quality management
Yeganeh Kamranmehr; Ezzatollah Asgharizadeh; Fatemeh Saghafi
Abstract
In recent years, artificial intelligence (AI) has emerged as a transformative force across industries, offering unprecedented capabilities in data analysis, process automation, and customer engagement. The insurance sector, inherently data-driven and operationally complex, stands to benefit significantly ...
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In recent years, artificial intelligence (AI) has emerged as a transformative force across industries, offering unprecedented capabilities in data analysis, process automation, and customer engagement. The insurance sector, inherently data-driven and operationally complex, stands to benefit significantly from AI adoption. While insurers in developed markets have begun to integrate these technologies strategically, organizations in emerging economies like Iran continue to face structural challenges in implementation. This study explores the potential of AI in improving the quality of insurance services within Iran’s Social Security Organization (SSO). Through an empirical model grounded in multi-criteria decision-making, the research identifies twenty-two AI-driven applications and evaluates their impact using expert feedback. By applying FARE (Fuzzy Analytic Relationship) for indicator weighting and MARCOS (Measurement Alternatives and Ranking According to Compromise Solution) for application prioritization, the study offers a structured pathway for digital transformation. The findings suggest that AI applications focused on data analysis and intelligent processing hold the highest potential for service enhancement. In contrast, functions like cybersecurity and customer interaction, while essential, play more supportive roles. This research contributes to the growing body of knowledge by contextualizing AI integration within Iran’s insurance landscape and proposing a practical decision-making framework for public sector innovation.IntroductionArtificial intelligence has become more than a technological trend—it is now a cornerstone of innovation in finance, healthcare, manufacturing, and insurance. As organizations contend with increasing volumes of data and rising expectations for personalized, efficient services, AI offers tools that not only automate routine tasks but also augment strategic decision-making. Within the insurance sector, AI applications span a wide array of functionalities, including predictive underwriting, fraud detection, customer service automation, and real-time risk evaluation. These advancements are reshaping insurance from a reactive, compensation-focused industry to a proactive, preventive service model. In Iran, despite visible interest and scattered efforts, the deployment of AI remains unsystematic and largely unstructured. The Social Security Organization, covering more than 45 million individuals, is a prime candidate for AI transformation. Yet the absence of a prioritized roadmap has hindered progress. This study responds to that gap by asking: Which AI applications should the SSO pursue to maximize improvements in service quality?Research BackgroundGlobally, the insurance industry has witnessed a surge in AI adoption. Companies such as AXA and Allstate utilize machine learning models to personalize policies and predict claims. Progressive and Lemonade implement chatbots and natural language processing to accelerate customer support. Zurich Insurance leverages robotic process automation to reduce operational latency, while Swiss Re and Munich Re deploy anomaly detection algorithms to prevent fraud and improve pricing accuracy.These examples underscore the strategic role AI plays, not only in optimizing internal processes but also in transforming customer engagement. International literature consistently identifies several application domains: predictive analytics, cybersecurity, automated claims processing, customer personalization, and intelligent document handling. However, within the Iranian context, existing studies have tended to focus narrowly—often examining isolated applications or technologies without offering a comprehensive prioritization framework. Moreover, public insurance institutions lack access to sector-specific evaluations that link technology capabilities to strategic outcomes like customer retention, operational cost reduction, and service satisfaction. This research seeks to bridge that divide by evaluating the functional relevance and strategic value of AI applications from an organizational perspective.MethodThe study adopts a quantitative and applied research design, supported by a positivist philosophical foundation. It uses a single cross-sectional survey to collect expert judgments and applies a hypothetico-deductive approach for analysis. The sample consists of ten carefully selected professionals affiliated with the SSO, each with proven expertise in insurance and artificial intelligence. These experts evaluated twenty-two performance indicators extracted from a comprehensive review of literature and industry practice. Indicators included operational metrics (e.g., fraud detection efficiency, speed of data processing), service quality measures (e.g., customer satisfaction, claim resolution accuracy), and strategic outcomes (e.g., risk prediction, loss minimization). To quantify the relative importance of each indicator, the FARE method was used. This enabled the assignment of nuanced weights based on expert consensus. Subsequently, each AI application was evaluated using the MARCOS ranking methodology, which integrates weighted scores with ideal and non-ideal performance scenarios to establish priority levels. This hybrid decision-making model ensures that each application is assessed on technical feasibility as well as on its strategic alignment with service enhancement goals.Discussion and ResultsThe analysis revealed clear priorities among the AI applications considered. The domains of data analysis and processing intelligence emerged as the most impactful in improving insurance service quality. These functions directly contribute to accurate risk assessment, efficient pricing, and streamlined claims management—areas that hold the greatest operational and strategic significance for public insurers. Applications such as predictive modeling, historical data mining, and intelligent classification of new information received the highest scores from experts. These tools allow insurance entities to transition from static, rule-based decision-making toward dynamic, data-driven strategies that respond to real-time conditions.In contrast, applications within cybersecurity and customer services, while essential, were deemed supportive rather than primary. Technologies such as chatbots, fraud detection systems, and encryption tools provide safeguards and enhance user experience but rely heavily on underlying analytical systems to deliver consistent value. One of the study’s conceptual contributions lies in distinguishing between direct AI applications and indirect effects, which enhance service through ancillary functions. This distinction has practical implications for implementation, especially in resource-constrained environments where prioritization is critical.Furthermore, the study highlighted a significant gap between global best practices and the current technological posture of Iran’s insurance sector. While international organizations experiment with generative AI and blockchain–AI hybrids, domestic insurers have yet to establish basic integration frameworks. This research, therefore, not only ranks applications but also serves as a blueprint for catching up with global innovation trajectories.ConclusionArtificial intelligence is no longer optional; it’s central to the future of insurance. For Iran's Social Security Organization, strategic investment in AI must begin with data analytics and intelligent processing, which offer the most immediate gains in speed, accuracy, and service quality. Applications in cybersecurity and customer service are also valuable, but their full potential depends on a solid analytical foundation. This study provides more than a ranking; it offers a practical mindset shift, from reactive service delivery to proactive, data-driven decision-making. The proposed framework is adaptable across sectors and scalable with future technological advances. Real success, however, hinges not on algorithms alone, but on how thoughtfully they are embedded into human-centered systems led by vision, collaboration, and continuous learning.
project management
ali mohaghar; Fatemeh Saghafi; Ebrahim Teimoury; Jalil Heidary Dahooie; Abdolkarim sabaee
Abstract
The application of supply chain management within the construction industry presents significant challenges due to the transient nature of construction projects, high levels of customization, low repeatability of activities, absence of a production line, and interdependent relationships among activities. ...
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The application of supply chain management within the construction industry presents significant challenges due to the transient nature of construction projects, high levels of customization, low repeatability of activities, absence of a production line, and interdependent relationships among activities. Construction supply chains are intricate systems, where the final performance results from numerous decisions made across multiple independent companies. Interactions among supply chain stakeholders and the unique characteristics of each project create complex phenomena with multiple interconnected elements and variables. The Viable System Model (VSM), rooted in organizational cybernetics, provides a structured approach to addressing complex and unstructured problems. This structured approach allows analysts to gain in-depth insights into the functional issues of the existing system and understand how to modify the system design to adapt to internal and external disruptions.MethodologyDespite the extensive capabilities of the Viable System Model as a diagnostic tool for assessing organizational structure and achieving viability, a systematic and distinct methodology for its application is lacking. Researchers in VSM often do not employ a specific methodology for systems analysis. In this study, we propose a methodology for applying the VSM as a diagnostic tool for organizations, derived from a review of theoretical foundations and practical requirements of VSM. Building on Jackson's methodology outlined in his book "System Thinking, Creative Holism for Managers," we have developed a methodology by integrating Jackson's approach with case study research. This methodology includes stages such as designing a diagnostic framework, selecting case studies, identifying systems, conducting system diagnosis, and validating the model. We applied this methodology to diagnose the supply chain of an Iranian petrochemical construction project, resulting in the development of a viable system model. The validity of the research methodology and findings was confirmed through expert participation and the application of multiple qualitative criteria.ResultsFollowing the selection of a case study and the identification of systems, we investigated the existence and function of five subsystems and communication channels within the focal system using a case study approach to gather information and develop the viable system model. Data was collected through semi-structured interviews conducted at various managerial and technical levels within a prominent project-oriented company in Iran's petrochemical industry. These interviews lasted between 45 and 60 minutes each. Data collection methods also included observation and document examination. The research involved a semi-structured interview with 18 individuals to explore complications within each of the five systems. Subsequently, the collected data was adapted to the model's requirements, and findings were extracted through intra-case analysis and coding. This process led to model development and the identification of weaknesses within the construction supply chain from the perspective of the five systems and communication channels, with a focus on achieving viability.ConclusionsThe developed model highlights weaknesses and bottlenecks within the focal system, shedding light on the most significant issues. A critical issue identified in the case study is the evident lack of coherence within System 4 and System 5. The results reveal that the incoherence of System 5, divided between parts of the company at level 0 and the parent company at a higher recursion level outside the focal system, results in defects within the communication channels related to this system, including C14 (Connection of System 4 with System 5), C9 (Algedonic channel), and C16 (Connection of System 5 with the homeostatic loop of Systems 3 and 4). Additionally, System 4, which is jointly managed by a segment of the company and the project management consultant, leads to disruptions in channels related to this system, particularly C13 (Homeostatic loop between Systems 3 and 4), C14 (Communication between System 4 and System 5), and C15 (Homeostat of System 4 with the future environment). Concerning common errors, the dominant error is E5, attributed to the lack of coherence between Systems 4 and 5 and the weak performance of System 2. This error largely stems from inconsistencies between the two operational units responsible for the engineering phase and the construction and installation phase. To achieve viability within the focal system, several measures should be taken, including the establishment of centralized Systems 4 and 5 within the company and strengthening communication channels with incomplete or insufficient capacity. These channels include the connection between System 4 and System 5 (C14), the Algedonic channel (C9), the connection of System 5 with the homeostatic loop of Systems 3 and 4 (C16), the homeostatic loop of System 3 and System 4 (C13), and the homeostat of System 4 with the future environment (C15). A crucial homeostatic link involves the communication and interaction between System 3 and System 4 (C13) to establish dynamic communication between the current project environment and its future. However, the interaction between these two systems is currently conflicting and misaligned due to the lack of coherence within System 4 and differences in functionality between System 3's perspective on the current state and System 4's perspective on the future state. Balancing the emphasis on System 4 and the future with the daily operations of the supply chain's operational units within System 1 is essential to avoid supply chain disruptions or inefficiencies. The lack of coherence within System 4 also affects the performance of other systems, particularly System 5, as well as the stability of System 4 in relation to the future environment. Inadequate information about the future environment can hinder informed decision-making within the system. By addressing these points within the model, the construction project's supply chain can move toward viability and better adapt to changes in the project environment. This research represents one of the limited studies in the implementation of VSM within the construction project environment.
