fuzzy interpretive structural modeling

This study aims to identify and analyze intraorganizational outsourcing risks within the Lorestan Gas Company for the period 2022–2023. The findings indicate that one of the intraorganizational factors, by paving the way for the emergence of other internal risks, could potentially lead to project failures and unsuccessful outsourcing activities.

Conducted in 2022–2023 within Lorestan Gas Company as a pilot in the oil industry, this study employs a mixed-method approach comprising both qualitative and quantitative phases. Quantitative data were analyzed using Failure Mode and Effect Analysis (FMEA) and Fuzzy Interpretive Structural Modeling (FISM). For the qualitative phase, Guba and Lincoln's (1982) evaluation method was applied, which is based on four criteria: credibility, transferability, dependability, and confirmability.

The developed model identifies risks associated with project definition and conceptualization, project design and planning, and project allocation, placing them at the fifth level, as the second most influential level affecting other potential outsourcing risks. The findings suggest that in any project, design and planning are crucial in steering a project towards success or failure, with failures in projects often proving costly.

 Most studies have focused solely on identifying outsourcing risks without providing a comprehensive model for analyzing these corresponding risks. This research, relying on selected analytical methods and identifying the most effective risks in outsourcing, presents a comprehensive model that helps fill this research gap, contributing to a deeper understanding of risk mitigation in outsourcing contexts.

One of the characteristics of the fourth industrial revolution is the creation of production intelligence through real-time data to make accurate and timely decisions. Therefore, data-driven statistical process control is expected to significantly contribute to the advancement of intelligent manufacturing. For this reason, statistical process control has become one of the most widely used tools to maintain an acceptable level of quality characteristics in the era of Industry 4.0. In this article, organizational readiness factors for the establishment of intelligent statistical process control in the age of the fourth industrial revolution in the gas industry were investigated. For this purpose, after identifying the control structure of the intelligent statistical process of applying the literature review, 12 factors of organizational readiness in order to achieve this goal were presented in the form of a framework. Then the relationship and sequence of these factors were determined by fuzzy interpretive structural modeling. Next, the obtained model was verified using the structured equation modeling approach. The presented model can be a guide for the gas industry in implementing an intelligent statistical process control system to provide better services and less defects.

The risks5 of buyer-supplier relationships cause the decreased performance and disruption in supply, but they can be managed by providing a suitable strategy. Despite the importance of these risks, a risk management model that determines the appropriate strategy for each supplier has not been developed so far. The purpose of this research is to provide a model for determining the strategy of buyer-supplier relationships risks for oil rotary equipment manufactures.

In the first stage, relationship risks and strategies were identified based on the research literature, and then experts' opinions about risks were collected to determine relationship risks in the studied industry. In the second stage, using the interpretive structural modeling method developed with fuzzy logic, the relationships between risks and those levels were examined and the most effective risks were determined by MICMAC analysis. The third stage is determined strategies by experts' opinions. Then fuzzy risk matrix with the dimensions of probability and intensity of impact is designed. Based on the risk priority (based on risk position in the matrix and its level based on interpretive structural model), a suitable strategy was determined for each supplier. In the last step, this model was validated by calculating the percentage of agreement.

The most effective risks of buyer-supplier relationships in this research include loss of objectivity, lack of trust and confidence, supplier's unwillingness to cooperate, Limited number of suppliers, supplier's opportunism, collusion and conflict of goals, which Their priority and relationships have been determined. Based on the priority of risk for each supplier, strategies for risk management have been determined.

The results of comparing the output of the model and the opinions of experts show the validity of the designed model and this model can be used for all industrial equipment manufacturer companies.

The purpose of this paper was to present the talent supply chain management model of the National Iranian Oil Company. In term of aim, this research is considered an applied and developmental research based on mixed method. The statistic population in qualitative step consisted articles in domstic and foreign scientific information bases related to talent management and in Quantitative step the human resources experts of company. The statistical samples for Meta synthesis included 12 articles related to talent supply chain management, which was determined through a multi-stage refinement of 109 papers and for Fuzzy interpretive structural modeling 22 HRM experts were selected by purposeful and judgmental method. Based on the research findings, talent supply chain network structure, talent supply chain key processes, managerial elements, core functions, talent supply chain performance and talent supply chain strategies are dimensions of the talent supply chain management model. In the presented model, the structure of the talent supply chain network has the highest driving power and the performance of talent supply chain management has the highest dependence degree.

In recent years, information technology has changed the practical methods in all corporations especially in the tourism industry. Nowadays, information technology is considered as one of the most important factors of everyday running processes in the tourism industry as the result of facilitating such important activities as producing, gathering, processing, applying, and communicating information. Therefore, to realize the importance of e-tourism industry, identifying the barriers of its application can be the first step, which contributes to the process of comprehensive development of the tourism industry. The research has offered a model by combining qualitative (Delphi) and quantitative (the second order of the confirmatory factor analysis) approaches which can be used by managers as planning and decision making guidelines. The statistical population was divided into two categories. The first was tourism experts and senior managers. The sampling method was snowball, and the sample size was 10. The second was the staffs and managers of ICHHTO. Its sampling method was simple probability, and the sample size was 60. For the purpose of this research, seven main barriers and 21 secondary barriers were identified by reviewing the literature and using the Delphi technique. Then, the main research model was structured by using SMART PLS. Also, the economic barriers were recognized as the most effective barriers, and software, educational and behavioral barriers were recognized as the most influenced ones, based on the created model by using the FISM technique. As the results indicated, tourism planners and managers are able to take proper actions to develop E-tourism.

Competition of today's organizations in the supply chain has increased by growing the level of the competitiveness caused by globalization, and is included all chain members and external organizations. Intra-organizational perspectives about quality have some limitations; therefore, manufacturers should pay attention to inter-organizational approaches of quality such as supply chain quality management (SCQM). SCQM will enable the manufacturing companies to satisfy their customer's needs in a competitive market. In this study, after identifying the structure of supply chain management and quality management operation through comprehensive review of the literature, a comprehensive and conceptual definition about supply chain quality management (SCQM) and finally after studying its dimensions, a conceptual framework with 7 factors are presented. Relationship and sequencing of these factors determined by fuzzy interpretive structural modeling (ISM). In this regard, after the assessment, these factors were placed in five levels. Then the obtained structural model confirmed by using path analysis. The proposed model can help the gas industry in implementation of quality management in their supply chain to achieve competitive advantages and satisfy the customer needs better.

The Iranian organizations high tendency for the establishment of total quality management (TQM) system has caused the researchers also do significant research in recent years in this area. But the lack of attention to basic obstacles and challenges in the successful implementation of quality management systems in Iranian organizations has made does not provide the necessary settings for the full realization of this system. Identifying barriers of TQM using in organizations can be considered the first step in implementing these systems, because as long as the managers and staff have not action is to overcome these obstacles, failure of this project seems inevitable. In this study, after identifying the key barriers to successful implementation of TQM in the gas industry through literature review and interviews with experts, 5 main obstacles detected. Then the communication and sequence of barriers were determined with Fuzzy Interpretive Structural Modeling. Given that intensity of relationships between some obstacles was not clear in FISM method, therefor intensity of the relationship between barriers was determined by using of fuzzy Dematel. The results showed that human resources barriers and conceptual obstacles that must influential barriers be a lot of attention to them. The proposed model can provide a plan to tackle barriers to successful implementation of TQM in the gas industry.