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Proposing a fuzzy multi-criteria decision making (MCDM) algorithm that is able to incorporate the heterogeneousness effect of DM group into the decision process, in order to determine the best remotely operated vehicle (ROV) design alternative to manufacture and developing a practical decision aid tool based on this algorithm. The paper aims to discuss these issues.

An algorithm utilizes fuzzy AHP Buckley’s approach for modeling heterogeneousness of the DM group, fuzzy AHP Chang’s extent analysis to calculate the priority values of criteria and Chen’s fuzzy TOPSIS for ranking the alternatives and finally group working technique for initiation issues is developed. MATLAB is used to implement the algorithm and generate a decision aid tool. Real life application and sensitivity analysis is performed by the help of generated tool. Literature and background explanations are also provided.

A MCDM algorithm that incorporates the heterogeneousness effect of the DM group into the decision process is introduced. Sensitivity analysis suggested the independence of the final result from DM group and criteria set. A practical decision aid tool is generated for ROV manufacturing companies.

A computerized MCDM aid tool that incorporates heterogeneousness of the DM group into the decision process is generated. Tool let ROV manufacturing companies to evaluate ROV design alternatives with respect to qualitative and quantitative criteria and determine proper choice.

Determination of the proper ROV design alternative to manufacture gap within the literature filled with an algorithm that provides more reliable results due to its incorporation the heterogeneousness of the DM group into the decision process characteristic. A practical decision aid tool is generated.

The purpose of this paper is to determine as to develop a fuzzy multi-criteria decision-making (MCDM) algorithm with self-check capability that can solve any manufacturing company's printed circuit boards (PCB) design computer aided design (CAD) tool selection problem and to implement it.

An algorithm that consists of two sub-algorithms that use same inputs and alternative pool is developed, thus self-check capability is introduced. The first sub-algorithm designed as an integration of fuzzy AHP and TOPSIS, where the second sub-algorithm composes of fuzzy analytic network process and TOPSIS. Fuzzy set theory and linguistic variables were utilized to handle uncertainty and usage of verbal expressions, respectively. MATLAB programming language was used for the implementation. The used MCDM methods’ and fuzzy set theory's explanations are given along with the literature review prior to real life application of the developed algorithm.

A MCDM algorithm with self-check capability is introduced. Moreover, a practical decision aid tool is generated for the usage of the manufacturing companies that are related with PCB design.

A practical computerized MCDM aid tool is generated. Using the tool let the manufacturers, i.e. high-tech device manufacturers, evaluate available PCB CAD design tools with respect to tangible and intangible criteria, and obtain a reliable result.

Self-check capability is incorporated into the decision process. Along with this capability, although the decision-making process takes place in a fuzzy environment, result of the algorithm becomes more reliable than the ones deprived of this characteristic. Furthermore, a practical computerized MCDM aid tool is generated.

Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show the usage of this information in financial decision processes.

Need for the Study: Main financial reports such as balance sheets and income statements can be analysed by statistical methods. However, an expanded financial reporting framework needs new analysing methods due to unstructured and big data. The study offers a solution to the analysis problem that comes with non-financial reporting, which is an essential communication tool in corporate reporting.

Methodology: Text mining analysis of annual reports is conducted using software named R. To simplify the problem, we try to predict the companies’ corporate governance qualifications using text mining. K Nearest Neighbor, Naive Bayes and Decision Tree machine learning algorithms were used.

Findings: Our analysis illustrates that K Nearest Neighbor has classified the highest number of correct classifications by 85%, compared to 50% for the random walk. The empirical evidence suggests that text mining can be used by all stakeholders as a financial analysis method.

Practical Implications: Combining financial statement analyses with financial reporting analyses will decrease the information asymmetry between the company and stakeholders. So stakeholders can make more accurate decisions. Analysis of non-financial data with text mining will provide a decisive competitive advantage, especially for investors to make the right decisions. This method will lead to allocating scarce resources more effectively. Another contribution of the study is that stakeholders can predict the corporate governance qualification of the company from the annual reports even if it does not include in the Corporate Governance Index (CGI).