsidy for the most financially vulnerable, RPA functions as highly structured software executing administrative tasks based on predetermined rules. Employing a sociomaterial perspective, this article analyses group interviews conducted in four Swedish municipalities, to explore if and how RPA adoption configures the dynamics of SA casework administration and practice. Findings suggest that RPA adoption does not inherently lead to expected all-encompassing enhancements of faster and fairer eligibility determinations as well as a more client-centric approach in SA casework. Instead, the instrumentalist approach of leveraging digital tools to achieve specific outcomes, combined with the task delineation required by RPA, appears incongruent with the nature of SA casework practice. As caseworkers attest to organizational adjustments and a more instrumental approach towards clients’ situations alongside RPA adoption, this study underscores the inadequacy of the rationalist notion of segmenting SA casework in order to achieve efficiency in casework that, by law, should be based on individual judgements by professional caseworkers.

Finance Receipting using Robotic Process Automation

Finance Receipting using Robotic Process Automation

The use of artificial intelligence for automatic analysis and reporting of software defects.

The COVID-19 pandemic marked a before and after in the business world, causing a growing demand for applications that streamline operations, reduce delivery times and costs, and improve the quality of products. In this context, artificial intelligence (AI) has taken a relevant role in improving these processes, since it incorporates mathematical models that allow analyzing the logical structure of the systems to detect and reduce errors or failures in real-time. This study aimed to determine the most relevant aspects to be considered for detecting software defects using AI. The methodology used was qualitative, with an exploratory, descriptive, and non-experimental approach. The technique involved a documentary review of 79 bibliometric references. The most relevant finding was the use of regression testing techniques and automated log files, in machine learning (ML) and robotic process automation (RPA) environments. These techniques help reduce the time required to identify failures, thereby enhancing efficiency and effectiveness in the lifecycle of applications. In conclusion, companies that incorporate AI algorithms will be able to include an agile model in their lifecycle, as they will reduce the rate of failures, errors, and breakdowns allowing cost savings, and ensuring quality.

Utilizing Robotic Process Automation and Artificial Intelligence in Auditing to Mitigate Audit Risks

Automating repetitive tasks significantly increases the efficiency of operations by identifying repetitive tasks, then choosing the appropriate tools for automation, designing successful workflows, testing, iteration, monitoring the performance of operations continuously, and making the necessary adjustments, which helps auditors accomplish their mission. This research aims to highlight the urgent need to develop the skills of accountants and auditors to maintain alignment with the swift advancements in digital technology in the accounting field to reduce fraud risks and protect stakeholders. The study relied on the applied aspect of 157 questionnaires distributed to a sample of auditors working in auditing firms and Federal Financial Supervision Bureau auditors. We used a five-point Likert scale to answer the questions on the questionnaire and the SPSS V.25 statistical package to look at the data and test our research hypotheses. We used Pearson correlation, simple regression, the coefficient of determination and interpretation R2, and the standard coefficient of regression B. Our research showed that using robotic process automation and artificial intelligence in auditing would make audited financial statements more reliable. Adding auditors' skills, artificial intelligence, and process automation will also significantly mitigate inherent control and detection risks. Furthermore, enhancing the quality of auditing

Multi-Agent Decisional System Based on Emotion Recognition in Speech for Controlling Exceptional Situations in Robotic Production Processes

The paper proposes a multi-agent decision-making system designed to manage exceptional situations in robotic production processes by recognizing human emotions in speech. The importance of human-robot collaboration was highlighted, with a focus on automatically detecting human operators’ emotions as potential warning signals for technical or safety issues. This approach contributes to improving the safety and efficiency of the production process by enabling agents to quickly adjust system settings according to the emotions expressed by operators in speech, for example, in cases of stress or frustration. The paper details the structural and functional scheme of the system. It presents the mathematical model and shows how deep learning algorithms implemented through hybrid neural networks (CNN and RNN) enable the system to accurately recognize and classify emotional patterns in an audio signal. The technology uses Python libraries such as TensorFlow and Keras to analyze acoustic characteristics such as tone, intensity, rhythm, and timbre of the voice. These parameters are used to identify emotions and initiate proactive or automatic corrective actions in robotic processes. Thus, the multi-agent system provides an excellent level of human-robot interaction, optimizing both productivity and safety in complex and dynamic industrial environments.

Optimization of Application Testing in Financial Companies Using Robotic Process Automation (RPA)

In the rapidly evolving era of digitalization, financial companies face the challenge of enhancing efficiency and accuracy in application testing processes. Manual testing, which is time-consuming and prone to human error, is a major obstacle in ensuring the reliability of applications. This study aims to optimize the application testing process in financial companies by implementing Robotic Process Automation (RPA) as a tool for automated testing. The methodology used includes the analysis of testing requirements, selection of the appropriate RPA tools, and the implementation and performance evaluation of the RPA system. The results of the study indicate that the implementation of RPA can reduce testing time and improve error detection accuracy. For the partner application, there are a total of 6 test sets, 31 test cases, 31 scenarios, and 112 test steps. Meanwhile, for the sales application, 6 test sets, 29 test cases, 29 scenarios, and 115 test steps were identified. Using RPA, the duration of manual testing, which was previously limited to 8 hours per day, significantly increased to 24 hours per day, allowing continuous testing without human intervention. The number of test cases handled increased from 7 cases per day to 63 cases per day, indicating a significant improvement in efficiency. The number of test steps also increased from 328 steps per day to 2952 steps per day. The implementation of RPA also positively impacted accuracy by reducing human errors that frequently occur in manual testing. Overall, this study demonstrates that the application of RPA in testing applications at AMF Finance significantly improves the capacity, efficiency, and accuracy of the application testing process.

Developing standard criteria for robotic process automation candidate process selection

<p>Robotic process automation (RPA) is a cutting-edge technology that provides software robots to repeat and mimic the repeatable tasks that a human user earlier performed. The use of software robots is encouraging because of their cost efficiency and easy implementation. Selecting and prioritizing a candidate process for automation is always challenging as all the business processes in an organization are not equally suitable for RPA implementation. Various studies have highlighted several criteria found in the literature for determining, prioritising, and selecting a business process for RPA. Nevertheless, there are no set standards for evaluating and analyzing a certain process or its tasks to determine whether they may be automated to use RPA. This paper aims to develop standard criteria and propose a consistent model to select and prioritize candidate process for RPA projects. To assess these criteria's applicability in the context of RPA, surveys among subject matter experts (SMEs) are used to validate them. Principal component analysis (PCA) and correlation are used to identify the top 20 criteria. Naïve Bayes algorithm is applied on the collected data for decision-making. The developed multi-criteria model exhibits strong precision and recall measures, with training and validation accuracy of 96% and 90%, respectively.</p>