Industry 4.0 Wireless Networks and Cyber-Physical Smart Manufacturing Systems as Accelerators of Value-Added Growth in Slovak Exports

Valášková, Katarína; Nagy, Marek; Zábojník, Stanislav; Lăzăroiu, George

doi:10.3390/math10142452

Cited by 48 publications

(28 citation statements)

References 75 publications

(80 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance of individual models in predicting corporate bankruptcy depends on the input data and the processing technique utilized (Valaskova et al, 2022). These models are built based on empirical data from a particular economy.…”

Section: Development Of Prediction Modelsmentioning

confidence: 99%

Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries

Valášková

Gajdosikova

Belás

2023

View full text Add to dashboard Cite

Research background: Effective monitoring of financial health is essential in the financial management of enterprises. Early studies to predict corporate bankruptcy were published at the beginning of the last century. The prediction models were developed with a significant delay even among the Visegrad group countries. Purpose of the article: The primary aim of this study is to create a model for predicting bankruptcy based on the financial information of 20,693 enterprises of all sectors that operated in the Visegrad group countries during the post-pandemic period (2020?2021) and identify significant predictors of bankruptcy. To reduce potential losses to shareholders, investors, and business partners brought on by the financial distress of enterprises, it is possible to use multiple discriminant analysis to build individual prediction models for each Visegrad group country and a complex model for the entire Visegrad group. Methods: A bankruptcy prediction model is developed using multiple discriminant analysis. Based on this model, prosperity is assessed using selected corporate financial indicators, which are assigned weights such that the difference between the average value calculated in the group of prosperous and non-prosperous enterprises is as large as possible. Findings & value added: The created models based on 6?14 financial indicators were developed using different predictor combinations and coefficients. For all Visegrad group countries, the best variable with the best discriminating power was the total indebtedness ratio, which was included in each developed model. These findings can be used also in other Central European countries where the economic development is similar to the analyzed countries. However, sufficient discriminant ability is required for the model to be used in practice, especially in the post-pandemic period, when the financial health and stability of enterprises is threatened by macroeconomic development and the performance and prediction ability of current bankruptcy prediction models may have decreased. Based on the results, the developed models have an overall discriminant ability greater than 88%, which may be relevant for academicians to conduct further empirical studies in this field.

show abstract

Section: Development Of Prediction Modelsmentioning

confidence: 99%

Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries

Valášková

Gajdosikova

Belás

2023

View full text Add to dashboard Cite

show abstract

“…This leads to an advancement culture in business development of an organisation. The major network of IoT are most of the physical objectives that are mainly highlighted with highest development to get the efficient culture and better activity within business development [18]. The physical objectives can economically be cost effective which mainly prioritised with the major advancement processes.…”

Section: Usage Of Ai In Iotmentioning

confidence: 99%

Exploring the Role of Artificial Intelligence in Internet of Things (IOT)

Balaji¹,

Fernando²

2022

TTITCCR

View full text Add to dashboard Cite

There is perfect role of artificial intelligence (AI) in Internet of things (IoT), and this get better result in the technological advancement processes. The major features of AI such as perception, deep learning, feature engineering, natural learning processing and robotics can make an effective development and establishment. There is major development can be featured with the technical process and this is possible with efficient application of AI in IoT. In this way business operation get feasible and this effective possible with the perfect intelligence. The bug data analytics and advanced techniques of technology can be maintained with the help of various new application which are especially be possible with proper management. Recent report has shown that revenue has been increased with the application of IoT. The main trends as also been used data driven which is combined with IoT to get better intelligence in major business conduction. This assist in maintaining huge growth in future development and also leads to the betterment in manufacturing procedure. Keyword : Artificial intelligence, Internet of things, Business operation

show abstract

“…The research problem and the literature gap developed thoroughly in the systematic review is whether the integration of artificial intelligence data-driven Internet of Things systems and sensing networks, autonomous decision-making algorithms, sustainable product lifecycle management, and real-time advanced analytics has furthered the swift advancement of cyber-physical production logistics in relation to cognitive manufacturing. This is the first systematic review covering cognitive capabilities (Lăzăroiu et al, 2017;Valaskova et al, 2022) of the smart manufacturing systems, as an extension of a hot emerging topic hardly covered in the literature (Andronie et al, 2021a(Andronie et al, , 2021b(Andronie et al, , 2021cLăzăroiu et al, 2022). We supplement prior research by consistently proving that computer vision algorithms harness image recognition tools, context aware systems, and machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing

Lăzăroiu

Androniceanu

Grecu

et al. 2022

View full text Add to dashboard Cite

Research background: With increasing evidence of cognitive technologies progressively integrating themselves at all levels of the manufacturing enterprises, there is an instrumental need for comprehending how cognitive manufacturing systems can provide increased value and precision in complex operational processes. Purpose of the article: In this research, prior findings were cumulated proving that cognitive manufacturing integrates artificial intelligence-based decision-making algorithms, real-time big data analytics, sustainable industrial value creation, and digitized mass production. Methods: Throughout April and June 2022, by employing Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) guidelines, a quantitative literature review of ProQuest, Scopus, and the Web of Science databases was performed, with search terms including ?cognitive Industrial Internet of Things?, ?cognitive automation?, ?cognitive manufacturing systems?, ?cognitively-enhanced machine?, ?cognitive technology-driven automation?, ?cognitive computing technologies,? and ?cognitive technologies.? The Systematic Review Data Repository (SRDR) was leveraged, a software program for the collecting, processing, and analysis of data for our research. The quality of the selected scholarly sources was evaluated by harnessing the Mixed Method Appraisal Tool (MMAT). AMSTAR (Assessing the Methodological Quality of Systematic Reviews) deployed artificial intelligence and intelligent workflows, and Dedoose was used for mixed methods research. VOSviewer layout algorithms and Dimensions bibliometric mapping served as data visualization tools. Findings & value added: Cognitive manufacturing systems is developed on sustainable product lifecycle management, Internet of Things-based real-time production logistics, and deep learning-assisted smart process planning, optimizing value creation capabilities and artificial intelligence-based decision-making algorithms. Subsequent interest should be oriented to how predictive maintenance can assist in cognitive manufacturing by use of artificial intelligence-based decision-making algorithms, real-time big data analytics, sustainable industrial value creation, and digitized mass production.

show abstract

Industry 4.0 Wireless Networks and Cyber-Physical Smart Manufacturing Systems as Accelerators of Value-Added Growth in Slovak Exports

Cited by 48 publications

References 75 publications

Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries

Bankruptcy prediction in the post-pandemic period: A case study of Visegrad Group countries

Exploring the Role of Artificial Intelligence in Internet of Things (IOT)

Artificial intelligence-based decision-making algorithms, Internet of Things sensing networks, and sustainable cyber-physical management systems in big data-driven cognitive manufacturing

Contact Info

Product

Resources

About