Industrial Management Journal (مدیریت صنعتی)

Objective : In the competitive global retail industry, achieving sustainable competitive advantage is a key factor for long-term success. This advantage arises when companies effectively utilize their unique resources and capabilities to outperform competitors. Operational efficiency and financial performance are critical for evaluating competitiveness and investment attractiveness. Data Envelopment Analysis (DEA) is a standard method for measuring efficiency, but classical DEA cannot fully rank efficient units. Integrating DEA with multi-criteria decision-making (MCDM) methods addresses this limitation, considering investor-relevant financial ratios. This study proposes a two-stage approach to evaluate and rank retail companies comprehensively.  Methodology: In the first stage, an input-oriented CCR model of DEA is applied, with assets, operating expenses, and the number of employees as inputs, and total revenue and net profit as outputs, to assess relative efficiency. In the second stage, financial indicators—asset turnover, dividend yield, return on equity (ROE), return on assets (ROA), and return on investment (ROI)—alongside DEA efficiency scores are evaluated using the PROMETHEE II method to generate a complete preference-based ranking of retailers. Results : DEA in the first stage provides relative efficiency insights but cannot rank efficient units. Employing PROMETHEE II in the second stage, and considering financial ratios, overcomes this limitation and produces a comprehensive ranking. Validation against DEA, hybrid DEA–PROMETHEE II, and hybrid DEA–AHP rankings demonstrates a strong alignment of the results with the actual market positions of retailers. Conclusion : The proposed method enables investors to identify high-performing companies and provides retailers with a strategic tool to monitor competitiveness, identify strengths and weaknesses, optimize resource allocation, and achieve a sustainable competitive advantage.

Objective : This study aims to identify and prioritize cybersecurity risks associated with IoT applications in the retail sector, an area critical to digital transformation and operational resilience. Given the challenges managers face in evaluating threats under uncertainty, the study introduces a novel methodological framework to enhance risk-based decision-making and strategic resource allocation.  Methodology: A hybrid approach combining Pythagorean fuzzy SWARA (PF-SWARA) and an alternative ranking technique based on adaptive standardized intervals (PF-ARTASI) within the FMEA framework is proposed. PF-SWARA is used to weight evaluation criteria, and PF-ARTASI ranks the identified risks. The model is applied to a case study in Iran’s retail sector. Sensitivity and comparative analyses are conducted to validate the robustness and effectiveness of the method. Results : The findings show that "Insecure Firmware/Software and Inadequate Patch Management" is the top cybersecurity risk, followed by "Lack of Standardization and Interoperability Issues" and "Physical Security concerns". The proposed PF-SWARA–ARTASI approach outperforms traditional FMEA and PF-MOORA methods in terms of result consistency, robustness, and practicality under uncertain conditions. Conclusion : This research makes four contributions: (1) It proposes the first integration of PF-SWARA and PF-ARTASI within FMEA; (2) applies a novel ranking method for risk prioritization; (3) provides an actionable list of prioritized cybersecurity risks in IoT-enabled retail; and (4) validates the model through extensive sensitivity and comparative analysis. The study provides a valuable decision-making tool for IT managers and contributes to the existing literature on fuzzy risk assessment in retail contexts.

Objective : Off-grid renewable energy systems are essential for ensuring a sustainable and reliable electricity supply in regions where access to the conventional grid is limited or economically unjustifiable. Designing such systems requires systematic and efficient methodologies that can guide planners in selecting appropriate technologies under diverse environmental and consumption conditions. This study aims to develop an intelligent sizing framework for hybrid renewable energy systems by integrating expert knowledge with geographical, climatic, and load-related variables. The proposed model seeks to identify the optimal combination of photovoltaic panels, wind turbines, battery storage systems, and diesel generators for off-grid applications in Iran.  Methodology: The research methodology employs a two-stage approach. In the first stage, key variables influencing system sizing were identified through a comprehensive review of prior studies and structured interviews with academic and industrial experts. These interviews provided valuable operational insights, enabling the determination of qualitative ranges for model inputs and outputs. In the second stage, the collected expert knowledge was translated into fuzzy rules and implemented within a fuzzy inference system developed in MATLAB, forming an intelligent decision-support engine capable of evaluating multiple operational scenarios. Results : The findings indicate that the proposed model accurately determines optimal configurations for various geographical locations and consumption profiles. Model outputs showed less than 10% deviation from 80% of expert assessments. Moreover, the model generates sizing recommendations within minutes, significantly improving the speed of decision-making. Conclusion : In summary, the developed framework provides a practical and efficient tool for planners and stakeholders involved in designing off-grid hybrid renewable energy systems.

Objective : This study investigates how waste storage, waste quality, and market dynamicity influence the economic and environmental performance of industrial symbiosis networks in Iran’s food sector.  Methodology: A hybrid simulation approach, combining agent-based modeling and discrete event simulation, is employed to analyze the dynamics of industrial symbiosis networks in the food sector in Iran. This integrated method enables a detailed examination of how waste quality, storage duration, and market dynamicity jointly affect network performance. The model is implemented and simulated using AnyLogic software. Results : The simulation results demonstrate that effective management of waste storage is essential for improving the economic and environmental performance of industrial symbiosis networks in the food sector. Extending the storage duration allows firms to better align waste supply with demand, which is particularly valuable in volatile markets. However, the benefits of longer storage depend on waste quality: for high-quality waste, additional storage costs are offset by higher exchange values, while for low-quality waste, prolonged storage mainly increases costs and reduces profitability. The study also finds that waste storage strategies can substantially buffer the negative effects of market fluctuations. Conclusion : This paper advances circular economy research by presenting an analytical framework that integrates agent-based modeling and discrete event simulation to analyze industrial symbiosis networks. The findings suggest that managing storage duration can improve economic and environmental outcomes, while waste storage strategies help firms mitigate the negative impacts of market volatility. These insights can help managers and policymakers improve waste management in Iran’s food sector.

Objective : This study aims to conceptualize the structure of digital servitization—the shift from product-centric to digital service-oriented business models. It seeks to integrate fragmented academic literature into a coherent framework and provide practical guidance for managers navigating this transformation.  Methodology: Through a systematic review methodology, a meta-synthesis was conducted to integrate and interpret prior research. Following the retrieval of 140 articles from major academic databases, multiple rounds of screening—based on criteria of quality, relevance, and novelty—culminated in the selection of 20 pivotal studies. These were categorized and analyzed through a conceptual model structured around the antecedents, processes, and outcomes of digital servitization. Results : Digital servitization can be structured into three core dimensions. Antecedents include technological readiness, human competencies, and organizational culture. Key processes involve designing digital platforms, implementing data-driven services, and reconfiguring business models. Outcomes result in enhanced innovation, organizational agility, and sustainable business performance. Conclusion : Successful digital servitization requires a systematic approach across antecedents, processes, and outcomes. The proposed framework offers an integrated view of the technological, human, and structural factors involved, helping managers avoid common pitfalls—such as strategy misalignment and employee resistance—and move beyond simple digitalization toward genuine service-based transformation.

Objective : This study examines the challenges and strategic approaches to developing and sustaining medical tourism supply chains (MTSC), particularly in developing countries. Medical tourism is one of the fastest-growing sectors within the global tourism industry, generating significant economic impact through the cross-border movement of patients seeking medical care. Despite its growth potential, numerous barriers hinder the effective design and implementation of sustainable MTSCs, especially in emerging economies such as Iran.  Methodology: The study uses the fuzzy Step-wise Weight Assessment Ratio Analysis (SWARA) to quantify the relative importance of various barriers to MTSC development. Subsequently, the fuzzy Weighted Aggregated Sum Product Assessment (WASPAS) method is used to prioritize strategic interventions to overcome the identified barriers. This hybrid fuzzy MCDM approach effectively handles the inherent uncertainty and subjectivity in expert evaluations, thereby providing a robust decision-making framework. Results : Findings indicate that among the main barriers, the lack of technological support to facilitate supply chain activities constitutes the most critical hurdle faced by Iran’s medical tourism industry. The limited adoption and implementation of technological innovations restrict efficient coordination, information sharing, and overall supply chain sustainability. In response to these challenges, the study identifies "economic and incentives-based strategies" as the foremost approach for overcoming barriers in MTSCs. This strategy emphasizes the creation of financial incentives, subsidies, and economic policies to encourage innovation, infrastructure development, and enhanced stakeholder collaboration. Conclusion : Policy implications suggest that governments and healthcare providers in developing countries should prioritize investments in technology and infrastructure, alongside devising incentive schemes tailored to the medical tourism sector. Collaboration among various stakeholders—including healthcare institutions, tourism agencies, technology providers, and policymakers—is critical to creating resilient supply chains that can adapt to evolving market demands and global health trends.

Objective : This paper develops a single-objective and a bi-objective mixed-integer linear programming model to optimize the post-earthquake relief logistics network involving transfer points, hospitals, and relief centers in Tehran, Iran. The primary aim is to minimize the total time required to transfer injured individuals through the system, while the bi-objective model additionally minimizes penalties for failing to transfer casualties due to capacity shortages.  Methodology: The methodology involves formulating location-allocation models in which demand points, transfer points, hospitals, and relief centers are represented by specific capacity and travel-time parameters. The models are applied to two earthquake scenarios in south-central Tehran: a magnitude-6 event with lower casualties and selective facility activation, and a magnitude-7 event requiring full capacity utilization and a 30% assumed increase in hospital capacity. Results : The model’s effectiveness in optimizing the relief network is demonstrated. For the magnitude-6 scenario, the model selects 10 transfer points, 15 hospitals, and 25 relief centers to minimize total transfer time. For the magnitude-7 scenario, utilizing all available facilities, the model optimally allocates casualties despite severe capacity constraints. Conclusion : The proposed models offer a practical decision-support tool for designing efficient humanitarian supply chains in earthquake-prone urban areas. They underscore the necessity of pre-disaster planning, including establishing transfer points with triage and outpatient capabilities, increasing hospital surge capacity, and ensuring public awareness to direct casualties to designated transfer points.

Objective : This study examines how different research and development (R&D) funding sources—business enterprise R&D (BERD), higher education R&D (HERD), and government-financed R&D (GOVERD)—influence national innovation and competitiveness, measured by the Global Innovation Index (GII) and Global Competitiveness Index (GCI). This study addresses a critical gap by moving beyond aggregate R&D spending to examine how funding composition shapes innovation capacity.  Methodology: Ordinary least squares (OLS) regression analysis was conducted on a sample of 47 countries using data from OECD R&D Statistics, World Bank Development Indicators, and GII/GCI reports. Missing values (< 3%) were imputed using mean substitution. Diagnostic tests were applied to verify normality, minimal multicollinearity, and compliance with heteroscedasticity assumptions. Two models were subsequently estimated with GII and GCI as dependent variables. Results : For the GII model (R² = 0.601, F = 12.37, p < 0.001), overall GERD intensity was significantly positive (β = 10.54, p = 0.040), while disaggregated components (BERD, HERD, GOVERD) showed no individual significance due to multicollinearity. GDP per capita was robust (β = 8.78e-05, p = 0.019). For the GCI model (R² = 0.651, F = 15.31, p < 0.001), GERD was non-significant; GDP per capita remained the strongest predictor (β = 0.0003, p < 0.001). Regression assumptions were satisfied (Jarque-Bera p > 0.44; Durbin-Watson ≈ 2.1). Conclusion : Overall R&D intensity significantly influences innovation, but relationships with disaggregated sources are complex. The importance of GDP per capita shows that the quality of institutions and the ability to absorb new ideas are both important for turning R&D spending into innovation and competitiveness benefits. Policymakers should prioritize both R&D funding levels and the institutional environment enabling effective R&D utilization.

Objective : This study addresses the critical gap in understanding how Industry 4.0 technologies—Artificial Intelligence (AI), Internet of Things (IoT), blockchain, and Extended Reality (XR)—synergistically transform customer-centric supply chains in creative industries (e.g., music, fashion, film). It moves beyond fragmented analysis to develop a unified framework for customer-integrated value delivery. Methodology: A systematic meta-synthesis was conducted following established seven-stage protocols. A targeted search across Scopus and Web of Science (2016–2025) identified relevant literature. After stringent screening and quality appraisal (the Critical Appraisal Skills Programme (CASP) checklist), 59 high-quality publications were analyzed through iterative coding and thematic analysis. Results : The analysis produced a novel framework of five interdependent components: (1) Customer-Integrated Value Creation (e.g., AI co-design), (2) Omnichannel & Immersive Fulfillment (e.g., XR commerce), (3) Dynamic Value Capture Models (e.g., fan-driven financing), (4) Algorithmic & Gamified Community Engagement , and (5) Networked Co-Creation Ecosystems . The framework demonstrates how AI, IoT, blockchain, and XR interconnect to transform linear supply chains into agile, experiential, community-integrated value networks. Conclusion : This study presents the first synthesized framework for Industry 4.0-enabled, customer-centric supply chains in creative industries. It bridges technology, culture, and operations management, offering researchers a structured model for future inquiry and providing practitioners with a strategic roadmap for building responsive, community-driven operations. The research fills a significant literature gap and offers a blueprint for competitive advantage in the digital creative economy.

Objective : The global increase in the elderly population has heightened the need for coordinated, tailored healthcare services that address the complex needs of older adults. This study aims to conceptualize the elderly healthcare ecosystem by identifying its key actors, classifying their roles, and examining the nature of their interactions. Methodology: A multi-stage methodological approach was employed. First, an extensive literature review—focusing on healthcare ecosystems and ageing studies—was conducted to develop an initial analytical framework. Based on this, ecosystem actors were identified and categorized using Mitchell et al.’s Stakeholder Salience Model. An expert panel was then consulted to validate actor attributes and refine classifications. To analyze interdependencies and determine influential actors, a Fuzzy Cognitive Map was constructed, enabling the assessment of causal relationships and the dynamic positioning of stakeholders within the ecosystem. Results : The analysis identified seven groups of actors within the healthcare ecosystem. FCM findings reveal that the elderly, families, and medical centers are the most influential actors. At the same time, the Ministry of Health and Medical Education, insurance and pension funds, and the Ministry of Cooperatives, Labor, and Social Welfare emerge as the most influential and central stakeholders in advancing ecosystem objectives. Conclusion : The study demonstrates that the elderly healthcare ecosystem is inherently dynamic, and stakeholder classifications should not be viewed as static. Attributes such as power, legitimacy, and urgency are fluid and context-dependent. The FCM results further highlight this dynamism by illustrating how shifts in causal relationships can reposition actors across stakeholder categories, underscoring the need for adaptive policymaking.