ABSTRACT This paper examines vertical outsourcing dynamics using three game-theoretic models: buyer's independent production, buyer-led outsourcing, and supplier-led outsourcing. The analysis systematically evaluates how government policy support affects outsourcing decisions, pricing strategies, and profit distribution in supply chains. Key results indicate that: (1) policy interventions significantly alter competitive dynamics, with buyer support encouraging price reductions to expand market share while supplier support enhances production efficiency and pricing power; (2) allocation of control rights directly determines profit distribution advantages, allowing the dominant party to secure higher profits through favorable contractual terms; (3) different policy types generate asymmetric profit effects, as buyer-oriented policies directly increase its profits whereas supplier-focused measures indirectly boost overall profitability through enhanced market competitiveness. Using Stackelberg game frameworks supported by numerical simulations, this research clarifies the dynamic interrelationships among policy interventions, control structures, and supply chain coordination, offering theoretical insights for industrial policy design and corporate outsourcing optimization.

ABSTRACT In many applications, the structural equation models with varying coefficients and changing quantiles are urgently needed. The estimation of the varying coefficients at different quantile levels brings difficulties and challenges especially in complex structural relations. In our article, we propose two kinds of quantile-based dynamic structural equation models: a quantile-based structural equation model with varying coefficients and a composite-quantile-based structural equation model with varying coefficients. Based on the above two new models, the paper first proposes a B-spline estimation algorithm based on the quantile regression estimation and then develops its extension algorithm based on the composite quantile regression estimation. The former algorithm allows the varying coefficients to be estimated at different quantiles, and the latter one simultaneously considers multiple quantile levels. A set of simulation studies is carried out to investigate the performances of our proposed algorithms and the paper finally applies the proposed models and algorithms to a real data example.

Along with several sectors, manufacturing industries have been concerned with the devastating impacts of COVID-19 on their supply chains (SCs). Many instantaneous decisions and strategies have been incorporated to mitigate the impacts of COVID-19. However, the significance of these strategies has not been analyzed in the literature considering long-term perspectives and sustainable implications. To fulfil this gap, we develop a sustainable recovery approach with a set of strategies that allow manufacturing SCs sustainable growth in the post-COVID-19 world. To analyze the results, we conduct a case study of automobile SCs of Bangladesh, an emerging economy. It identifies and assesses 16 post-COVID-19 sustainable recovery strategies and evaluates their causal relationships. The findings imply that managers should concentrate on ‘Developing a robustness-based risk management system for eliminating SC vulnerabilities’ and ‘Investing in collaborative strategic planning’. The findings can help SC managers and practitioners to develop sustainable recovery strategies in the post-pandemic era.

While conventional models have already demonstrated exceptional performance in time series forecasting, several challenges remain to be tackled. The emergence of large language models (LLMs) has introduced disruptive changes to forecasting methodologies, including architectural frameworks and forecasting processes. To conclude existing literature regarding the application of LLMs in time series forecasting, this study, distinct from prior reviews, offers a comprehensive and up-to-date general pipeline block structure that integrates various modeling techniques in time series forecasting with LLMs, with specific emphasis on data scenario usage. To guide practical applications, this study proposes three collaborative patterns for incorporating LLMs in time series forecasting workflows and provides a comparison of LLMs’ performances with conventional models. Overall, this study organizes and evaluates the latest research advancements, delivering key insights and propositions for the potential future development of LLMs in the field of time series forecasting.

Modernization affects social, environmental, and economic structures in ways that are both positive and negative, and it makes it impossible to maintain equilibrium in sustainable practices. In this context, optimization has received a lot of attention. Even though the comprehensive models that integrate queuing systems, uncertainty, and reverse logistics emphasize the necessity. This study considers a four-echelon closed-loop green supply chain including a third-party collector under a stochastic environment. The products are transported through fleets to different centers forming a queue system during loading/unloading time. The effective energy consumption and carbon emission occur at the time of production, transportation, waiting in the loading center, reverse logistics, disposal, etc. To reduce this environmental impact, a mathematical model has been developed under different collection strategies. A numerical example is considered for the proposed model, followed by a sensitivity analysis, to investigate the significant role of the parameters. The findings indicate that the extensive use of returned product can have a positive impact on the environment and influence all the supply chain decisions. Therefore, the management policy should place major emphasis on the best collection strategy that can benefit the ecological and economic environments.

This paper introduces a novel inventory model tailored for retailing systems aligned with the Product Life Cycle (PLC), encompassing the introduction-growth, maturity, and decline stages. Diverging from previous inventory models that integrate sales incentive policies without consideration of PLC characteristics, this study pioneers an integrated framework. The paper introduces an innovative decreasing time-based discount policy tailored for the introduction-growth stage. Following that, a quantity discount policy is devised for the maturity phase, while a delay payment policy and an incremental time-based discount policy are formulated for the decline phase. Recognizing the product's heightened market recognition during the maturity phase, a partial backorder system with variable shortage costs is proposed to address shortages. The findings indicate that extending the duration of the maturity phase, along with elevating the costs associated with each unit of shortage in this phase, results in an augmentation of the retailer's profit.

Artificial intelligence (AI)-based ChatGPT application has begun to be discussed in many areas due to its rapid impact on human life. This study provides an overview of the applicability of ChatGPT for quality management and discusses the advantages, disadvantages, limitations, different perspectives, and future concerns of ChatGPT. The current review study reveals the contribution levels of ChatGPT to the effectiveness of decisions that need to be taken into account and implemented in quality management, which is a human-oriented approach. The top 9 (3 main and 6 sub-subjects) titles, abstracts, and keywords for each search term (a total of 166 publications) were analyzed independently to examine all aspects of the topic of this study. PubMed, Scopus, Web of Science (WoS), medRxiv, arXiv, and SSRN databases were used to select relevant articles. Studies emphasize that the ChatGPT application has inevitably become an indispensable tool to provide better service, increase efficiency, and reduce errors by supporting quality management processes. In addition, while analyzing a certain point with specific techniques in quality-related studies, the studies examined indicate that ChatGPT provides continuous improvement and accuracy in quality management because it is a constantly developed and updated model. However, users should note that ChatGPT has many limitations regarding quality management and the need for human expertise and control. As a result, the present review study will contribute to researchers by providing basic suggestions for future quality applications of the ChatGPT tool.

Effective mitigation of supply disruption is a crucial issue for many retailers. Although prior studies of supply disruption mainly focused on the sale of a single product, the bundling of products is one of the important marketing tools that significantly impact businesses’ profitability. In this study, we investigate a retailer who uses a dual-sourcing strategy for complementary products. The problem is formulated under different selling strategies, i.e., separate selling, pure bundling, and mixed bundling. In each strategy, the concavity of the objective function is explored, and a solution algorithm is proposed. The results show that pure bundling is more sensitive to supply disruption than mixed bundling and separate selling. Actually, under pure bundling, the retailer transfers its sourcing strategy from dual to single sourcing even under a low probability of supply disruption. Furthermore, when bundle price elasticity is high, separate selling outperforms bundling and mixed bundling.

The objective of this research is to assess financial institutions across various countries and regions from 2011 to 2020 using hybrid machine learning methodologies. Machine learning is employed for data analysis and prediction, proving particularly effective for extensive datasets. This paper analyzes a real bank credit dataset to examine the functionality of bank credit. The random forest method identifies Net Interest Income (gross profit and loss) as the most influential factor. Using Fuzzy C-means, we categorize the data into five clusters across the studied years. With Cluster-Induced Ordered Weighted Averaging (CIOWA), 2013 is identified as the best-performing year. This study contributes to the field by applying hybrid machine learning methods to forecast the future performance of financial institutions. Additionally, a comprehensive literature review on related issues is incorporated into this model.

This work holistically evaluates the factors that can aid decision makers in finding the optimal location for food businesses in developing economies. It integrates the analytic hierarchy process (AHP) in determining criteria weights (i.e. benefit, opportunity, cost, risk) and the newly introduced three-way decision – Technique for Order Preference by Similarity to Ideal Solution (3WD-TOPSIS) method in identifying the priority factors. Applying the AHP yields the assignment of more priorities to benefits and opportunities rather than costs and risks, reflecting the benefit – or opportunity-oriented attitudes of food businesses. Meanwhile, the implementation of 3WD-TOPSIS results in the identification of government regulations and restrictions, proximity to consumers, parking capacity, supply chain strategy, and socio-economic status as the most crucial location decision factors. Findings from the comparative analyses show a high agreement between the results and those of other comparable methods. Managerial insights from these findings are outlined in this work.