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.

The efficient management of reverse supply chains, particularly the collection and remanufacturing of defective products, plays a critical role in reducing production costs and determining the final pricing of remanufactured products. While existing research extensively explores warranty policies and maintenance services to enhance customer satisfaction and profitability, the integration of vehicle routing for product collection and sustainability advertising strategies remains underexplored. Addressing this gap, this study introduces a comprehensive two-stage reverse supply chain model that captures the interactions between manufacturers (MFRs) and remanufacturers (RMFRs) through a Stackelberg game framework. Methods: The proposed model incorporates interactive production constraints, vehicle routing problem (VRP) for optimizing collection logistics, and sustainability advertising to influence consumer behavior towards remanufactured products. Utilizing mixed nonlinear programming (MINLP) and nonlinear programming (NLP) techniques, the model simultaneously optimizes pricing strategies, collection efforts, and advertising investments for both MFRs and RMFRs. Numerical analyses are conducted to solve the optimization problems, accompanied by sensitivity analyses to evaluate the impact of key parameters such as production costs, defect rates, and routing constraints. The numerical results demonstrate that increases in production costs for MFRs lead to higher selling prices, thereby reducing their profit margins and negatively impacting RMFR profitability due to decreased demand for remanufactured products. Sensitivity analysis reveals that higher defect rates (α ≥ 0.8) significantly diminish overall supply chain profitability by lowering customer acceptance of RMPs. Additionally, expanding the allowable vehicle routing distance L effectively reduces collection costs, enhancing RMFR profits and enabling greater investment in sustainability advertising. The study shows that the integration of VRP and advertising strategies proves crucial in balancing cost efficiencies and market competitiveness, ultimately fostering a more sustainable and profitable reverse supply chain.

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.