The study explores the impact of digitalization and supply chain integration on the financial performance of the Tunisian agro-industrial sector, considering the growing importance of digital technologies in modern business operations. The objective is to examine the relationship between these two factors and financial performance in this sector. Data were collected through a questionnaire and analyzed using SPSS26 software, employing principal component analysis and linear regression. The results show that digitalization and supply chain integration significantly influence the financial performance of agro-industrial organizations in Tunisia. This study makes a theoretical contribution by shedding light on how digital advancements and supply chain strategies affect financial outcomes in this context. In conclusion, the findings highlight the significant effects of digitalization and supply chain integration on the financial performance of Tunisian agro-industrial companies.
This study diagnoses the systemic sustainability challenges in Iran's national automotive supply chain using Soft Systems Methodology to analyze the interrelated economic, environmental, social, technological, and managerial dimensions. The goal is to develop a conceptual model that reflects these complexities, validate it with real-world case data, and propose practical and desirable changes to improve sustainable supply chain management practices. The research addresses a critical gap in sustainability strategies for emerging markets with structurally constrained and politically sensitive industrial ecosystems. The study adopts an integrated approach combining systemic methodology and thematic analysis, utilizing semi-structured interviews with senior automotive industry experts and a targeted literature review. Rich pictures, conceptual modeling, and iterative validation ensured alignment between systemic challenges and practical realities, with data coded to identify barriers and enablers across the supply chain. Comparing conceptual and real-world models revealed key areas for intervention, including blockchain-enabled traceability, water recycling, supplier diversification, and digital integration to enhance sustainability. Findings highlight issues such as reliance on foreign suppliers, currency volatility, limited domestic manufacturing capacity, inefficient logistics, weak governance, and slow adoption of green technologies. Environmental concerns include water scarcity, inadequate vehicle scrappage systems, and high emissions from diesel transport. The study provides actionable recommendations for policymakers and industry managers, such as diversifying suppliers, implementing digital platforms, and introducing transparent governance. These measures can improve efficiency, reduce environmental impact, and increase resilience against political and economic disruptions. The research also offers future research needed to test the proposed interventions and explore policy frameworks for large-scale adoption.
The question of cybersecurity has turned into a necessity of credible digital finance, defining the expansion capacity of fintech sectors to increase access to secure and safe financial services. The phenomenon of fintech inclusion is becoming subject to the impact of institutional and corporate protection factors, which minimize perceived risk, and safeguard users, as well as confidence in digital financial channels. This paper is based on institutional and risk governance views and explores the influence of cybersecurity practices on fintech inclusion and suggests digital policies as a mediating factor that converts cybersecurity preparedness to inclusive fintech performance. Although increased attention is paid to cybersecurity and the modernization of regulations, there is still a lack of empirical data on the impact of cybersecurity practices on the inclusion of fintech based on policy-driven processes in a unified analytical framework. The 300 respondents who worked in the positions of information security managers, compliance and risk officers, fintech product managers, IT governance specialists, and senior executives were sampled to gather the data across the fintech-enabled organizations. The proposed hypothesized relationships were tested using Structural Equation Modeling based on the PLS-SEM. These results suggest that cybersecurity practices affect considerably digital policies and fintech inclusion, whereas digital policies affect considerably fintech inclusion. The mediation analysis ensures that the effect of cybersecurity practices on fintech inclusion is partly transmitted through the digital policies, which indicate that a better result is observed when backed by the clear governance structure and policy frameworks. The findings have practical implications to managers and policymakers who aim to improve the inclusion of fintech using secure and policy-driven digital finance environments.
This study explores how the digital business ecosystem enhances purchasing and supply management performance in the hospitality industry, focusing on the mediating role of service innovation capabilities and the moderating roles of collaborative network capabilities and knowledge sharing. Data were gathered through surveys from 401 managers of four- and five-star hotels in Türkiye, including purchasing managers and supply management professionals, and analyzed using structural equation modeling and moderated mediation analysis based on Hayes’ PROCESS macro. Digital business ecosystems positively influence purchasing and supply management performance, both directly and indirectly, via their service integration capability. Collaborative network capability and knowledge sharing serve as moderators in the research model. The findings confirm that integrating digital infrastructure, innovation capabilities, and collaborative networks improves purchasing efficiency, supplier collaboration, and overall supply management effectiveness in service-intensive environments.
This study develops a generalized finite-horizon inventory model that integrates complex demand patterns, deterioration, seasonal effects, and stochastic variability within a Monte Carlo-based simulation framework in MATLAB. The model incorporates fixed, holding, procurement, deterioration, transportation, backordering, and lost sales costs, enabling comprehensive cost evaluation. Simulation results show that deterioration and seasonality significantly affect optimal replenishment cycles and order quantities, and that the model yields robust cost estimates under uncertainty. Sensitivity analysis further indicates that the base demand rate has the strongest effect on cost outcomes, followed by procurement and backorder costs, whereas seasonality and holding cost parameters exhibit negligible influence.
The digital transformation has been a main engine of economic growth by transforming the organizational structures, operations and strategic decisions with the help of sophisticated digital technologies. The mediating role of Big Data Analytics in this transformation is that it helps organizations to transform digital initiatives into real economic performance. It will utilize the Technology Acceptance Model and recent literature on the concept of digital transformation and the capabilities of data to identify the effects of digital transformation on economic growth in the mediator Big Data Analytics. The research paper is based on three aspects of digital transformation: preparations of digital infrastructure, process integration based on technology and data-driven decisions support. A sample of 300 respondents was taken, who are representatives of digitally intensive organizations, including senior executives, technology managers, and analytics specialists. Structural Equation Modeling on the Partial Least Squares algorithm was used to test the research framework. The results prove that digital transformation has a positive impact on economic growth and improves the capabilities of Big Data Analytics to a large extent. Big Data Analytics, in its turn, can help improve economic outcomes due to its contribution to enhancing efficiency, the ability to innovate, and responsiveness. The mediation analysis supports the claims and confirms that Big Data Analytics is a major transmission channel upon which the digital transformation initiatives create sustainable economic value. The research has its implications on organizational leaders, technology strategy, and policymakers. Digital transformation can only be maximized by strategic investment in digital infrastructure, development of analytics capability and data governance. To create the digital policies that can support both the data-driven innovation and inclusive economic growth, policymakers can rely on these findings.
Green supply chain management (GSCM) has emerged as a crucial strategy for organizations to address environmental sustainability concerns. This study aims to examine the knowledge domain of GSCM practices by conducting a bibliometric analysis of Scopus data from 2010 to 2023. Using the VOS viewer software, the researchers examined the publication trends, influential authors, institutions, journals, and keywords in the GSCM research landscape. The methodology involves systematically searching and retrieving relevant publications from the Scopus database, then data cleaning and preprocessing. The resulting dataset comprises 530 articles, which are then analyzed using the VOS viewer to create a network visualization map. Various bibliometric indicators, such as co-authorship, co-citation, and keyword co-occurrence, are used to uncover patterns and relationships among the articles. The bibliometric analysis revealed a growing interest in GSCM, with a significant increase in publications over the 14 years. The study identified the most influential authors, institutions, and journals in the field, highlighting the prominent contributors to GSCM research. Also, looking at how often keywords were used together revealed the main theme groups in the GSCM field, such as sustainable supply chain management, sustainable operations, sustainable governance, and sustainable organizational performance. The findings of this study provide valuable insights into the intellectual structure and evolution of GSCM research. Visualizing the knowledge domain using the VOS viewer facilitates a comprehensive understanding of the interconnections between various aspects of GSCM, such as the adoption of green practices, environmental performance, and supply chain sustainability. These insights can inform researchers, practitioners, and policymakers in developing strategic initiatives and future research directions to advance the field of GSCM.
With the increasing complexity of supply chain management, supply chain concentration (SCC) has become a prominent research topic in academia and practice. To clarify the developmental context and research trends within this field, this study utilizes the Web of Science core collection as the data source, selecting 362 English-language publications from 1975 to 2025. CiteSpace 6.2 was employed to conduct a visual bibliometric analysis, systematically examining the social structure, conceptual structure, and intellectual structure of SCC research through co-authorship, co-word, and co-citation analyses. The results indicate rapid growth in SCC research since 2020, with China and the United States being the major contributing countries, and collaborations exhibiting regional characteristics. High-frequency keywords prominently include "customer concentration," "supplier concentration," and "performance," with research themes progressively extending toward frontier topics such as "digital transformation," "green innovation," and "corporate social responsibility." Co-citation analysis identified representative works by authors such as Panos Patatoukas, Dan Dhaliwal, and Murillo Campello, highlighting a shift in research focus from traditional performance perspectives to governance mechanisms and sustainable strategies within a digital context. This study summarizes core literature clusters, evolutionary paths of clusters, and significant citation bursts, revealing interdisciplinary integration and paradigm shifts in SCC research. The paper provides a systematic review of future directions in SCC studies.
In the face of digitization in manufacturing industries, the judicious evaluation and selection of cutting-edge CNC machines play a pivotal role in achieving production-grade precision, accuracy and manufacturing agility. The evaluation of 3-axes CNC machines incorporates most sought-after subjective and objective criteria having significant relative weights and green impacts. This research paper presents a novel heterogeneous expert based decision making (HGEDM) framework incorporating a diversified combination of experts having distinct impact factors. The experts’ impact factors so calculated impart significant contributions in computing weighted aggregated performance ratings of the alternatives. To establish the effectiveness of the suggested approach, three practical selection problems are illustrated. The calculated findings are validated with few well-established approaches demonstrating the realistic nature of the suggested methodology. To assess the stability and robustness of the proposed approach, a sensitivity analysis is performed. Besides, Spearman’s rank correlation measure demonstrates that the ranks obtained using the proposed approach are highly close to those derived from several existing methods. Furthermore, both Pearson correlation coefficient and Sample correlation coefficient measures show a strong association between the proposed approach and existing ones. Therefore, the proposed HGEDM approach is considered to be a consistent and effective tool for supporting optimal selection.
Auto accessories such as car covers provide an added extra in automotive styling both in the look and construction. Any fault in these components will reduce customer satisfaction and result in higher warranty expenses among manufacturers. Automotive sector as per IATF 16949 requirements requires a very effective and strong control of its processes to reduce the defects and enhance productivity. Thus, improved methods for defect identification and higher levels of quality assurance during production are critical issues of current concern. This research focuses on the use of Artificial intelligence (AI) in the automotive industry with an emphasis of using computer vision for superior improvement of quality KPIs. The purpose is to provide an efficient system and organizational approach to the further optimization of the end-of-line inspection of covers for vehicles, and to improve the efficiency of the identification of defects under IATF 16949 regulations. This study is unique in adopting a case based on smart splicing technology implemented in the cutting area of the automobile manufacturing lines. This paper simultaneously applies AI and IoT in order to understand its degree of influence in the definitive performance KPIs. Insignificance may be identified through the application of linear regression used to analyze the correlation between the applied technology and subsequent performance gains. Experimental outcome shows a significant decline on the number of defects that are identified at the last inspection process as well as an improvement on the rate of production. AI particularly contributed to enhancement of inspection processes thereby minimizing non-value adding activities and hence improving overall quality of the products. The current study also encourages manufacturers to adopt intelligent technologies since the AI technologies implemented within the IATF 16949 standards can boost the automotive production quality and decrease the costs and customer dissatisfaction. The automotive industry has changed today due to the implementation of IoT and AI in manufacturing, as this work has shown, with an exciting horizon of the constant automation process and increasing quality indications to deliver on the promise of the redefined definition of success in this industry.
This paper presents a new integrated framework combining the Joint Replenishment Problem (JRP) with a generalized Vendor Managed Inventory (VMI) system. The model under consideration represents a three-level supply chain consisting of a supplier, manufacturer, and retailer. The model incorporates multiple product types, each produced on a dedicated machine at the manufacturer, subject to setup costs, and major and minor ordering costs. The primary objective of this research is to optimize a set of critical decision variables, including the common order interval, order frequencies for each item, backorder levels at the retailer, and production initiation times at the manufacturer for each product type, under both deterministic and stochastic demand scenarios. This analysis will provide valuable insights for improving joint replenishment operations in manufacturing. The research begins with a deterministic model fit for the particular issue area derived from the canonical JRP. Within a VMI context, the manufacturer, acting as the supply chain leader, utilizes shared information to derive initial feasible solutions. Subsequently, an optimization technique is employed, combining marginal cost-based and cumulative cost-based algorithms, while leveraging embedded discrete Markov chain decomposition method adapting Jacobi stepping method to determine steady-state probabilities. A cost function is then defined for each action state within this framework. The integration of the VMI policy into the JRP model can significantly reduce the whole cost of the supply chain, through balancing between production initiation and backorders under both the deterministic and stochastic models.
This paper presents a framework for reverse logistics aimed at managing reusable items within supplier-buyer relationships to promote sustainability and reduce environmental impact. In this model, the supplier produces and inspects items, shipping only perfect items to buyers, while recycling or disposing of imperfect ones. Returned items from consumers are categorized as either reusable or damaged at a collection center. The concept of a circular economy encourages the return and refilling of reusable items, while damaged items are recycled. Additionally, the model incorporates carbon emissions considerations across production, storage, transportation, and landfilling, emphasizing the importance of environmental factors. To evaluate the sustainability and economic efficiency of the supply chain network, both Stackelberg and Nash equilibrium strategies are employed. The paper provides a mathematical framework based on lemmas to analyze the impact of the network and promote sustainable supply chain practices. In this cycle, consumers use the items and eventually discard them. To support a zero-waste policy, the supplier labels the bottles with barcodes to identify used items upon collection. The supplier has two different rates at which they purchase used bottles from consumers. Refilled bottles are sent back for reuse, while damaged bottles are either repurposed as raw materials or disposed of. The research paper aims to develop a mathematical model that determines the buyer's cycle time and the number of deliveries from the supplier to the buyer, ensuring that the buyer's demand is met without shortages.
In today's dynamic and uncertain environments, effective decision-making processes are essential for navigating complex challenges. This paper proposes an innovative approach utilizing Fermatean fuzzy sets to enhance decision-making within heterogeneous group dynamics. Through a systematic mathematical framework, our method integrates expert preferences to find out the comparative weight of decision attribue, leveraging both Fermatean fuzzy sets and entropy calculations. Furthermore, we introduce a novel technique to assess the significance of individual experts' opinions, accounting for specific contextual factors. By synthesizing performance ratings, criteria weights, and expert inputs, our approach offers a comprehensive decision-making model. We introduce the concept of the proximity coefficient to address existing methodological limitations, enhancing the accuracy of decision outcomes. To validate our methodology, we apply it to a practical scenario involving warehouse location selection. Additionally, analysis of sensitivity is conducted to evaluate the robustness of our method across diverse scenarios, demonstrating its efficacy in uncertain environments. This research contributes to advancing decision-making practices in complex and uncertain contexts, offering a valuable tool for addressing real-world challenges.
Textile industry involves a lengthy process from upstream to downstream, making supply chain integration crucial for enhancing firm performance. This study explores various factors that can boost supply chain integration and company performance in Indonesia's textile sector, including strategic agility, innovation capability, and technology adoption. The research is grounded in resource-based-view and market-based-view theories, suggesting that companies can optimize their resources and collaborate effectively with supply chain partners to enhance industry performance. Additionally, the study considers environmental turbulence as a moderating variable. Utilizing a quantitative approach with judgmental sampling, the research collected data through a structured questionnaire, resulting in 270 valid responses. The data was analyzed using the partial least squares structural equation modeling (PLS-SEM) method with SmartPLS 4.0 software. Findings indicate that strategic agility, innovation capability, and technology adoption significantly influence firm performance through supply chain integration, while environmental turbulence notably moderates the relationship between innovation capability and supply chain integration on firm performance. The study recommends that textile companies prioritize agility, strategic innovation, and technology adoption to enhance their integration with supply chain partners. It underscores the critical role of supply chain integration in improving company performance and the impact of environmental turbulence as a moderating factor.
The new economic context has brought new challenges to the supply chain and has increased the complexity of its processes. The digitalization; as one of these challenges, is a rapidly evolving paradigm that transforms supply chains by integrating data and communication technologies to optimize operations, enhance sustainability, and improve overall performance. Digital twin technology emerged as one of the most promising digital tools that offer an innovative approach to supply chain management. However, the adoption of digital twins in the supply chain is still in its early stages. Previous research papers presented limited overviews of the applications of digital twin technology in supply chain systems that need to be extended, as it is inevitably a work in progress. In this matter, we conducted a systematic literature review built upon 31 articles to determine the applications of supply chain digital twins (SCDT). This study is divided into three core themes; the first is a comprehensive review of the paradigm of digital supply chain with a focus on digital twin technology and its primary features. The second theme presents an analysis of the 31 papers where we explore the different purposes of SCDTs and their integration. in the third theme by using VOSviewer to conduct a network analysis. We aim; through this paper, to contribute significantly to the supply chain management field by summarizing and analyzing existing research and developments in the applications of digital twins in the different areas of supply chains.
In today's dynamic and uncertain environments, effective decision-making processes are essential for navigating complex challenges. This paper proposes an innovative approach utilizing Fermatean fuzzy sets to enhance decision-making within heterogeneous group dynamics. Through a systematic mathematical framework, our method integrates expert preferences to find out the comparative weight of decision attribute, leveraging both Fermatean fuzzy sets and entropy calculations. Furthermore, we introduce a novel technique to assess the significance of individual experts' opinions, accounting for specific contextual factors. By synthesizing performance ratings, criteria weights, and expert inputs, our approach offers a comprehensive decision-making model. We introduce the concept of the proximity coefficient to address existing methodological limitations, enhancing the accuracy of decision outcomes. To validate our methodology, we apply it to a practical scenario involving warehouse location selection. Additionally, analysis of sensitivity is conducted to evaluate the robustness of our method across diverse scenarios, demonstrating its efficacy in uncertain environments. This research contributes to advancing decision-making practices in complex and uncertain contexts, offering a valuable tool for addressing real-world challenges.
This study uses the Resource-Based View (RBV) and technology, organization, and environment (TOE) theories to examine how smart supply chain (SSC) practices affect financial performance (FP) in enterprises of various sizes. Our results show that SSC benefits larger enterprises more financially than smaller firms. SSC has a statistically significant effect on green supply chain management (GSCM) and sustainable supply chain performance (SSCP), and the strength of the relationship declines with a decline in firm size. Smaller enterprises are more receptive to competitive pressure and implement GSCM alongside SSC. Our findings show that SSCP improves financial performance, while GSCM does not, even in large enterprises. Further, mediation effects show that GSCM mediates the relationship between SSC and SSCP, whereas it does not mediate between SSC and FP across all sizes. The impact of SSC on FP is sequentially mediated via GSCM and SSCP. Using a non-linear approach (ANN), we also rank independent variables for small, medium, and large firms. Our research provides important implications.
This study analyzes the relationships between knowledge sharing culture, information technology support, and process innovation on supply chain sustainability in Indonesia's digital printing SMEs, with market uncertainty as a moderating variable. Employing an explanatory approach, the research utilizes a cross-sectional survey involving 225 SME owners. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings reveal that knowledge sharing culture has a significant positive impact on both process innovation and supply chain sustainability. Conversely, information technology support negatively affects these two variables. Process innovation positively contributes to supply chain sustainability, albeit with a small effect. Market uncertainty strengthens supply chain sustainability but weakens the relationship between process innovation and sustainability. The mediating role of knowledge sharing culture through process innovation highlights a critical pathway for enhancing supply chain sustainability. This study offers theoretical and practical implications regarding the importance of knowledge sharing culture and process innovation, alongside challenges in technology adoption. The study's limitations include its cross-sectional approach and focus on digital printing SMEs in Indonesia. Future research is recommended to adopt a longitudinal approach to explore dynamic changes in this context. The novelty of this study lies in its integrated understanding of factors influencing supply chain sustainability amid market uncertainty.
There are few studies addressing the mediating effect of readiness in supply chains in developing countries. We use the theory of dynamic capabilities to investigate the impact of integration (INT) on readiness (REA) and on innovative product performance (IPP) and to examine the mediating effect of REA on INT and IPP. We conducted a survey with a sample of 213 supply chain (SC) managers of machinery and equipment for transport and lifting heavy loads in Brazil. To this end, we used structural equation modeling to test the hypotheses and PROCESS macro to confirm the indirect effect. The empirical results indicate a significant effect between INT and REA and REA and IPP. However, the indirect impact of REA was compromised. The literature review demonstrates that the microfoundations of dynamic capabilities (Sensing, Seizing and Reconfiguration) strengthen supply chain links, particularly between INT, REA and IPP. This article helps managers understand the functioning of SC routines and operations. To this end, they should develop strategies that strengthen SC ties with INT, REA and IPP to face future crises. This paper advances the findings on integration, readiness, and innovative product performance in an SC. The findings provide insightful implications for managers to improve their strategies. In doing so, we theorize how the microfoundations of dynamic capabilities support the efficiency of supply chain operations.
In food processing factories, especially when dealing with perishable items, managing inventory is crucial as it impacts sales, quality, and customer satisfaction. However, inventory management is often complicated by the costs and intricacies of the supply chain. This research aims to create a conceptual model that connects the costs and complexities of the supply chain with satisfaction, sales, and quality through optimized inventory management. The study involves a case analysis of thirteen food processing factories, using a structured questionnaire for data collection. To validate the proposed framework, PLS-SEM was employed. The framework addressed five key research questions, and the results confirmed that inventory management is essential for maintaining quality, sales, and satisfaction, and that supply chain costs and complexity influence inventory management.
