quality management
Pedram Esmaeilzadeh; Abolfazl Kazzazi; Amiri Maghsoud; Jahanyar Bamdadsoofi
Abstract
Despite the recognized importance of strategic alignment for competitiveness and quality management maturity for operational excellence, a significant literature gap persists in systematically integrating these concepts. This study addresses how to systematically align strategic alignment dimensions ...
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Despite the recognized importance of strategic alignment for competitiveness and quality management maturity for operational excellence, a significant literature gap persists in systematically integrating these concepts. This study addresses how to systematically align strategic alignment dimensions with quality management maturity. Using a systematic literature review and thematic analysis, 843 documents were screened, resulting in 47 articles for data coding and analysis. The primary contribution is the "Cross-mapping Table of Strategic Alignment and Quality Management Maturity Dimensions," which delineates interrelationships between six strategic alignment dimensions (skills, governance, value, communication, stakeholder participation, and scope) and seven quality management maturity dimensions (context, leadership, process management, resource management, performance analysis and evaluation, and improvement, learning and innovation). The research provides an integrated framework elucidating interaction mechanisms between these domains. Practically, it offers managers a diagnostic tool for assessing organizational status and developing simultaneous enhancement strategies for both quality maturity and strategic alignment. This framework fosters a paradigm shift in managerial approaches and facilitates leveraging existing platforms to strengthen the interaction between strategic and quality management systems, ultimately supporting sustained organizational excellence.
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.
quality management
Ali Ebrahimi Kordlar; Hossein Safari; Helyeh Sadat Aghamiri; Fatemeh Sharifi Tabar; Mohsen Moradi Moghaddam
Abstract
In today’s competitive environment, organizations need innovative capabilities and strategies for competitive advantage, with organizational capabilities playing a key role in success. Excellence models like EFQM help identify improvement areas and enhance performance. Since the organizational ...
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In today’s competitive environment, organizations need innovative capabilities and strategies for competitive advantage, with organizational capabilities playing a key role in success. Excellence models like EFQM help identify improvement areas and enhance performance. Since the organizational capabilities of the Mobile Communications of Iran (MCI) have not been assessed using the latest EFQM model, this study aims to identify key capabilities and develop a mathematical optimization model. Using a descriptive survey with an applied purpose, the research targeted academic experts, excellence practitioners, and MCI specialists. First, a systematic literature review identified and categorized critical capabilities. Then, expert judgment and a fuzzy inference system modeled causal links between capabilities and the EFQM framework. Mathematical equations quantified each capability, forming an integrated model. A genetic algorithm was used to optimize parameters and determine the best capability combination. The study concludes with practical implementation recommendations and suggestions for future research.IntroductionIn today’s globalized and competitive environment, organizations face increasing competition and a dynamic external landscape. To survive and lead, organizations must differentiate themselves by creating a competitive advantage through innovation. This requires management excellence models that help organizations adapt to these changing conditions. The competitive environment, characterized by geographical dispersion and organizational innovation, demands unique capabilities known as dynamic capabilities, which help organizations create, expand, and maintain their core resources. The 2020 edition of the EFQM model, based on design thinking, has evolved from an assessment tool into a vital framework for addressing the changes and disruptions organizations face daily. Its strategic focus, combined with operational performance and a results-oriented approach, makes it an ideal framework for examining the alignment of an organization's ambitions. The aim of this article is to develop a mathematical model of organizational capabilities within the EFQM 2020 excellence model, helping organizations evaluate and improve their current performance.Materials and MethodsThis research is applied in nature with a comparative approach. It follows a quantitative methodology and is based on library research. The research strategy is survey-based, and from a goal perspective, it falls under the descriptive category. Data collection is conducted through interviews and questionnaires. Organizational capabilities are first extracted from scientific sources, then matched with the sub-criteria of the model by reviewing guidelines. Based on the findings, if-then rules and a fuzzy inference system are designed using MATLAB software.Meta-heuristic methods are used to solve complex optimization problems where classical optimization and heuristic methods are ineffective. Among these, the genetic algorithm is commonly used as a function optimizer. In this model, due to the complex, non-linear, and fuzzy relationships in the fuzzy inference system within the objective function, it can be compared to a neural network. The genetic algorithm is then applied to solve the model.ResultsThe desired capabilities for the fuzzy inference system are determined by specifying the capabilities of each criterion and sub-criterion of the EFQM model. A fuzzy inference system is defined for each of the 23 sub-criteria, and at the criterion level, the systems of the sub-criteria are combined. Sensing, learning, integration, coordination, and reconfiguration routines are used to measure the capabilities of the EFQM excellence model.This research focuses on MCI. By comparing the current values with the target and the scores obtained from the genetic algorithm, it is found that, within the budget limits, the desired goal can be achieved for 38 capabilities. For capabilities such as sensing, abduction, business model development, reporting, environmental management, networking, modeling, and social responsibility, the values fall within the target range. However, for three capabilities—organizational governance development, transformation management, and improvement—the target values fall outside the selected range. These differences are minor and can likely be ignored. The transformation management capability score (25.6) is close to the minimum value of 26, indicating that improvement is not feasible within the current budget for this sub-criterion. Increasing the budget could raise the score. The organizational governance development score differs by almost 4 points, which may be due to the fuzziness in scoring and inaccuracies in the budget values assigned to each sub-criterion.ConclusionOrganizational excellence models are generally frameworks that organizations use to develop a culture of excellence, and each model attempts to provide a set of management principles that are generally employed by organizations in their geographical areas of influence. Organizational resources and capabilities are the key success factors for the organization. In this research, using the fuzzy inference system, the combination of organizational capabilities in the sub-criteria of the EFQM 2020 excellence model was designed, and the mathematical model was developed using linear programming. Finally, a genetic meta-heuristic algorithm was used to solve the model. Each sub-criterion is a fuzzy inference system composed of the organizational capabilities related to it. A set of organizational capabilities makes up each of the sub-criteria of the excellence model, and we have a point limit for each capability. The budget limit defined in this model consists of the total budget dedicated to each organizational capability constituting the relevant sub-criterion. A case study was used to check the validity of the model and its practical application in an internal organization. In this research, the studied organization is MCI.
quality management
amir mohammad khani; Abolfazl Kazzazi; Soraya birami
Abstract
The aim of this study was to investigate the relationship between comprehensive productive maintenance, TQM, supply chain management, learning organization characteristics and operational performance. In order to conduct research operations, the conceptual framework of the research was first presented ...
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The aim of this study was to investigate the relationship between comprehensive productive maintenance, TQM, supply chain management, learning organization characteristics and operational performance. In order to conduct research operations, the conceptual framework of the research was first presented by studying the theoretical foundations. In the next stage, by compiling and distributing a questionnaire among 180 people from the statistical population of the research consisting of senior, middle and operational managers aware of the subject under study, export companies of Golestan province were distributed randomly, the information needed to test research hypotheses was collected. Finally, 146 completed return questionnaires were covered by the structural equation modeling technique based on the partial least squares method. The results obtained based on this technique showed that the maintenance and repair of comprehensive productivity has a positive effect on total quality management, while the maintenance and repair of comprehensive productivity alone could not achieve an acceptable result to achieve operational performance and requires comprehensive quality management. . Another result obtained indicates the positive effect of total quality management and supply chain management and the mediating role of supply chain management in achieving operational performance between the two variables of total quality management and operational performance. In addition to these results, it was found that total quality management has a positive effect on the learning organization and the learning organization has an important role between total quality management and operational performance of export companies in Golestan province.
