تحلیل سلسله مراتبی فازی

With the entry of global industries into the Fourth Industrial Revolution and the observation of the lagging domestic industries, there is a need for scientific efforts to identify and address the barriers to the development of local industries. The manufacturing industries in Bushehr province, as active enterprises in one of the regions with industrial potential and abundant natural resources, grapple with challenges across different domains, and the academic communities in the province feel a responsibility to work towards resolving these issues. The objective of the present study is to analyze the factors contributing to the underdevelopment of manufacturing industries in Bushehr province.

The statistical population of this study comprises experts active in manufacturing companies within Bushehr province. In the first phase of study, scientific resources and theoretical foundations were utilized to extract the criteria for identifying impairments in manufacturing industries. Subsequently, a focus group was formed, and feedback was solicited from experts in the industries of Bushehr province, leading to the prioritization and finalization of the indicators through fuzzy methods. In the second phase, the extracted components and indicators were weighted and ranked using the Analytic Hierarchy Process technique. The research instrument in both phases was a researcher-constructed questionnaire, which was validated for content by professors and experts in the field. The reliability of the questionnaire was confirmed using the consistency ratio

Out of 74 identified indicators from the theoretical framework, 39 indicators were deemed to have higher weight and significance for inclusion in the second phase. These indicators were categorized into eight overarching criteria. The weights of the eight general components were as follows: restrictive government policies (0.163), weaknesses in strategic decision-making (0.158), weaknesses in optimal resource management (0.141), macroeconomic drivers (0.129), organizational financial issues (0.110), workforce and human resource challenges (0.107), lack of attention to technological advancements (0.099), and environmental barriers (0.093).

Originality/Value: 

This research identifies the challenges faced by the manufacturing industries in Bushehr Province from the perspective of experts in these industries, and by ranking and categorizing these issues, it offers a clear representation for addressing and resolving these problems.

As environmental policies are highlighted in the green supply chain, it plays an essential role in improving the supply chain capabilities. On the other hand, considering the development of technology and the Internet of Things intelligence and their potentials in goal achievement, it is important to investigate and study their applications in this field. This study aims to identify the most important criteria in selecting a green supplier and investigate the impact of the Internet of Things (IoT) on this selection. To achieve these objectives, the fuzzy cognitive map (FCM) method is used to identify the effective weights of each criterion and introduce the most effective criterion by examining the centrality. The fuzzy hierarchical analysis (FAHP) is used to investigate the role of IoT in the green supply chain. The results indicate that air pollution control is the most important criterion for evaluating the suppliers and also has the greatest impact on IoT.

Anxiety for incorrect risks management is the most important obstacle to the development of investment projects. Risk management is the most important part of project management in organizations discussed below, and there is not systematic structural approach to the risk assessment in this study, we use a combination model of Fuzzy Analytic Hierarchy Process (FAHP) and Fuzzy Decision-making Trial and Evaluation Laboratory(FDEMATEL) method to rank and valuate risk factors in Power Plant Projects. Firstly, the risk factors of construction projects are figured out through literature review and expert interview in the second step, the main criteria and sub-criteria are weighted using fuzzy hierarchical analysis is in the third stage, interrelationships among risk factors are calculated by fuzzy DEMATEL. The results of the research identifying causal factors can determine the main risk factors in the powerhouse projects. The populations of the investigation are managers and experts in the field of research. Eleven experts being familiar with construction project management are selected using judgmental sampling. In this study, for the investigation of the distribution of the type of data, Kolmogorov- smirnov test has been used. The prioritization of these factors show that among the main criteria for research, implementation and construction achieved the highest rank, and other factors are important in the next position. Also, among the 53 sub-criteria research, increasing construction costs, the lack of skilled labor, confiscation of property, and land ownership hold the highest priority and are of utmost importance in the standpoint of respondents. The implementation and construction of risk have the first rank., Also, have the highest weight and have the highest interaction with other criteria by DEMATEL in terms of innovation, in most of the topics of risk management this weakness in the present article has been solved.

Business intelligence (BI) is the tool used by enterprises to collect, manage and analyze structural and non-structural data and also information by taking advantage of modern information technology. It utilizes a substantial amount of collected data during the daily operational processes, and transforms the data into information and knowledge to avoid the supposition and ignorance of the enterprises. In the last decade, Business intelligence has evolved as one of the critical applications in organizations to provide useful insight, support decision-making, and drive organizational performance. However, to avoid the ineffective experiences during the deployment, it is important to clarify the impact factors of a BI system and find out a Suitable assessment method to evaluate the performance of BI systems. Evaluating the effectiveness of business intelligence systems is vital to our understanding of the value and efficacy of management actions and investments. This research is a descriptive survey. We construct an Assessment model to assess the performance of business intelligence systems based on the outcomes of previous researches. Furthermore, an expert questionnaire using fuzzy analytic hierarchy process (AHP) was used to filter out useful performance indicators. The analytic hierarchy process is a structured technique for organizing and analyzing complex decisions. Finally, we analyze a real case by using the constructed assessment model for Business Intelligence systems. We choose an automotive parts manufacturer as a case. The survey sample population consisted of 66 randomly taken employees and managers of the case. The results indicate that the performance of Business Intelligence system in our case was suitable.

Banks as financial institutions must estimate the credit risk of their debtors. This is the basis of pricing a loan, determining appropriate interest rates and determining the mortgage required to each borrower. Since the continuity of bank activities largely depends on the amount of credit losses in a particular period, banks should consider the credit quality of their loan portfolio as a collection of debts.In this paper, the calculation of credit risk of corporates applying for loans has been investigated. Using fuzzy analytic hierarchy process, effective criteria for credit risk have been analyzed.The neural network is used to extract an open box model that describes the relationship between effective criteria and the credit risk of the companies who apply for a loan. Neural network model has been run with historical data. Observations have been based on 174 corporate who had taken out a loan from a major Iranian bank named Mellat (All loans had been made during 2005 to 2008).The output of the model can predict credit risk of a corporate by at least 84% accuracy.

The aim of this paper, using a combination approach for fuzzy hierarchical analysis and balanced score card to assess the business climate in the industry in Iran. In the first part of the situation and favorable business environment in more reviews and Economic Intelligence Unit the results based on four aspects of balanced score card categories are analyzed. Indicators extracting fuzzy hierarchical analysis process has been weighted in proportion to their empowerment strategies for private sector survey design experts and 62 major strategies to identify and then select the most effective strategies to prioritize TOPSIS method used and finally 16 poll Grand strategy was selected. Appropriate strategies and operational solutions to the above interviews with experts and Library Studies to identify and extract the most effective solutions, expert persons development organizations affiliated to the Ministry of Industries and Mines of choice (a total of 30 organizations from every 5 persons) and questionnaire design those with the most important guidelines applicable to the study population and a total of 64 large-scale solution, was selected.