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.
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 combined with the Malmquist Productivity Index. Analyzing a panel dataset of 24 Middle Eastern container ports from 2009 to 2016, the research uniquely incorporates the Port Liner Shipping Connectivity Index as a distinct input variable to model global network integration. This framework enables the decomposition of Total Factor Productivity 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.
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.
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 5and 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.
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.
The rapid advancement in information technology has made customer data more accessible, significantly enhancing firms' interest in personalized pricing. This study investigates how personalized pricing and big data analytics capability interact in supply chains using a Stackelberg game-theoretic model. The analysis examines how manufacturers and online platforms strategically determine optimal pricing and big data investment under an agency selling format. The findings are threefold. First, higher big data analytics efficiency does not always lead to increased investment in analytics capability. Second, manufacturers may strategically set base prices below production costs. This strategy incentivizes platforms to invest in analytics capability, which indirectly enhances profitability. Third, platforms do not always benefit from higher analytics efficiency, particularly under moderate efficiency conditions. This research provides managerial insights into the nuanced role of big data capability in pricing strategies.
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.
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.
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.
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. These findings substantiate the contention that principled integration of DTs with Industry 4.0 paradigms furnishes a transformative yet pragmatic pathway for manufacturing-oriented SHM. The framework's verified capacity to enhance prognostic fidelity while simultaneously yielding sizeable operational dividends delineates a clear trajectory toward more resilient and resource-efficient industrial assets.
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.
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.
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.
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.
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.
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.
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. Economic analysis revealed 28% maintenance cost reduction, 37% unplanned downtime decrease, and 15% overall equipment effectiveness improvement, yielding 183% return on investment with a 6.5-month payback period. These findings demonstrate the practical viability and substantial economic benefits of hybrid deep learning approaches for industrial predictive maintenance, establishing a foundation for enhanced operational efficiency in Industry 4.0 manufacturing systems.
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. It highlights the importance of resources and capabilities and ecosystem integration in enabling SMEs to transition to service-oriented business models, providing a valuable roadmap for organizations embarking on a digital servitization journey.
This study examines the impact of supply chain management practices on the organization’s marketing and financial performance. We present the results of a survey conducted with 100 Pakistani fan manufacturing firms. Statistical analysis reveals that the industry struggles with information sharing and joint operations within the supply chain. PLS-SEM analysis of the survey data shows that supply chain performance is significantly correlated with organizational performance. Both customer and supplier relationship management have positive and significant effects on the performance of the supply chain and the organization. However, the impact of customer relationship management is stronger as its path coefficient is greater. Additionally, although internal supply chain management also impacts both supply chain and organization performance positively, the impact is slightly short of being statistically significant. This study contributes to the supply chain management literature by providing empirical evidence from an understudied manufacturing sector from a developing country.
