Fig 1 - available via license: Creative Commons Attribution 4.0 International
Content may be subject to copyright.
![Strong vs. Weak AI, own presentation based on [14]](publication/319012077/figure/fig1/AS:525700342190080@1502348136790/Strong-vs-Weak-AI-own-presentation-based-on-14.png)
Source publication
![Fig. 1. Strong vs. Weak AI, own presentation based on [14]](publication/319012077/figure/fig1/AS:525700342190080@1502348136790/Strong-vs-Weak-AI-own-presentation-based-on-14_Q320.jpg)
![Fig. 3. Incremental enhancement, source: [17]](publication/319012077/figure/fig5/AS:631652455817255@1527609089713/Incremental-enhancement-source-17_Q320.jpg)
The continuous optimization of business processes is still a challenge for companies. In times of digital transformation, faster changing internal and external framework conditions and new customer expectations for fastest delivery and best quality of goods and many more, companies should set up their internal process at the best way. But what to d...
Similar publications
The domains of complex event processing (CEP) and business process management (BPM) have different origins but for many aspects draw on similar concepts. While specific combinations of BPM and CEP have attracted research attention, resulting in solutions to specific problems, we attempt to take a broad view at the opportunities and challenges invol...
Citations
... Artificial Intelligence (AI) learns algorithms designed to integrate intelligence into software, enabling the performance of specific tasks [8]. The ongoing advancements in digital technologies signify substantial progress in the field of artificial intelligence. ...
The extensive globalization of services and rapid technological advancements driven by IT have heightened the competitiveness of organizations in introducing innovative products and services. Among the noteworthy innovations is enterprise resource planning (ERP). An integral field in computer science, known as artificial intelligence (AI), is undergoing a transformative integration into various industries. Grasping the concept of artificial intelligence and its application in diverse business applications is crucial, given its broad and intricate nature. The primary focus of this paper is to delve into the realm of artificial intelligence and its utilization within enterprise resource planning. The study not only explores artificial intelligence but also delves into related concepts such as machine learning, deep learning, and neural networks in greater detail. Drawing upon existing literature, this research examines various books and online resources discussing the intersection of artificial intelligence and ERP. The findings reveal that the impact of AI is evident as businesses attain heightened levels of analytical efficiency across different ERP domains, thanks to remarkable advancements in AI, machine learning, and deep learning. Artificial intelligence is extensively employed in numerous ERP areas, with a particular emphasis on customer support, predictive analysis, operational planning, and sales projections.
... The popularity of machine learning technology is rooted in a much lower cost intensity than in the case of using human labour (Castelli et al., 2016). Research among senior managers shows that the areas in which machine learning is most often used are optimisation and automation of business processes, core business activities, improvement of business models and forecasting) (Paschek et al., 2017). AI algorithms can be used to analyse data and provide managers with insights and recommendations that they might not have been able to see on their own. ...
Nowadays, the need for digitisation and digitalisation of enterprises, as well as the use of solutions based on Artificial Intelligence (AI), are coming to the fore. The use of intelligent systems in organisations is not a strictly technical issue, and is also important in the management of modern enterprises. The aim of this article is to provide a theoretical analysis of the phenomenon of Artificial Intelligence in management sciences by means of a systematic review of the literature using Scopus database records. Bibliographic analysis of Artificial Intelligence in management sciences in this article points to this topic as something relatively new in the case of management sciences, although rapidly developing. As part of the bibliographic analysis we propose an agenda regarding the issue of AI in management sciences, consisting of thematic clusters related to technologies based on and complementary to AI, the goals of using AI in organisations, human-AI relations and issues related to ethics and sustainable development.
... For some companies, this comes in the form of an enterprise resource planning (ERP) system (Ivan ci c et al., 2019). This is the foundation for future business process optimizations (Colli et al., 2019;Paschek, Luminosu, & Draghici, 2017). ...
Purpose
Digital transformation (DT) projects are complex and often unsuccessful. While researchers have suggested many guidelines and best practices on how to successfully roll out DT projects and how they are spread among a large number of scientific papers. The aim of this paper is to synthesize these guidelines into clear overviews.
Design/methodology/approach
A systematic literature review was conducted on both Scopus and Web of Science to search for papers suggesting DT guidelines or best practices. In total, 150 papers dealing with DT and guidelines were fully analyzed.
Findings
Eight main DT guidelines were found and each one was expanded with several best practices on how to implement these. The results are eight tables giving an overview of the commonly agreed-upon best practices for each DT guideline.
Research limitations/implications
These overviews are useful for both researchers and practitioners, to guide future work and to be inspired respectively. This paper calls for more research on how these guidelines are followed in practice, how these differ per industry and what their impact is on the overall success of DT projects.
Originality/value
The synthesis of DT guidelines organized into an accessible format has not yet been conducted before, and can serve as a seminal pinpoint for future research.
... Therefore, targeted, and consistent process automation is a critical success factor for both now and in the future. It is becoming increasingly important for companies to use their data's power and make intelligent and profitable decisions (Paschek, Luminosu, and Draghici, 2017). Bakarich and O'Brien (2021) conducted a survey to gather insight from professionals regarding the extent that AI is used in accounting firms and conclude that accounting firms are currently using AI on a limited basis and suggest that AI has potential for future research and integration into accounting and business education. ...
... For this reason, already in 2010, a study [50] expected that sustainability accounting research will continuously evolve and better enhance management decision making. In this direction, new technologies as artificial intelligence, machine learning, automation, remote control have been recognized as the next powerful tools able to change the management decision-making process in a business [51]. Multiple technologies exist to help SD and SR preparers, for instance ERPs, web-tools, and SaaS. ...
This paper presents the implications of blockchain technologies on sustainability reporting and disclosure, and specifically proposes blockchain use-cases as a possible solution for problems experienced in the field of supply chain carbon information. This study addresses how the reliability of supply chains’ carbon-related information can become more transparent and reliable through a decentralized approach based on blockchain thinking (BT), issues that have been identified as a gap in the literature and in the practice. Scenario analysis and design science research (DSR) are used as a methodological driver to conceptualize over the nature of practical solutions using unified modeling language (UML) diagrams. The resulting use-case focuses on data retrieval in the supply chain. The paper also presents implications for the audit industry and their role in the assurance of such technological architecture implementations. The study is visionary as it offers a conceptualization based on scenario analysis. Developing a scenario enables researchers to depict a prospective situation, develop ability to solve future problems, and to back cast them in current policies, technologies, and actions.
... According to the theory of open innovation [28], a holistic cognitive approach should allow a company to effectively explore internal knowledge and assimilate external knowledge about a dynamic environment [29][30][31]. On the other hand, innovation has been defined as a tool that "combines existing knowledge in new ways" [32,33], highlighting the limits and potential of the organization's cognitive substratum to promote sustainability and innovation. ...
This paper presents original research on the identification and modeling of quality requirements for structural products in aircraft structures. As the main objective of this research, the authors focused on identifying the relationship between the previously mentioned requirements and the technical knowledge necessary to improve the quality of the processes involved. This scientific paper presents research in terms of defining a global engineering process addressing, as a starting point, the requirements necessary to be met in the manufacture of structural products for the structure of aircraft. The authors have identified directions for improvement in the global engineering process which will certainly lead to the creation of a sustainable, competitive advantage for the organization where the research was conducted. Based on the concept of intellectual capital and its components, the authors developed a model of knowledge analysis related to the requirements in order to later develop knowledge matrices at the subprocess level. This research presents a pragmatic evaluation based on the experience of those involved, but also on a study focused on pre-modeling the knowledge necessary to be considered and involved in the global engineering process. After this desideratum, the relationship between knowledge–requirements, but also the relationship between requirements–knowledge, was achieved, establishing a direct connection with the global quality of the analyzed products from the perspective of streamlining the global engineering process. This research is based on an entire procedure of analysis and modeling of the processes and subprocesses considered that could lead to favorable results from an economic point of view, but also from a technical point of view, the recommendation being to implement those presented to increase the competitive advantage in the profiled market.
... In achieving the company's business goals, a strong coherence between business and IT (Information Technology) has become an important factor of competition in all markets and almost all sectors are entering the industrial revolution 4.0 [1]. Enterprise Architecture (EA) is an approach to facilitate the integration of strategy, business, information systems and technology towards a common goal and mastering organizational complexity through the development and use of architectural descriptions [2]. ...
... A study conducted in 2019 [27] highlights that, in terms of impediments for Industry 4.0 adoption, most companies are struggling with the lack of digital competences, lack of technology and infrastructure, and lack of skills and guidance for transformation. Ideas related to the needs for changes and transformations are being already announced by previous studies [28,26]. ...
Industry 4.0 refers to the transformation of industry through the adoption of techniques and processes based on information and communication technologies (ICT) to manage and optimize all aspects of the manufacturing processes and supply chain. Even if Industry 4.0 adoption has significant advantages for companies in terms of increased productivity and decreased costs, it also raises various challenges for companies. At the same time, more and more experts start to voice their concerns regarding Industry 4.0 and discuss the advent of Industry 5.0 with a focus on sustainability as opposed to productivity. During the discussions regarding Industry 5.0 and the necessity of an improved collaboration between humans and technological systems, it is relevant to evaluate the way companies from the manufacturing sector are adopting Industry 4.0, considering all the challenges and costs imposed by the process. The present study aims at providing an overview regarding the presence of the Industry 4.0 elements in European Union manufacturing companies using publicly available data from the European Statistical Office.
... In order to achieve real-time capability, cloud computing offers immediate access to hardware resources as well as lowers IT 225 barriers and opens up new ways to innovate [43]. Machine learning standards, tools, methods and approaches can facilitate the automation and optimization of processes as well as support BPM through new algorithms [44]. In the context of BPM, Information Systems (IS) supporting the processes produce the structured data, i.e., logs, which are analyzed with process mining techniques used 230 to discover processes and bottlenecks, check compliance, and propose process improvements [45,46]. ...
... The management of business processes by supporting digitization and au-505 tomated processes via AI has great potential for organizations. The consistent redesign of processes to implement the I4.0 environment is critical, as is the opportunity for companies to use their data and make intelligent, profitable and real-time decisions [44]. ML-related standards, tools, methods, and approaches promote process automation and optimization as well as facilitate BPM through 510 new algorithms and independent learning through continuous data analysis [44]. ...
... The consistent redesign of processes to implement the I4.0 environment is critical, as is the opportunity for companies to use their data and make intelligent, profitable and real-time decisions [44]. ML-related standards, tools, methods, and approaches promote process automation and optimization as well as facilitate BPM through 510 new algorithms and independent learning through continuous data analysis [44]. ...
Business process management (BPM) supports the management and transformation of organizational operations. This paper provides a structured guideline for improving data-based process development within the BPM life cycle. We show how Industry 4.0-induced tools and models can be integrated within the BPM life cycle to achieve more efficient process excellence and evidence-based decision-making. The paper demonstrates how standards of machine learning (CRISP-ML(Q)), BPM, and tools of design science research can support the redesign phases of Industry 4.0 development. The proposed methodology is carried out on an assembly company, where the proposed improvement steps are investigated by simulation and evaluated by relevant key performance indicators.
... BPM is recognized as a set of methods and techniques to discover a business process, to develop designs for that process, monitor it by measuring data, as well as by optimizing and automating the process with human, technological and financial resources (Paschek et al., 2017). Similarly, past researchers have visualized this set in a BPM lifecycle with subsequent phases to address a business process, namely iterations that begin with process identification and process discovery, then process analysis and redesign, leading towards implementation, and finally monitoring and control (Dumas et al., 2013a). ...
