scholarly journals Konaklama İşletmelerinin İş Süreçlerinde Yapay Zekâ Teknolojileri ve Akıllı Otel Uygulamaları: Avantajlar ve Dezavantajlar (Artificial Intelligence Technologies and Smart Hotel Practiceses at Hospitality Organizations Business Processes: Advantages and Disadvantages)

2021 ◽  
Vol 9 (1) ◽  
pp. 203-219
Author(s):  
Çağla Demir
Keyword(s):  
Artificial Intelligence ◽  
Business Processes ◽  
Advantages And Disadvantages ◽  
Hospitality Organizations

Trends in the Development of Electronic Document Management: Prospects, Problems, Opportunities

2021 ◽  
Vol 27 (12) ◽  
pp. 963-970
Author(s):  
S. A. Zhutyaeva ◽  
T. A. Lysova
Keyword(s):  
Artificial Intelligence ◽  
Legal Status ◽  
Business Processes ◽  
Document Management ◽  
Electronic Document ◽  
Work Processes ◽  
Blockchain Technology ◽  
Electronic Document Management ◽  
Corporate System ◽  
The Impact

Aim. The presented study aims to determine the role and place of electronic document management in the corporate system of Russian enterprises, outlining the prospects for its development.Tasks. The authors examine the legislative acts of the Russian Federation on the prospects for the implementation of electronic document management; assess the impact of the pandemic on the digitalization of document management; analyze the business costs of paper document management; identify the advantages of using electronic document management and promising technologies in document processing.Methods. This study uses theoretical and empirical research methods. The dialectic method is used to determine the role, significance, and legal status of electronic document management. Through a logical approach, the essence of such concepts as 'electronic document' and 'electronic document management' is identified.Results. The study presents directions for the development of electronic document management using blockchain technology, which will improve workflows by processing, sorting, exchanging data and documents protected from unauthorized access, and artificial intelligence, which can help organizations process documents faster by simplifying operational procedures. Obstacles that prevent companies from actively using electronic document management are identified. These include additional investment, time costs, and reorganization of management. The volume of innovative services is analyzed by the type of economic activity, and the costs of creating, storing, and processing paper documents are considered.Conclusions. Recent trends in legislation indicate the government's firm commitment to the speedy introduction of electronic document management in Russia. Its use frees up a lot of resources, including time, labor, and finances. The 2020 pandemic has emphasized the importance of digitalizing business processes to ensure their continuity in unforeseen situations. Integrated into the automation of work processes, blockchain technology will ensure the protection of information from unauthorized tampering. Artificial intelligence will open up new opportunities for processing electronic documents.

scholarly journals AI for augmenting human judgement in Business Processes Management

2021 ◽  
Vol 201 (3) ◽  
pp. 507-518
Author(s):  
Łukasz Osuszek ◽  
Stanisław Stanek
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Support ◽  
Business Processes ◽  
Workflow Management ◽  
Organizational Memory ◽  
Machine Learning Algorithms ◽  
Analytical Models ◽  
Case Based Reasoning ◽  
Historical Case

The paper outlines the recent trends in the evolution of Business Process Management (BPM) – especially the application of AI for decision support. AI has great potential to augment human judgement. Indeed, Machine Learning might be considered as a supplementary and complimentary solution to enhance and support human productivity throughout all aspects of personal and professional life. The idea of merging technologies for organizational learning and workflow management was first put forward by Wargitsch. Herein, completed business cases stored in an organizational memory are used to configure new workflows, while the selection of an appropriate historical case is supported by a case-based reasoning component. This informational environment has been recognized in the world as being effective and has become quite common because of the significant increase in the use of artificial intelligence tools. This article discusses also how automated planning techniques (one of the oldest areas in AI) can be used to enable a new level of automation and processing support. The authors of the article decided to analyse this topic and discuss the scientific state of the art and the application of AI in BPM systems for decision-making support. It should be noted that readily available software exists for the needs of the development of such systems in the field of artificial intelligence. The paper also includes a unique case study with production system of Decision Support, using controlled machine learning algorithms to predictive analytical models.

scholarly journals Combining Regression and Clustering for Financial Analysis

2020 ◽  
Vol 2 (1) ◽  
pp. 109-118
Author(s):  
Andreea Ioana Chiriac
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cluster Analysis ◽  
Business Processes ◽  
Financial Analysis ◽  
Two Dimensions ◽  
Machine Learning Algorithms ◽  
Financial Ratio ◽  
Research Perspective ◽  
Systems Learning

Abstract Artificial Intelligence is used in business through machine learning algorithms. Machine learning is a part of computer science focused on computer systems learning to perform a specific task without using explicit instructions, relying on patterns and inference instead. Though it might seem like we’ve come a long way in the last ten years, which is true from a research perspective, the adoption of AI among corporations is still relatively low. Over time it became possible to automate more tasks and business processes than ever before. The benefit of using artificial intelligence is that does not require to program every step of the process, predicting at each step what could happen and how to resolve it. The algorithms decide for themselves in each case how the problems should be solved, based on the data that is used. I apply Python language to create a synthetic feature vector that allows visualizations in two dimensions for EDIBTA financial ratio. I use Mean-Square Error in order to evaluate the success, having the optimal parameters. In this section, I also mentioned about the purpose, goals, and applications of cluster analysis. I indicated about the basics of cluster analysis and how to do it and also did a demonstration on how to use K-Means.

Design of Portal for Improvement Controlling Sales Channel

2014 ◽  
Vol 693 ◽  
pp. 445-450
Author(s):  
Matus Peci ◽  
Pavel Vazan ◽  
Vladimir Surka
Keyword(s):  
Performance Indicators ◽  
Web Application ◽  
Business Processes ◽  
Initial Conditions ◽  
Content Management ◽  
Appropriate Technology ◽  
Content Management System ◽  
Sales Channel ◽  
Advantages And Disadvantages ◽  
Financial Costs

This paper presents design of portal that improves controlling of sales channel at initial conditions. The customer needed to display key performance indicators on the portal and thus significantly improve the efficiency of business processes after the deployment of Microsoft Dynamics CRM 2011. The aim of this paper is to choose appropriate technology to create a portal and design possible architecture of solution. Microsoft Dynamics CRM 2011 with Web Application Microsoft SharePoint platform provide a solution for process control of business processes in company that is primarily offered like as the best solution. On the other hand, we found the possibility of open source content management system DotNetNuke, which meets customer's requirements and therefore, there is a possibility to reduce financial costs when selecting this system. Finally, we evaluated considered technologies, prepared more price variations of architecture and compared advantages and disadvantages of these variations.

Modelling blockchain-based business processes: a comparative analysis of BPMN vs CMMN

2021 ◽  
Vol 27 (2) ◽  
pp. 638-657
Author(s):  
Fredrik Milani ◽  
Luciano Garcia-Banuelos ◽  
Svitlana Filipova ◽  
Mariia Markovska
Keyword(s):  
Design Methodology ◽  
Business Processes ◽  
Process Analysis ◽  
Process Models ◽  
Disruptive Technology ◽  
Content Type ◽  
Advantages And Disadvantages ◽  
Blockchain Technology ◽  
Block Chain ◽  
Insight Into

PurposeBlockchain technology is increasingly positioned as a promising and disruptive technology. Such a promise has attracted companies to explore how blockchain technology can be used to gain significant benefits. Process models play a cardinal role when seeking to improve business processes as they are the foundation of process analysis and redesign. This paper examines how blockchain-oriented processes can be conceptually modelled with activity- (BPMN) and artifact-centric (CMMN) modelling paradigms.Design/methodology/approachThis paper discusses how commonly occurring patterns, specific to block-chain-based applications, can be modelled with BPMN and CMMN. Furthermore, the advantages and disadvantages of both notations for accurately representing blockchain-specific patterns are discussed.FindingsThe main finding of this paper is that neither BPMN nor CMMN can adequately and accurately represent certain patterns specific for blockchain-oriented processes. BPMN, while supporting most of the patterns, does not provide sufficient support to represent tokenization. CMMN, on the other hand, does not provide support to distinguish between activities executed and data stored on-chain versus off-chain.Originality/valueThe paper provides insight into the strengths and weaknesses of BPMN and CMMN for modelling processes to be supported by blockchain. This will serve to aid analysts to produce better process models for communication purposes and, thereby, facilitate development of blockchain-based solutions.

REVISITING THE METHODOLOGICAL DEVELOPMENT IN SOIL EROSION RESEARCH

Ensemble ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 145-165
Author(s):  
Tanmoy Sarkar ◽  
◽  
Tapas Pal ◽  
Keyword(s):  
Artificial Intelligence ◽  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Soil Erosion ◽  
Geographic Information ◽  
Methodological Development ◽  
Advantages And Disadvantages ◽  
The Future ◽  
Soil Erosion By Water

Soil erosion (by water) is a major land degradation process that may threat the Sustainable Development Goals (SDG) by its negative impact on environment and human well-being. Soil erosion research demands scientific methods, tools and techniques to assess soil erosion with more accuracy and reliability. Soil erosion research has had experienced crude field-based techniques in early twentieth century to model-based approaches since the 1970s and very recent machine learning and artificial intelligence models to predict soil erosion susceptibility and risk. The paper aims to review the trend in methodological development in soil erosion by water through time. The brief background of different approaches, their relative advantages and disadvantages are reviewed. Depending on the time of establishment and wide application the approaches are classified and represented as erosion plot/runoff approach, erosion pin technique followed by environmental tracer method and model approach in combination with Remote Sensing (RS) and Geographic Information System (GIS). Recent advancement in artificial intelligence and application of statistical techniques have a great potential to contribute in soil erosion research by identifying various degrees of susceptibility in large scale and also to quantify the erosion rate with high accuracy. The Remote sensing (RS) and Geographic Information System (GIS) contribute to develop regional scale data base with exploration of real time data and spatial analysis. The combination of RS & GIS and process-based models must be more effective than the traditional soil erosion model in the context of prediction with greater reliability and validity. The future research on soil erosion is better to focus on the theoretical analysis and development of erosion prediction model with more quantitative refinement and to model the future.

Artificial Intelligence in Healthcare

2020 ◽  
Author(s):  
Ivana Ognjanovic
Keyword(s):  
Artificial Intelligence ◽  
Business Processes ◽  
Technology Development ◽  
Rapid Development ◽  
Modern Technology ◽  
Quality Monitoring ◽  
Intelligent Support ◽  
Treatment Plans ◽  
Healthcare Processes ◽  
Personalised Treatment

Modern technology development created significant innovations in delivery of healthcare. Artificial intelligence as “a branch of computer science dealing with the simulation of intelligent behaviour in computers” when applied in health care resulted in intelligent support to decision-making, optimised business processes, increased quality, monitoring and delivering of personalised treatment plans and many other applications. Even though the benefits are clear and numerous, there are still open issues in creating automation of healthcare processes, ensuring data protection and integrity, reduction of medical waste etc. However, due to rapid development of AI techniques, more advances and improvements are still expected.

Innovation and information technology in management

2021 ◽  
pp. 68-72
Author(s):  
Muna Shehada ◽  
F.V. Akulinin
Keyword(s):  
Artificial Intelligence ◽  
Information Technology ◽  
Economic Development ◽  
Supply Chain ◽  
Business Processes ◽  
Resource Planning ◽  
Operating Costs ◽  
Financial Sustainability ◽  
Chain Management ◽  
Reducing Costs

Information technology, artificial intelligence and other innovative areas of economic development allow manufacturers to reduce their operating costs by improving resource planning. Optimizing supply chain management (SCM) can improve the efficiency of all business processes in an organization, while reducing costs and increasing financial sustainability.

Mobile Technology for Knowledge Management

2011 ◽  
pp. 1625-1632
Author(s):  
Volker Derballa ◽  
Key Pousttchi
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Data Warehouse ◽  
Mobile Technology ◽  
Business Processes ◽  
Technological Infrastructure ◽  
It Support ◽  
Mobile Processes ◽  
Design Requirements ◽  
Mobile Context

IT support for knowledge management (KM) is a widely discussed issue. Whereas an overemphasis on technology is often criticized, the general consensus is that a well-balanced combination of technical and social approaches can be a rewarding departure (Alavi & Leidner, 1999). The usage of knowledge management systems (KMSs) (i.e., information systems including for example data warehouse techniques and artificial intelligence tools) is seen as a factor that can beneficially support different KM processes (Frank, 2001; Wiig, 1995). Due to the fact that an increasingly large proportion of work is not conducted in the context of stationary workplaces anymore, it becomes necessary to make KMSs available to those mobile workers (Rao, 2002; Sherman, 1999). Considering the different technological infrastructure in the stationary, as well as the mobile context, a KMS that so far is only available at a stationary workplace cannot simply become mobile without any changes. Further, the aspect of mobility implies specific design requirements for KMS. Taking together the rapid developments in the field of technology, allowing more and more mobile processes to be potentially supported through mobile KMS, as well as the current social and occupational developments, resulting in more mobile workplaces and business processes (Gruhn & Book, 2003), the relevance of mobile KM can be expected to increase in the future.

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