scholarly journals BUSINESS PERFORMANCE AND FINANCIAL HEALTH ASSESSMENT THROUGH ARTIFICIAL INTELLIGENCE

2021 ◽  
Vol 15 (2) ◽  
pp. 38-51
Author(s):  
Tomas Krulicky ◽  
Jakub Horak
Keyword(s):  
Cluster Analysis ◽  
Financial Distress ◽  
Business Performance ◽  
Financial Analysis ◽  
Health Assessment ◽  
Value Added ◽  
Decision Support Tool ◽  
Support Tool ◽  
Construction Companies ◽  
Kohonen Networks

Research background: Globalisation and the development of technology introduce new requirements for effective business management. Every business must constantly adapt to the environment, analyse and know its competitors and its customers’ requirements, and meet other stakeholders’ commitments. An unsuccessful business will go into liquidation. The intention of any business should be not only to avoid this situation, but to thrive and prosper and create value for its shareholders. Purpose of the paper: The aim of this study is to propose an appropriate tool for cluster analysis and determine the ability of a business to survive a potential financial distress. Methods: Details from financial statements of construction companies operating in the period 2015-2019 in the Czech Republic are analysed. Attention is mainly directed to items that represent the capital and asset structures of a company, liquid assets, and the ability to generate sales and profit. Artificial neural networks in the form of Kohonen networks are used for the purpose of cluster analysis. Financial analysis is used to examine the underlying dataset as well as for a detailed analysis of selected clusters, i.e. the contribution margin and ratio indicators. Findings & Value added: The basic analysis clearly shows that companies in liquidation attempt to reduce the value of inventories and engage additional foreign capital with a view to survival, while there is a certain solidarity between companies’ key persons. Cluster analysis using Kohonen networks is quite successful. The present methodology and approach can still be applied to the design of an enterprise decision support tool. Further research may study whether the representation of businesses in the different clusters will change over time, or whether the development of the construction industry can indeed be predicted based on an analysis of the leaders.

scholarly journals Performance assessment of alternative SSS networks by combining KPIs and factor-cluster analysis

2020 ◽  
Vol 12 (1) ◽  
Author(s):  
Patrizia Serra ◽  
Gianfranco Fancello
Keyword(s):  
Cluster Analysis ◽  
Performance Assessment ◽  
Decision Support Tool ◽  
Transport Systems ◽  
Quantitative Methodology ◽  
Support Tool ◽  
Performance Benchmarking ◽  
Relevant Variables ◽  
Quantitative Results

Abstract Performance assessment is a fundamental tool to successfully monitor and manage logistics and transport systems. In the field of Short Sea Shipping (SSS), the performance of the various maritime initiatives should be analyzed to assess the best way to achieve efficiency and guide related policies. This study proposes a quantitative methodology which can serve as a decision-support tool in the preliminary assessment and comparison of alternative SSS networks. The research is executed via a Mediterranean case study that compares a hypothetical Mediterranean ro-ro SSS network developed in the framework of a past Euro-Mediterranean cooperation project with the network of existing ro-ro liner services operating in the area. Performance benchmarking of the two networks is performed using a set of quantitative Key Performance Indicators (KPIs) and applying a factor-cluster analysis to produce homogeneous clusters of services based on the relevant variables while accounting for sample heterogeneity. Quantitative results mostly confirm the overall better performance of the prospective network and demonstrate that using KPIs and factor-cluster analysis to investigate the performance of maritime networks can provide policymakers with a preliminary wealth of knowledge that can help in setting targeted policy for SSS-oriented initiatives.

VP163 Patient Involvement In The Development Of Multi-Criteria Decision Tool

2017 ◽  
Vol 33 (S1) ◽  
pp. 223-223
Author(s):  
Marie-Pierre Gagnon ◽  
Sylvain L'Espérance ◽  
Carmen Lindsay ◽  
Marc Rhainds ◽  
Martin Coulombe ◽  
...  
Keyword(s):  
Decision Support ◽  
Delphi Study ◽  
Value Added ◽  
Decision Support Tool ◽  
Strategic Choices ◽  
Healthcare Organizations ◽  
Support Tool ◽  
Health Technologies ◽  
Modified Delphi ◽  
Patient Representatives

INTRODUCTION:Healthcare organizations should assess the relevance of both existing and new practices. Involving patients in decisions regarding which health technologies and interventions should be prioritized could favor a better fit between strategic choices and patients needs.METHODS:Following a systematic review of existing multi-criteria decision support tools and a consultation with hospital clinicians and managers, a set of potentially relevant criteria was identified. A three-round modified Delphi study was then conducted among four groups (hospital managers, heads of department, clinicians, and patient representatives) in order to reach consensus on criteria that should be considered in the tool.RESULTS:In total, seventy-four participants completed the third round of the Delphi study. Consensus was obtained on twelve criteria. There were some significant differences between groups in priority scores given to criteria. Patient representatives differed significantly from other groups on two criteria. Their ranking of the accessibility criteria was higher, and their ranking of the organizational aspect criteria was lower than for the other groups.CONCLUSIONS:Patient representatives can be involved in the development of a multi-criteria decision support tool to identify, evaluate and prioritize high value-added health technologies and interventions in order to enhancing clinical appropriateness The fact that accessibility aspects were more important for patient representatives calls for specific attention to these criteria when prioritizing health technologies or interventions. Furthermore, we need to ensure that the decisions made regarding the relevance of these technologies and interventions also reflect patients’ preferences.

scholarly journals Financial Distress and Bankruptcy Prediction in Conditions of Slovak Republic

2021 ◽  
Vol 92 ◽  
pp. 08017
Author(s):  
Filip Rebetak ◽  
Viera Bartosova
Keyword(s):  
Financial Distress ◽  
Prediction Models ◽  
Financial Analysis ◽  
Slovak Republic ◽  
Value Added ◽  
Theoretical Background ◽  
Bankruptcy Prediction ◽  
Important Place ◽  
Non Profit ◽  
Analysis And Synthesis

Research background: Prediction of bankruptcy has an important place in financial analysis of an organization in the globalized economy. Ever since the first publication of a paper on bankruptcy prediction in 1932, the field of bankruptcy prediction was attracting researchers and scholars internationally. Over the years, there have been a great many models conceived in many different countries, such as Altman’s Z score or Ohlson’s model for use for managers and investors to assess the financial position of a company. Globalization in last few decades has made it even more important for all stakeholders involved to know the financial shape of the company and predict the possibility of bankruptcy. Purpose of the article: We aim in this article to examine the financial distress and bankruptcy prediction models used or developed for Slovakia to provide an overview of possibilities adjusted to specific conditions of the Slovak Republic in context of globalization. We will also look at the possibility of use of these prediction models for assessing financial status of non-profit organizations in the Slovak Republic. Methods: We will use analysis and synthesis of current research and theoretical background to compare existing models and their use. Findings & Value added: We hope to contribute with this paper to the theoretical knowledge in this field by summarizing and comparing existing models used.

SUSTAINABILITY-ORIENTED INNOVATION, TECHNOLOGIES & FINANCE IN CONSTRUCTION: A MULTI-CRITERIA METHODOLOGY

2021 ◽  
Vol 5 (11) ◽  
Author(s):  
Adriano A. R. Barbosa ◽  
Martins Vilnits ◽  
Walter Leal Filho
Keyword(s):  
Decision Making ◽  
Developing Countries ◽  
Environmental Awareness ◽  
Decision Support Tool ◽  
Developed Countries ◽  
Analytic Hierarchy ◽  
Support Tool ◽  
Construction Companies ◽  
Planning Decision ◽  
The Impact

Considering the efforts to adjust the great demand of the civil construction in the developing countries and the economic interests of the sector, in line with current sustainable trends, this article presents a methodology to support the planning and decision making of construction companies. An analysis of the elements involved in the process, their interaction and proposed application priorities are evaluated using the AHP (Analytic Hierarchy Process) method as a multi-criteria decision support tool. The results obtained allow us to observe the parameters that guide the decision-making of the managers in the construction sites, the impact of low productivity and limited investments in innovation and processes, which still adopts the traditional methods of construction. However, exists a margin that is sustainable, without major financial impact, through the management and encouragement of the environmental awareness of investors, entrepreneurs and construction workers. The actions that interfere with the process and its application priorities in construction are related, within the context of innovation, technology and finance oriented to sustainability. Supported by the concepts of management and SOI (Sustainability-oriented innovation) topics, a discussion of the civil construction scenario in Brazil is carried out through the economic, social, technological and managerial issues typical of developing countries, guided by the strong trends in the macro-economics adjustments and rational use of natural resources in the coming decades. It highlights the main challenges of the sector and presents a analysis, planning, decision-making approach and collective impact actions that could support the development actions of the sector, in order to reduce the distance from the concepts of environmental awareness and attitudes existing in developed countries.

scholarly journals Artificial Intelligence for Cluster Analysis: Case Study of Transport Companies in Czech Republic

2021 ◽  
Vol 14 (9) ◽  
pp. 411
Author(s):  
Eva Kalinová
Keyword(s):  
Cluster Analysis ◽  
Czech Republic ◽  
Financial Analysis ◽  
Transport Sector ◽  
Economic Sectors ◽  
Accounting Data ◽  
The Czech Republic ◽  
Kohonen Networks ◽  
Further Development

What is the situation of the transport sector in the Czech Republic and what is its importance for the economy of the Czech Republic? How and to what extent do businesses operating in this sector influence the sector as such, and how many businesses in this sector have such influence? Additionally, what happens if the most important businesses in the transport sector go bankrupt, and which businesses are the most important ones? Searching for the answers to these questions is a subject of this contribution, focusing primarily on the cluster analysis using artificial neural networks (ANN), specifically with Kohonen networks, which represent the main method for processing a large volume of not only accounting data on transport companies. In this research, the dataset consists of the financial statements of transport companies for the years 2015–2018. The research part of the contribution deals mainly with the issue of the transport sector’s development in the years 2015–2018 with the companies operating in this sector and tries to identify the most important companies in terms of their importance for this sector. The results show that the whole transport sector is influenced mainly by the two largest companies, whose potential changes can affect companies themselves but to a great extent also the development of the whole transport sector. For the two companies, financial analysis is carried out using ratios, whose results show that despite the negative values of the important value generators of one of these companies, the company is still able to significantly influence the situation in the transport sector of the CR. This information is a clear guide for experts, development analysts, to determine the further development of the whole sector when focusing on the development of the two specific companies only. A question arises as to how the created model can be applied to other economic sectors, especially in other EU countries.

scholarly journals PRACTICE OF SMALL AND MEDIUM-SIZED ENTERPRISE PERFORMANCE EVALUATION IN LATVIA

2016 ◽  
Vol 4 ◽  
pp. 335 ◽  
Author(s):  
Inta Kotane
Keyword(s):  
Performance Evaluation ◽  
Performance Measurement ◽  
Business Performance ◽  
Research Study ◽  
Financial Analysis ◽  
Value Added ◽  
Financial Indicators ◽  
Scientific Publications ◽  
Expert Survey ◽  
Small And Medium Business

The total number of small and medium-sized enterprises signifies an essential share of the national economy; SMEs’ importance is evaluated by the value added and the new jobs created. Despite the growing research interest in the small and medium business performance measurement, there is no consistent opinion among researchers regarding small and medium business performance indicators, their measurement, and methods of assessment. The research study is based on an analysis of literature and scientific publications, the assessment of the financial indicators used by the Latvian institutions for the company’s financial analysis, and an expert survey. The general scientific research methods are used in the research study: information analysis and synthesis, logical construction, monographic, an expert survey, data grouping, and the graphical method. The aim of the research – to carry out an analysis of the performance evaluation practice for small and medium-sized enterprises in Latvia. A study of the small and medium sized business performance measurement and management is carried out and the analysis of the financial indicators used for the performance measurement of small and medium-sized enterprises is performed in the result.

scholarly journals iCCareD: The Development of an Algorithm to Identify Factors Associated With Distress Among Caregivers of Children and Youth Referred for Mental Health Services

2021 ◽  
Vol 12 ◽  
Author(s):  
Shannon L. Stewart ◽  
Ashley Toohey ◽  
Jeffrey W. Poss
Keyword(s):  
Mental Health ◽  
Mental Health Services ◽  
Health Services ◽  
Assessment Tool ◽  
Health Assessment ◽  
Decision Support Tool ◽  
Well Being ◽  
Children And Youth ◽  
Caregiver Distress ◽  
Support Tool

Caregiver well-being plays an important role in children's development and a number of factors have been found to impact distress levels among caregivers of children and youth referred for mental health services. Further, caregiver distress impacts youth psychopathology, its acuity as well as related mental health interventions. The purpose of this study was to develop and validate an algorithm for identifying caregivers who are at greatest risk of experiencing caregiver distress. This algorithm was derived from, and will be embedded in, existing comprehensive interRAI child and youth instruments. Ontario data based on the interRAI Child and Youth Mental Health assessment instruments (ChYMH and ChYMH-DD) were analyzed to identify predictors of distress among caregivers of children and youth ages 4–18 years. Starting with proactive aggression, the algorithm uses 40 assessment items to assign one of 30 nodes that are grouped into five levels of risk. The interRAI ChYMH Caregiver Distress (iCCareD) algorithm was validated using longitudinal data from mental health agencies across Ontario and was found to be a good predictor among this sample with a c-statistic of 0.71 for predicting new or ongoing caregiver distress and 65% for both sensitivity and specificity using algorithm values of 3 or greater. This algorithm provides an evidence-based decision-support tool embedded within a comprehensive assessment tool that may be used by clinicians to inform their selection of supports and services for families.

scholarly journals Outcomes of Implementing the Women's Health Assessment Tool and Clinical Decision Support Tool Kit

2016 ◽  
Vol 12 (3) ◽  
pp. 313-323
Author(s):  
Terry Silvestrin ◽  
Anna Steenrod ◽  
Karin Coyne ◽  
David Gross ◽  
Canan Esinduy ◽  
...  
Keyword(s):  
Decision Support ◽  
Women’S Health ◽  
Women's Health ◽  
Clinical Decision Support ◽  
Assessment Tool ◽  
Health Assessment ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Tool ◽  
Clinical Decision Support Tool

scholarly journals The application of Kohonen networks for identification of leaders in the trade sector in Czechia

Equilibrium ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. 739-761 ◽  
Author(s):  
Jaromír Vrbka ◽  
Elvira Nica ◽  
Ivana Podhorská
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Czech Republic ◽  
Value Added ◽  
Financial Statements ◽  
Data Set ◽  
The Czech Republic ◽  
Return On Equity ◽  
Kohonen Networks ◽  
Trade Sector

Research background: The trade sector is considered to be the power of economy, in developing countries in particular. With regard to the Czech Republic, this field of the national economy constitutes the second most significant employer and, at the same time, the second most significant contributor to GNP. Apart from traditional methods of business analyzing and identifying leaders, artificial neural networks are widely used. These networks have become more popular in the field of economy, although their potential has yet to be fully exploited. Purpose of the article: The aim of this article is to analyze the trade sector in the Czech Republic using Kohonen networks and to identify the leaders in this field. Methods: The data set consists of complete financial statements of 11,604 enterprises that engaged in trade activities in the Czech Republic in 2016. The data set is subjected to cluster analysis using Kohonen networks. Individual clusters are subjected to the analysis of absolute indicators and return on equity which, apart from other, shows a special attraction of individual clusters to potential investors. Average and absolute quantities of individual clusters are also analyzed, which means that the most successful clusters of enterprises in the trade sector are indicated. Findings & Value added: The results show that a relatively small group of enter-prises enormously influences the development of the trade sector, including the whole economy. The results of analyzing 319 enterprises showed that it is possible to predict the future development of the trade sector. Nevertheless, it is also evident that the trade sector did not go well in 2016, which means that investments of owners are minimal.

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