Background: Creative Micro, Small, and Medium Enterprises (MSMEs) in Indonesia are increasingly exposed to global trade policy volatility, particularly following the 2025 US reciprocal tariffs. This study examines how green entrepreneurial orientation (GEO) strengthens green supply chain resilience (GSCR) through adaptation strategy and green product innovation (GPI). Methods: Data were collected through a survey of 150 creative MSMEs in West Java, Indonesia, and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The proposed Green Entrepreneurial Supply Chain Resilience (GESCR) model is grounded in Dynamic Capabilities Theory, conceptualizing GEO as a sensing capability that activates adaptive (seizing) and innovative (transforming) mechanisms. Results: The findings reveal that GEO significantly enhances GSCR both directly and indirectly through the partial mediating roles of adaptation strategy and GPI. The results suggest that resilient supply chains are shaped not merely by reactive adjustments but by proactive, sustainability-oriented entrepreneurial behavior, particularly in industries highly dependent on imported inputs. Conclusion: This study validates the GESCR framework as a mechanism linking sustainability-driven entrepreneurial orientation to supply chain resilience under trade policy shocks. The findings contribute to theory and practice, highlighting the importance of strengthening access to green financing, enhancing capability development, and promoting collaborative supply chain platforms to support resilient and sustainable MSME ecosystems. Future studies should employ longitudinal or cross-regional designs to improve the generalizability of the framework.
Background: The development of Smart Mobility 4.0 in Polish cities remains uneven and is largely shaped by institutional, organizational, and social conditions influencing urban transport systems. Materials and Methods: This study combines quantitative research conducted on a sample of 1,500 respondents (N=1,500) with in-depth interviews involving representatives of local governments. Results: The findings reveal key barriers to the implementation of advanced mobility solutions, including a misalignment between the rapid pace of technological advancement and the maturity of management structures, the level of data integration, and user readiness. Conclusions: The transition to a Smart Mobility 4.0 model requires strengthening data governance, improved coordination in mobility management, and the development of both administrative capabilities and societal readiness.
Background: The printing industry remains a vital component of the information, cultural, and manufacturing ecosystem, yet it is undergoing profound structural transformation driven by digitalisation, automation, and the emerging human-centric paradigm of Industry 5.0. This study investigates the development of the printing sector in the Visegrad Four (V4) countries through an integrated economic, ecological, and technological perspective, with emphasis on technological readiness, sustainability transitions, and long-term competitive capacity. Methods: A mixed-method research design was applied, combining bibliometric analysis with quantitative evaluation based on Eurostat structural business statistics covering the period 2012-2024. Economic performance across the V4 countries was assessed using descriptive indicators and one-way ANOVA, while the ALR framework was employed to support interpretation of sustainability performance and technological readiness. All assumptions required for ANOVA were verified and satisfied. Results: The findings reveal statistically significant differences in the economic performance of the V4 printing industries, with 99.91% of the total variance attributable to inter-country disparities. Poland exhibits a pronounced three- to four-fold advantage in key indicators, including sectoral GDP contribution, sales, exports, and number of enterprises, reflecting higher levels of technological integration and innovation capacity. These results highlight the central role of digitalisation, automation, and sustainability-oriented technological upgrading in shaping regional competitiveness. Conclusions: This study advances an integrative analytical model linking the adoption of Industry 4.0 and Industry 5.0 to sustainability outcomes and adaptive competitiveness within the printing industry. The results contribute to ongoing theoretical discourse on sustainable industrial transformation and offer actionable implications for policy formulation, innovation support, and technological upgrading across the EU, particularly in regions affected by structural disparities in digital readiness.
Background: Passenger rail demand forecasting is essential for designing efficient and sustainable transport systems. This study develops a machine-learning framework to forecast both regional and long-distance passenger rail workloads in Poland, based on representative lines from each voivodeship. The modelling pipeline integrates ensemble tree-based algorithms, evolutionary feature selection, hyperparameter optimization, and post-hoc explainability using SHAP to ensure both predictive accuracy and interpretability. Methods: The framework combines ensemble machine learning models with evolutionary feature selection and hyperparameter optimization. Model interpretability and the contribution of individual variables were examined using SHAP analysis. Results: Random Forest consistently provides robust baseline performance, while optimally tuned boosting algorithms, particularly XGBoost, achieve the highest predictive accuracy, with a mean sMAPE of 0.595. Evolutionary feature selection reduced dimensionality while improving model accuracy, indicating that a smaller subset of variables captures the key determinants of passenger demand. SHAP analysis shows that regional demand is primarily driven by population size, accommodation capacity, and commuting intensity, with multimodal public transport connections amplifying these effects. In contrast, long-distance demand is mainly shaped by commuter flows, spatial accessibility, and feeder networks, while population size plays a secondary role. Conclusions: The proposed framework contributes both methodological and practical insights. Methodologically, it demonstrates how ensemble models can be combined with evolutionary feature selection and interpretability techniques to produce transparent demand forecasts. Practically, the results provide guidance for transport planning: regional demand is strongly influenced by population (21.7%), the number of small service units (16.9%), and tourism-related factors ( 16.5%), supported by multimodal integration and local commuting flows. Long-distance demand is primarily shaped by commuting intensity (28.5%), population (16.3%), and the transport accessibility of railway stations and stops (15.5%), measured by the number of bus and tram connections serving them. These findings support evidence-based planning of passenger rail services in Poland and may also be relevant for rail transport planning in Central and Eastern Europe.
Background: There is growing interest in improving supply chain management (SCM) using artificial intelligence and large language models (LLMs). However, the use of LLMs or generative AI presents inherent challenges. Therefore, recognizing these challenges within organizations and understanding how they are interrelated is crucial. Although there is an emerging focus on the use of LLMs in SCM, there remains limited peer-reviewed research exploring the challenges associated with their use in the field. Hence, this study aims to identify the challenges of using LLMs in SCM and to examine the interrelationships among these challenges. Materials and methods: The challenges identified in the literature were validated through the opinions of sixteen experts, including supply professionals, AI specialists, and academics. The study employed Interpretive Structural Modeling (ISM) and Matrice d'Impacts Crois & eacute;s Multiplication Appliqu & eacute;e & agrave; un Classement (MICMAC) analysis to develop a framework consisting of autonomous, driving, linkage, and dependent challenges. Results: The findings show that "Multiple data points in SCM network (C6)" emerges as the key challenge with the highest driving power, whereas "Cost of better optimization (C13)" and "Managerial suspicion toward adopting real-time decision making based on LLM outputs (C14)" are the key dependent challenges. The study also highlights non-technical aspects of these challenges, such as trust and legal considerations, and emphasizes technology adoption from a human and managerial perspective. Conclusions: These findings offer valuable insights into the ranking of challenges as well as their driving and dependent relationships. They can help SCM practitioners and LLM developers address these challenges and facilitate the effective adoption of LLMs in SCM.
Background: Moroccan industrial companies operate in an increasingly unstable environment in which Industry 4.0 technologies-such as the Internet of Things (IoT), artificial intelligence, and big data-represent powerful levers for strategic adaptation. The dynamic capabilities framework, based on sensing, seizing, and reconfiguring, provides a relevant lens for understanding how organizations sense signals of change, mobilize resources, and reconfigure processes to sustain competitive advantage. Methods: The study is based on a cross-sectional survey conducted among 276 Moroccan industrial companies from heavy and light industry as well as the construction sector. Data were analyzed using the PLS-SEM method with 5,000 bootstrap samples. The measurement model demonstrated strong reliability and validity (factor loadings approximate to 0.857-0.912; composite reliability approximate to 0.934-0.939; AVE approximate to 0.778-0.793; HTMT < 0.85). Results: The findings reveal robust relationships among the three dimensions of dynamic capabilities. Sensing strongly predicts seizing (beta = 0.917, p < 0.001), while seizing significantly influences reconfiguration (beta = 0.725, p < 0.001). Collectively, sensing (beta = 0.385), seizing (beta = 0.232), and reconfiguration (beta = 0.362) contribute significantly to the development of overall dynamic capabilities (all p < 0.001). The model exhibits high explanatory power for seizing (R-2 = 0.84), moderate explanatory power for reconfiguration (R-2 = 0.53), and moderate explanatory power for dynamic capabilities as a whole (R-2 approximate to 0.33). All six hypotheses are empirically supported, highlighting the role of environmental vigilance, the mobilization of smart technologies, and organizational reconfiguration in strengthening dynamic capabilities. Conclusions: This research demonstrates that Industry 4.0 (I4.0) technologies constitute concrete foundations for the development of dynamic capabilities in an emerging economy context. It advances a systemic model that links the detection of external signals, the use of intelligent technologies, and organizational responsiveness. By doing so, the study contributes to a deeper understanding of how I4.0 is integrated into organizational practices and offers an operational framework for managers to navigate highly uncertain environments.
Background: Last-mile delivery accounts for over 50% of total logistics costs, representing a major operational and sustainability challenge in Saudi Arabia's rapidly expanding e-commerce sector. Conventional siloed fleet models often result in underutilized capacity, delivery inefficiencies, and elevated carbon emissions. This study proposes a machine learning-driven smart-sharing framework that integrates predictive analytics with operations research to optimize last-mile delivery in the Kingdom of Saudi Arabia. Methods: Using the Regional Delivery Data in Saudi Arabia (2024), which aggregates quarterly order volumes across 13 regions, this study develops demand forecasting models (SARIMAX, LightGBM, and CatBoost) as well as ETA predictions enriched with event-and calendar-based features. These outputs inform a vehicle routing problem with time windows (VRP-TW) implemented using Google OR-Tools and a CP-SAT facility location model for parcel locker siting. A digital twin simulation built in SimPy is then used to stress test performance under peak demand conditions (e.g., Ramadan) and weather-related disruptions. Results: The findings show that pooled fleet sharing reduces routing costs by more than 65%, decreasing traveled distance from 7,906 to 2,370 units, while increasing the share of on-time deliveries from 37% to nearly 100%. The introduction of parcel lockers further improves system efficiency, reducing kilometers traveled per parcel by up to 90% and lowering CO2 emissions from 0.77 to 0.06 kg per parcel. Stress-test experiments confirm the resilience of the shared model, which maintains service-level agreement (SLA) compliance even under demand surges and operational disruptions. Conclusions: This research presents the first reproducible, Colab-native machine learning and optimization pipeline applied to Saudi Arabia's regional delivery data. The proposed framework provides actionable insights for logistics managers, urban planners, and policymakers seeking data-driven approaches aligned with Saudi Arabia's Vision 2030 sustainability and efficiency goals.
Background: Artificial intelligence (AI) is increasingly integrated into supply chain management (SCM) to support forecasting, coordination, and disruption management. However, outcomes remain uneven because the value derived from AI depends not only on technical capabilities but also on how managers form expectations, evaluate performance during use, and update beliefs over time. Drawing on Expectation Confirmation Theory (ECT), this study examines managers' satisfaction with AI-enabled decision support in SCM. Methods: A latent-variable model linking initial expectations, perceived performance, belief confirmation, and satisfaction was operationalized using 7-point Likert items adapted from prior information systems and AI research. Data were collected from 279 firms across primary, secondary, tertiary, and quaternary sectors using Computer-Assisted Telephone Interviewing (CATI) and Computer-Assisted Web Interviewing (CAWI). Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to assess measurement quality and test the hypothesized relationships. Results: the measurement model demonstrated high reliability and validity (rho A, composite reliability, and Cronbach's alpha > .94; AVE > .78; HTMT < .85). Initial expectations positively influenced perceived performance (beta = .922, p < .001) and belief confirmation (beta = .208, p < .001). Perceived performance strongly predicted belief confirmation (beta = .786, p < .001). Satisfaction was explained by both belief confirmation (beta = .361, p < .001) and perceived performance (beta = .643, p < .001), with strong explanatory power (R & sup2; = .850 for perceived performance, .961 for belief confirmation, .998 for satisfaction). Conclusions: The findings support ECT as an explanation of managers' satisfaction with AI-enabled decision support in SCM. Perceived performance emerges as the primary driver, shaping both belief confirmation and satisfaction. Practically, organizations should manage expectations, enhance the transparency and reliability of AI tools, and support managers in interpreting AI-generated insights to facilitate sustained integration of AI into supply chain decision-making.
Background: The Industry 4.0 (I4.0) paradigm is fundamentally reshaping managerial environments, positioning Project Management (PM) as a key discipline for harnessing these technologies to enhance performance. However, the academic literature on the synergy between I4.0 and PM remains fragmented, leaving a gap in understanding how I4.0 technologies integrate with PMBOK (R) Guide processes. Methods: This study employed amixed-methods design, beginning with a Systematic Literature Review (SLR) conducted in January 2024 using the Scopus and Web of Science databases. Structured search strings combining "Project Management" and "Industry 4.0" yielded a final sample of 25 articles. Subsequently, four PM experts from diverse sectors were interviewed using a semi-structured format. Their textual responses were analyzed using Correspondence Factor Analysis (CFA) through IRaMuTeQ software to identify discursive patterns. Results: The SLR identified Artificial Intelligence (AI) and Cloud Computing as prominent technologies across PMBOK (R) knowledge areas, with AI showing particular relevance to the domain of "Uncertainty." In contrast, Cyber-Physical Systems (CPS) and the Project Closing process group received comparatively limited attention in the literature. The qualitative findings indicate that, although experts recognize the disruptive potential of AI and Big Data Analytics, they report few mature or systematically implemented applications for conventional scope, schedule, and cost management. In practice, they continue to rely primarily on established tools like MS Project. The triangulation of quantitative and qualitative findings thus reveals a substantial gap between theoretical promise and practical adoption, largely attributable to cost constraints, perceived risks, training demands, and organizational resistance. Conclusions: This study demonstrates a clear disparity between the extensive theoretical exploration of I4.0 technologies and their effective integration into central PMBOK (R) processes. To advance digital transformation in PM, future research should prioritize systemic integration frameworks and empirically grounded applications. Developing applied case studies and practitioner-oriented guidelines is essential to translate theoretical insights into operational value and to mitigate barriers to adoption.
Background: The rapid expansion of global operations has significantly increased the strategic importance of the logistics industry. Logistics performance plays a vital role in ensuring the efficient execution of international trade. The operational effectiveness of companies in this sector directly influences a country's economic structure; therefore, the performance of logistics firms has become a critical component of national economies. This study aims to propose a hybrid multi-criteria decision-making (MCDM) model to assess the performance of logistics companies listed in the Fortune 500 T & uuml;rkiye. Methods: This study applies a hybrid decision-making model integrating the SPC, MPSI, LOPCOW, and RAWEC methods. The evaluation is based on key criteria, including Net Sales, Change in Net Sales, Earnings Before Interest and Taxes (EBIT), Change in EBIT, Total Assets, Equity, Export Amount, and Number of Employees. Criterion weights are calculated using the SPC, MPSI, and LOPCOW methods, after which the RAWEC method is employed to evaluate and rank the logistics companies. Additionally, sensitivity and comparative analyses are conducted to assess the robustness of the results. Results: The results indicate that the Number of Employees is the most influential criterion, while Net Sales is the least influential. According to the RAWEC analysis, Mars, Noatum, and Netlog demonstrate the highest performance levels among logistics firms listed in the Fortune 500 T & uuml;rkiye. Moreover, the comparative analysis reveals a strong consistency between the proposed hybrid model and other applied MCDM methods, with Netlog and Mars emerging as the top-performing companies. Conclusion: The findings of this study offer valuable insights for policymakers, industry practitioners, and decision-makers aiming to benchmark and improve the performance of logistics firms in T & uuml;rkiye within an increasingly competitive global landscape.
Background: Limited production capacity within existing factory layouts poses a significant challenge for manufacturers striving to meet rising demand. In the case of a factory producing automotive sensor circuit boards, three product types, Products A, B, and C, are manufactured to serve different automotive sensor applications. The projected demand indicated substantial growth across all three product categories, necessitating an optimized layout to enhance production capacity and operational efficiency. Previous studies have rarely integrated Multi-Criteria Decision-Making (MCDM) methods, such as the Order Preference by Similarity to Ideal Solution (TOPSIS), with Systematic Layout Planning (SLP), despite the potential benefits of incorporating weighted criteria into the layout design process. However, some studies have now examined how such an integrated approach can be applied to improve layout adaptability and capacity expansion under dynamic demand conditions in the automotive electronics industry. Methods: This study applied SLP methodology to develop three alternative factory layout designs. Alternative 1 prioritizes minimizing material movement distances. Alternative 2 prioritizes lower operation costs and faster implementation and Alternative 3 focuses on material movement distance together with the allocation of preparatory workstation close to the Surface Mount Technology product line, which can potentially help improving workflow. The TOPSIS was then applied to evaluate the alternatives, using weighted criteria to determine the most suitable layout. Results: Through the integration of SLP and TOPSIS, Layout Alternative 2 was identified as the optimal solution, achieving the best trade-off among operating cost, installation lead time, and material flow efficiency. Implementation of this layout led to significant capacity improvements, with monthly output increasing by +67% for Product A, +21% for Product B, and +50% for Product C, thereby enabling the factory to meet the forecasted demand. Conclusions: The integration of SLP and TOPSIS provides a robust decision-support framework for plant layout optimization under dynamic demand conditions. The study contributes to theory by extending SLP with weighted MCDM evaluation, and to practice by demonstrating significant capacity gains in a real manufacturing setting.
Background: The three-dimensional container loading problem (CLP) is an NP-hard optimization problem with significant implications for logistics efficiency and sustainability. Even minor improvements in space utilization can substantially reduce transportation costs and environmental impact. Although numerous heuristics and metaheuristics have been proposed, clustering-based preprocessing approaches-particularly K-Means-have received limited attention. Objective: This study proposes a hybrid framework that integrates K-Means clustering with a two-level three-dimensional (3D) heuristic to improve space utilization, computational efficiency, and practical applicability. Methods: The proposed approach follows a two-stage design that combines clustering and heuristic packing. In Stage 1, items are grouped according to dimensional similarity using K-Means, with the number of clusters determined by the Elbow, Silhouette, Davies-Bouldin, and Calinski-Harabasz indices. In Stage 2, each cluster is packed as a compact block using a two-level 3D Best-Fit heuristic, subject to feasibility, weight, and stackability constraints. Results: Experiments on benchmark and industrial datasets achieved container utilization improvements of 12-15% over a baseline 3D Best-Fit heuristic and 19-24% over manual loading, while reducing runtime by approximately 30-35x (from over one hour to less than two minutes). Conclusions: K-Means-based preprocessing substantially enhances heuristic 3D loading container performance, yielding higher space utilization and significantly shorter runtimes compared with a baseline heuristic. The proposed approach, therefore, offers a transparent, scalable, and computationally efficient alternative that bridges the gap between simple greedy heuristics and complex metaheuristics.
Background: Perishable supply chains face persistent challenges arising from product perishability, demand fluctuations, and frequent disruptions. Conventional models lack the real-time adaptability required to effectively manage spoilage while maintaining service levels. This paper proposes an integrated decision-support framework that leverages Digital Twin (DT) technology, Mixed-Integer Programming (MIP), and Agile Quality Tools to enhance resilience and quality preservation in perishable supply chains. Methods: The proposed framework integrates DT-enabled real-time monitoring, predictive analytics, and adaptive re-optimization. MIP is employed to support routing and inventory decisions, while Agile Quality Tools, such as Failure Mode and Effects Analysis (FMEA) and control charts, are used to dynamically adjust model parameters in response to freshness deviations and disruption signals. Case studies from food and pharmaceutical supply chains were conducted to evaluate performance under both stable and disrupted operating conditions. Results: The DT-driven framework significantly outperformed baseline models in both domains. In the food supply chain, spoilage was reduced by more than 35%, service levels improved by over 7%, product freshness increased, and total costs declined by nearly 9%. In the pharmaceutical cold chain, spoilage was reduced by over 60%, service levels exceeded 97%, and recovery from disruptions was 37% faster, with costs reduced by about 12%. Across all scenarios, the framework demonstrated high adaptability and operational efficiency, achieving near real-time optimization with an average CPU time of 2.45 seconds. Conclusions: Integrating DT, MIP, and Agile Quality Tools offers a robust, adaptive, and cross-domain solution for perishable supply chains. The proposed framework enhances resilience, minimizes spoilage, reduces operational costs, and ensures higher service levels under uncertainty. Its multidisciplinary nature offers clear value for both academic research and industrial practice.
Background: Recent advancements in supply chain management, supported by information technology, have enabled reductions in inventory levels, among other operational improvements. Nevertheless, inventory-related challenges persist. Economic factors, particularly various forms of uncertainty, often necessitate holding inventory to ensure product availability. Demand variability, which is frequently unpredictable, remains a major challenge in numerous industries and requires the creation of safety buffers. Addressing this issue calls for increasingly sophisticated forecasting methods within replenishment models. Forecasts based solely on traditional time series methods offer limited improvements, whereas advanced approaches using machine learning and deep neural networks provide significantly greater potential. These models are capable of identifying factors that influence customer purchasing decisions, leading to more accurate demand forecasts and, consequently, a stronger foundation for improving replenishment processes. Objective: The primary aim of the research was to illustrate the extent to which advanced demand forecasting models can improve replenishment efficiency, particularly by reducing inventory levels. Methods: Simulation techniques were employed to replicate the replenishment process under various forecasting scenarios based on historical data. This dataset consisted of demand patterns for 1,000 Walmart stock-keeping units (SKUs), publicly released by the retailer for research purposes. The forecast methods examined included a benchmark arithmetic mean model, seven traditional time series-based models, and five advanced models employing machine learning and deep learning techniques. All simulations were conducted using the Reorder Cycle replenishment model with a uniform inventory review cycle across products. The second control parameter, the maximum inventory level (S), was fixed for the benchmark model and dynamically adjusted for the remaining twelve models according to their respective forecasts. A total of 13,000 replenishment simulation cycles were performed. Key performance indicators included average inventory and the service level (SL alpha), defined as the probability of fully satisfying demand within a replenishment cycle. The parameter S was calibrated to ensure a consistent service level across models. Consequently, the inventory level index was the primary measure of replenishment efficiency, enabling comparisons between the twelve forecasting models and the benchmark. Additionally, the relationship between this index and forecast quality improvement was analyzed using forecast error measurements, specifically the root mean square error (RMSE). Results: The findings confirm that inventory levels can be reduced by an average of 10% through the application of machine learning and deep neural network-based forecasting methods, without compromising service quality. The magnitude of the reduction varied depending on specific temporal demand patterns. Conclusions: The observed improvements can be attributed to two main factors: increased forecast accuracy and the dynamic adjustment of the maximum inventory level (S), based on current forecasts and their associated error estimates. Furthermore, replenishment efficiency may be enhanced further by selecting the most appropriate forecasting method for individual products during specific time periods.
Background: In the modern global economy, postal and courier services serve as critical infrastructure, ensuring the uninterrupted flow of goods, documents, and communication across all segments of society. Despite their essential role, occupational safety in this sector remains an understudied and undervalued dimension within the broader discourse of workplace health and risk management. While technological interventions such as GPS tracking or drop boxes have reduced some vulnerabilities, they cannot fully eliminate human-centered threats such as verbal abuse, physical assault, or lack of emergency response in the case of incidents. Hence, there is a growing need for empirical research that focuses on comments by postal delivery workers themselves and explores their lived experiences with safety, threats, and institutional support. The present study seeks to fill this research gap by providing a comprehensive analysis of safety perceptions among postal delivery workers in Croatia. Methods: Based on a structured questionnaire administered to 157 delivery personnel employed by Croatian Post, the study evaluates a wide range of variables, including perceived threats (e.g., dog attacks, verbal threats, physical violence), situational risk factors (e.g., remote delivery areas, late-night deliveries), experience with security incidents, and attitudes toward protective measures and institutional responses. Results: By providing empirical insights into the Croatian context, this study also aims to contribute to the wider international discourse on logistics labor, frontline worker safety, and risk governance in public service delivery. This research invites a re-examination of delivery work not merely as a logistical function, but as a profession requiring targeted safety protocols, institutional support, and monitoring of frontline risks. It positions postal workers as agents whose insights are critical for shaping occupational safety strategies in a transforming delivery sector. Conclusions: By integrating empirical insights from workers themselves, this research contributes to the growing discourse on frontline labor safety in public service sectors. The findings underscore the urgency of developing evidence-based, worker-informed, and context-sensitive safety strategies to protect the health, dignity, and operational continuity of postal services in an increasingly demanding delivery environment.
Background: The paper investigates consumer engagement in reverse logistics (RL) for plastic waste, focusing on gender-and age-related differences in environmental attitudes and behaviours. Understanding how demographic factors shape participation, perception of regulatory measures, and responses to communication strategies is essential for effective RL system design. Methods: The study used a mixed-method approach based on an online survey conducted in Poland in 2024 (n = 1,200). The Mann-Whitney U test was applied to identify statistically significant differences between respondent groups. Results: The results show that women and older consumers exhibit stronger pro-environmental attitudes, higher trust in regulations, and greater willingness to segregate and return plastic waste. Younger and male respondents are more strongly influenced by convenience and technological innovation. Emotional communication proved more persuasive for women, while rational appeals were more effective for men. Conclusions: Demographic differences influence consumer behaviour within RL systems. Tailoring communication approaches and infrastructure solutions to specific gender and age groups can enhance participation and improve system efficiency. The findings offer practical guidance for targeted interventions aligned with circular economy goals.
Background: Organizations face the challenge of making strategic decisions to build a carbon-neutral and competitive future through the adoption of digital technologies. This pressure arises from rapidly evolving global market conditions and the need to achieve required emission reductions. For a successful deployment of Industry 4.0 technologies to support decarbonization, it is crucial for organizations to assess their competencies and establish a clear path forward. Digital maturity models are effective frameworks for measuring an organization's digital capabilities, and several such models have been developed. However, existing models often overlook environmental dimensions, focusing primarily on profit-driven growth. To address this gap, a Digital Decarbonization Maturity Model has been developed. This paper aims to evaluate the model's effectiveness and applicability by assessing the current state of digital decarbonization within organizations and identifying opportunities for advancement, drawing on empirical insights from a focus group. Methods: The design science research approach is employed to test, verify, and validate the proposed Digital Decarbonization Maturity Model, treating it as an IT artefact. Design science research aims to generate practice-relevant knowledge about the design of artefacts such as constructs, models, or methods. The model is tested and validated using empirical insights from a focus group composed of industry actors from various manufacturing sectors. Results: The assessment of the proposed model produced several key findings across its dimensions. First, partnerships with diverse stakeholders were identified as a critical success factor. Additional findings highlight the importance of leveraging employee expertise, digital technologies, and business model innovation. Finally, the assessment also revealed several challenges associated with the use of digital technologies for decarbonization. Conclusions: The proposed model supports organizations in determining their current position in digital transformation and decarbonization, and in identifying targeted growth opportunities to advance their maturity. Progressing through these maturity stages can help organizations more effectively align digital initiatives with CO2 reduction goals. The findings indicate that pathways to decarbonization are diverse and can be adapted to support both short-term actions and long-term strategic objectives. Organizations can therefore prioritize initiatives according to their existing resources, capabilities, and progress to date, enabling them to develop a decarbonization trajectory that fits their specific context.
Background: Smart logistics has emerged as a transformative approach to supply chain management, leveraging advanced information technologies and intelligent systems to enhance operational performance. Within this context, accurate demand forecasting is critical, as it shapes inventory planning, resource allocation, cost control, and customer satisfaction. However, demand is inherently volatile-driven by stochastic fluctuations, seasonal patterns, and sudden disruptions arising from external events or shifts in consumer behavior. These characteristics make traditional forecasting models insufficient for capturing the complexity of real-world demand dynamics, underscoring the need for more intelligent and adaptive forecasting approaches. Methods: This study introduces Zebra-SparseEchoNet, a novel hybrid AI forecasting framework designed for high-accuracy prediction in dynamic logistics environments. The methodology begins with extensive data preprocessing, including cleaning, exploratory analytics, and visualization to ensure reliable inputs. Feature transformation is subsequently applied through categorical encoding and numerical normalization to enhance model interpretability, stability, and overall predictive performance. The curated dataset is then divided into training and testing sets to enable unbiased evaluation. The core forecasting engine merges two complementary components. First, a Sparse Self-Attention Transformer (SAT) captures long-range temporal dependencies while reducing computational load by selectively attending to the most informative interactions within the sequence. Second, an Echo State Network (ESN) offers lightweight, memory-efficient modeling of short-term dynamics using its fixed recurrent reservoir. To further strengthen forecasting capability, the Zebra Optimization Algorithm (ZOA) is utilized for intelligent hyperparameter tuning, ensuring optimal architectural configuration and accelerated convergence across components. Results: Experimental results demonstrate that Zebra-SparseEchoNet outperforms conventional deep learning models across multiple forecasting error metrics. The model also achieves higher R2 values and explained variance, indicating a better fit to underlying demand structures and more reliable forecasts. Conclusion: For smart logistics applications, the Zebra-SparseEchoNet framework delivers significant gains in both computational efficiency and forecasting precision. By effectively capturing intricate temporal patterns and maintaining scalability through the integration of Zebra Optimization, Echo State Networks, and Sparse Self-Attention Transformers, the model demonstrates strong potential as a dependable and sophisticated solution for contemporary supply chain and inventory management challenges.
