Aug 2026· Management Decision· pp. 1-19· 0 citations· 36 references
Abstract
This research aims to present the Framework for Enhancing Network Integrity eXcellence (FENIX) model, integrating technological, organizational, human, and economic dimensions to enhance risk assessment in complex socio-technical systems.
The FENIX model employs fuzzy logic, multi-criteria prioritization, and non-additive aggregation methods to enhance risk assessment in complex socio-technical systems. Its applicability is demonstrated through a case study involving the introduction of a new aircraft.
The findings indicate that technological risks can be reduced through diagnostic and monitoring systems, whereas operational and human factors continue to play a significant role. Training, communication, and fatigue management are essential for minimizing systemic vulnerability. The economic dimension highlights the feasibility of reconciling mitigation effectiveness with financial sustainability.
The methodology assists decision-makers by optimizing the RPN, improving RTO reliability, and incorporating ERI and EPNmetrics, thereby allowing organizations to align risk management strategies with operational resilience and financial constraints.
In contrast to conventional FMEA, the FENIX framework incorporates uncertainty, nonlinear interdependencies, and cost–benefit considerations, offering a more comprehensive and flexible approach to risk prioritization and resource allocation.
Against the backdrop of increasing global uncertainty and the growing acceptance of sustainable development principles, enhancing supply chain resilience has become a core issue for enterprises in managing risks and ensuring operational security. Based on a review of the literature and theoretical analysis, this study constructs an evaluation system comprising 12 third-level indicators across three dimensions: proactive defense capability, green operational capability, and collaborative recovery capability. When determining whether there are interdependent relationships among the indicators, this study introduces an extended Bayesian fusion method based on trapezoidal fuzzy numbers to evaluate and confirm these relationships, thereby reducing biases arising from subjective judgments. By quantifying experts’ assessments of the relationship strength and confidence levels between indicators using trapezoidal fuzzy numbers, this method effectively integrates the opinions of multiple experts, reducing the randomness and subjectivity associated with individual judgments. During the ANP weight calculation stage, to overcome the ambiguity and uncertainty inherent in traditional pairwise expert comparisons, trapezoidal fuzzy numbers were similarly used to quantify the comparison results. These were then defuzzified using the mean area metric to construct a precise judgment matrix. Finally, using the publicly available annual reports and ESG disclosure data from three multinational corporations—one in the semiconductor manufacturing sector (Company T), one in industrial digital manufacturing (Company S), and one in the food and beverage industry (Company N)—as empirical samples, the cross-industry applicability and validity of the constructed evaluation system were verified. The results demonstrate that this method can systematically reflect the key factors influencing sustainable supply chain resilience and their weighting structure.
Tongtong Nie, Zhi-Hao Zhang· Applied System Innovation· 0 citations
Operational risk assessment in complex industrial systems like mineral processing plants is hindered by inherent uncertainty and incomplete information. This study presents an integrated grey system theory-based framework to address this challenge. Combining Grey Multi-Criteria Decision-Making (GST-MCDM) and Grey Relational Analysis (GRA), the methodology enables a systemic analysis that prioritizes risks, quantifies interdependencies, and measures cumulative burden across four key objectives: time, cost, quality, and safety. Applied to a case study at the Gohar Zamin iron ore processing plant (Iran), the model analyzed 26 operational risks, classifying them into Critical (8 risks), Significant (9), and Controllable (9) tiers. Electrical power shortage (RPS: 0.19) and raw material supply delay (RPS: 0.21) were identified as the most critical risks. The analysis quantified that the safety objective bears the highest cumulative risk burden at 32%, primarily due to human factor vulnerabilities, while cyber-physical threats ranked among the top 8 critical risks. Strong interdependencies were revealed, notably a quality cascade (relational grade: 0.84) between poor consumable materials and final product failure. Sensitivity analysis confirmed high model robustness (Spearman’s p = 0.91). The framework provides managers with an actionable tool for strategic, cluster-based mitigation and evidence-based resource allocation, emphasizing investment in human capital as a core risk reduction strategy. This research contributes a replicable, quantitative methodology for enhancing operational resilience under uncertainty in capital-intensive industries.
Mohammad Naeim Zeidabadi-Nejhad, H. Khoshdast, T. Niedoba et al.· Minerals· 0 citations
This paper proposes a sustainable and systemic model for the transition from traditional Total Cost Management (TCM) practices toward a broader and more integrated Systemic Value Management (SVM) framework, incorporating Environmental, Social, and Governance (ESG) criteria. Building on previous work within the AICE/ICEC Scientific Commission, the study introduces the concept of a general utility function as a unifying and quantifiable measure capable of integrating economic, financial, social, environmental, and institutional dimensions in project assessment. The proposed approach aims to provide a rational foundation for comparing, ranking, and optimizing projects in complex socio-economic environments where traditional financial indicators alone are insufficient. The methodology combines established project evaluation tools (e.g., NPV, IRR, ROI, DSCR, BEP) with social and environmental indicators into a single multidimensional utility function. Each domain contributes a weighted component, normalized within a [0, 1] range, allowing dynamic updates of weights through experience, expert judgment, or data-driven learning. This aggregation model enables both multi-criteria optimization and sensitivity analysis, revealing how variations in weights or component utilities affect overall project ranking and robustness. In this sense, the proposed model serves not only as an evaluative tool but also as a framework for iterative improvement and adaptive management across multiple domains. The paper further demonstrates how Multidimensional Data Analysis (MDA)—including Principal Component Analysis (PCA), Factor Analysis, Multivariate Regression, and Machine Learning—canbe employed to empirically derive or calibrate the partial utilities and weights that constitute the general utility function. This integration between MDA and utility theory establishes a data-informed pathway for constructing systemic value models that evolve through continuous feedback from real-world projects. An illustrative example, implemented both in textual and spreadsheet formats, shows how the approach can be applied to a simplified two-dimensional case (economic and environmental), complete with normalization, aggregation, and sensitivity testing. Finally, the study discusses a theoretical extension of the model toward multidimensional tensor calculus, acknowledging its conceptual relevance for representing higher-order interdependencies (projects × criteria × domains × time × scenarios), while emphasizing that such complexityis best reserved for future research. The paper concludes that a gradual, data-supported evolution from TCM to SVM/ESG is both feasible and desirable, leveraging artificial intelligence and multidimensional analytics as enabling tools for sustainable, value-centered project management and decision-making.
The article substantiates the integration of environmental risk management into the economic security system of an organisation as a key factor in sustainable development in the context of global environmental transformation.The aim of the study was to develop theoretical and methodological foundations and practical recommendations for integrating environmental risk management systems into the economic security system to ensure its long‐term financial and competitive sustainability.Systems approach, comparative analysis of ESG and risk management standards, case study method, expert assessments and scenario modelling.The authors developed and verified a three‐block integration model (organisational and management modules, process and methodological modules and information and analytical modules) aimed at transforming qualitative environmental threats into quantitative financial risks. Scenario analysis showed that a proactive approach provides competitive advantages, while a reactive approach leads to additional costs and a loss of financial stability.Environmental risks are currently strategically significant and their superficial analysis creates gaps in business protection. The authors' proposed model improves financial stability and business reputation and further research is aimed at developing methods for monetary valuation of environmental risks.
N. G. Gadzhiev, S. Konovalenko, M. Trofimov et al.· South of Russia: ecology, de...· 0 citations
Resilience is a vital attribute of modern industrial systems, particularly in the chemical process industry, where complex dynamics and high‐risk operations require robust approaches to ensure safety, security, and operational continuity. This study presents a semi‐quantitative framework for resilience assessment that integrates safety and security considerations with key operational performance indicators related to productivity and quality. The framework relies on dynamic simulations to quantify resilience as a time‐dependent system response to disruptions and introduces a triangular visualization tool for integrated performance evaluation. The proposed methodology is applied to a continuous‐stirred tank reactor (CSTR) to assess its performance under both safety‐related incidents and security‐related threats. The integration of safety and security dimensions in resilience assessments provides a more holistic understanding of system robustness, enabling the identification of targeted improvement strategies to mitigate risks. The results highlight critical vulnerabilities, such as the impact of valve failures on production continuity and the consequences of cyberattacks on system stability. This novel approach not only enhances the understanding of system resilience but also provides practical and actionable insights to improve resilience in complex industrial environments.
Tommaso Capomagi, Ming Yang, Valerio Cozzani· Safety Science and Technolog...· 0 citations
Introduction/purpose: In this paper, a multi-criteria evaluation framework for pavement condition assessment using the Fuzzy Analytic Hierarchy Process (Fuzzy AHP) is proposed, integrating technical, economic, and environmental considerations. Road infrastructure is a key component of transportation systems, and its sustainability depends on effective management and maintenance. This study aims to provide a scientific basis for optimizing road maintenance planning by identifying and prioritizing the key factors affecting pavement quality. The framework considers technical, economic, and environmental dimensions to ensure a comprehensive evaluation. Methods: A hierarchical structure of evaluation criteria was developed, including technical sub-criteria such as surface roughness, bearing capacity, traffic characteristics, and environmental conditions, alongside economic and material-related factors. Weights were assigned using the Fuzzy AHP method, with consistency checks validating the reliability of the results. A case study was applied to National Road RN101, linking Sidi Bel Abbès to Aïn Témouchent. Results: The analysis revealed significant deficiencies in certain segments, including excessive roughness and insufficient structural strength. Sections subject to high traffic loads and severe environmental conditions showed the most deterioration. Technical aspects, particularly bearing capacity and material quality, were found to exert the strongest influence on pavement performance and durability. Conclusion: The findings demonstrate the relevance and applicability of the proposed Fuzzy AHP-based evaluation model in supporting decision-making for road maintenance prioritization. The methodology can be extended to other road networks requiring a structured, reliable approach to pavement quality assessment.
