Industrial Management Studies

Abstract The transition toward sustainable industrial systems has underscored the necessity of integrating Circular Economy (CE) principles with Industry 4.0 technologies. Despite expanding studies in both fields, an integrated and process-oriented framework that explains the systematic interaction of these approaches to enhance sustainable performance remains absent in the existing literature. This research aimed to identify existing gaps and propose an integrated conceptual framework for improving sustainable performance based on the synergy between CE and Industry 4.0. Employing a systematic literature review (SLR) following Sandelowski and Barroso's seven-step approach, a systematic search was conducted across reputable international databases for the period 2015–2026. After applying inclusion and exclusion criteria, selected articles were analyzed. A total of 407 initial codes were extracted and, following conceptual refinement, organized into 49 indicators and 33 criteria. These indicators were categorized into five sustainable performance improvement processes: procurement, design and development, production and operations, distribution, use, and maintenance. Findings revealed that the literature focuses predominantly on production and operations, while upstream and midstream value chain processes remain under-explored. The proposed framework demonstrated that Industry 4.0 technologies act as enablers for implementing the ten principles of the Circular Economy. By rearranging five organizational processes, these technologies simultaneously enhance the economic, environmental, and social dimensions of sustainable performance. The research's novelty lay in providing an integrated, process-oriented model that elucidates the mechanisms for achieving practical sustainable performance through the nexus of CE and Industry 4.0.
Introduction
The transition toward sustainable industrial systems has highlighted the necessity of integrating Circular Economy (CE) principles with Industry 4.0 technologies. Despite growing research, an integrated, process-oriented framework explaining their systematic interaction to enhance sustainable performance remains absent. This study addressed this gap by proposing a conceptual framework demonstrating how the synergy between CE and Industry 4.0 can improve sustainable performance through reorganizing five supply chain processes: procurement, design and development, production and operations, distribution, and use and maintenance. The CE literature has evolved from 3R approaches to comprehensive frameworks such as the 10R model (Potting et al., 2017). Industry 4.0 technologies (IoT, AI, big data analytics, blockchain, additive manufacturing, and cyber-physical systems) are recognized as enablers for circular practices (Rajput & Singh, 2019; Han et al., 2023). However, existing studies focused primarily on technological dimensions or conceptual relationships, with limited attention to process-oriented implementation frameworks (Rosa et al., 2020; Patyal et al., 2022; Alsaoudi et al., 2025). Most studies focused on macro-level relationships or review technologies and principles, with less attention to practical process-oriented frameworks. Many studies do not simultaneously address all three sustainability dimensions, predominantly focusing on environmental and economic aspects. A systemic approach explaining the dynamic interaction of CE principles and Industry 4.0 within supply chain processes is rarely observed. Moreover, a study that systematically extracts and classifies sustainable performance indicators within a coherent framework is not available.
Methodology
This research employed a systematic literature review (SLR) based on qualitative meta-synthesis. SLR served as the method for systematic search, screening, and selection of articles based on a specific protocol to prevent selection bias and ensure transparency and replicability (Kitchenham & Charters, 2007; Okoli & Schabram, 2010). Meta-synthesis served as the method for analyzing and synthesizing qualitative findings, enabling extraction of codes, concepts, and indicators into a new conceptual framework (Sandelowski & Barroso, 2007; Walsh & Downe, 2005). The study followed Sandelowski and Barroso's seven-step approach (2007). A systematic search was conducted across Scopus, Web of Science, and ScienceDirect, and complementary Persian databases (Civilica, Magiran, SID) for 2015–2026. After applying inclusion/exclusion criteria and CASP quality assessment, 32 articles were selected. Data analysis employed open, axial, and selective coding, extracting 407 initial codes, refined into 49 indicators and 33 criteria. Reliability was assessed using Cohen's kappa (κ = 0.847), indicating strong agreement.
Findings
Indicators were categorized into five processes: (1) procurement, (2) design and development, (3) production and operations, (4) distribution, and (5) use and maintenance. Quantitative distribution showed production and operations with the highest share (16 indicators, 32.7%), followed by use and maintenance (11, 22.4%), design and development (10, 20.4%), distribution (7, 14.3%), and procurement (5, 10.2%). This revealed a significant research imbalance, indicating upstream and midstream processes have been underexplored. Industry 4.0 technologies acted as enablers for implementing 10R principles across all five processes, simultaneously enhancing economic, environmental, and social dimensions.
Discussion and Conclusion
Sustainable performance is achieved through strategic alignment of CE and Industry 4.0 within organizational processes. The novelty lay in providing an integrated, process-oriented model that operationalizes indicators within five concrete processes and explains how technologies enable 10R implementation. This alignment between methodology and findings validated the framework as an outcome of analyzing scattered literature data, not a predetermined model. Limitations include: (1) Framework based on literature review lacking empirical testing; (2) Selected articles limited to specific databases; (3) Time frame (2015–2026) may exclude key earlier studies; (4) CASP assessment involves subjectivity; (5) Implementation requires digital infrastructure and organizational maturity. Future research should focus on: (1) empirical validation using quantitative methods across industries; (2) investigating moderating variables including organizational size, digital maturity, and institutional pressures; (3) developing social dimensions of sustainable performance; (4) applying system dynamics to analyze long-term behavior and feedback loops; (5) conducting comparative studies across industries and developed versus developing countries.

Abstract Accurate demand forecasting in online retail is a fundamental challenge in supply chain management, as demand patterns are typically influenced by complex temporal relationships, cross-product network interactions, and item-specific semantic features. To address this challenge, this study introduced a novel forecasting framework based on a Transformer architecture, capable of modeling long-lag temporal dependencies, cross-product network effects, and item semantic information. This empirical-computational research evaluated the proposed model using online retail II datasets. The proposed model was subsequently trained utilizing a multi-head attention mechanism within the Transformer framework, incorporating semantic representations extracted from product descriptions. The results demonstrated that the model achieved a Mean Absolute Error (MAE) of 2.68 and a Root Mean Square Error (RMSE) of 3.85, outperforming both the Autoregressive Integrated Moving Average model (MAE: 4.37, RMSE: 5.96) and the Gradient Boosting algorithm (MAE: 3.91, RMSE: 5.12). This improvement indicated that the framework effectively identifies complex demand patterns and cross-product relationships.
Introduction
Precise and adaptive demand forecasting is paramount in the digitally transformed retail landscape. Online platforms generate vast data, presenting opportunities and challenges. Demand is shaped by trends, seasonality, promotions, network externalities (influences between complementary/substitute products), and product characteristics. Traditional models like ARIMA capture linear temporal dependencies but often miss nonlinear relationships and external factors. While advanced machine learning models handle nonlinearity, they may not adequately integrate diverse data types (time series, network structures, textual semantics). This study proposed a unified deep learning framework, the Transformer, excelling at capturing long-range dependencies via its attention mechanism. By explicitly modeling temporal lags, product network effects, and semantic features, our approach aimed for a more comprehensive and accurate representation of demand generation in online retail, pushing forecasting boundaries.
Literature Review
Demand forecasting in online retail is vital for supply chain and inventory management. Traditional time-series models like ARIMA and Exponential Smoothing capture linear temporal dependencies but are limited by the increasing complexity of modern online retail environments, characterized by intricate temporal relationships, inter-product network effects, and rich semantic item characteristics (Tiwari, 2025; Chowdhury et al., 2025). Contemporary deep learning architectures, including RNNs (LSTMs, GRUs), model complex temporal patterns but may struggle with long-range dependencies or nuanced interactions (Cai et al., 2021).
The Transformer architecture, with its self-attention mechanism, excels at capturing long-range and context-dependent relationships, revolutionizing sequence modeling (Rahman et al., 2025). Its adoption in time-series forecasting, including retail demand prediction, is growing (Wang, 2025; Li, 2023). However, existing Transformer-based research often focuses primarily on historical sales data (temporal lags) in isolation. A significant gap remains in simultaneously modeling the multifaceted influences on demand, specifically the interplay of:

Network Effects: Product demand is interdependent; influenced by complements and substitutes (Caetano et al., 2025). Capturing these structural interdependencies is crucial for dynamic patterns, especially in large catalogs.
Temporal Lags: The impact of events or changes may not be immediate. Accurately modeling these varied time delays is vital for precise forecasting (Tarighat et al., 2025).
Product Semantics: Product characteristics and descriptions significantly influence consumer choice. Leveraging semantic information can uncover hidden relationships and improve accuracy (Rahikka & Mikkola, 2025).

While studies have explored these factors individually or in limited combinations (e.g., Cai et al., 2021; Wang, 2025), a comprehensive framework integrating temporal dependencies, network effects, and product semantics within a single Transformer architecture remains an active research area. This study bridged this gap by proposing a novel Transformer-based framework to jointly model these dimensions for a more accurate and holistic approach to dynamic retail demand forecasting.
Method
This study employed an empirical-computational approach using the Online Retail II dataset. Data preprocessing involved handling missing values, correcting errors, and removing irrelevant transactions. The dataset was split chronologically into an 80% training set and a 20% testing set.
Our core forecasting model was the Transformer architecture, utilizing its self-attention mechanism to weigh historical time steps for predicting future demand, thus capturing complex temporal patterns and long-range dependencies. Positional encodings were added to input embeddings for sequence information.
Our framework innovatively integrated three key information sources:

Temporal Lags: Explicitly accounts for lagged demand patterns. While attention captures temporal relationships, features representing past demand values at different lags can be incorporated.
Network Effects: Models product interdependencies through a product interaction graph (nodes=products, edges=relationships like co-purchase frequency). Graph-derived features (e.g., centrality) processed by techniques like Graph Convolutional Networks (GCNs) are integrated.
Product Semantics: Extracts rich semantic embeddings from product descriptions using NLP techniques (e.g., pre-trained models). These capture nuanced characteristics and are integrated alongside temporal and network features.

The model was trained end-to-end using Mean Squared Error (MSE) or Mean Absolute Error (MAE), optimized via an Adam optimizer. Hyperparameters were tuned for optimal performance. Final evaluation used MAE and RMSE on the test set.
Results
The empirical evaluation on the Online Retail II dataset showed the superiority of our proposed Transformer-based framework. On the test set, the model achieved an MAE of 2.68 and an RMSE of 3.85. This substantially outperformed baseline models: ARIMA yielded MAE 4.37, RMSE 5.96; XG-Boost achieved MAE 3.91, RMSE 5.12. These quantitative results highlighted the enhanced predictive accuracy of our integrated approach, attributed to its ability to capture the complex interplay of temporal, network, and semantic factors. Visualizations of predicted versus actual demand confirmed the model’s superior tracking of demand fluctuations. Analysis of attention weights also provided insights into influential historical periods and product relationships, offering a degree of interpretability.
Discussion
The findings underscored the advantages of a Transformer-based architecture for dynamic retail demand forecasting when augmented with explicit modeling of temporal lags, network effects, and product semantics. The accuracy improvements over ARIMA and XG-Boost highlighted the limitations of models neglecting the interconnected nature of retail demand. Our approach achieved a more comprehensive representation by jointly considering these factors. The attention mechanism effectively captured long-range temporal dependencies and contextual relationships. Explicit incorporation of network effects revealed the critical influence of product interdependencies, often overlooked in single-item forecasting. Leveraging product semantics allowed deeper understanding of demand drivers beyond historical sales. This integrated approach offered retailers enhanced capabilities for inventory management, demand planning, and strategic decision-making, potentially reducing costs and stockouts while improving customer satisfaction. Attention weight analysis offered valuable insights for model trust and adoption.
Conclusion
This research successfully developed and validated a novel Transformer-based framework for dynamic retail demand forecasting, integrating temporal lags, network effects, and product semantics. Empirical results on the Online Retail II dataset demonstrated the model’s superiority, achieving significantly lower error metrics (MAE: 2.68, RMSE: 3.85) compared to ARIMA (MAE: 4.37, RMSE: 5.96) and XG-Boost (MAE: 3.91, RMSE: 5.12). This work contributed a unified and highly accurate methodology for forecasting in complex retail environments, offering substantial practical implications for inventory management and strategic decision-making. Future research could incorporate additional external factors, explore advanced GNN architectures, and develop more sophisticated interpretability methods for real-world adoption.

Abstract The development of renewable electrical energy in the industrial sector requires multidimensional alignment across economic, social, environmental, and technological dimensions. This study aimed to present a multidimensional alignment framework for sustainability and to analyze indicator interactions using fuzzy cognitive maps. Key indicators were extracted from literature review and expert opinions of 20 energy policymakers, industrial managers, and faculty members, with causal relationships analyzed via specialized software. Findings identified electricity price and demand, production self sufficiency, energy saving, solar system installation, and climate change as main drivers; variables such as working capital and fossil fuel reduction played dual roles, while CO₂ reduction emerged as the response variable. The proposed framework supported strategic decision-making and roadmap development. Proposed solutions include smart monitoring and control systems based on IoT and digital twins, integrated energy management with employee self help, non governmental investment, public joint stock companies, participatory models for small and medium sized factories, and energy storage methods.
Introduction
The industrial sector occupies a strategic position in national energy policymaking because of its substantial share of electricity consumption and its essential contribution to production continuity, employment, and economic growth. A reliable electricity supply is therefore fundamental to industrial productivity, competitiveness, and resilience. Nevertheless, electricity imbalances during peak-demand periods often lead to supply restrictions for industrial units, causing reduced production capacity, lower efficiency and profitability, and, in some cases, temporary shutdowns. The existing electricity-generation portfolio remains highly dependent on fossil fuels, particularly natural gas. Constraints in fuel supply, increasing electricity demand, environmental concerns, and climate-change impacts demonstrate that the current energy system cannot adequately support long-term industrial growth. Renewable electrical energy (including solar, wind, and other clean sources) offers an important pathway for diversifying the electricity-generation mix, reducing fossil-fuel dependence, improving energy security, and mitigating environmental pollution and greenhouse-gas emissions.
However, renewable electricity development in the industrial sector is not solely a technological issue. Its sustainability depends on coherent alignment among economic, social, environmental, and technological dimensions. In practice, this alignment is challenged by inconsistent policies, inadequate infrastructure, limited financing mechanisms, insufficient operational coordination, and varying levels of industrial readiness. The absence of an integrated analytical framework for simultaneously examining these dimensions may lead to fragmented policies and ineffective investments.
Accordingly, this study aimed to develop a multidimensional alignment framework for the sustainability of renewable electrical energy in the industrial sector. It also investigated the causal interactions among the effective indicators through the Fuzzy Cognitive Map (FCM) approach. The study addressed four questions: identifying the key indicators, determining causal relationships and influence levels, classifying indicators according to their roles in the system, and explaining their priorities within the proposed multidimensional framework.
Methodology
This research employed a fuzzy cognitive mapping approach to model the complex causal relationships among the indicators affecting renewable electricity sustainability in the industrial sector. FCM was an appropriate method for analyzing systems characterized by uncertainty, interdependence, feedback mechanisms, and reliance on expert knowledge.
The research process was conducted in four stages. First, relevant indicators were identified through a review of theoretical foundations, previous studies, and energy-sustainability literature. The indicators were classified into four dimensions: economic, social, environmental, and technological. Second, the preliminary indicators were reviewed and validated by a panel of 20 experts, including energy policymakers, managers from public and private industrial organizations, and university faculty members. All participating experts had more than five years of relevant professional or academic experience.
Third, the experts assessed the existence, direction, and strength of causal relationships among the identified indicators. Data were collected through a structured questionnaire distributed via the Porsa platform of the Iranian Research Institute for Information Science and Technology. The questionnaire was designed to require responses to all items; therefore, no missing data were observed. Expert judgments were aggregated to generate the final weighted adjacency matrix of the fuzzy cognitive map.
Results and Discussions
The study identified a set of key indicators influencing the alignment and sustainability of renewable electrical energy in the industrial sector across the four economic, social, environmental, and technological dimensions. The FCM analysis revealed that electricity price and demand, self-sufficiency in electricity generation, industrial energy saving, installation of solar-energy systems, and climate change are the principal driving variables in the system. These variables have high outgoing influence and play a decisive role in shaping the development path of renewable electricity in industry.
Electricity price and demand affect investment incentives, consumption behavior, and the economic feasibility of renewable-energy projects. Industrial self-sufficiency in electricity generation reduces dependence on the national grid and improves resilience against supply restrictions. Energy-saving practices decrease industrial electricity demand and complement renewable-energy deployment. Solar-system installation contributes directly to decentralized clean-electricity production, while climate change acts as an important environmental driver that increases the urgency of energy transition and emission-reduction policies.
The findings also indicated that working capital in industries, industrial self-reliance, reduced fossil-fuel consumption, and environmental protection have bidirectional roles. These variables both influence and are influenced by other factors in the system. For example, access to working capital supports investment in renewable technologies, but it is itself affected by electricity costs, industrial performance, and policy incentives. Similarly, reduced fossil-fuel consumption contributes to environmental protection, while environmental policies and concerns can stimulate further reductions in fossil-fuel use.
The reduction of carbon dioxide emissions was identified as the main response variable. This indicated that emission reduction is primarily an outcome of coordinated improvements in electricity self-sufficiency, renewable-energy capacity, energy efficiency, financing, technology adoption, and environmental governance.
The proposed framework provided a structured basis for prioritizing policy interventions and industrial actions. It emphasizes smart consumption-monitoring and control systems, intelligent scheduling of industrial electricity use, Internet of Things (IoT)-based remote control, digital twins, integrated energy-management systems, employee participation in energy-saving initiatives, renewable-energy investment models, small-scale combined power plants for small and medium-sized enterprises, and electricity-storage solutions.
Conclusion
This study developed a multidimensional alignment framework for renewable electrical energy sustainability in the industrial sector using fuzzy cognitive maps. The findings demonstrated that sustainable renewable-energy development requires coordinated action across economic, social, environmental, and technological dimensions rather than isolated technological investments. The results identified electricity price and demand, electricity self-sufficiency, industrial energy saving, solar-system installation, and climate change as the system’s main drivers. Working capital, industrial self-reliance, fossil-fuel reduction, and environmental protection served as bidirectional variables, whereas carbon dioxide emission reduction is the main response variable. These results suggested that policymakers and industrial decision-makers should prioritize interventions that strengthen the system’s key drivers and improve coordination among interdependent variables.
The framework supported strategic decision-making and the preparation of a national or sectoral roadmap for renewable electricity development in industry. Practical measures include implementing smart energy-management systems, expanding decentralized renewable generation, promoting collective and public investment models, supporting small and medium-sized industries through participatory projects, deploying energy-storage technologies, and using advanced analytical tools for demand and generation forecasting. Future studies may extend the framework by incorporating additional stakeholders, comparing industrial subsectors, and conducting scenario-based simulations under alternative energy-policy conditions.

Abstract The flow shop scheduling problem with sequence-dependent setup times and operator constraints represents a complex and practically relevant production planning challenge due to the simultaneous integration of sequencing, timing, and human resource allocation decisions, making its exact solution computationally intractable for medium- and large-scale instances. This study proposed an integrated modeling and solution framework aimed at minimizing total order tardiness while jointly incorporating key operational constraints, including technological precedence, machine non-overlapping, sequence-dependent setup times (satisfying the triangle inequality), and multi-skilled operator capacity limitations. In this regard, a mixed-integer linear programming (MILP) model was first developed to explicitly capture sequencing, scheduling, and operator assignment decisions within a unified structure. Given the computational complexity of the model and the inefficiency of exact methods for real-world scales, an approximate solution approach based on Particle Swarm Optimization (PSO) was designed; to accommodate the combinatorial structure of the problem, a continuous encoding mechanism combined with a constructive decoder (based on FIFO logic) was implemented to enforce key constraints during solution evaluation. The performance of the proposed algorithm was evaluated through an industrial case study, a comparative benchmark against a Genetic Algorithm (GA), and multiple independent runs with different random seeds, assessing key performance indicators such as the best objective value, mean and standard deviation of results, computational time, and convergence behavior. Results demonstrated that the proposed PSO achieved high-quality and stable solutions with acceptable computational effort, exhibiting significant superiority over the benchmark algorithm in terms of average solution quality, stability, and runtime; furthermore, sensitivity analysis of parameters and problem dimensions indicated that stronger exploration enhances robustness and solution quality at the cost of longer runtime, whereas exploitative settings accelerate convergence but reduce solution quality. Overall, combining precise operational constraint modeling with flexible metaheuristic algorithms provided an efficient approach for solving complex scheduling problems in real-world production environments and can serve as an effective decision-support tool in production management.
Introduction
Production scheduling in multi-stage manufacturing systems represents a fundamental operational challenge that directly impacts delivery reliability, customer service levels, resource utilization, and tardiness-related financial penalties. In modern process and assembly industries such as the chemical, food, and detergent manufacturing sectors, production efficiency depends heavily not only on processing times but also on sequence-dependent setup operations resulting from line washouts, tooling adjustments, and material transitions. Concurrently, human resources serve as critical shared constraints across production stations, where operations cannot proceed without the presence of a dedicated, skilled operator. Classical flow shop models frequently overlook operator limitations or treat setup durations as negligible, leading to unrealistic schedules, unexpected bottlenecks, and significant delivery delays. To bridge this gap, this study investigated a permutation flow shop scheduling problem with sequence-dependent setup times (satisfying the triangle inequality) and multi-skilled operator constraints, aimed at minimizing total order tardiness. By integrating human workforce dynamics with technical workstation constraints, this research provided a comprehensive decision-making framework to balance order sequencing, setup overheads, and labor allocation.
Methodology
The study initially developed a rigorous Mixed-Integer Linear Programming formulation that unifies order sequencing, machine precedence, non-overlapping constraints, sequence-dependent transitions, and dedicated operator commitments across both setup and processing phases. Given the NP-hard nature of the problem and the computational intractability of exact solvers for industrial-scale instances, an approximate optimization approach based on Particle Swarm Optimization was developed. To effectively map the continuous search space of the swarm algorithm into discrete combinatorial schedules, a dual random-key continuous encoding mechanism was utilized to govern both job sequence permutations and operator-to-operation assignments. A constructive simulation decoder incorporating a first-in, first-out priority dispatching rule resolved operator contention dynamically and enforced all technological and resource constraints during solution evaluation. The proposed framework was validated using real-world industrial data from a detergent manufacturing facility involving multi-station lines, sequence-dependent changeovers, and constrained multi-skilled operators. Furthermore, the algorithm was evaluated through comparative benchmarking against a Genetic Algorithm with an identical decoding mechanism, multi-seed statistical replications, and systematic sensitivity analyses across algorithm hyperparameters and problem dimensions.
Findings
Computational experiments demonstrated the superior performance, stability, and computational efficiency of the proposed Particle Swarm Optimization algorithm. In the industrial case study, both metaheuristics identified the minimum tardiness objective of 24.6 hours; however, the proposed Particle Swarm Optimization framework achieved statistically superior average tardiness (25.27 versus 26.85), significantly lower standard deviation (1.43 versus 2.15), and reduced computational runtime (3.26 seconds versus 4.18 seconds) compared to the Genetic Algorithm. Convergence trajectory analyses indicated rapid search progression within the first fifty iterations, effectively avoiding premature stagnation through dynamic inertia control and perturbation mechanisms. Sensitivity analysis demonstrated that explorative configurations with larger swarm sizes enhanced solution robustness and consistency, whereas overly exploitative settings accelerate convergence at the expense of solution quality. In addition, scalability assessments across medium-scale (20 jobs, 5 machines) and large-scale (50 jobs, 10 machines) problem instances confirmed that the proposed framework delivered high-quality schedules within reasonable computational time frames when iteration limits are calibrated to problem scale.
Discussion and Conclusion
The findings underlined that achieving optimal delivery performance in modern production lines requires the synchronized optimization of machine schedules, sequence-dependent changeovers, and human labor assignments. Treating operator constraints independently from sequencing decisions leads to suboptimal or practically unfeasible production schedules. The developed framework served as an effective decision-support tool for operations managers, enabling them to evaluate operational trade-offs between delivery commitments, labor availability, and setup losses under varying production scenarios. Methodologically, coupling a continuous swarm intelligence algorithm with a constraint-aware constructive decoder proved to be an adaptable and robust approach for handling complex shop-floor constraints without altering the core optimization engine. Future research can extend this framework by incorporating stochastic processing and setup times through robust or fuzzy optimization, developing hybrid metaheuristic variants, and addressing multi-objective criteria such as energy efficiency and carbon emissions.

Abstract In the competitive automotive parts manufacturing industry, identifying and prioritizing failure modes is essential for preventing financial losses, ensuring passenger safety, and maintaining quality. This applied, descriptive-analytical study aimed to identify and prioritize failure modes in the constant-velocity (CV) joint assembly process of an Iranian automotive parts manufacturer, integrating the Fuzzy Best-Worst Method (FBWM) and Fuzzy VIKOR (FVIKOR). Fifteen purposively selected experts participated in a three-round Delphi survey to screen risk factors and failure modes; retained factors were weighted using FBWM and failure modes ranked using FVIKOR. Severity received the highest weight among retained risk factors, and failure modes associated with tensile strength and dimensional and spline tolerances of CV-joint components received the highest corrective-action priority. Expert pairwise comparisons showed high consistency, and combining Delphi screening with fuzzy multi-criteria weighting and ranking improved prioritization accuracy compared with the traditional risk priority number approach, providing production managers with a reliable basis for preventive decision-making.
Introduction
The automotive parts manufacturing sector in Iran faces persistent challenges related to product quality, safety, and reliability. Failure Mode and Effects Analysis (FMEA) is one of the most widely used preventive quality management tools, designed to identify and prioritize potential failures before they occur (McDermott et al., 2009; Baghery et al., 2018). The traditional Risk Priority Number (RPN)—the product of severity, occurrence, and detection—has well-documented limitations: identical RPN values can arise from different risk combinations, equal weighting is assumed, and crisp scores cannot capture expert judgment ambiguity (Liu et al., 2013; Yousefi et al., 2018). Recent literature called for replacing RPN with fuzzy multi-criteria approaches (Liu, 2016; Akkus, 2024; Liou et al., 2024). This study integrated three complementary methods (Delphi screening, FBWM for criterion weighting, and FVIKOR for failure mode ranking) in the context of CV-joint assembly, a safety-critical powertrain component. No prior study had simultaneously applied all three elements in the Iranian automotive parts sector, nor provided empirical sensitivity validation and an RPN comparison.
Methodology
This applied, descriptive-analytical study was conducted via a survey approach. A panel of fifteen purposively selected experts from a CV-joint manufacturer—drawn from quality, production engineering, R&D, sales, and senior assembly roles, each with at least ten years of experience—participated in all study phases. Delphi screening used a five-point Likert scale with an acceptance threshold of 3; termination required a Kendall’s W change of less than 0.02 between rounds and no further item elimination. Five candidate criteria (severity, occurrence, detection, cost, time) were screened in three rounds. The four retained criteria were weighted using FBWM with the nonlinear optimization model solved in Lingo; weights were defuzzified using the Graded Mean Integration Representation (GMIR) method (Chen & Hsieh, 1999) as standardized in Guo and Zhao (2017). The consistency ratio was computed as CR = k/CI. Seventy-one potential failure modes were reduced to 68 through three Delphi rounds. The 68 retained failure modes were evaluated using fuzzy linguistic scales (Patil & Kant, 2014) and ranked by FVIKOR (Opricovic, 2011) with compromise parameter v = 0.5, defuzzifying S, R, and Qusing (l + 2m + u) / 4. A sensitivity analysis across v ∈ {0, 0.25, 0.5, 0.75, 1} and an illustrative RPN comparison were also conducted for a 12-FM subsample.
Findings
FBWM yielded weights of 0.408 (severity), 0.265 (cost), 0.186 (occurrence), and 0.139 (detection). The consistency ratio CR ≈ 0.064 confirmed high expert agreement. The “time” criterion was eliminated in Delphi round 1 (mean = 2.933). Three failure modes were eliminated in the failure-mode Delphi round 1. Cronbach’s alpha was 0.91 (round 2) and 0.90 (round 3); Kendall’s W was 0.247 (round 2) and 0.254 (round 3). FVIKOR ranked all 68 failure modes; the five highest-priority were: FM32—Insufficient tensile force in the trunnion test (Q = 0.0195); FM65—Low stopper bearing tensile strength (Q = 0.0349); FM56—Spline deformation (Q = 0.0352); FM4—Dimensional non-conformance (Q = 0.0364); FM13—Shaft contact with boot edge (Q = 0.0468). The RPN comparison showed that failure modes with high detection difficulty but low severity (FM64, FM67) were over-ranked by the simple additive approach relative to FVIKOR. The sensitivity analysis demonstrated full robustness: {FM3, FM4, FM65, FM66} occupied the top four positions across all five v values, and the ordering of the remaining eight subsample failure modes was completely unchanged.
Discussion and Results
The findings confirmed that integrating Delphi screening, FBWM weighting, and FVIKOR ranking provided a robust approach to failure mode prioritization. The dominance of severity (weight = 0.408) reflected the safety-critical nature of the CV joint. FVIKOR’s superiority over simple additive scoring was most apparent when criticality was concentrated in high-severity or high-cost criteria. The study extended existing literature as the first to simultaneously apply all three elements (Delphi, FBWM, FVIKOR) in Iranian automotive parts manufacturing, and provided empirical sensitivity validation and an RPN comparison. A key limitation was that raw decision matrix data could not be fully disclosed due to company confidentiality.
Conclusion
This study demonstrated that the Delphi–FBWM–FVIKOR framework effectively identified and prioritized failure modes in CV-joint manufacturing. The three highest-priority failure modes—insufficient tensile force in the trunnion test, low stopper bearing tensile strength, and spline deformation—warrant immediate corrective action. The consistency (CR ≈ 0.064) and robustness (full rank stability across all v values) confirmed the reliability of the approach. Future research should replicate the framework across multiple production lines and explore K-means expert clustering prior to FBWM weighting, following Amoozad Mahdiraji et al. (2022).

Abstract The oil and gas industry faces challenges such as the complexity of a multi-layered and global supply network, operational and geopolitical risks, cybersecurity threats in critical infrastructure, sustainability pressures, and the need for real-time data-driven decision-making. The main objective of the study was to design and analyze an autonomous supply chain framework based on artificial intelligence, big data, and the Internet of Things in the oil and gas industry with an emphasis on sustainability and cybersecurity. The research was applied and developmental in terms of its purpose; qualitative-quantitative in terms of the nature of the data; and descriptive in terms of the data collection method, which was conducted using the Fuzzy Cognitive Mapping method. The study period was summer and fall 2025 and winter 2026. The population of the study was Iranian oil and gas industry experts, and 23 experts completed a structured questionnaire using purposive sampling. Achieving autonomy in the oil and gas supply chain was contingent upon the synergy and application of advanced digital technologies. Within this context, operational intelligence served as a crucial mediating factor, transforming technological capabilities into economic and environmental value. In other words, the establishment of data-driven digital infrastructure was the primary prerequisite for attaining sustainability, while resilience and trust within this ecosystem could only be realized through robust cybersecurity. The proposed model, functioning as a decision support system, empowered managers in this industry to optimally prioritize digital investments, evaluate the implications of security policies, and simulate various digital transformation scenarios prior to operationalization. Introduction As one of the most strategic industries in the world, the oil and gas industry is highly dependent on the effective performance of its supply chain. It has a multi-layered, extensive, and interconnected chain that ranges from the provision of specialized equipment and raw materials to extraction, refining, transmission, and distribution operations. The capital-intensive nature, geographical dispersion of assets, dependence on critical infrastructure, sensitivity to geopolitical fluctuations, and the requirement for operational continuity have presented the oil and gas industry with significant management complexities. In addition, disruptions caused by geopolitical crises, fluctuations in the global energy market, environmental constraints, and rapid technological changes have made the need to redesign the industry’s supply chain architecture more apparent than ever. In such a context, advanced digital technologies can play an important role in reinventing the oil and gas industry’s supply chain. Artificial intelligence, through machine learning algorithms and predictive analytics, enables demand forecasting, inventory optimization, automated operation scheduling, and predictive maintenance of equipment. The Industrial Internet of Things (IoT) enables real-time monitoring of the status of assets, transmission lines, and operational equipment by creating a network of smart sensors. Big data also provides strategic insights for decision-making by aggregating and analyzing a huge volume of structured and unstructured data. The synergy of these technologies facilitates the transition from reactive management to predictive and autonomous management in the oil and gas industry. Despite these capabilities, the deployment of an autonomous supply chain in the oil and gas industry faces serious challenges. One of the most important of these challenges is the issue of cybersecurity. The widespread digitalization of operational infrastructure and the convergence of information technology (IT) and operational technology (OT) have significantly increased the level of cyber attacks. The connection of field equipment, industrial control systems, smart pipelines, and remote-control centers, in the absence of appropriate security mechanisms, can lead to widespread disruptions, economic losses, environmental threats, and even human hazards. Along with security considerations, the issue of sustainability has also become one of the central concerns of the oil and gas industry. Increasing pressures from environmental regulations, carbon emission reduction commitments, stakeholder expectations, and sustainable development requirements have forced organizations active in this industry to review their operating models. Accordingly, the main objective of the present study was to design and analyze an autonomous supply chain framework based on artificial intelligence, big data, and the Internet of Things in the oil and gas industry with an emphasis on sustainability and cybersecurity. Methodology The present research was applied and developmental in terms of its purpose; qualitative-quantitative in terms of the nature of the data; and descriptive in terms of the method of data collection. The study period was summer and fall 2025 and winter 2026. The research aimed to design an autonomous supply chain framework based on artificial intelligence, big data, and the Internet of Things in the oil and gas industry with a sustainability and cybersecurity approach, and used the fuzzy cognitive mapping method. The statistical population of the research included experts in the following fields in the oil and gas industry: supply chain management, digital transformation and information technology, cybersecurity, sustainability management, and HSE. The sampling was carried out purposively (judgmentally), and the criteria for selecting experts were: at least 10 years of relevant professional experience; managerial or specialized experience in digital or operational projects; and familiarity with analytical systems and smart infrastructures. A total of 23 experts completed the questionnaire. In this study, Mental Modeler software was used to model the causal structure of an autonomous supply chain based on artificial intelligence, big data, and the Internet of Things. Findings and Conclusion Findings from modeling using fuzzy cognitive mapping showed that the transition to an autonomous supply chain in the oil and gas industry was not a purely technological process, but rather a systemic and multidimensional transformation that requires synergy between digital infrastructure, smart decision-making capabilities, cyber governance mechanisms, and sustainability requirements. The results of the study showed that the relationships between these dimensions were nonlinear, dynamic, and feedback in nature, and the impact of each variable became meaningful in interaction with other network components. The first important finding of the study was the determining role of artificial intelligence maturity, big data infrastructure, and IoT integration as the main drivers of autonomous supply chain transformation. Analysis of centrality and degree of influence indices showed that these variables had the greatest causal impact on other model components and acted as the driving force of digital transformation. One of the most important findings of the study was the identification of “real-time decision-making” as the most central variable of the network, indicating its pivotal role in connecting digital enablers to performance outcomes. Findings from sensitivity analysis and scenario building showed that cybersecurity played a role beyond a control or protection factor and acted as a stabilizing pillar of the entire autonomous structure. A decrease in the maturity level of cyber governance or an increase in cyber threats caused a significant decline in digital trust, reduced chain transparency, weakened real-time decision-making, and ultimately a decline in system resilience. The research results showed that sustainability in the autonomous supply chain was a direct consequence of the smartization of operational processes. Simulation scenarios showed that strengthening digital enablers indirectly reduced waste, increased energy efficiency, reduced carbon emissions, and strengthened economic sustainability.

Abstract Artificial intelligence is reshaping knowledge work and creating new challenges for employee experience in mission-oriented higher education. This study aimed to identify and prioritize factors affecting knowledge worker experience in the AI era and develop a managerial roadmap for a technology university. Using a mixed-methods approach, data were collected from 31 knowledge workers and experts through a researcher-developed questionnaire based on ISO 34000. ISM and MICMAC analyzed structural relationships, driving power, and dependence, while ANP determined factor importance. Results identified nine interrelated dimensions. Intelligent leadership and organizational and cultural experience emerged as fundamental drivers, while intention to remain was the final dependent outcome. ANP ranked intelligent leadership, organizational and cultural experience, and digital maturity as the most important factors. Based on these findings, a five-phase managerial roadmap was developed, covering digital foundation, managerial and cultural transformation, work experience optimization, innovation and scientific excellence, and knowledge capital retention. Introduction Artificial intelligence (AI) is transforming knowledge work, creating both opportunities and challenges for employee experience, particularly in mission-oriented higher education. As technology universities adopt AI, technological progress often outpaces organizational and human adaptation. However, integrated frameworks that capture the interdependencies among these factors and translate them into actionable strategies remain limited. This study therefore aimed to identify, analyze, and prioritize the factors affecting knowledge worker experience in the AI era and develop a context-sensitive managerial roadmap for a mission-oriented technology university. By moving beyond isolated technological investments toward an integrated organizational and cultural approach, the study provides a strategic foundation for sustaining knowledge capital, enhancing digital maturity, and fostering scientific excellence. Methodology This applied study used a survey-based, cross-sectional, mixed-methods design with a descriptive-analytical approach in a mission-oriented technology university. Initial factors were identified through a literature review, analysis of international and domestic employee experience models, and the ISO 34000 Human Resource Management Standard, with emphasis on knowledge-intensive work and AI. The indicators were then integrated and adapted to the study context. Data were collected from 31 knowledge workers and experts using a researcher-developed 37-item questionnaire covering nine dimensions: digital work experience, organizational and cultural experience, intelligent leadership, digital maturity, engagement and work enthusiasm, job and organizational integration, digital organizational citizenship behavior, innovation and knowledge performance, and intention to remain. Content validity was established through literature, ISO 34000 alignment, and expert review, while Cronbach’s alpha of 0.83 confirmed satisfactory reliability. Purposive sampling was used given the expert-oriented analytical methods. Descriptive statistics assessed the dimensions, while ISM identified relationships and hierarchical levels, MICMAC classified factors by driving power and dependence, and ANP determined their relative importance considering interdependencies and feedback. Finally, the integrated findings were used to develop a five-phase managerial roadmap. Results and Discussion Descriptive findings showed an overall mean of 2.98 across the nine dimensions, indicating a moderate to relatively weak condition of knowledge worker experience. Innovation and knowledge performance had the highest mean (3.28), followed by digital work experience (3.18), while job and organizational integration (2.65), digital maturity (2.82), and organizational and cultural experience (2.84) had the lowest scores. This suggested that technological and task-related benefits had advanced faster than organizational, cultural, and relational aspects. ISM positioned organizational and cultural experience and intelligent leadership as foundational factors, while engagement and integration occupied intermediate levels and digital citizenship, innovation and knowledge performance, and intention to remain appeared at higher levels. MICMAC confirmed F1–F4 as independent, F5–F6 as linkage, and F7–F9 as dependent factors, highlighting intelligent leadership and organizational and cultural experience as major system drivers. ANP ranked intelligent leadership first (0.185), organizational and cultural experience second (0.172), digital maturity third (0.145), and digital work experience fourth (0.120). The consistency of ISM, MICMAC, and ANP indicated that improving knowledge worker experience should begin with organizational and managerial foundations rather than isolated technological investments. Accordingly, a five-phase managerial roadmap was developed: Phase 1 (0–12 months), digital foundations and maturity; Phase 2 (12–24 months), managerial and cultural transformation; Phase 3 (24–36 months), work experience optimization; Phase 4 (36–48 months), innovation, knowledge performance, and scientific excellence; and Phase 5 (48–60 months), knowledge capital retention. This sequence provided a practical pathway from foundational capabilities to sustainable retention. Conclusion This study developed an integrated, context-sensitive model of knowledge worker experience in the AI era within a mission-oriented technology university. Findings showed that employee experience is not simply a result of technological adoption but emerged from the interaction of organizational, cultural, managerial, technological, and behavioral factors. Intelligent leadership and organizational and cultural experience emerged as fundamental drivers, while intention to remain was the final dependent outcome. ANP reinforced these findings by assigning the highest weights to intelligent leadership, organizational and cultural experience, and digital maturity. Thus, sustainable AI-driven transformation required organizational and cultural foundations, capable leadership, digital maturity, and employee participation, rather than technology acquisition alone. Based on these findings, a five-phase roadmap was developed covering digital foundation, managerial and cultural transformation, work experience optimization, innovation and scientific excellence, and knowledge capital retention. The roadmap provided managers with a structured basis for prioritizing interventions and resources. Theoretically, the study conceptualized knowledge worker experience as an interconnected system; methodologically, it integrated ISM, MICMAC, and ANP to assess structural relationships, driving power, dependence, and relative importance. Future research may test the model using larger samples, structural equation modeling, longitudinal and comparative designs, and objective indicators of innovation, knowledge performance, and retention.