Table 1 lists the main concepts to be addressed in the research.

Table 1

Concepts	Description
Key operational and logistic activities or tasks	"Activities in purchases, production, sales, and marketing; these are supported by RPA technology in tasks such as receiving the payroll application (by email), reading the application, and entering relevant data in the Enterprise Resource Planning (ERP)" (Axmann & Harmoko, 2020).
Robotic Process Automation (RPA)	RPA is a software-based solution that automatically executes repetitive and routine tasks, emulating a human worker by interacting with information systems through existing user interfaces. Its use generates benefits because it is a booming technology, relatively fast to implement, creating solutions and increasing performance, productivity, and efficiency by automating specific tasks that would be developed at a high speed (Pozo-Martínez, 2016). It has been used with the objective of continually improving the work environment for its collaborators and the provision of services in a more agile way, positively impacting the external client (Baviskar et al., 2021).
Productivity behavior	The definition of productivity is described by authors from different countries as an economic measure that calculates how many goods and services have been produced for each factor used (worker, capital, time, land, etc.) during a given period (Hradecká, 2019; Baranauskas, 2018).
Industry 4.0 in manufacturing	According to Vidosav et al. (2021) , "it is an advanced model of automation of manufacturing systems, based on the connection and decentralized control of cyber-physical systems, using the IoT and the support of cloud computing and Artificial intelligence (AI)."
Maturity models in manufacturing processes	The authors Cadena et al. (2020) mention that the maturity of the automation levels used in organizations require models to determine the degree of incorporation of technology used in their processes to identify practices that favor the development of these and are used to standardize them. Referring to the Gartner Inc. Model, five levels are proposed: Level 0. Recognition of operational inefficiencies, Level 1. Understanding of processes, Level 2. Control and automation of the process, Level 3. Control and automation among processes, Level 4. Control and evaluation of the organization and Level 5. Agile business structure.
Business processes	For the development of activities there must be an organized and uniform structure in the areas of the company, minimizing process variability. In the case of business models, it is defined as "a set of logically related tasks that are performed in a certain sequence and form, that use the organization's resources to provide results in support of its objectives" (Hernández-González, 2005).
Logistics processes	Zoubek and Simon (2021) mention "Internal logistics covers the planning, implementation, control, and flow of efficient storage of materials, semi-finished and finished products in a production environment."

Source: Own elaboration.

In the systematic literature review by Angreani et al. (2020) , 9 types of dimensions were identified for Maturity Models (MM) and their application to Industry 4.0: strategy, leadership, customers, products, operations, culture, people, governance, and technology. After the analysis of 17 primary studies, in response to the questions posed, it is concluded, as mentioned by the authors, that the more categories of dimensions the researchers adopt in their MM, the more complex their construction will be. In addition, research shows that development techniques can be implemented in manufacturing and logistics; in this way, Industry 4.0 has its application relevance in manufacturing and logistics.

Figure 1 Source: Own elaboration based on Bibliometrix.

Figure 1 shows a thematic map of the relevant concepts in the research based on the state of the art considering their relevance and development, classifying them into emerging/recurrent, driving, basic, and specialized topics. Ng et al. (2021) in their systematic literature review, seek to provide an analysis regarding the technical and usage challenges of AI in the world, which in turn contributes to the progress of information technology (IT), cognitive, and process automation. Within the research, there is evidence of strong interest in showing the capabilities of intelligent automation to enhance business growth considering the contributions of a series of AI domains from cognitive technology, operational efficiency, and visibility.

The objective of the research presented by Enriquez et al. (2020) is to offer a systematic review in the field of RPA, both in academic literature and of the solutions available and applied in the industry. As mentioned by the authors, the use of RPA technology occurs in a greater proportion in scientific-academic contexts than in industrial ones. This is due to the research interest in knowing the current state of the technology compared to the application that implies time and resources with uncertainty about benefits. The authors conclude that, at an industrial level, the most suitable companies to implement an RPA are those whose business is based on back-office areas (inbound and internal logistics activities), with software proposals focused on specific environments.

Case studies proposed by Marciniak and Stanislawski (2021) intend to show how the application of RPA generates greater impacts when implemented in specific cases than in organizations as a whole, regardless of whether the services of an external entity that offers RPA are used for said implementation or if the company creates its center of excellence or individual local team for the process. However, they highlight that the solution may involve various types of threats (risks) such as the incorrect selection of processes to be automated.

Considering the 89 potential writings to answer the research questions, 62 articles that respond directly are taken as a research contribution to the construction of knowledge.

After carrying out the search based on the equations used and implementing the analytics produced by Scopus and WoS, a series of criteria are developed to classify categories that will help information searches and the respective analysis know the development state of the subject matter, nationally and internationally. Next, the criteria and the number of levels used for the analysis of the information are described (Table 5) and Appendix 2 - Detail of the criteria and levels for the analysis of the information.

Table 5

Criteria	Levels	Definition
Countries	34	It defines the countries that are developing an RPA technology and its application in some sector according to national and international scope.
Main content	6	It describes the type of information and the source from which it is extracted according to the corporate name and type of institution handled.
Geographical applications	6	The scope and use of RPA technology is defined within a national and international geographical framework.
Business sector	17	It considers the different economic sectors of the countries in which the use of technology is applied and generates benefits.
Processes and areas	12	It refers to the different processes and areas of application of RPA or any other type of IT technology for SMES process automation.
Types of research	10	It allows defining, for each of the sources of consultation, what is its main object and how to reach its development, considering types of research such as applicative, descriptive, and case study, among others.
Types of information sources	1	It is important to specify the categories of consultation and research sources that were used for the construction of the state of the art, including review articles, research articles, and research papers.
Complementary technologies	26	It shows some of the technological tools or applications that resulted from the research findings, which do not directly involve RPA, but as complementary technologies used in organizations for process automation purposes.
Key words	38	It results from the analysis of the words used in the information collected in relation to the subject matter.
Use of RPA	3	The use of technology in reference to the organizational area of the manufacturing and service economic sector.
Key activities/ tasks	4	The analysis of RPA technology in the industrial logistics phases of related organizations.
Automation maturity	6	The level of structuring and organization of automation within the organizations studied.
Productivity behavior	5	The impact generated by the use of RPA technology in relation to productivity and the benefits found in SMES.

Source: Own elaboration.

According to the review of the analyzed literature, the answer to Q1 of this research can be argued, considering for this the interpretation of Figure 8 that shows the relationship among the countries in front of the main contents of the study and the contributions of different authors.

For the manufacturing business sector, the authors Zhang and Liu (2018) from China, Penttinen et al. (2018) , Marciniak and Stanislawski (2021) , and Helo and Hao (2021) from Finland, and Madakam et al. (2019) from India, carried out studies of business information, reflection, and case study and general automation information, whose contribution focuses on the implementation of RPA, the positive and negative impacts, the contribution of complementary technologies (highlighting AI) in the various areas and operations of the company (Figure 8).

Figure 8 Source: Own elaboration.

In the service business sector, the authors from China Qiu and Xiao (2019) , Maalla (2019) , Ma et al. (2019), and Carden et al. (2019) , carried out studies on business information, reflection, and case study and general automation information, whose contribution focuses on detailing the inputs and implementation resources and RPA support such as data acquisition, the technical level of experts and the use of cloud technology. It also emphasizes on the outputs reflected in the implementation performance results in terms of improvement in efficiency, effectiveness, and productivity for the optimization of business processes.

For India, the authors Ruchi et al. (2017) , Hiren-Timbadia et al. (2020) , and Vijai et al. (2020) document automation general information and contribute with comparative analysis for the back office (inbound and internal logistics activities) and front office (outbound logistics activities), about traditional business management models and RPA implementation models.

Figure 9 shows the relationship of the business sector with the highest share of RPA use focused on the back office and internal logistics activity. The authors Hradecká (2019) , Echeverri-Arias et al. (2021) , and (Chuong et al., 2019) have studied benefits and advantages of implementing RPA in production agility, reduction of operating costs, and fraud prevention for the manufacturing sector.

Figure 9 Source: Own elaboration.

For their part, the authors Kaya et al. (2019) , Mazhar-Hussain (2019) , and Willcocks et al. (2017) highlight, for the service sector, the connection between integrated and complementary systems (Business Process Management System [BPM/BPMS], I4.0, Robotic Service Orchestration [RSO], and AI), the data collection problems, inappropriate information (Qiu & Xiao, 2019), participation of the area involved, testing, execution of the technology, communication strategies with the work team, careful choice of the process to intervene, training, maintenance, security, and technical support (Hallikainen et al., 2018).

Figure 10 shows a great share in the areas of software application, customer service, and production. Specifically for manufacturing processes in the production area. The authors Doyle and Cosgrove (2019) , Chuong et al. (2019) , and Hradecká (2019) argue that Industry 4.0 requires an evaluation of internal logistic preparation and it is necessary to integrate the areas of the production process (Zoubek & Simon, 2021).

Figure 10 Source: Own elaboration.

For SMES, the objective of RPA implementation generally seeks digitization, product quality, cost reduction, and production capacity, to improve the business model (Ascúa, 2021). Complementing the above, the authors Nanda and Balaramachandran (2018) , Kedziora and Kiviranta (2018) , (Hallikainen et al., 2018), and Willcocks et al. (2017) , highlight the benefits of RPA and complementary technologies (ROM - robotic operating model, BPM/BMPS, CRM, BPO, Middleware), for the area of customer service for the service sector, in increasing productivity, efficiency, competitiveness, strengthening of supply chain management (SCM) and profitability, in addition to the need to implement roadmaps (Aguirre & Rodríguez, 2017; Mazhar-Hussain, 2019; Donny & Harsiti, 2019).

Finally, the authors Zhang and Liu (2018) ; Madakam et al. (2019) , and Díaz et al. (2018) contribute to the manufacturing and service sectors, in the software application support processes, the importance of using a diagnostic instrument to define the need to apply Industry 4.0, before a preliminary automation phase to assess the level of maturity in RPA implementation (Sobczak, 2021), to then define a team of automation experts.

Figure 11 shows that the most representative complementary technologies are ERP and AI, mainly in the information technology, manufacturing, and services sectors. The authors Chuong et al. (2019) and Syreyshchikova et al. (2020) provide, for the manufacturing sector, that RPA technology is complemented by AI, optical character recognition (OCR), CRM, ERP, cyber physical system (CPS), material requirements planning (MRPII) to standardize high-frequency transactions, digitization, use of operational resource data, and business process timing changes (Doyle & Cosgrove, 2019; Geyer-Klingeberg et al., 2018).

Figure 11 Source: Own elaboration

Teja Yarlagadda (2018) , for the service sector, contribute to research regarding the impact of automation on labor and salary in the case of financial institutions. He mentions that RPA does not replace existing software applications, but improve them in terms of productivity, data quality, and refined processes (Donny & Harsiti, 2019; Siderska, 2020).

Madakam et al. (2019) contribute to the manufacturing and service sectors with RPA based on robot software or AI workers that can support the detection of fraud in data management, using tools such as virtual agents, machine learning, text analytics for customer queries, business development for handling consumer data and industrial applications (Sander-Tavallaey & Ganz, 2019).

Figure 12 shows that RPA technology evidences the impact on performance achievement for organizations that implement it. For the manufacturing sector, the authors Chuong et al. (2019) , Geyer-Klingeberg et al. (2018) , Radke et al. (2020) , and Hradecká, (2019) agree that the implementation of RPA technology results in a decrease or reduction in operating costs, production times, human failure, while Wewerka and Reichert (2020) , Syreyshchikova et al. (2020) , and Echeverri-Arias et al. (2021) mention the decrease in internal and external failures, as well as quality risks, waste in factors and processes. Geyer-Klingeberg et al. (2018), Hradecká (2019), and Shi et al. (2019) agree on the increase in return on investment and key performance models (KPI) with the implementation of RPA in this sector. Wewerka and Reichert (2020) and Chuong et al. (2019) conclude that productivity levels can be increased, while Doyle and Cosgrove (2019) , Agostinelli et al. (2020) , Radke et al. (2020), and Echeverri-Arias et al. (2021), document improvements about control and planning, compliance levels, data accuracy, on-time delivery, quality, and times in the execution of the processes.

Figure 12 Source: Own elaboration.

For the services sector, the authors Donny and Harsiti (2019) , Kaya et al. (2019) , and Hofmann et al. (2019) agree that the implementation of RPA minimizes operating costs, added to savings in delivery times, customer response, time spent by collaborators, and minimization of waste and loss of jobs (Lacity et al., 2015; Aguirre & Rodríguez, 2017; Kedziora & Kiviranta, 2018).

To answer Q2, 62 research articles were used that are related to the use of RPA technology, as well as the main pillars that makeup Industry 4.0, developed in the research study areas. The characteristics are classified considering the complementary technologies and their operation in the operational (O), logistic (L), and business (B) areas. Table 6 shows a share of 40.3 % in O, 26.9 % in L and 32.8 % in B, where the most representative percentage is presented in the pillars of cyber-physical systems and integration with 29.9 % each.

Table 6

Author	Big data			Simulation			IoT			Cyberphysical systems			Integration
	O	L	B	O	L	B	O	L	B	O	L	B	O	L	B
Zhang and Liu (2018)										x		x			x
Syreyshchikova et al. (2020)	x		x			x									x
Baranauskas (2018)										x		x
Shi et al. (2019)							x	x	x						x
Sobczak (2021)						x				x		x	x		x
Echeverri-Arias et al. (2021)										x	x	x	x		x
Díaz et al. (2018)	x	x		x	x		x	x		x	x
Chacón-Montero et al. (2019)				x						x
Viorel-Costin (2020)													x	x	x
Hradecká (2019)							x	x		x	x	x	x	x
Chuong et al. (2019)						x				x		x	x
Osman (2019)	x	x	x										x	x	x
Madakam et al. (2019)					x	x
Sangkeun et al. (2018)					x		x			x				x	x
Radke et al. (2020)	x	x	x							x	x		x	x

Source: Own elaboration.

Figure 13 shows the overall scenario of the RPA characteristics from the operational, logistic, and business aspects, relating the main pillars of technology 4.0 in manufacturing organizations. A great impact on the pillar of cyber-physical systems in the operational area and the integration pillar in the business area can be observed. The big data pillar stands out in the operational area, the simulation pillar in the business area and finally the IoT pillar in the operational area. Next, the main pillars of Industry 4.0 and the relationship with the technologies investigated for manufacturing organizations are detailed.

Figure 13 Source: Own elaboration.

Process automation must have the experience of experts and project coordinators for the adoption of technology with innovative approaches that adapt to these new pillars based on standardized and mature processes (Osman, 2019). For the tasks, process operational and logistic activities, data, and in particular master data, are essential in digital initiatives, ranging from KPI to optimization algorithms and prescriptive analysis classified as descriptive (Radke et al., 2020).

According to Syreyshchikova et al. (2020) master data presented in an erroneous way impact productivity and the return on investment (ROI) of technology systematization that, in these cases, the intervention of the worker can solve. They mention that due to the complexity and scope of the production tasks faced by a manufacturing company, it is required to process a large volume of high-quality information to make decisions at different management levels.

This technology helps collect content for different types of formats with different types of sources and devices. These formats can be text, image, audio, or video, between structured and unstructured data. Technology can also collect data from the web through deep learning techniques and with other complementary technologies such as AI, big data, and analytics that will help depend on the technology model used in the organization (Madakam et al., 2019).

As mentioned by Díaz et al. (2018) according to the interventions that Industry 4.0 has had in manufacturing case studies, in supply chains there are uniform models, people involved in the process, who can collect, and process all the information involved in the production process to have better planning and better decision making.

When information is collected and distributed via networked smart devices from production, data can be managed and transferred using the web by sharing data with other cloud or on-premises systems and applications in the organization. Integrating modular solutions of the website supervisor (SWM) and Standard Motor Interface (SMI) with finished product storage robotic technology, data reference quantities needed from the beginning of the production process to the distribution of the organization are obtained (Hradecká, 2019).

According to Radke et al. (2020) , the scope of robotics technology for other cases about the quality of master data helps with the coding of the standard operating procedure, and robot coding skills are acquired with the training materials, prioritizing the needs of the robots and obtaining more agile iterations.

Robotization is closely related to various types of manufacturing industries, including food, clothing, and metalworking. According to Hradecká (2019) , the contribution of technology to the process is mentioned in three aspects: (i) detection of risk points in various stages of production; (ii) review of the manufacturing process to reduce operating and production costs; and (iii) implementation of the computer application of robotic internal audit to ensure interoperability of production data and enterprise information system.

Economic and productive sectors need applications that complement data collection through programming and software application systems, aiding with the organization of an industry such as that of the manufacturing sector. This helps respond to the different internal and external obligations of the company, including accounting, financial, logistical, productive, commercial, and laws imposed by the government (Asatiani & Penttinen, 2016) . As mentioned by Cabello-Ruiz et al. (2022) :

Figure 14 shows in which pillars of Industry 4.0 the study technology is applied with different areas of research hierarchically mentioned.

Figure. 14 Source: Own elaboration.

According to Sobczak (2021) the implementation of this technology not only seeks to reduce costs, but also allows guaranteeing the continuity of ongoing processes, generating digital innovations with the need for a preliminary phase of implementation of the automation of robotic processes, a role fulfilled by the centers of excellence in the organization.

The models used in logistic and operational areas must be considered in the face of the use of RPA technologies for the development of the activities and tasks of the manufacturing sector.

For the development of activities and tasks in the current manufacturing models, it is necessary to have a technological infrastructure that avoids wear, loss of production, and high resource consumption. This modern technology must meet the demands of Industry 4.0 (Hradecká, 2019).

The internal audit uses automation technology in cases such as the agricultural and food industry, specifically the brewery, to impact productivity in transportation activities, and raw material collection, transforming the implementation of robotics and advanced digital industrial systems. Furthermore, according to Baranauskas (2018) , starting from Anagnoste (2018) , these automated solutions are not only projected to processes and Computer Integrated Manufacturing (CIM) but with greater scope to the entire SCM, the relationship with customers (CRM) and data security in the cloud.

For Syreyshchikova et al. (2020) the application of ERP systems allows simultaneous programming standards considering the stock available in the warehouse and the planned purchase and production receipts for the calculation of MRP, leading to increased efficiency process management, reduction of costs, and higher productivity through automation results such as the reduction of the time necessary for the process maintenance.

For Felsberger et al. (2020) , monitoring the processes allows the control over production and in many cases, does not require human intervention, instead, more reliable automated indicators (KPI) can be implemented, while improving product quality, reducing operating costs in business and production model, helps reduce downtime, increasing overall equipment effectiveness (OEE) with high system availability.

For IT models, according to Zhang and Liu (2018) , the dimensions of communications, competency measurement, governance, association, scope and architecture, and skills are a model of strategic alignment for the business areas.

Figure 15 shows identified logistic and manufacturing models concerning the use of RPA technologies and other emerging technologies in the activities and tasks of the researched sector, referencing the percentage of use in each of the organizational models analyzed as a result of the analysis for Q3.

Figure 15 Source: Own elaboration.