Robotic Process Research Articles

Intelligent automation of financial and accounting processes in business process outsourcing centers in Poland

Purpose: The aim of the article is to assess the degree of intelligent automation of financial and accounting processes in services centres that operate in the business process outsourcing (BPO) market in Poland and to identify future trends and prospects.Methodology/approach: The main research data comprise various reports on the modern business services sector in Poland for 2017–2023. The methods used in the research include a literature review, case study analysis, and deductive reasoning.Findings: The research results have shown that the financial and accounting process outsourcing market in Poland is changing. There has been a gradual advancement of services provided by BPO, shared services centres (SSC) and global business services (GBS) centres. An undoubted driver of the change is technology, which brings trans-formative potential. This technology is changing the nature of work – from repetitive work to work that requires in-depth analysis and complex problem-solving. There is an increasing tendency to implement intelligent process automation (IPA), which includes, among other things, robotic process automation (RPA) solutions using artificial intelligence (AI), in service centres. The increasing automation of repetitive financial and accounting processes is driven by the need to optimize costs, increase the speed of service and improve the quality of services provided by outsourcing centres.Research limitations/implications: The conclusions presented in the article are important for the development and improvement of financial and accounting processes. They are of cognitive value and can be analyzed by decision-makers at various management levels in service centres in Poland and worldwide.Originality/value: The article narrows the research gap in the field of intelligent automation of financial and accounting processes. The research is part of the trend to continuously improve business processes. The conclusions can be a starting point for future research conducted from a macroeconomic perspective, with a particular focus on qualitative research.

The accounting of virtual robots

Purpose: The aim of the article is to present the principles and methods of measuring the activity of virtual robots. We link the identified metrics to analogies of perceiving a bot as a machine, an automated process, or a digital employee. The latest phenomenon of the anthropomorphization of bots receives special attention.Methodology/approach: We used text analysis of technical and marketing documentation as the research method. We conducted the analysis as a case study using the software of a leading international RPA (Robotic Process Automation) provider.Findings: The results lead to the following conclusions: accounting using bots is multidimensional, embedded in technology, and carried out in real time. The virtual robot accounting solutions avoid the analogy of virtual employees; measurement covers both bots and operators, and the representation of activities is incomplete and distorted.Research limitations/implications: The research was focused on subjective recommendations of the RPA technology provider, taking into account their marketing goals. Originality/value: The research shows that embedded measurement technology automates raw data collection and enables real-time accounting for both bots and their operators. It thus contributes to the development of hybrid accounting. Simultaneously, this technology has a constraining effect, distorting measurement and redefining well-known and well-established effectiveness metrics.

The benefits of implementing Robotic Process Automation in Accounting

Purpose: The purpose of the paper is to identify and evaluate the main benefits associated with implementing process automation in accountingMethodology/approach: The empirical part of the article includes the results of quantitative research, which was conducted using the survey method, employing purposive sampling of respondents. The study was conducted in the first quarter of 2024 on a sample of 109 enterprises based on a research questionnaire. Descriptive statistics measures were utilized in data analysis, taking into account the nature of the questions and scales included in the research tool.Findings: The research demonstrates the following key benefits of implementing ac-counting process automation in the surveyed enterprises: process transparency, risk minimization, increased enterprise productivity, and improved employee satisfaction.Research limitations/implications: The empirical research was exploratory and referred to a limited group of enterprises. However, they can constitute the basis for further research in assessing the use of process automation in accounting.Originality/value: The paper discusses the relatively unexplored area of modern accounting and presents empirical research findings on the benefits of implementing process automation in accounting. This study partially fills the research gap and contributes to the discussion on the use of modern technologies in accounting. The paper is one of the first studies to present the results of research conducted among business entities operating in Poland.

ORPP—An Ontology for Skill-Based Robotic Process Planning in Agile Manufacturing

Ontology plays a significant role in AI (Artificial Intelligence) and robotics by providing structured data, reasoning, action understanding, context awareness, knowledge transfer, and semantic learning. The structured framework created by the ontology for knowledge representation is crucial for enabling intelligent behavior in robots. This paper provides a state-of-the-art analysis on the existing ontology approaches and at the same time consolidates the terms in the robotic task planning domain. The major gap identified in the literature is the need to bridge higher-level robotic process management and lower-level robotic control. This gap makes it difficult for operators/non-robotic experts to integrate robots into their production processes as well as evaluate key performance indicators (KPI) of the processes. To fill the gap, the authors propose an ontology for skill-based robotics process planning (ORPP). ORPP not only provides a standardization in the robotic process planning in the agile manufacturing domain but also enables non-robotic experts to design and plan their production processes using an intuitive Process-Task-Skill-Primitive structure to control low-level robotic actions. On the performance level, this structure provides traceability of the KPIs down to the robot control level.

Robotic Process Automation and its Impact on Purchasing and Supply Chain Management

Robotic Process Automation and its Impact on Purchasing and Supply Chain Management

Implementation of Quality 4.0 framework in the electronics sector using ANP and QFD methodologies

PurposeQuality 4.0 is essential to the Industry 4.0 framework, notably in the electronics sector. It evaluates product quality in real-time using automatic process controls, quality tools and procedures. The implementation of Quality 4.0 criteria in the electronics industry is the subject of this study’s investigation and analysis. In this study, nine Customer Requirements (CRs) and 18 Design Requirements (DRs) have been defined to adopt Quality 4.0, aiming to increase yield while reducing defects. This study has developed a Quality 4.0 framework for effective implementation, incorporating the People, Process and Technology categories.Design/methodology/approachMany CRs and DRs of Quality 4.0 exhibit interdependencies. The Analytic Network Process (ANP) considers interdependencies among the criteria at various levels. Quality Function Deployment (QFD) can capture the customer’s voice, which is particularly important in Quality 4.0. Therefore, in this research, we use an integrated ANP-QFD methodology for prioritizing DRs based on the customers' needs and preferences, ultimately leading to better product and service development.FindingsAccording to the research findings, the most critical consumer criteria for Quality 4.0 in the electronics sector are automatic systems, connectivity, compliance and leadership. The Intelligent Internet of Things (IIOTs) has emerged as the most significant design requirement that enables effective control in production. It is observed that robotics process automation and a workforce aligned with Quality 4.0 also play crucial roles.Originality/valueExisting literature does not include studies on identifying CRs and DRs for implementing Quality 4.0 in the electronics industry. To address this gap, we propose a framework to integrate real-time quality measures into the Industry 4.0 context, thereby facilitating the implementation of Quality 4.0 in the electronics industry. This study can provide valuable insights for industry practitioners to implement Quality 4.0 effectively in their organizations.

Effect of Artificial Intelligence (AI) on Financial Decision-Making: Mediating Role of Financial Technologies (Fin-Tech)

The main objective of the current study is to shed light on the mediating effect of financial technology (Fin-Tech) on the relationship between artificial intelligence (encompassing natural language processing (NLP), machine learning algorithms, computer vision, predictive analytics, robotic process automation (RPA), blockchain technology, and deep learning) and financial decision-making from the perspective of financial managers within Jordan's commercial banking sector. Realizing this objective required the use of quantitative methodology. A questionnaire was self-administered by 86 financial managers in the Jordanian banking sector. Primary data was analyzed using AMOS. Results of analysis confirmed that FinTech plays a significant mediating role between AI applications and financial decision-making. Machine learning was identified as the most impactful AI technique, facilitating more informed decisions through advanced data analysis and pattern recognition beyond the scope of traditional analysis methods. The novelty of current research is in the fact that it offers valuable insights into the intersection of AI and Fin-Tech within the Jordanian financial sector. It contributes to the understanding of how advanced AI techniques can enhance financial decision-making, emphasizing the importance of multidisciplinary expertise in the development of AI-driven financial systems. The findings have significant implications for both theoretical understanding and practical application in the finance industry. Doi: 10.28991/HIJ-2024-05-03-015 Full Text: PDF

ICT ADOPTION AMONG REAL ESTATE AGENTS IN MALAYSIA

The success of real estate agents relies on the ability to provide better services to their clients efficiently and seamlessly. The use of Information and Communication Technology (ICT) is benefiting real estate agents by making it more convenient for real estate agents to find clients, advertise the property, and facilitate the property transaction process, which is indubitable. Therefore, the main objective of this study is to assess the level of ICT usage by real estate agents in valuation and agency firms in Klang Valley, Malaysia. Data were gathered through a survey conducted with these firms, and 400 responses were collected using a quantitative study and analysed using descriptive statistics and the Relative Importance Index (RII) ranking. The study revealed that most respondents adopt ICT in their practices, with the most frequently used tools being Mobile Instant Messaging, Social Media, and Real Estate Agent Websites. However, technologies such as Virtual Reality, Augmented Reality, Robotic Process Automation, and Metaverse Real Estate are rarely utilised. The study concluded that the widespread application of ICT in real estate is evident. However, inadequate training, higher cost of acquisition, and shortage of skilled people are the barriers that limit its full potential within the sector. This study recommends that the top management of valuation and agency firms provide training programs and ongoing support that cater to the specific needs of real estate agents.

Towards discovering erratic behavior in robotic process automation with statistical process control

Companies that frequently use robotic process automation often encounter difficulties in maintaining their RPA portfolio. To address these problems and reduce time spent investigating erratic behavior of RPA bots, developers can benefit from exploring methods from process sciences and applying them to RPA. After a selection process, we examine how variability and deviations impact robotic process automation. Indicators of statistical dispersion are chosen to assess variability and analyze RPA bot behavior. We evaluate the performance of RPA bots on 12 processes, using statistical dispersion as a measure. The results provide evidence that variability is an undesirable form of erratic behavior in RPA, as it strongly correlates with the success rate of the bots. Importantly, the results also show that outliers do not affect the success rate of RPA bots. This research suggests that variable analysis can help describe the behavior of RPA bots and assist developers in addressing erratic behavior. Additionally, by detecting variability, we can more effectively handle exceptions in RPA.

How does robotic process automation create value for firms?

RPA’s potential for efficiency improvement highly recommends the analysis of its applicability to the organizations’ processes. This paper analyses the value of RPA as its impact on the main activity groups in the value chain: Procurement, Internal Operations and Marketing and Sales. The analysis of RPA’s contribution for each of the activity groups addresses a research question through an exploratory approach: “How does RPA create value for organizations and which RPA features are critical for such value creation?”. The data was collected through interviews, analyzed using content analysis, and shows that RPA has impacts on both downstream and internal dimensions. Regarding upstream dimension further research is required to ascertain RPA’s potential.

Navigating digital transformation: Insights from recent studies on process automation and innovation

The integration of process automation and digital transformation is reshaping modern business practices, driving efficiency, innovation, and strategic agility. Studies reviewed provide comprehensive insights into how technologies such as Artificial Intelligence (AI), Robotic Process Automation (RPA), and the Internet of Things (IoT) are revolutionizing various sectors, particularly manufacturing and process industries. Research by Bichel et al. (2023) explores how AI and RPA are transforming workplaces, emphasizing the need for businesses to adapt to rapidly changing environments and promote employee creativity. Their study highlights the importance of establishing intelligent IT ecosystems and integrating new strategies across departments to leverage digital transformation effectively. Sjödin et al. (2018) focus on the development of "smart factories," which use advanced digital technologies to enhance operational efficiency, product quality, and sustainability. They identify the benefits of smart factories, including reduced costs and increased adaptability, while also noting the significant challenges of implementing such large-scale transformations. Their proposed maturity model emphasizes the need for cultivating digital skills, adopting agile processes, and using modular technologies. Chirumalla (2021) investigates how digitalization and Industry 4.0 technologies can improve process industries by enhancing product quality and operational flexibility. The study develops a framework with 19 dynamic capabilities for transitioning from traditional to digitally-enabled process innovations, identifying key challenges and enablers for successful implementation. Malik et al. (2021) examine the broad application of digital transformation across engineering domains, highlighting its role in automating traditional processes and addressing big-data challenges. The editorial presents 52 applications of digital transformation, showcasing advancements in areas such as cybersecurity, intelligent manufacturing, and advanced construction.

Overview of structure and drive for wheel-legged robots

Wheel-legged robots are a type of mobile robot that combines the advantages of wheeled robots, such as fast and stable movement and high efficiency, with the adaptability of legged robots to complex and unstructured environments. Therefore, wheel-legged robots have great potential for application in fields such as deep space exploration, disaster relief, and wilderness exploration. This paper categorizes and summarizes the structural forms and driving modes of wheel-legged robots, dividing them into three categories: wheel-legged hybrid robots, wheel-legged separation robots, and wheel-legged transformation robots based on their structural characteristics. Finally, this paper summarizes the structure and driving aspects of wheel-legged robots and provides an outlook on their development in these two areas. The research results presented in this paper help researchers understand the development process of wheel-legged robots and serve as a valuable reference for future research.

Achieving Housing Affordability in the U.S. through Sustained Use of AI and Robotic Process Automation for Prefabricated Modular Construction

The rising housing issues occasioned by high costs in the United States (US) are traceable to the high costs of building materials and construction. This study proposes the combined leveraging of AI and Robotic Process Automation (RPA) for prefabricated modular construction as a strategic means of reducing high costs of housing and increasing efficiency in construction. It argues that doing so would pave way for housing affordability in the US. Deploying descriptive survey and qualitative method alongside the applicable interpretive and descriptive techniques, the study demonstrates in the course of its analysis that AI and RPA are viable means of reducing costs of construction and housing, and increasing efficiency. It submits that amidst the established constraints to the ideal extent of leveraging AI for construction activities, the leveraging of AI and RPA for prefabricated modular construction yields huge positive results among which are reduced costs of construction and housing and increased efficiency in construction. Government and construction organizations are charged to play enabling roles in the adoption and sustainability of AI and RPA in construction activities, including prefabricated modular construction, so as to achieve reduced costs of housing and increase efficiency in construction.

About managing the number of simultaneously functioning software robots of different types

Objectives. The study sets out to justify the relevance and investigate approaches for solving the problem of managing the number of simultaneously functioning software robots of various types under conditions of limited computational resources and changes in sets of executable tasks.Methods. A proposed solution is based on models and methods of scenario management, linear programming, inventory management, queuing theory, and machine learning. The described methods are valid for different compositions and preconditions for generating initial data, as well as ensuring the relevance horizons of the obtained solutions.Results. The initial data is obtained via the presented approach for determining the computational resource parameters for operating a single software robot. The resources are determined by analyzing the composition of the software and information services used by an actual software robot. Problem statements and mathematical models are developed for cases involving scenario management and linear programming methods. Methods for real-time management of the number of software robots and their sequential local optimization are proposed based on the abovementioned solution sequences. The developed method for generating statistical data based the results of applying the sequential local optimization method is used to identify deficient and non-deficient computational resources. Some results of working in the multi-functional center of RTU MIREA software robots developed on the Аtоm.RITA platform are outlined.Conclusions. The emerging problem of managing the number of simultaneously operating software robots of various types for cases involving scenario control methods and linear programming is formalized. This problem is relevant in the field of automation of business processes of organizations. The use of mathematical methods for solving this problem opens up opportunities for expanding the functional capabilities of robotic process automation platforms, as well as increasing their economic efficiency to create competitive advantages by optimizing the use of IT infrastructure components.

Acceptance of Rpa in Public Sector Institutions

ABSTRACT Due to the increasing demands for modern communication channels and the economical fulfillment of public tasks, innovative technologies are needed to improve the efficiency and interoperability of existing IT infrastructures in public institutions. Robotic process automation (RPA) is a promising technology that has the potential to pragmatically automate administrative processes in public institutions, thereby providing much-needed work load reduction for public employees. However, any RPA project will not be successful if the relevant stakeholders’ acceptance of the new technology is insufficient. Until today, research did not address which factors influence the acceptance of RPA in public sector institutions. A relevant question nonetheless, as the peculiarities of the public sector in the operation of processes are well known. Therefore, this study examines the drivers of RPA acceptance and usage behavior in public sector institutions. As a result of a grounded theory study, a theoretical framework with factors for evaluating RPA user acceptance is developed and an understanding of its facilitators is gained. The findings also provide practical recommendations for guiding RPA implementation in public sector institutions.

Robotic Process Automation at Top Accountants: An Adaptable Case Designed to Evaluate Processes and Create Bots with Various Levels of Complexity

Robotic Process Automation at Top Accountants: An Adaptable Case Designed to Evaluate Processes and Create Bots with Various Levels of Complexity

Pineapple Detection with YOLOv7-Tiny Network Model Improved via Pruning and a Lightweight Backbone Sub-Network

High-complexity network models are challenging to execute on agricultural robots with limited computing capabilities in a large-scale pineapple planting environment in real time. Traditional module replacement often struggles to reduce model complexity while maintaining stable network accuracy effectively. This paper investigates a pineapple detection framework with a YOLOv7-tiny model improved via pruning and a lightweight backbone sub-network (the RGDP-YOLOv7-tiny model). The ReXNet network is designed to significantly reduce the number of parameters in the YOLOv7-tiny backbone network layer during the group-level pruning process. Meanwhile, to enhance the efficacy of the lightweight network, a GSConv network has been developed and integrated into the neck network, to further diminish the number of parameters. In addition, the detection network incorporates a decoupled head network aimed at separating the tasks of classification and localization, which can enhance the model’s convergence speed. The experimental results indicate that the network before pruning optimization achieved an improvement of 3.0% and 2.2%, in terms of mean average precision and F1 score, respectively. After pruning optimization, the RGDP-YOLOv7-tiny network was compressed to just 2.27 M in parameter count, 4.5 × 109 in computational complexity, and 5.0MB in model size, which were 37.8%, 34.1%, and 40.7% of the original YOLOv7-tiny network, respectively. Concurrently, the mean average precision and F1 score reached 87.9% and 87.4%, respectively, with increases of 0.8% and 1.3%. Ultimately, the model’s generalization performance was validated through heatmap visualization experiments. Overall, the proposed pineapple object detection framework can effectively enhance detection accuracy. In a large-scale fruit cultivation environment, especially under the constraints of hardware limitations and limited computational power in the real-time detection processes of agricultural robots, it facilitates the practical application of artificial intelligence algorithms in agricultural engineering.

AI-enhanced inventory and demand forecasting: Using AI to optimize inventory management and predict customer demand

The advent of artificial intelligence (AI) has ushered in a new era of efficiency and accuracy across various industries, with inventory management and demand forecasting being at the forefront of these advancements. Traditional inventory management techniques, often reliant on historical data and simple statistical models, fall short in addressing the dynamic and complex nature of contemporary markets (Chopra & Meindl, 2016). AI, with its advanced algorithms and machine learning capabilities, offers a transformative approach to these critical business functions. This paper explores the integration of AI technologies in optimizing inventory management and predicting customer demand. AI-enhanced inventory management involves the application of various AI technologies such as machine learning, natural language processing (NLP), computer vision, and robotics process automation (RPA) (Ivanov et al., 2017). Machine learning algorithms analyze vast amounts of historical data to identify patterns and trends, enabling more accurate predictions and adjustments in inventory levels. NLP processes unstructured data from sources like social media and customer reviews to provide deeper insights into market trends and customer preferences (Cambria & White, 2014). Computer vision technologies assist in real-time monitoring of inventory levels and identifying discrepancies through visual data, while RPA automates repetitive tasks like order processing and inventory tracking, thereby reducing human error and increasing efficiency (Aguirre & Rodriguez, 2017). This paper highlights significant improvements in forecast accuracy and inventory turnover rates achieved through AI implementation and discusses future implications for supply chain management.

Leveraging Cost-Effective AI and Smart Technologies for Rapid Infrastructural Development in USA

High cost of building makes houses expensive for US citizens and residents. Thus, this study proposes the leveraging of cost-effective artificial intelligence (AI) and smart technologies (ST) for rapid infrastructural development in US. It considers them as sustainable means of tackling the challenges for the attainment of affordable houses. The study explores the potentials of prominent AI and smart technologies capable of reducing the cost of building houses in the US, for which houses would become affordable for all. The primary data are obtained from telephone interviews with 10 construction workers and 5 experts of AI, alongside observation and introspection. The secondary data are drawn from library and the internet. Qualitative method, thematic and content analyses, systematic review, and descriptive and interpretive tools are employed. The results show Machine Learning, Natural Language Processing, Computer Vision, Reinforcement Learning, and Robotic Process Automation to be prominent cost-effective AI technologies, while Building Automation Systems, Internet of Things, Renewable Energy Systems, and Smart Water Management Systems are cost-effective smart technologies. The study concludes that the identified AI and smart technologies are not only cost-effective, but also transformative and innovation-driven and can be leveraged to increase efficiency, productivity, quality delivery and satisfactory services. The study recommends them to government and organizations for cost-effectiveness towards attaining rapid infrastructural development in the USA.

Robotic Process Automation in Banking for Better Customer Experience

Robotic Process Automation (RPA) has emerged as a transformative technology in the banking sector, offering opportunities to streamline operations, reduce costs, and enhance customer experiences. This explores the impact of RPA implementation on customer experience within the banking industry. By automating repetitive, rule-based tasks such as account maintenance, loan processing, and customer inquiries, banks can significantly improve operational efficiency and accuracy while freeing up human resources to focus on higher-value activities. Furthermore, we discuss the challenges and considerations associated with RPA adoption in banking, including data security, regulatory compliance, and employee upskilling. Ultimately, this underscores the pivotal role of RPA in reshaping the banking landscape, driving innovation, and delivering superior customer experiences in an increasingly digital world.