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Aesthetic Workshop 2008 Business Intelligence Advancement Workshop Download And Install
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Reconstructing The Future By Birkhäuser
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By Poliana Gomes Poliana Gomes Scilit Preprints.org Google Scholar 1, *, † , Luiz Verçosa Luiz Verçosa Scilit Preprints.org Google Scholar 2, *, † , Fagner Melo Fagner Melo Scilit Preprints.org Google Scholar 1, † Vincícius Silva , Vincícius Silva Silva Scilit Preprints.org Google Scholar 2, † , Carmelo Bastos Filho Carmelo Bastos Filho Scilit Preprints.org Google Scholar 2, † and Byron Bezerra Byron Bezerra Scilit Preprints.org Google Scholar 2, †
Department of Administration, Faculdade de Administração e Direito da Universidade de Pernambuco, Campus Benfica, Universidade de Pernambuco, Pernambuco 50720-001, Brazil
Eportfolio Performance Support Systems By Parlor Press, Llc
Departamento de Engenharia da Computação, Escola Politécnica da Universidade de Pernambuco, Campus Benfica, Universidade de Pernambuco, Pernambuco 50720-001, Brazil
Received: January 5, 2022 / Revised: February 4, 2022 / Accepted: February 17, 2022 / Published: February 23, 2022
Companies tend to be overwhelmed with data that they may not be able to take advantage of. On the other hand, artificial intelligence (AI) techniques are known to “learn more” as the data grows. In this context, our research question arises, what artificial intelligence-based methods could be used in the literature to automate business processes and support the decision-making processes of companies? To fill this gap, in this article we reviewed the literature to identify these techniques. We ensured the use of the methods as they enabled reproducibility and extension. We applied our search string to the Scopus and Web of Science databases and discovered 21 relevant articles related to our question. In these articles, we identified methods that automate tasks and help analysts make solid decisions when designing, extending, or redesigning business processes. The authors used various artificial intelligence techniques such as K-means, Bayesian networks and swarm intelligence. Our analysis provides statistics on the techniques and problems to be addressed and points to possible future directions.
Business process management (BPM) is a discipline that includes concepts, methods, and techniques for planning, executing, measuring, and configuring business processes [1]. Adam Smith, Frederick Taylor and Henry Ford were the fundamental forerunners of today’s configuration of BPM, demonstrating the benefits of division of labour, scientific management and production lines in industry.
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The beginning of the computer era, around 1950, had another profound impact on BPM. It changed corporate structures as they began to rely on information systems. It has become essential to model cross-organizational processes that can document procedures and gain insight [2].
In recent decades, we – the world – have become a digital society; data is collected everywhere. The data comes from mobile phones, personal computers and smart home appliances. As data “continually grows”, organizations face challenges around exploring such data in order to increase the value of their operations [3]. Business intelligence (BI) tools and process-aware information systems (PAIS) can help extract knowledge from data through computing tools and decision makers. However, these tools can be limited when dealing with large amounts of data, as the output must be further analyzed by professionals in time-critical environments.
Artificial intelligence (AI) is used in learning algorithms, i.e. decisions are made in milliseconds in a “Big Data” context. For example, the subfields of AI machine learning and deep learning have achieved great success in complex tasks such as natural language processing, speech recognition, computer vision, medical diagnosis, recommender systems, and many others [4]. It has been successfully applied in many disciplines, including biology, experimental psychology, communication theory, game theory, mathematics, statistics, logic, and philosophy [5]. Swarm intelligence is another branch of artificial intelligence that is primarily used for optimization tasks. This field includes a variety of bio-inspired metaheuristics capable of finding relevant solutions in high-dimensional search spaces. Swarm intelligence algorithms have achieved good results in a wide variety of tasks, including telecommunications, industry, social sciences, and military operations [6]. AI techniques have changed the corporate world by offering faster and less error-prone techniques.
Business processes (BPs) are becoming more powerful by incorporating techniques of the digital age. Thanks to these changes, organizations can focus on decision-making and business strategies that are different from manual and repetitive operations. This new context leads to more mature and predictable processes, highly scalable operations, and an overall improvement in organizational performance [7].
A Review Of Open Source Image Analysis Tools For Mammalian Cell Culture: Algorithms, Features And Implementations [peerj]
Artificial intelligence is critical because of the complexity of the changes required to integrate the new organization into the larger organization [8]. Investors and business leaders alike believe that AI and machine learning will transform their organizations by reducing costs, streamlining operations, managing risk, accelerating growth, and boosting innovation [9]. However, once a business problem or opportunity is identified as potentially transforming and optimizing with AI, it is not always clear how to implement and develop the solution. Here, we look at optimization from a business perspective. Therefore, we are interested in developments in the company’s business processes that lead directly to better decision-making processes and the achievement of company goals. For example, we can cite artificial intelligence solutions that automate manual and time-consuming procedures, provide insights for decision-makers, or align process goals with the company’s business goals. The article therefore raises the question: what AI-based methods does the literature use to automate business processes and support the decision-making processes of companies? This article answers this question through a systematic literature review (SLR). It is important to note that (i) we did not include multiple scientific databases in the search, and (ii) we excluded the use of artificial intelligence that was not associated with a method, such as an approach that focuses solely on improving a model. Instead, our selected approaches used AI as part of a multi-step method to achieve a specific goal. The rest of the article is organized as follows. Section 2 introduces related studies, background concepts, and the context of this study. In Section 3, we describe the search and selection criteria used to identify relevant studies. Section 4 presents the profile of the identified papers and the classification of AI-based methods. Finally, Section 5 presents the discussion, and Section 6 provides the conclusion, limitations, and future work.
Garcia et al. [10] carried out a systematic survey of process mining techniques and their applications in different industry segments. They included 1,278 peer-reviewed articles between 2002 and 2018 and identified process discovery, conformance control, and architecture and tool development as the most active topics in the field. Healthcare, ICT and manufacturing were the most recurring fields of application. Similarly, Maita et al. [11] carried out systematic mapping to survey the process mining area. From 2005 to 2014, 705 papers were analyzed by identifying process mining and data mining tasks and the types of techniques used in the literature. It was observed that 38% of the analyzed articles used graph structure-based techniques, 9% used evolutionary computing, and 6% used decision trees. The authors conclude that little importance has been attached to computational intelligence and machine learning techniques in the field of process mining. Looking at the types of process mining, the authors found that the most frequently performed tasks were process discovery, business process compliance, and business process development, in that order. The two articles mentioned earlier mapped the (then) current work in process mining by identifying relevant statistics such as recurring themes and application areas. In contrast, our work is an SLR focused on the application of AI methods in the field of business processes. Taymouri et al. [12] conducted a systematic literature review of methods used to analyze process variations. This field consists of approaches used to analyze related event logs that differ based on certain predicates, such as a company’s country of operation. The authors selected 29 studies and created a taxonomy for the type of input data required, the outputs provided, the type of analysis, and the algorithms used. Pourshahid et al. [13] performed SLR using aspect-oriented approaches for BP adaptation. They focused on articles that
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