International Journal of Industrial Engineering and Management
Published: quarterly | ISSN 2217-2661 | ISSN (online) 2683-345X Editor-in-Chief: Uglješa Marjanović, PhD Subject Areas: 1. Engineering, Industrial, 2. Management Science, 3. Operations Research International Journal of Industrial Engineering and Management (IJIEM) is an open-access interdisciplinary international scientific journal that publishes theoretical and empirical peer-reviewed articles from the field of industrial engineering and production management. IJIEM is published quarterly - March, June, September and December. CiteScore 7.2 (2025) | Impact Factor 4.2 (2025) | Journal Category M22 (2025)
University of Novi Sad, Faculty of Technical Sciences
IJIEM is an Open Access Journal. All articles can be downloaded free of charge and used in accordance with the Creative Commons CC-BY 4.0 licence. This allows readers to read, download, copy, distribute, print, search, or link to the full texts of the articles. Benefits of open access include:: Free access for all users worldwide Increased visibility and readership Rapid publication No spatial constraints Please visit our current issue or archives journal pages to access all articles.
Integrative technologies in traceability systems: advancing Industry 5.0 in a Brazilian company
This study aims to investigate how the implementation of an automated traceability system facilitates the transition from Industry 4.0 (I4.0) to Industry 5.0 (I5.0) in a medium-sized Brazilian company. A qualitative single case study approach was employed, involving the application and development of the traceability system through barcode reading on stainless steel capacitor tanks, the RAST 4.0 system). The analysis focuses on the contribution of the RAST 4.0 system, which incorporates I4.0 technologies, to production efficiency and product quality, while also fostering more sustainable and human-centered industrial practices. Focus groups, documentary analysis and direct observations were utilized to collect data, thereby enabling a deeper understanding of the system’s implementation. The system provided improved control through real-time information, enabling informed decisions for continuous improvement and management of non-conformities in production. In addition, it was possible to achieve the three pillars of I5.0 and propose a conceptual model. The results of this study may encourage other companies looking to adapt to I5.0 to invest in similar systems to improve their processes, resulting in a more humane, productive, and sustainable work environment. Article history: Received (May 13, 2025); Revised (April 3, 2026); Accepted (May 4, 2026); Published online (June 12, 2026)
Copyright (c) 2026 International Journal of Industrial Engineering and Management https://creativecommons.org/licenses/by/4.0
Unlocking Innovation Value to Fuel Competitive Advantage: The Innovation Pivot Canvas
Business model innovation is widely recognized as critical for enhancing competitive advantage. Within managerial practice, Lean methodologies and the Business model canvas are widely adopted for model design; however, the success of a new venture often hinges on the ability to integrate short-term viability with long-term sustainability and scalability. To address this gap, we present the Innovation Pivot Canvas, an agile strategic analysis tool and step-by-step methodology designed to assist the process of building a strategy to unlock the innovation’s market value. Having been extensively validated across numerous entrepreneurial settings, the tool’s efficacy is further demonstrated in this paper through a detailed case study involving the strategic redesign of a technology-intensive startup. Article history: Received (July 26, 2025); Revised (March 12, 2026); Accepted (April 20, 2026); Published online (June 12, 2026)
When Modularity Meets Mind: Product Innovation through a Behavioral Lens
Modular Product Design (MPD) is a promising strategy for managing complexity in product development. However, researchers disagree on the MPD’s impact on product innovation. Some researchers argue that MPD facilitates innovation, while others contend that it hinders it. This conceptual paper develops a framework that aims to reconcile these contradictory findings from the literature. Thus, we build on the current understanding in the field of MPD, identifying two literature streams that have different views on the impact of MPD on product innovation. To reconcile these views, we adopt an interdisciplinary approach that integrates bodies of research on MPD and innovation with insights from behavioral science. Finally, after analyzing the relevant literature on MPD and behavioral science, we conceptualize a framework that proposes that designers’ cognitive behavior moderates MPD’s impact on product innovation, explaining this long-standing debate in the field. In this way, the proposed conceptual framework reconciles the conflicting arguments on MPD’s impact on product innovations and potentially opens a new field of study, shedding light on this behavioral blind spot of existing research. Article history: Received (September 30, 2025); Revised (March 9, 2026); Accepted (March 29, 2026); Published online (May 9, 2026)
Framework for Understanding Design Heuristics and Sources of Bias in New Product Development
Product development, whether incremental or radical, is inherently complex and uncertain, requiring rational decision-making by product designers to enable effective ideation. However, heuristics (or mental shortcuts) often impact this decision-making process, introducing cognitive biases that can significantly impact design outcomes. This is especially problematic for New Product Development (NPD). Despite this impact, research on the sources of these cognitive biases within NPD, particularly during the ideation phase, remains limited. Through a systematic literature review, the present study first identifies the body of research dealing with heuristics and cognitive biases in NPD. Secondly, the study identifies a comprehensive set of available design heuristics and cognitive biases, which are four design heuristics and 37 cognitive biases, and subsequently provides an overview. Finally, we develop a framework that proposes a new view on cognitive bias sources composed of three perspectives: 1) information related to the product design, 2) the nature of the design problem, and 3) designers’ cognitive reasoning. Article history: Received (December 16, 2025); Revised (April 30, 2026); Accepted (May 5, 2026); Published online (June 26, 2026)
Innovative Quality Determination by Integrating Process Incapability Index into Variable Sampling Plan Design
This study presents an innovative acceptance sampling plan for variables that integrates the process incapability index with a resubmitted sampling scheme (Cpp-RSP) to enhance the efficiency of lot sentencing. While conventional single-sampling plans (SSP) are attractive for their simplicity, they often require large fixed sample sizes, which generate substantial inspection costs for both producers and customers. To overcome this constraint, the proposed sampling plan leverages the statistical properties of Cpp to construct a nonlinear optimization model that determines the optimal plan parameters subject to prescribed quality standards and risks for producers and consumers. This parametric framework yields closed-form operating characteristic (OC) and average sample number (ASN) functions, leading to practical design tables for different quality and risk settings. Numerical investigations demonstrate that across a broad range of quality levels and risk combinations, the Cpp-RSP reduces the per-stage sample size required by roughly 25–50% compared with the corresponding SSP. These results indicate that incorporating capability indices and resubmission mechanisms into acceptance sampling can substantially reduce routine inspection effort while maintaining rigorous control of decision risks, offering a promising direction for more adaptive and economical quality control systems. Article history: Received (April 11, 2025); Revised (February 16, 2026); Accepted (February 23, 2026); Published online (May 9, 2026)
Bridging the Gap: How Process Mining Practitioners and Researchers Address Data Quality Issues
Process mining integrates process science and data science to analyze process workflows using event logs. Initially an academic discipline, it has seen rapid adoption in industry, often combined with machine learning and automation. This study explores how researchers and practitioners approach data quality issues found in event logs and how they apply preprocessing techniques to minimize or solve said issues. Results show that practitioners often undervalue data quality challenges and rely on basic methods, likely due to limited experience and dependence on commercial tools like Celonis. On the other hand, researchers prioritize diverse and advanced preprocessing techniques and view data quality issues as critical in process mining projects. Respondents with dual roles demonstrate specific expertise, addressing diverse challenges with data quality issues and applying more complex preprocessing techniques. The study emphasizes the need for collaboration between academia and industry, integrating process mining into education, and enhancing tool capabilities. These steps can bridge knowledge gaps, promote best practices, and advance research and practical application in process mining. Article history: Received (February 11, 2025); Revised (February 27, 2026); Accepted (March 19, 2026); Published online (May 9, 2026)
Digital Twin-Enabled Decision Support Framework for Tailings Dam Operations and Safety
Tailings storage facilities deal with urgent decisions amid unsure water and strength conditions. This study presents a digital twin–enabled decision support framework that connects live sensing, physics-based simulation, and clear decision rules to support safe and open operations. The goal is to design and test a closed-loop workflow that changes monitoring signals into suggestions for water release and deposition scheduling. The framework links a sensor-instrumented 1:60 physical model of a downstream-raised dam with a matched numerical model. State estimation applies a Kalman filter. Seepage and stability get solved with Richards flow and limit-equilibrium analysis. Scenario tests include routine operation, heavy rainfall, and a quick-load case. A weighted cost function balances safety standards, pond goals, and production changes to pick actions. Compared to a rule-based baseline, the framework reduced time to first action from 34 to 21 minutes, increased the minimum factor of safety from 1.28 to 1.36, and decreased peak pond level from 0.33 to 0.29 m. State estimation errors for pond level and pore pressure stayed small. The framework moves operations from reactive checks to proactive control, with water balance acting as the key tool. The method's new aspect is the closed loop that combines sensing, hydro-mechanical simulation, and direct action in one workflow. In practice, sites should start early pond release and shortterm deposition cuts as the factor of safety nears about 1.35. Article history: Received (January 26, 2026); Revised (April 23, 2026); Accepted (May 5, 2026); Published online (June 26, 2026)
Investigating the Impact of Circular Economy Strategies on Financial Performance with the Mediating Role of Human-Centric Sustainable Supply Chain Management
Industry 5.0 has shifted attention from automation alone to the human conditions under which Circular Economy Strategies (CES) create financial value. This study examines whether Human-Centric Sustainable Supply Chain Management (HC-SSCM) mediates the relationship between CES and Financial Performance (FP) in Saudi manufacturing firms. Data were gathered through a structured questionnaire from 240 firms and analyzed with a two-stage design that combined Partial Least Squares Structural Equation Modeling and Artificial Neural Network (ANN) analysis. The results show that CES has a positive direct association with competitor-relative FP and a strong positive association with HC-SSCM, while HC-SSCM is also positively associated with Financial Performance. The indirect association from CES to FP through HC-SSCM is significant, with a Variance Accounted For of 47.9%, indicating complementary partial mediation. ANN results further show that cognitive ergonomic support and human-robot collaboration are stronger predictors of self-reported FP than the technical circular variables alone. These findings suggest that circular strategies are more strongly associated with favorable financial outcomes when reverse-logistics and recovery activities are supported by worker-centered decision support, collaborative technologies, and adaptive problem solving. Article history: Received (December 8, 2025); Revised (April 17, 2026); Accepted (May 5, 2026); Published online (June 26, 2026)
Learning Equitable and Efficient Shift Rosters with Multi-Objective Deep Reinforcement Learning and Counterfactual Fairness Constraints in Operations
Shift rostering systems often optimize cost and coverage while treating fairness as a secondary objective. This study develops a multi-objective deep reinforcement learning framework for workforce scheduling that integrates Proximal Policy Optimization with a graph neural network encoder, a Conditional Value-at-Risk term for limiting extreme burden concentration, and a counterfactual fairness regularizer for reducing demographic sensitivity in assignment decisions. The framework was evaluated on 18 months of workforce data from a tertiary hospital and an industrial facility in Uzbekistan. Relative to mixed-integer linear programming and vanilla reinforcement learning baselines, the proposed model reduced the burden Gini coefficient by 41.3%, narrowed weekend disparity by 36.5%, and achieved adverse impact ratios above 0.97 for gender and age. These equity gains were obtained with a 1.8% increase in operational cost and a 9.4% reduction in understaffing minutes under demand shocks. The results indicate that fairness constraints can be embedded directly into policy learning while preserving service levels and improving resilience in high-constraint scheduling environments. Article history: Received (December 3, 2025); Revised (April 17, 2026); Accepted (May 13, 2026); Published online (August 13, 2026)
Optimizing Safety Logic Device Selection with Machine Learning: A Classifier-Based Comparison
In Industry 4.0 manufacturing, selecting appropriate safety logic devices during machinery risk assessment is critical yet complex. This study applies supervised machine learning classification to predict safety logic device categories. We assembled an expert-informed dataset of 306 machine cases, each described by safety-related parameters and labelled with one of four target classes (Relay, CR30, GMX, GLX). Using the WEKA toolkit, we evaluated 32 classifiers across four families (rule-based, instance-based, neural network, and tree/forest) using training-set evaluation and 5- and 10-fold cross-validation. On the holdout training data, classifiers achieved very high accuracy (average 97.1%), but cross-validation accuracies dropped to only 58–59%, indicating overfitting. Tree/forest ensembles (Random Forest, Random Tree, OptimizedForest) performed best overall (95.9% holdout test, 69.1% 5-fold cross-validation, 68.8% 10-fold cross-validation) compared to rule-based, instance-based, and multi-layer perceptron models. These results suggest that machine learning can effectively guide safety device selection, potentially reducing design time and cost in industrial safety engineering, while highlighting the need for expert oversight and larger datasets. Article history: Received (August 13, 2025); Revised (January 20, 2026); Accepted (February 24, 2026); Published online (May 9, 2026)
A Frugal Engineering Approach to Foster Digital Transformation in Emerging Countries: A Digital Inventory Management case study
Digital Transformation Processes (DTPs) are essential for companies seeking to remain competitive in an increasingly digitalized environment. While DTPs can enhance manufacturing control and operational efficiency, their implementation often poses significant challenges for small and medium-sized enterprises (SMEs) due to the complexity of available technologies and the substantial investments required. This paper presents a novel methodology for digital transformation based on frugal innovation, specifically designed to address the resource and infrastructure limitations commonly faced by SMEs. Unlike traditional maturity models, which demand comprehensive integration and high costs, the proposed approach offers a structured, step-by-step framework that enables organizations to focus on core functionalities, minimize expenditures, and achieve effective performance using accessible, low-cost digital solutions. The methodology is demonstrated through a case study involving an Argentine SME, where it facilitated rapid and measurable improvements in inventory management. Key capabilities of the frugal approach include modular implementation, adaptability to existing workflows, and the ability to deliver tangible results without extensive technical expertise or financial investment. By targeting the most critical processes and leveraging familiar technologies, the proposed frugal method empowers SMEs to overcome barriers to digital transformation and achieve sustainable operational gains. This work addresses a gap in the literature by providing a practical and scalable alternative for resource-constrained organizations, illustrating how frugal innovation can drive successful digital transformation in realworld settings. Article history: Received (April 4, 2025); Revised (October 16, 2025); Accepted (November 28, 2025); Published online (January 30, 2026)
Optimizing Japanese Entrepreneurship for Startup Growth and Sustainability in ASEAN Lower-Middle-Income Economies
This study investigates how Japanese entrepreneurship contributes to the strengthening of startup ecosystems in lower-middle-income ASEAN economies. Even with the dynamic expansion of these economies, they continue to grapple with historic challenges such as the Middle-Income Trap, institutional weaknesses, and limited access to capital. Japanese firms contribute to the enhancement of the scalability and competitiveness of ASEAN startups by implementing production management strategies—including lean concepts, digitalization, and cross-cultural collaboration. Through a qualitative examination of Japanese-ASEAN partnerships, the study identifies key success factors as technology transfer, structured business processes, and adaptation to markets. While Japanese investment and management expertise increase the effectiveness of operations, cultural disparities and regulatory mismatches remain significant obstacles to seamless collaboration. To address the challenges, the study proposes integrative solutions for conciliating Japanese models of production with ASEAN business culture with a view to enabling sustainable entrepreneurial growth. Applying the Grounded Theory Method, the research adds theory to international entrepreneurship and production management literature while also offering practitioner implications. By optimizing Japanese investment trends and institutional alignment, the study offers policymakers, investors, and entrepreneurs’ actionable recommendations to strengthen ASEAN startup ecosystems. Enhancing Japanese-ASEAN economic collaboration is imperative to foster innovation and long-term economic development in the region. Article history: Received (February 27, 2025); Revised (November 29, 2025); Accepted (December 12, 2025); Published online (February 18, 2026)
EDITORIAL: Advancing Industrial Innovation and Human-Centric Technologies in the Era of AI
In 2025, International Journal of Industrial Engineering and Management (IJIEM) received so far over 350 manuscript submissions, marking a clear signal of the vibrant growth of industrial engineering and production management research. What is striking is that across the three issues of Volume 16 (March, June, September) many of these submissions coalesce around the theme of Artificial Intelligence (AI) and its deepening role in industrial systems. Within the March and June issues, for example, we see manuscripts focused on machine-learning-driven scheduling in smart manufacturing, human-resource and machine-integration in Industry 4.0 settings, and cybersecurity and IoT for power, agro-supply-chain and manufacturing infrastructures. The September issue then brings several studies directly addressing AI in production: a self-assessment model for digital retrofitting of legacy manufacturing systems; federated-learning approaches for predictive maintenance; reinforcement-learning plus digital twin frameworks for energy-efficient production scheduling.
Copyright (c) 2025 International Journal of Industrial Engineering and Management https://creativecommons.org/licenses/by/4.0
Smart HVAC Heat Exchanger Network Optimization Through Collaborative IoT-Enabled Predictive Analytics for Manufacturing Facilities
Manufacturing facilities rely on sophisticated Heating, Ventilation, and Air Conditioning (HVAC) systems to ensure precise environmental conditions; however, operating these systems in isolated silos often results in substantial energy inefficiencies. This study addresses this challenge by developing and validating a collaborative Internet of Things enabled framework that optimizes heat exchanger networks using privacy-preserving predictive analytics. A distributed IoT architecture comprising 1,234 sensors was deployed across eight diverse manufacturing facilities (chemical, electronics, and automotive) in Saudi Arabia. The framework utilized federated Long Short-Term Memory neural networks. Using the Federated Averaging algorithm, these networks collaboratively trained a global optimization model without sharing proprietary local data. Over a 12-month operational period compared against a three-month baseline, the framework achieved a 29.1% average reduction in HVAC energy consumption (p < 0.001) and improved temperature control precision by 37%. Furthermore, the federated learning model significantly outperformed isolated control strategies, reducing prediction error by 61.8% and preventing 94% of inter-zonal operational conflicts. These results demonstrate that collaborative, privacy-preserving intelligence offers a scalable, robust solution for industrial energy management, effectively bridging the gap between localized control and system-wide optimization in support of Industry 5.0 sustainability goals. Article history: Received (August 19, 2025); Revised (December 5, 2025); Accepted (January 23, 2026); Published online (April 23, 2026)
The Synergistic Effect of Lean Practices and Technological Innovation Adoption on Hotel Service Performance: An Empirical Study in Vietnam
The growth of competition in the hospitality sector requires hotel managers to adopt effective management practices and invest in technological innovation to differentiate their services. This research simultaneously investigates the benefits of lean practices and technological innovation adoption of hotels operating in Vietnam market. The responses collected from 122 management representatives from hotels show that lean practices and technological innovation adoption have positive relationships with performance improvement, staff and customer satisfaction. The synergistic effects between lean practices and technological innovation adoption in hotel service were also confirmed. It is suggested that an effective combination between lean and technological innovation is necessary in driving the growth of firms in the hospitality sector. Article history: Received (July 3, 2025); Revised (December 5, 2025); Accepted (January 20, 2026); Published online (April 22, 2026)
In-Depth Analysis of the Effective Factors in Green Supply Chain Management in the Offshore Industry
The proposed study is based on the hybrid framework, which is a convergence between machine learning algorithms and fuzzy decision-making techniques to determine and rank the most important factors regarding the Green Supply Chain Management (GSCM) within the offshore industry under the influence of climate change. The research follows a three-phase methodology: (1) systematic literature review and expert consultation to establish the dimensions of relevancy in GSCM (2) hybrid fuzzy Delphi machine learning to quantify uncertainty and elicit expert opinion (3) an Analytic Network Process to establish interdependency and global priorities. The framework was applied to Saudi Arabia’s Arabian Gulf offshore sector, where four primary GSCM dimensions and twelve operational indicators were validated with expert consensus levels between 0.82 and 0.93. Results show that climate change adaptation mechanisms represent the most influential dimension (global weight = 0.334), while Climate Risk Assessment Protocols rank as the top indicator (0.127). The hybrid model got an accuracy of 0.863 which was 34.4 percent higher than the traditional methods in predicting disruption and 44.3 percent in risk assessment accuracy. Three offshore validation indicated performance improvement of 11 to 25. These results have shown that the process of combining machine learning and fuzzy logic makes GSCM decision-making much more effective, providing a pragmatic and climate-adaptive architecture to make offshore operations more sustainable and resilient. Article history: Received (August 19, 2025); Revised (November 20, 2025); Accepted (December 18, 2025); Published online (February 18, 2026)
The Effect of Liner Shipping Connectivity on Container Ports’ Efficiency in The Middle East: Data Envelopment Analysis Approach
Existing efficiency models frequently rely on aggregated country-level data, obscuring the specific impact of individual port-level liner shipping connectivity on technical efficiency. This study addresses this limitation by investigating how granular connectivity metrics influence port productivity using Data Envelopment Analysis (DEA) combined with the Malmquist Productivity Index (MPI). Analyzing a panel dataset of 24 Middle Eastern container ports from 2009 to 2016, the research uniquely incorporates the Port Liner Shipping Connectivity Index (PLSCI) as a distinct input variable to model global network integration. This framework enables the decomposition of Total Factor Productivity (TFP) into technological and efficiency changes, isolating the specific drivers of performance variations. The results reveal significant heterogeneity: median productivity gains reached 38.1% in Abu Dhabi and 19.3% in Jubail, driven primarily by efficiency improvements. Conversely, established hubs like Jebel Ali relied on technological change to achieve 3.7% growth, while other ports faced declines of up to 6.6%, indicating a critical need for targeted operational reforms. Consequently, this study provides a quantitative basis for strategic investments in maritime network integration and infrastructure to enhance long-term regional competitiveness. Article history: Received (October 4, 2025); Revised (December 18, 2025); Accepted (February 11, 2026); Published online (April 20, 2026)
Developing Robust Pattern Recognition in Imbalanced Data Using Cost-Sensitive Multi-Channel Convolutional Neural Networks
Detecting abnormal patterns in control charts is critical for ensuring quality in smart manufacturing, where sensor proliferation generates voluminous, noisy, and imbalanced data. Minority-class abnormal patterns, though rare, signal critical process deviations that can escalate costs and compromise product quality if undetected. This study addresses three critical challenges—severe class imbalance, data noise, and sensitivity to temporal perturbations—through a novel dual-channel cost-sensitive convolutional neural network framework. We propose CS-2CCNN, which processes raw one-dimensional time series data alongside two-dimensional regression graph images to extract complementary temporal and spatial features, combined with cost-sensitive learning to prioritize minority-class detection. We further introduce DeepHybridCS-2CCNN, which enhances CS-2CCNN by integrating empirical Bayesian wavelet denoising to remove noise and XGBoost classification for robust, adaptive prediction. Evaluated on simulated datasets with varying imbalance ratios (1:20 and 1:200) and the real-world Wafer dataset, DeepHybridCS-2CCNN achieves G-mean values exceeding 0.86 for severely imbalanced patterns, representing a 123% improvement over the baseline cost-sensitive CNN and outperforming traditional resampling methods (SMOTE, ADASYN) by 28–32%. The model attains F1-scores above 0.85, Matthews Correlation Coefficient values exceeding 0.80, and Area Under the ROC Curve scores surpassing 0.93 for critical patterns, demonstrating balanced performance across normal and abnormal classes. Unlike conventional oversampling approaches, this work framework minimizes sensitivity to data perturbations and enhances minority-class detection without introducing synthetic noise, offering a scalable, computationally efficient solution for industrial quality control in smart manufacturing environments. Article history: Received (August 19, 2025); Revised (October 30, 2025); Accepted (November 28, 2025); Published online (February 12, 2026)
Digital Twin-Enabled Thermal Energy Management System for Sustainable Manufacturing Process Optimization
Manufacturing processes consume substantial thermal energy, yet siloed management approaches cannot exploit facility-wide synergies. This study develops and validates an integrated Digital Twin (DT) that fuses physics-based thermal models with machine-learning forecasts and multi-objective optimization to coordinate process heat, waste-heat recovery, thermal storage, and on-site renewables in real-time. Deployed across four heterogeneous manufacturing facilities, the DT generated operator-ready knee-point recommendations that balanced energy use, operating cost, and emissions under changing production and weather conditions. Across sites, deployment produced substantial, sustained gains in thermal-energy efficiency and marked reductions in carbon intensity (approximately 27% higher efficiency and about one-third lower emissions in aggregate), demonstrating that system-level orchestration outperforms isolated component upgrades. Novelty lies in plant-scale, real-time co-optimization of process heat, waste-heat recovery, thermal storage, and on-site renewables using a hybrid physics–ML digital twin with uncertainty-aware multi-objective control, field-validated across four heterogeneous manufacturing sites. Article history: Received (August 19, 2025); Revised (October 21, 2025); Accepted (November 13, 2025); Published online (January 30, 2026)
Deep Learning Enhanced Predictive Maintenance Framework Using Industrial Internet of Things Sensors for Smart Manufacturing Systems
Manufacturing systems generate massive sensor data, yet transforming this information into actionable maintenance insights remains challenging due to traditional threshold-based approaches suffering from high false positive rates and insufficient advance warning. This study developed and validated a hybrid deep learning framework combining convolutional neural networks for spatial feature extraction with long short-term memory networks for temporal pattern recognition in smart manufacturing environments. The methodology involved collecting 18 months of operational data from 127 industrial machines across three Saudi Arabian facilities, encompassing 1.2 million sensor readings and 3,452 maintenance events from vibration, temperature, current, pressure, and acoustic sensors. The hybrid CNN-LSTM framework achieved 94.3% accuracy in predicting equipment failures 48 hours in advance with a 2.1% false positive rate, demonstrating statistically significant superiority over Random Forest (15.4 percentage point improvement), Support Vector Machines (15.1 percentage points), and threshold-based monitoring (25.9 percentage points). Significance was assessed on paired predictions using McNemar’s test (two-sided, alpha = 0.05) with Bonferroni correction across model comparisons; improvements were significant (p < 0.001). Cross-facility validation confirmed robust generalization capabilities. Article history: Received (August 1, 2025); Revised (October 2, 2025); Accepted (October 13, 2025); Published online (November 14, 2025)
An Industry 4.0 Framework for the Smart Production Management of Renewable Energy and Water Systems: An Application of AI, IoT, and Digital Twin Technologies
This study develops an integrated Industry 4.0 framework for smart production management in renewable energy systems applied to water processes. The framework combines artificial intelligence, the Internet of Things, and digital twin technologies to improve production planning, system reliability, and environmental performance. A neural network model was implemented for predictive analytics and achieved high accuracy (MAE = 0.82, R² = 0.92), enabling precise forecasting for energy generation and operational scheduling. Optimization algorithms, including genetic algorithms and particle swarm optimization, increased energy utilization efficiency from 65% to 85% and reduced operational costs by 15%. The IoT utilization enhanced real-time monitoring and reduced fault detection time from 120 minutes to 15 minutes, significantly improving maintenance response. Digital twin simulations allowed process optimization and predictive maintenance, further increasing production efficiency to 92% and system uptime to 99.5%. The approaches also led to a 20% reduction in CO₂ emissions, demonstrating both economic and environmental benefits. Overall, this framework offers a practical and data-driven solution for improving the efficiency and sustainability of renewable energy systems in water applications and contributes to the advancement of smart manufacturing in industrial engineering. Article history: Received (August 1, 2025); Revised (October 21, 2025); Accepted (October 30, 2025); Published online (November 20, 2025)
Integration of Digital Twin Technology and Industry 4.0 Principles for Real-Time Structural Health Monitoring in Smart Manufacturing Facilities
Within Industry 4.0 manufacturing environments, Structural Health Monitoring (SHM) is recognized as mission-critical; nevertheless, extant Digital Twin (DT) implementations seldom achieve deep fusion with the production layer and consequently struggle to co-optimize structural integrity alongside operational efficiency. This paper therefore introduces, and subsequently validates, an integrated DT framework expressly conceived to close that lacuna. Four objectives guided the inquiry: first, to architect a distributed digital-twin topology underpinned by edge–cloud analytics capable of real-time SHM; second, to operationalize a machine-learning-driven predictive-maintenance regime that causally couples structural response data with both manufacturing process signatures and ambient environmental variables; third, to embed the resultant framework within incumbent MES/ERP ecosystems spanning multiple production facilities; and fourth, to quantify the concomitant reductions in maintenance expenditure, production downtime, and energy utilization. A longitudinal, 24-month, multi-site investigation furnished empirical corroboration. The framework couples a high-fidelity DT to legacy MES/ERP strata through a distributed edge-cloud fabric; an ensemble of machine-learning algorithms—Long Short-Term Memory networks prominent among them—was deployed for predictive anomaly detection. The system attained 96% anomaly-detection accuracy (F1-score: 0.95) and translated this diagnostic precision into demonstrable operational gains: maintenance costs fell by 42.1%, downtime by 31.1%, and energy intensity by 23.2% (p < 0.001). The edge-centric architecture reduced processing latency by 67%, thereby enabling sub-50 ms integration with MES/ERP layers, while inter-site model transfer achieved 94.0% adaptation efficacy. Article history: Received (July 9, 2025); Revised (September 25, 2025); Accepted (November 11, 2025); Published online (November 28, 2025)
Kanban in Aviation Maintenance: A Case Study of a Chinese Aircraft Maintenance Enterprise
With the rapid expansion of the Chinese civil aviation market, aircraft maintenance enterprises are under increasing pressure to improve both operational efficiency and profitability. However, many continue to rely on traditional management practices that result in inefficient workflows, poor resource utilization, and high production costs. To address these challenges, this paper presents a case study on the implementation of the Kanban method as a lean management tool within a Chinese aircraft maintenance enterprise. This study aims to explore the feasibility and effectiveness of the Kanban method in enhancing the development and competitiveness of the Chinese aviation maintenance sector. Empirical observations over a one-year period reveal that Kanban significantly improves maintenance scheduling, reduces operational costs, and enhances overall productivity. This research contributes to both practice and academia by offering practical insights and underscoring the effectiveness of Kanban as a robust management tool for enhancing operational efficiency in China’s rapidly growing aviation industry. Article history: Received (May 12, 2025); Revised (August 26, 2025); Accepted (November 8, 2025); Published online (November 25, 2025)
NextGen 5.0 Leadership in Manufacturing: Tackling Workforce Challenges with a Multicountry and Multicompany Perspective
As Generation Z enters the job market, it is crucial for managers to understand how to foster their commitment, especially in the face of challenges like the Great Resignation and the Quiet Quitting. These challenges jeopardize organizational knowledge, innovation, and competitive advantage. In this way, this paper aims to identify the most effective leadership model for managing, engaging, and retaining Generation Z. The study adopts an abductive methodology, combining a systematic literature review on empirical studies about leadership styles that foster engagement with insights from a focus group of 13 manufacturing professionals across different countries. This article enhances the scientific literature with a leadership model tailored to the context of Industry 5.0, Generation Z and manufacturing organizations. Dubbed the NextGen 5.0 Leadership Model, it is built on three core pillars: a growth mindset, emotional intelligence, and a triple purpose. Article history: Received (January 24, 2025); Revised (August 28, 2025); Accepted (October 2, 2025); Published online (November 4, 2025)
Development of a didactic solution for teaching concepts related to Digital Twins using Educational Robot
Engineering education faces ongoing challenges in keeping pace with the technological demands driven by the need to apply Industry 4.0 concepts in student training. In response to this, the present study introduces the development of a pedagogical tool built around an educational robot produced through 3D printing. The aim is to integrate active learning methodologies with key Industry 4.0 technologies, such as Digital Twins, asset administration shells, and computational simulation, to create a practical and dynamic learning environment. This hands-on approach was implemented in a graduate-level course on Advanced manufacturing engineering, specifically in the Process simulation module. The activity involved the use of the robot alongside a real-time, bidirectional computational model capable of reading and writing data, applying the Digital Twin concept. The study followed a mixed-method action research design. Results from a student feedback survey revealed that 100% of participants agreed the experience with the computational robot helped solidify theoretical concepts. Furthermore, 77.3% gave the learning experience the highest possible rating, and 95.5% recognized the robot’s potential for use in other modules and courses. These findings suggest that this approach offers an effective strategy for engineering education that is aligned with the principles of Industry 4.0. Article history: Received (April 23, 2025); Revised (October 2, 2025); Accepted (October 31, 2025); Published online (November 20, 2025)
Managing Projects in Large Companies – Project Success Factors in the Crisis and Post Crisis Period: Evidence from Serbia and Slovenia
This paper investigates project success factors (SF) and project success criteria (SC) in large firms and aims to identify which contribute the most to project success. The results of this study are based on a survey of large firms in Slovenia and Serbia. A sample of 175 large firms is included. The Mann-Whitney test was used to compare groups across countries and between the COVID-19 and post COVID-19 periods. A comparison study of project SF and SC between the period of COVID-19 crisis and post COVID-19 is presented. Findings suggest a high degree of alignment between both countries: both prioritise user appreciation as the most important project SC and clear goals and objectives were identified as the most critical project SF. The results also show that a well-defined project management process is the most critical factor for successful project implementation. Project managers were constantly the most dominant decision makers on projects during and after the COVID-19 period. Analysis shows no significant differences between project SF and SF during and after the COVID-19 period, indicating that large companies are resilient in managing project success. Article history: Received (May 5, 2025); Revised (September 11, 2025); Accepted (December 8, 2025); Published online (February 12, 2026)
Intellectual capital determinants of customer value added in a transitional economy: An unconvincing role of knowledge supportive technology
This study investigates whether and how a firm’s utilization of knowledge management-supportive information technology (IT) moderates the effects of three-partite intellectual capital on customer value and, further, market performance. We draw on a combination of thus-far isolated literature streams namely: intellectual capital, technology-based knowledge management, and marketing to build a research model, which is tested on a survey of firms in the transitional economic context of Serbia. The survey data is analyzed using structural equation modelling–partial least squares (SEM-PLS). The results show that structural capital and relational capital have a positive effect on customer value, which further positively affects market performance. Surprisingly, IT practices may even decrease the extent to which an organization produces value for its customers by capitalizing on its intellectual capital. Our findings demonstrate that technological excellence cannot solve everything and that an optimal balance between “tech” and “human-based” resources must be found for superior customer value added. This brings an interesting nuance to the discussion concerning the interaction between knowledge resources and technological capabilities in facilitating performance. The findings also demonstrate that heavy reliance on IT-based knowledge management in certain economic contexts may backfire with respect to a firm’s customer value and eventually deteriorate its performance. Article history: Received (April 7, 2025); Revised (December 19, 2025); Accepted (December 30, 2025); Published online (February 18, 2026)
Digital Twin-Enabled Just-In-Time and Kanban Implementation Framework for Industry 4.0 Transformation in SMEs
Implementing Industry 4.0 technologies is a big problem for small and medium-sized businesses. JIT (Just-In-Time) and Kanban are both lean tools, but they might take too much time and effort. We plan to fil this gap by creating and testing a working framework that combines cloud-hosted Digital Twins with streamlining JIT-Kanban loops. The framework’s effectiveness was assessed in real-world contexts during a 30-month action research project involving twelve small and medium-size automotive parts manufacturers in Saudi Arabia. Using machine learning algorithms, a shop floor connected to the Internet has been turned into a virtual version. Because of the system, changes to how things are done on the shop floor are now possible immediately.This strategy led to a substantial improvement in performance. Lead times were down by 28%, equipment efficiency went up by 39%, and inventories came down by 47%. It was demonstrated that the framework had a payback period of less than 18 months, which demonstrated its financial viability. It is remarkable that 78 percent of implementations were successful, compared to 31% of digital deployments in conventional settings. Article history: Received (July 9, 2025); Revised (August 15, 2025); Accepted (September 9, 2025); Published online (October 27, 2025)
Orchestrating resources and capabilities for platform-based servitization: recommendations for SMEs to overcome tensions
This paper explores how small and medium-sized enterprises (SMEs) can orchestrate internal and external resources and capabilities to achieve platform-based servitization, offering practical recommendations to navigate the tensions and challenges associated with this transformation. The study employs a case study methodology based on 16 in-depth interviews with internal personnel and external actors involved in the digital servitization journey of a SME in the food and beverage sector. The interviews reveal key resources, capabilities, tensions, and solutions associated with the servitization process. The findings reveal that successful digital servitization in SMEs requires a strategic orchestration of internal resources like physical assets and human capital with external contributions from ecosystem actors. Tensions include aligning organizational structures with digital goals, managing financial risks and addressing customer-related challenges. Solutions involve a phased transformation approach, top management commitment, critical assessment of internal resources, leveraging the ecosystem and orchestrating multi-actor collaborations. This paper contributes to the literature on digital servitization by focusing on the under-researched area of SMEs, offering empirical insights and practical recommendations. Article history: Received (March 27, 2025); Revised (September 25, 2025); Accepted (October 2, 2025); Published online (November 5, 2025)
Privacy-Preserving Federated Learning for Predictive Maintenance in Smart Manufacturing Networks
Smart manufacturing environments require advanced predictive maintenance capabilities, yet implementation faces significant barriers due to data privacy concerns and proprietary knowledge protection requirements. Traditional machine learning approaches necessitate centralized data repositories, creating obstacles for collaborative maintenance optimization across organizational boundaries. This research develops and evaluates a federated learning framework that enables effective predictive maintenance while preserving data privacy in manufacturing networks. The study implemented a horizontal federated learning architecture with secure aggregation protocols and differential privacy techniques across multiple aerospace manufacturing facilities. System performance was evaluated through comparative analysis against centralized and standalone approaches across multiple predictive maintenance use cases. The federated approach achieved 93.7% of centralized model accuracy while eliminating cross-facility data sharing, with failure prediction lead times approaching centralized performance while substantially outperforming standalone models. Computational overhead increased modestly, but network data transfer requirements decreased by 94%. Privacy analysis confirmed that proprietary process parameters could not be reconstructed from shared model updates. Research advances smart manufacturing capabilities by providing practical implementation framework for privacy-preserving predictive maintenance across organizational boundaries. Article history: Received (April 30, 2025); Revised (June 23, 2025); Accepted (July 24, 2025); Published online (August 18, 2025)