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Robotic Process Automation and Artificial Intelligence in Industry 4.0 – A Literature review

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Jorge Ribeiro
Jorge Ribeiro
Jorge Ribeiro
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Rui Lima at University of Minho
Rui Lima
Tiago Eckhardt
Tiago Eckhardt
Tiago Eckhardt
  • This person is not on ResearchGate, or hasn't claimed this research yet.
Sara Paiva at Polytechnic Institute of Viana do Castelo
Sara Paiva
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Abstract

Taking into account the technological evolution of the last decades and the proliferation of information systems in society, today we see the vast majority of services provided by companies and institutions as digital services. Industry 4.0 is the fourth industrial revolution where technologies and automation are asserting themselves as major changes. Robotic Process Automation (RPA) has numerous advantages in terms of automating organizational and business processes. Allied to these advantages, the complementary use of Artificial Intelligence (AI) algorithms and techniques allows to improve the accuracy and execution of RPA processes in the extraction of information, in the recognition, classification, forecasting and optimization of processes. In this context, this paper aims to present a study of the RPA tools associated with AI that can contribute to the improvement of the organizational processes associated with Industry 4.0. It appears that the RPA tools enhance their functionality with the objectives of AI being extended with the use of Artificial Neural Network algorithms, Text Mining techniques and Natural Language Processing techniques for the extraction of information and consequent process of optimization and of forecasting scenarios in improving the operational and business processes of organizations.
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ScienceDirect
Available online at www.sciencedirect.com
Procedia Computer Science 181 (2021) 51–58
1877-0509 © 2021 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)
Peer-review under responsibility of the scientific committee of the CENTERIS - International Conference on ENTERprise
Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on
Health and Social Care Information Systems and Technologies 2020
10.1016/j.procs.2021.01.104
10.1016/j.procs.2021.01.104 1877-0509
© 2021 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0)
Peer-review under responsibility of the scientic committee of the CENTERIS - International Conference on ENTERprise
Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health
and Social Care Information Systems and Technologies 2020
Available online at www.sciencedirect.com
ScienceDirect
Procedia Computer Science 00 (2019) 000–000
www.elsevier.com/locate/procedia
1877-0509 © 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the CENTERIS - International Conference on ENTERprise Information Systems /
ProjMAN – International Conference on Project MANagement / HCist - International Conference on Health and Social Care Information
Systems and Technologies
CENTERIS - International Conference on ENTERprise Information Systems / ProjMAN -
International Conference on Project MANagement / HCist - International Conference on Health
and Social Care Information Systems and Technologies 2020
Robotic Process Automation and Artificial Intelligence in Industry
4.0 – A Literature review
Jorge Ribeiro*, Rui Lima, Tiago Eckhardt, Sara Paiva
Instituto Politécnico de Viana do Castelo, Portugal.
Abstract
Taking into account the technological evolution of the last decades and the proliferation of information systems in society, today
we see the vast majority of services provided by companies and institutions as digital services. Industry 4.0 is the fourth industrial
revolution where technologies and automation are asserting themselves as major changes. Robotic Process Automation (RPA) has
numerous advantages in terms of automating organizational and business processes. Allied to these advantages, the complementary
use of Artificial Intelligence (AI) algorithms and techniques allows to improve the accuracy and execution of RPA processes in the
extraction of information, in the recognition, classification, forecasting and optimization of processes. In this context, this paper
aims to present a study of the RPA tools associated with AI that can contribute to the improvement of the organizational processes
associated with Industry 4.0. It appears that the RPA tools enhance their functionality with the objectives of AI being extended
with the use of Artificial Neural Network algorithms, Text Mining techniques and Natural Language Processing techniques for the
extraction of information and consequent process of optimization and of forecasting scenarios in improving the operational and
business processes of organizations.
© 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the CENTERIS - International Conference on ENTERprise
Information Systems / ProjMAN – International Conference on Project MANagement / HCist - International Conference on Health
and Social Care Information Systems and Technologies
* Corresponding author.
E-mail address: jribeiro@estg.ipvc.pt
Available online at www.sciencedirect.com
ScienceDirect
Procedia Computer Science 00 (2019) 000–000
www.elsevier.com/locate/procedia
1877-0509 © 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the CENTERIS - International Conference on ENTERprise Information Systems /
ProjMAN – International Conference on Project MANagement / HCist - International Conference on Health and Social Care Information
Systems and Technologies
CENTERIS - International Conference on ENTERprise Information Systems / ProjMAN -
International Conference on Project MANagement / HCist - International Conference on Health
and Social Care Information Systems and Technologies 2020
Robotic Process Automation and Artificial Intelligence in Industry
4.0 – A Literature review
Jorge Ribeiro*, Rui Lima, Tiago Eckhardt, Sara Paiva
Instituto Politécnico de Viana do Castelo, Portugal.
Abstract
Taking into account the technological evolution of the last decades and the proliferation of information systems in society, today
we see the vast majority of services provided by companies and institutions as digital services. Industry 4.0 is the fourth industrial
revolution where technologies and automation are asserting themselves as major changes. Robotic Process Automation (RPA) has
numerous advantages in terms of automating organizational and business processes. Allied to these advantages, the complementary
use of Artificial Intelligence (AI) algorithms and techniques allows to improve the accuracy and execution of RPA processes in the
extraction of information, in the recognition, classification, forecasting and optimization of processes. In this context, this paper
aims to present a study of the RPA tools associated with AI that can contribute to the improvement of the organizational processes
associated with Industry 4.0. It appears that the RPA tools enhance their functionality with the objectives of AI being extended
with the use of Artificial Neural Network algorithms, Text Mining techniques and Natural Language Processing techniques for the
extraction of information and consequent process of optimization and of forecasting scenarios in improving the operational and
business processes of organizations.
© 2019 The Authors. Published by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Peer-review under responsibility of the scientific committee of the CENTERIS - International Conference on ENTERprise
Information Systems / ProjMAN – International Conference on Project MANagement / HCist - International Conference on Health
and Social Care Information Systems and Technologies
* Corresponding author.
E-mail address: jribeiro@estg.ipvc.pt
52 Jorge Ribeiro et al. / Procedia Computer Science 181 (2021) 51–58
2 Jorge Ribeiro et al. / Procedia Computer Science 00 (2019) 000–000
Keywords: Robotic Process Automation, Artificial Intelligence, Industry 4.0.
1. Introduction
The availability of digital services is seen as a growing trend at a company-level, taking into account the greater
use of the proliferation of information systems in society and the technological evolution that we are witnessing at
various levels. The form of communication between citizens, companies and institutions started to be mostly through
the exchange of digital information. In view of the high volume of information and digital documentation exchanged
between entities, in general, it is humanly impossible to respond in a timely manner to the processing of all information
and to follow up on processes internally. In this sense, we highlight the importance of Robotic Process Automation
(RPA), which can be defined as a “technique that results in the automatic execution of administrative, scientific or
industrial tasks” [1] which uses robotics as a “set of techniques concerning the operation and use of automata (robots)
in the execution of multiple tasks in place of man” [1] for “how to do a thing; standard; method; system” [1]. In this
context and in a nutshell, the RPA tools correspond to a set of techniques that aim to improve the work by reducing
the number of repetitive tasks, automating them [2]. In addition to the use of RPA, the complement with Artificial
Intelligence (AI) - algorithms and techniques - allows to improve the precision of the execution of automated
processes. Industry 4.0 reviews a set of technologies and sensors that allows an even greater advance in the processes
and applications of automation of AI applications for organizational processes, contributing to a better performance
and presenting new opportunities.
The main contribution of this paper is essentially in providing a review of AI and RPA contributions to Industry
4.0 as well as in the analysis and comparison of several proprietary and opensource tools regarding their
functionalities. This document is structured as follows. In chapter two the general concept about Robotic Process
Automation is presented and in section 3 the general concept of Artificial Intelligence and Industry 4.0. In chapter 4,
several proprietary and open source tools are presented and their main characteristics so that in chapter 5 a discussion
about the tools is made. In chapter 6 the conclusions are presented and then the references that support this work are
presented.
2. Robotic Process Automation
Robotic Process Automation (RPA) is the automation of services tasks that reproduce the work that humans do [3].
The automation is done with the help of software robots or AI workers that are able to perform , accurately, repetitive
tasks. The task instructions are set by the developer using some form of screen recording and defining variables. These
tasks include actions like logging into applications, copying and pasting data, opening emails, filling forms, among
others [4]. Van der Aalst et al. [3] state that “RPA is an umbrella term for tools that operate on the user interface of
other computer systems”. Although traditional forms of process automation (like screen recording, scraping and
macros) also rely on the computer’s user interface, RPA’s core function is via element identification and not by screen
coordinates [4] or XPath selections. This, in most cases, provides a more intelligent interaction with the user interface.
Commercial vendors of RPA tools report a surge in demand since 2016 [3], and we see some research where these
tools are used for automating digital forensics [4], auditing [5] and industry [6]. The advent of the fourth industrial
revolution (Industry 4.0) is paving the way for new ways to automate mundane rules-based business processes, using
RPA tools on information obtained from smart devices [6]. For business processes, RPA is the extrapolation of a
human worker’s repetitive tasks by a robot (where those tasks are done quickly and profitably). This aims to replace
people by automation in an outside-in manner. Unlike traditional methods, RPA is not part of the information
infrastructure but rather sits on top of it, implying a low level of intrusiveness [7] possibly reducing costs. Some
reports present a 30% to 50% decrease in operational costs of transactional activities within shared services with the
use of RPA technologies [8].
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3. Artificial Intelligence and Industry 4.0
At one time AI was a concept divided into major fields of application. Some of those fields where natural language
processing, automatic programming, robotics, computer vision, automatic theorem proving, intelligent data retrieval,
etc. Nowadays these application areas are so extensive that each could be considered a field in and of itself. AI is now
best described as a group of core ideas that underline many of these applications [9]. The use of AI by machines to
complete complex tasks, reduce costs and improve the quality of goods and services is the core principle of smart
factories and industry 4.0 [10]. AI technologies are permeating the manufacturing industry and merging the physical
and virtual worlds with the help of cyber-physical systems. The use of AI makes the manufacturing industry smart
and capable of addressing modern challenges like customizable requirements, reduced time to reach the market and
increasing number of sensors used in equipment [11]. The use of flexible robots combined with AI allows for easier
manufacturing of different products. AI methods (like data mining) are capable of analyzing large volumes of real-
time data gathered from various sensors [12].
4. RPA Tools with IA support
In recent years, AI algorithms [13] and Machine Learning (ML) approaches have been successfully applied in real-
world scenarios, such as commerce, industry and digital services. ML [14] is used to “teach” machines how to deal
with data more efficiently, simulating the learning concept of rational beings and can be implemented with AI
algorithms (or techniques), reflecting the paradigms / approaches of rational characteristics such as connectionist,
genetics, statistics and probabilities, based on cases, etc. With the AI algorithms and based on the ML approach, it is
possible to explore and extract information to classify, associate, optimize, group, predict, identify patterns, etc. Given
the scope of the applicability of AI, RPA has gradually been adding, to its automation features, implementations of
algorithms or AI techniques applied in certain contexts (e.g.: Enterprise Resource Planning, Accounting, Human
Resources) to classify, recognize, categorize, etc. In recent years, some academic studies have been published as
challenges and potential, as well as case studies of the applicability of RPA and AI, as are the cases of articles [15] in
the field of automatic discovery and data transformation, in the audit area, [17] in the application of Business Process
Management and in productivity optimization processes [18]. Other studies on the intelligent automation of processes
using RPA have been published, such as that of the consultancy Delloite [19], which presents the potentialities of the
applicability of AI algorithms and techniques, but it should be applied in well-defined, stabilized and mature processes,
like in strategic areas focused on customer tasks, increasing employee productivity (optimizing routine tasks),
improving accuracy in categorizing and routing processes, improving the experience with customers and employees,
enhancing the analytical data analysis, reduce fraud and payment of “fines” processes for non-compliance with dates
or procedures defined by government institutions. In this context, and based on the above, if on the one hand there are
challenges and potentialities of the concept of automation using RPA, these may be further enhanced with the
application of algorithms and AI techniques. The following sections present commercial and open source tools that
we consider representative of the recent applicability of RPA (ideally with the application and some AI techniques or
algorithms).
4.1. UiPath
UiPath [20-26] is a tool that allows the development of RPA functionalities in its framework to create and execute
programming scripts, allowing it to be programmed with an interface of blocks and multiple plugins for the business
process customizations. The RPA UiPath platform is currently structured in three modules, UiPath Studio, UiPath
Robot and UiPath Orchestrator, in which the latter allows the possible orchestration of robots [20]. The UiPath Studio
module corresponds to a tool that allows to design, model and execute workflows [ 21] and help in the creation and
maintenance of the connection between robots, as well as to ensure the transfer of packages, management of queues.
In turn, with the storage of log records and linked with Microsoft's Information Services Server and SQL Server, as
well as with Elasticsearch (which is open source and built on the Apache License search engine) with a Kibana data
visualization plugin also allows to potentiate the view of analytical information associated with the execution of RPA
processes. These features can be found in more detail in [22-24]. Some Artificial Intelligence techniques or algorithms
54 Jorge Ribeiro et al. / Procedia Computer Science 181 (2021) 51–58
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are currently available through the UiPath tool through its UIAutomation module [25] and which are disclosed on its
official page [26], of which the following stand out: recognition, optimization, classification and information
extraction. In terms of AI algorithms, for the information consulted they use image and character recognition,
optimization, classification.
4.2. Kofax
Kofax [27-33] is a company that develops process automation software in companies and organizations. The tool
offers a set of modules oriented to RPA, business process orchestration through procedural flows of software activities,
document recognition (through Optical Character Recognition - OCR processes) and advanced data analysis. Being a
proprietary tool and not having been able to obtain a test version to this study, several sources of information were
consulted [27-34] in order to collect as much information about the tool. As RPA automation processes, this tool
makes it possible to extract data from documents, other sources (web, e-mail, local files) in various formats and design
and allows the execution of procedural flows between computer applications to optimize tasks associated with
Enterprise Resource Planning (ERP) information systems. As with other tools, it also provides modules associated
with the implementation of techniques or algorithms associated with AI. Being able to be more or less profound in
terms of the application of these techniques, the tool allows for example to recognize the content and context of a
document [28], or through the classification and recognition of information in emails, web portals and paper [34]. The
use of ML approaches combined with the recognition and classification of OCR documents and the analysis o f the
contents of e-mails or web pages can be considered as forms of supervised learning since a set of prior information is
required to classify and validate the contents. On the other hand, the application of natural language processing,
depending on the technique or algorithms, can be used in supervised learning for classification or unsupervised
learning to analyze content through information clustering (“clustering”) or density extraction. In this sense, it appears
that some AI techniques or algorithms are currently available through the Kofax tool through the Intelligent
Automation platform [32] and its Cognitive Document Automation module [33].
5.3 Automation Anywhere
Automation Anywhere [35-41] is another tool oriented towards RPA processes with the particularity of also providing
information on the applicability of AI techniques / algorithms. As an RPA tool applied to ERP contexts and like other
tools previously described, it covers several areas of applicability such as human resources, Customer Relationship
Management, Supply Chain, being especially liable to be integrated or interconnected with ERPs from SAP and
Oracle, and can be interconnected with other ERP's from other companies. Allied to the RPA is the most automatic or
intelligent process called “Digital Workers”. The RPA tool incorporates a module called cognitive automation and
analytical data analysis tools applied to RPA processes. Being an application with numerous functionalities, it provides
a set of information that allows the configuration, operation and implementation of RPA processes [35-41]. The
Automation Anywhere tool through its Bot tool [40], internally provides the execution of some Artificial Intelligence
techniques and algorithms such as fuzzy logic, Artificial Neural Networks, and natural language processing for the
extraction of information from documents and consequently improve efficiency in document validation. In this sense,
it appears that some AI techniques or algorithms are currently available by the Automation Anywhere intelligent word
processing tool through the IQ Bot platform [40].
4.3. WinAutomation
The WinAutomation tool [42] provides a set of features associated with automation processes that are incorporated in
the RPA processes, namely, automation of emails, files in various formats (eg PDF and Excel), OCR and other features
associated with the post employees' work environment (desktop or web). In turn, softomotive is an RPA solutions
company that created WinAutomation. WinAutomation is aimed at desktop environments that have built-in process
design, desktop automation, web automation, macro recording, multitasking, automatic task execution, mouse and
keyboard automation, User Interface designer, email automation, excel automation, file and folder automation, system
monitoring and triggering, auto-login, security, File Transfer Protocol automation, exception handling, repository and
Jorge Ribeiro et al. / Procedia Computer Science 181 (2021) 51–58 55
Jorge Ribeiro et al. / Procedia Computer Science 00 (2019) 000–000 5
control images, command line control, web data extraction, PDF automation, scripting, OCR capabilities, computer
vision, non-participatory and participatory automation, advanced synchronization, auditing and logs, web recorder,
inactive and non-interactive execution, database and SQL, cognitive and terminal emulation [43]. In terms of RPA
functionalities, the tool provides a set of modules through the “processrobot” module and through a partnership with
the company CaptureFast allows to extend its RPA functionalities with information capture engines using AI, data
extraction in documents and systems automatic and hybrid document classification. Based on the analyzed literature
[42-45], the Cognitive module allows integrating the functionalities with the analytical information analysis engines
from Microsoft, IBM and Google's Cognitive. However, it appears that at the level of availability of AI functionalities,
the tools do not present evidence.
4.4. AssistEdge
The AssistEdge tool, owned by EdgeVerve Systems (a subsidiary of Infosys), is a proprietary tool, but with an
“opensource” version for the community [49]. Based on institutional information [46-49], its functionalities are OCR
reading for processing documents based on the context associated with the type of document. Based on information
from automation processes, it uses AI algorithms (e.g. Artificial Neural Networks) [49] for automatic data capture,
data analysis through the analysis of process variations based on individual process monitoring and classification of
information for recommendation processes.
4.5. Automagica
The Automagica tool [50] is proprietary with an opensource version (for non-commercial purposes), with its code
being made available on GitHub [51]. Developed mainly in the Python language, it can be exploited by other
implementations by the community (e.g. of AI techniques or algorithms). Among the basic features of RPA, such as
reading OCR, extracting texts from PDF files, automating information in word files, excel, information collected via
the browser and creating automation processes, it also allows interconnection with Google Tensorflow for image and
text recognition.
5. Discussion
Based on the study of the most representative RPA tools described in this document, a comparative table of
technologies is presented below, specifying which objectives of Artificial Intelligence and AI algorithms they use:
Table 1. Comparison of technologies and goals associated to IA
Artificial Intelligence Techniques or Algorithms used
Tool
Recognition
Optimization
Classification
Information
extraction
Computer
Vision
(*)
F
uzzy
M
atching
Statistic
methods
Artificial
Neural
Networks
Decision Trees
Fuzzy Logic
Natural
Language
Processing
Text Mining
Recomendation
Systems
UiPath
X
X
X
X
X
X
X
X
Kofax
X
X
X
X
X
X
Automation
Anywhere
X X X X X
X
X
WinAutomation
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
AssistEdge
X
X
X
X
X
X
X
Automagica
X
X
X
X
X
X
X
(*) Computer vision - Considering the application of AI algorithms such as Artificial Neural Networks.
NA – Information Not Available.
56 Jorge Ribeiro et al. / Procedia Computer Science 181 (2021) 51–58
6 Jorge Ribeiro et al. / Procedia Computer Science 00 (2019) 000–000
Analyzing information on the web and in digital libraries, a set of tools were identified that implement automation
processes associated with RPA. Those tools have the implementation of “smart” processes associated with automation
routines but mainly are associated with the implementation of techniques and algorithms of AI. It appears that the
proprietary tools have a greater set of information and RPA functionalities with AI. Open source tools depend on the
community of developers, they are still under development or growing in the number of functionalities such are the
tools TagUI [52, 53], TaskT [54], Robot Framework Foundation [55].
UiPath is a tool with a lot of features and a lot of documentation. It has several plugins that can be programmed
allowing adaptability to other applications such as PowerShell, SAP ERP, Oracle and Microsoft Dynamics. The Kofax
and Automation Anywhere tools implement several RPA processes with interconnection to ERP’s mainly to ERP
SAP. AssistEdge tool demonstrated the possibility of being integrated with Microsoft (Azure Machine Learning) and
Google (cognitive Services) cognitive systems, which allows to enhance the usability of the implementations of these
two large technological companies.
In the case of proprietary companies (where the largest number of RPA functionalities and information is more
available) the adoption of RPA processes with AI or the interconnection with other ERPS will always be associated
with a licensing cost. In the case of open source RPA tools, initiatives and implementations are still growing. However,
in recent years, numerous implementations (at the academic level) of algorithms are used in free programming
languages (such as R and python), and some initiatives of RPA may take advantage of implementations of Artificial
Intelligence techniques and algorithms. On the other hand, as a result of the development and availability of Artificial
Intelligence algorithms by Microsoft, the possibility of using the .NET framework from Azure’s Machine Learning
platform, allows for a direct way to explore RPA implementations.
6. Conclusions
This document presents an investigation on RPA with AI for ERP-related processes. It was based on the analysis
of information researched in digital libraries on the web (corporate websites and tools, blogs, etc.), as well as in
scientific digital libraries. A set of proprietary tools (UiPath, Kofax, Automation Anywhere and WinAutomation) and
Opensource tools (AssistEdge and Automagica) were identified, and for each of them a characterization of their RPA
features, their integration with ERPs and support for ERPs was made. We conclude that most of the proprietary tools
implement algorithms associated with the objectives of AI, such as recognition, optimization, classification and
extraction of knowledge from either RPA documents or processes. It also enhances their optimization and exploration
of the information by the users of these applications. The AI techniques and algorithms that these tools implement,
focus on computer vision (image recognition using for example Artificial Neural Networks), statistical methods,
decision trees, neural networks for classification and prediction, fuzzy logic and implementation of techniques
associated with text mining, natural language processing and recommendation systems.
On the other hand, Industry 4.0 - the revolution we are experiencing today - lives on the fusion of the Internet of
Things, intelligent automation, intelligent devices and processes and cyber-physical systems. The combination of all
these concepts and technologies brings a significant change in industrial processes, affecting the workflow of digital
processes throughout the company. Nowadays, and to improve these processes, they are incorporating automation of
some steps through robots (RPA). In addition, RPA nowadays incorporates, in many tools as we have shown in this
paper, intelligent techniques and algorithms (AI), which allows to reach levels of intelligence in the automation of
processes within a company.
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... Because of the increasing number of robot applications, the requirements of robots in the workplace are gradually increasing, and smart manufacturing requires robots to be applied with a high degree of autonomy and precision, as well as a higher degree of safety, which requires robots to have the ability to perceive and self-regulate the environment. The robot's ability to interact with the unknown environment is particularly important in robot control technology, intelligent robots should have the ability to collect the environment and analyze the working state, through its own sensor feedback and self-check, analyze whether the working environment is normal, and always detect the working state during the work process, can be self-corrected [5][6][7][8]. ...
Research on flexible control method of robot based on visual servoing
Article
  • Apr 2025
Flexibility control and vision of robots are important acquisition and feedback links in robot control, and the study of multi-sensor data fusion is becoming more and more important as the complexity of robot tasks increases. This paper describes the robot kinematics and inverse kinematics process by studying the knowledge of D-H model theory and parameter definitions in the machine kinematics model, reveals the changing relationship between the robot joint control and end pose, and establishes a kinematics-based vision servo control model. On this basis, the coupling error compensation algorithm is used to combine the visual position control quantity as well as the force sensing position correction quantity to form the final visual and force sensing supple control strategy. Meanwhile, for the lack of adaptability of classical impedance force control on unknown constraint environments, a two-fuzzy adaptive sliding mode controller is designed according to the Lyapunov stability theorem to drive the robot end in order to achieve the actual position tracking expectation. The results of simulation experiments and motion contour tracking experiments show that the control algorithm proposed in this paper has better control accuracy and is more robust to noise and uncertainty, and the controller is also able to reduce the effect of torque saturation on the robot system.
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... La digitalización de la industria 4.0 ha hecho que las prácticas de auditoría contable sean más eficientes y efectivas para los auditores independientes y la profesión de auditoría (Alvarez-Aros & Bernal-Torres, 2021; Ribeiro et al., 2021). Las tecnologías de la industria 4.0 impulsan la transformación digital en las empresas, mejorando la competitividad a través de la sostenibilidad ambiental, el crecimiento, la diversificación y la innovación (Nwachukwu et al., 2021). ...
TENDENCIAS ACTUALES: LA TECNOLOGÍA EMERGENTE COMO RECURSO CLAVE PARA LA PRÁCTICA DE LA AUDITORÍA
Book
  • Jan 2025
Los procesos de auditoría han evolucionado significativamente con la integración de tecnologías emergentes. Este estudio ha analizado cómo la digitalización ha transformado la auditoría financiera y gubernamental a través de una revisión sistemática basada en el método Prisma. El objetivo de esta investigación fue examinar el impacto de las tecnologías emergentes en los procesos de auditoría, mediante la evaluación de su influencia en el rol del auditor y los desafíos asociados con su adopción en distintos tipos de firmas auditoras. Para ello, se consultaron las bases de datos Google Académico, Dialnet y Scopus, y se establecieron criterios de búsqueda que permitieron recopilar fuentes publicadas entre 2021 y 2025. Como resultado, se identificaron 1268 fuentes, las cuales fueron sometidas a un proceso de filtrado basado en criterios de selección, lo que llevó al descarte de una gran cantidad de documentos. Finalmente, se seleccionaron 16 artículos para su análisis. Los textos analizados destacan el papel de la tecnología en la transformación de los procesos de auditoría y la optimización del rol del auditor. Entre las herramientas más relevantes se encuentran big data, blockchain y la inteligencia artificial, las cuales proporcionan alternativas funcionales para la elaboración de informes contables, el análisis de grandes volúmenes de datos y la predicción de posibles casos de fraude en el futuro.
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... In the era of big data, artificial intelligence (AI) and machine learning (ML) solutions are increasingly being applied across diverse engineering fields, such as manufacturing [17,51], predictive maintenance [12,56], energy [68,19], transportation [62,70], healthcare [72,53], design [64,71], computer vision (CV) [13,26], natural language processing (NLP) [43,29], construction [66,6], robotics and automation [49,55], agriculture [52,9], materials science [7,15], etc. The application of AI and ML in engineering domains is expected to revolutionize traditional practices, accelerating smarter, more efficient, and * Corresponding author greener solutions [27,47,34]. ...
Engineering Artificial Intelligence: Framework, Challenges, and Future Direction
Preprint
Full-text available
  • Apr 2025
Over the past ten years, the application of artificial intelligence (AI) and machine learning (ML) in engineering domains has gained significant popularity, showcasing their potential in data-driven contexts. However, the complexity and diversity of engineering problems often require the development of domain-specific AI approaches, which are frequently hindered by a lack of systematic methodologies, scalability, and robustness during the development process. To address this gap, this paper introduces the "ABCDE" as the key elements of Engineering AI and proposes a unified, systematic engineering AI ecosystem framework, including eight essential layers, along with attributes, goals, and applications, to guide the development and deployment of AI solutions for specific engineering needs. Additionally, key challenges are examined, and nine future research directions are highlighted. By providing a comprehensive perspective, this paper aims to advance the strategic implementation of AI, fostering the development of next-generation engineering AI solutions.
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... Artificial Intelligence (AI) has been used successfully to support the automation of processes, helping production systems to accurately and robustly perform repetitive tasks (Ribeiro et al., 2021;Rajawat et al., 2021) as well as to identify critical malfunctioning (Syafrudin et al., 2018;Zhao, 2019) or to handle unexpected and non-systematic behaviors (Lin, 2020;Van der Aalst et al., 2018;Carvalho et al., 2019) introducing the concept of smart manufacturing. Integrating a modeling step within control schemes has been proven a valid alternative to improve the functioning of automated systems for accurate positioning applications . ...
Motion stage precision prediction for photonic integrated circuit assembly
Article
Full-text available
  • Apr 2025
  • J INTELL MANUF
The consistently growing demand for robust automated Photonic Integrated Circuits assembly, testing and packaging, is increasingly oriented towards high volume and continuously sets newer challenges to overcome concerning throughput and cost effectiveness. Production processes’ intrinsic complexity, combined with short product life cycle and the necessity of quickly ramping up those to high volume, requires smarter solutions to guarantee high yield as well as low cycle time. Robust production demands for motion systems capable to realize repeated movements with precision and resolution in the range of tens of nanometers. These constraints on precision do not allow to operate the system at its highest overall speed; ideal working conditions are thereby preserved by slowing down motion, ending up trading cycle time for precision. Finally, the optimal trade-off between motion speed and repeatability is also expected to depend on hardware conditions and its optimization is therefore impossible without scheduling downtime and performing long evaluation processes. In this paper it is presented a solution for predicting linear stages’ motion inaccuracies from controller features by means of Machine Learning and Deep Learning modeling. The proposed formulation introduces a metric for calculating motion analytical imprecision that includes only the difference between successive position measurements, thus allowing a separation of short term repeatability from other error terms by removing the mean from the evaluation. Successive differences are interpreted as single-motions’ expected errors that can be aggregated into a repeatability estimate, serving as target distribution for the learning problem; predictions of single-motion metrics ensure the proposed approach to work in production scenarios when non identical movements are performed, opening up the possibility to realize advanced control paradigms and predictive maintenance for smart manufacturing.
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Aplicación de herramientas de automatización robótica de procesos (RPA) en procesos de pentesting para MiPyMEs
Article
Full-text available
  • Apr 2025
El hacking ético, también conocido como pentesting, es una práctica clave para identificar vulnerabilidades en sistemas de Tecnologías de la Información (TI) mediante simulaciones controladas de ataques cibernéticos, lo que permite mejorar la seguridad informática. Sin embargo, el enfoque tradicional, que depende de intervenciones manuales, se enfrenta a limitaciones debido al aumento exponencial de activos tecnológicos y la complejidad de las infraestructuras, lo que implica un alto consumo de tiempo, recursos y la necesidad de experiencia técnica especializada. Este artículo explora la integración de la Automatización Robótica de Procesos (RPA) en el pentesting como una solución para optimizar estos procesos. A través de un análisis comparativo de metodologías documentadas y herramientas RPA disponibles, se propone una herramienta específica para automatizar el pentesting en un entorno controlado y seguro. Los resultados experimentales obtenidos indican que esta herramienta es una alternativa viable para mejorar la eficiencia, accesibilidad y escalabilidad de las auditorías de seguridad, lo que la convierte en una solución efectiva en el ámbito de la seguridad informática.
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Artificial Intelligence in the Hospitality Industry – Challenges and Sustainability Solutions
Chapter
  • Apr 2025
Based on theory and practice it takes the student through the key issues focusing on the hospitality industry, particularly the hotel sector. It analyses environmental, economic and social value in the industry, and what can be done to maximise the good for all the stakeholders in the long term.
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Strategic recruitment of the Iraqi workforce in light of digital automation
Research
Full-text available
  • Apr 2025
There is a correlation between achieving sustainable economic development and the availability of an effective and highly skilled workforce that has the ability to innovate, create and keep pace with modern technological developments, especially as we live in the world of digital automation where mechanization, equipment and robots based on artificial intelligence in various industrial sectors .
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Artificial Intelligence Adoption in Service Industries: A Systematic Literature Review of key Drives, Barriers, Challenges, and Strategies
Article
Full-text available
  • Apr 2025
Artificial Intelligence (AI) is reshaping service industries by automating processes, enhancing decision-making, and delivering personalized customer experiences across sectors like tourism, healthcare, finance, and governance. This systematic literature review consolidates findings from over 100 studies to explore the drivers, barriers, and strategies influencing AI adoption. While AI-driven advancements such as robotic process automation (RPA) and predictive analytics enable efficiency and innovation, significant challenges like infrastructural limitations, ethical concerns, and organizational resistance hinder its widespread adoption. High implementation costs, socioeconomic disparities, and data privacy issues further complicate integration efforts, particularly in underdeveloped regions and resource-constrained industries. To address these barriers, the study highlights strategies like targeted training, policy-driven investments in digital ecosystems, and robust data governance frameworks. Additionally, balancing AI automation with human interaction emerges as a critical factor for stakeholder trust and acceptance. This review emphasizes the importance of interdisciplinary collaboration to align technological advancements with societal and organizational goals, ensuring that AI adoption fosters sustainability, inclusivity, and long-term growth in service industries.
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Prospecção Tecnológica do Reconhecimento Óptico de Caracteres na Automação de Processos 4.0
Article
Full-text available
  • Apr 2025
O presente trabalho abordou os aspectos do reconhecimento óptico de caracteres (OCR) na automação de processos 4.0. A evolução da tecnologia OCR nos processos digitais contribui, decisivamente, para a agilidade e eficácia dos processos de análise e de tomada de decisões. Por essa razão, foram analisados os trabalhos científicos, os registros de softwares e os pedidos de patentes vinculados ao sistema OCR com intuito de identificar a evolução, as tendências e os impactos na automação de processos 4.0. Para tanto, foi realizada a prospecção tecnológica nas bases de dados IEEE Explore e Science Direct, Instituto Nacional de Propriedade Industrial (INPI) e Orbit Intelligence®. Os resultados apontaram uma corrida para o desenvolvimento de patentes tecnológicas baseadas no sistema OCR e na automação de processos, contribuindo para a formação de uma visão sistêmica do processo de evolução das tecnologias e a importância de investimentos voltados para Ciência, Tecnologia e Inovação.
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Robotic Process Automation: A Scientific and Industrial Systematic Mapping Study
Article
Full-text available
  • Feb 2020
The automation of robotic processes has been experiencing an increasing trend of interest in recent times. However, most of literature describes only theoretical foundations on RPA or industrial results after implementing RPA in specific scenarios, especially in finance and outsourcing. This paper presents a systematic mapping study with the aim of analyzing the current state-of-the-art of RPA and identifying existing gaps in both, scientific and industrial literature. Firstly, this study presents an in-depth analysis of the 54 primary studies which formally describe the current state of the art of RPA. These primary studies were selected as a result of the conducting phase of the systematic review. Secondly, considering the RPA study performed by Forrester, this paper reviews 14 of the main commercial tools of RPA, based on a classification framework defined by 48 functionalities and evaluating the coverage of each of them. The result of the study concludes that there are certain phases of the RPA lifecycle that are already solved in the market. However, the Analysis phase is not covered in most tools. The lack of automation in such a phase is mainly reflected by the absence of technological solutions to look for the best candidate processes of an organization to be automated. Finally, some future directions and challenges are presented.
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A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence
Article
Full-text available
  • Jul 2019
This introduction to this special issue discusses artificial intelligence (AI), commonly defined as “a system’s ability to interpret external data correctly, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.” It summarizes seven articles published in this special issue that present a wide variety of perspectives on AI, authored by several of the world’s leading experts and specialists in AI. It concludes by offering a comprehensive outlook on the future of AI, drawing on micro-, meso-, and macro-perspectives.
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Let the robots do it! - Taking a look at Robotic Process Automation and its potential application in digital forensics
Article
Full-text available
  • Jun 2019
The challenges of tackling increasing caseloads, large volumes of digital data and maintaining examination efficiency in order to adhere to tight criminal justice system deadlines persist. As the field looks towards techniques for improving efficiency, forms of automation are both simultaneously touted as a potential solution, whilst also attracting criticism. The potential for techniques which mechanise parts of the digital forensic examination process, and do it reliably, is great, however developing the capability to do this remains a challenge. This work provides an introductory discussion to Robotic Process Automation, a form of service task automation. Its potential application is debated and two case studies are offered demonstrating potential areas of applicability. An objective evaluation is offered, debating whether technology has a place in improving efficiency in this field. Keywords: Digital forensics, Robotic Process Automation, Investigation, Automation
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Smart manufacturing systems for Industry 4.0: Conceptual framework, scenarios, and future perspectives
Article
Full-text available
  • Jan 2018
Information and communication technology is undergoing rapid development, and many disruptive technologies, such as cloud computing, Internet of Things, big data, and artificial intelligence, have emerged. These technologies are permeating the manufacturing industry and enable the fusion of physical and virtual worlds through cyber-physical systems (CPS), which mark the advent of the fourth stage of industrial production (i.e., Industry 4.0). The widespread application of CPS in manufacturing environments renders manufacturing systems increasingly smart. To advance research on the implementation of Industry 4.0, this study examines smart manufacturing systems for Industry 4.0. First, a conceptual framework of smart manufacturing systems for Industry 4.0 is presented. Second, demonstrative scenarios that pertain to smart design, smart machining, smart control, smart monitoring, and smart scheduling, are presented. Key technologies and their possible applications to Industry 4.0 smart manufacturing systems are reviewed based on these demonstrative scenarios. Finally, challenges and future perspectives are identified and discussed.
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Industry 4.0: Managing The Digital Transformation
Book
Full-text available
  • Jan 2018
This book provides a comprehensive guide to Industry 4.0 applications, not only introducing implementation aspects but also proposing a conceptual framework with respect to the design principles. In addition, it discusses the effects of Industry 4.0, which are reflected in new business models and workforce transformation. The book then examines the key technological advances that form the pillars of Industry 4.0 and explores their potential technical and economic benefits using examples of real-world applications. The changing dynamics of global production, such as more complex and automated processes, high-level competitiveness and emerging technologies, have paved the way for a new generation of goods, products and services. Moreover, manufacturers are increasingly realizing the value of the data that their processes and products generate. Such trends are transforming manufacturing industry to the next generation, namely Industry 4.0, which is based on the integration of information and communication technologies and industrial technology. The book provides a conceptual framework and roadmap for decision-makers for this transformation
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Automation of a Business Process Using Robotic Process Automation (RPA): A Case Study
Conference Paper
Full-text available
  • Aug 2017
Robotic Process Automation (RPA) emerges as software based solution to automate rules-based business processes that involve routine tasks, structured data and deterministic outcomes. Recent studies report the benefits of the application of RPA in terms of productivity, costs, speed and error reduction. Most of these applications were carried out on back office business process where the customer is not directly involved, therefor a case study was conducted on a BPO provider to verify the benefits and results of applying RPA to a service business process with front and back office activities. The results show that productivity improvement is the main benefit of RPA, nevertheless time reduction was not achieved on this case.
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Applying robotic process automation (RPA) in auditing: A framework
Article
  • Nov 2019
  • Int J Account Inform Syst
Robotic process automation (RPA) has been widely adopted in many industries, including the accounting industry, to automate well-defined and repetitive tasks; however, its application to auditing has lagged behind because of the unique nature of this industry. This study applies RPA in the auditing area. An RPA framework is proposed that frees auditors from doing repetitive and low-judgment audit tasks and enables them to focus on tasks that require professional judgment. This paper also demonstrates the feasibility of RPA by implementing a pilot project that applies RPA to the confirmation process.
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The Future Digital Work Force: Robotic Process Automation (RPA)
Article
  • Jan 2019
The Robotic Process Automation (RPA) is a new wave of the future technologies. Robotic Process Automation is one of the most advanced technologies in the area of computers science, electronic and communications, mechanical engineering and information technology. It is a combination of both hardware and software, networking and automation for doing things very simple. In this light, the research manuscript investigated the secondary data - which is available in google, academic and research databases. The investigation went for totally 6 months, i.e., 1-1-2018 to 30-6-2018. A very few empirical articles, white papers, blogs and were found RPA and came across to compose this research manuscript. This study is exploratory in nature because of the contemporary phenomenon. The keywords used in searching of the database were Robotic Process Automation, RPA, Robots, Artificial Intelligence, Blue Prism. The study finally discovered that Robots and Robotic Process Automation technologies are becoming compulsory as a part to do business operations in the organizations across the globe. Robotic Process Automation can bring immediate value to the core business processes including employee payroll, employee status changes, new hire recruitment and on boarding, accounts receivable and payable, invoice processing, inventory management, report creation, software installations, data migration, and vendor on boarding etc. to name a few applications. Besides, the Robotic Process Automation has abundant applications including healthcare and pharmaceuticals, financial services, outsourcing, retail, telecom, energy and utilities, real estate and FMCG and many more sectors. To put in the right place of RPA in business operations, their many allied technologies are working at the background level, artificial intelligence, machine learning, deep learning, data analytics, HR analytics, virtual reality (second life), home automation, blockchain technologies, 4D printing etc. Moreover, it covers the content of different start-ups companies and existing companies - their RPA applications used across the world. This manuscript will be a good guideline for the academicians, researchers, students, and practitioners to get an overall idea.
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Robotic Process Automation for Auditing
Article
  • Jul 2018
The advent of Robotic Process Automation (RPA) has the potential to disrupt the traditional audit model. With its capability to automate rules-based tasks that are repetitive and manual, RPA is expected to repurpose the role of the auditor by replacing perfunctory tasks and emphasizing higher order thinking skills that will eventually lead to enhanced audit quality. This editorial envisages the future of audit by introducing the concept of RPA and describing its usage in auditing. Moreover, considerations for RPA-based audits and a series of research questions are presented with the objective to create a dialogue in this evolutionary area.
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