Recently, a growing number of upstream factories are entering the territory of the national brand manufacturer (also called the original equipment manufacturer, denoted by OEM) by establishing their private labels (PLs) in a platform-based supply chain. Yet, existing literature rarely considers how the factory’s entry strategy, non-entry, entry via the platform’s marketplace channel, or entry via the platform’s resale channel, interacts with the OEM’s decision of whether (and how) to introduce a marketplace channel, although this phenomenon is common in reality. In a three-tier supply chain consisting of a factory, an OEM and a platform, we utilize game theory to discuss the relationship between the factory’s entry strategy and the OEM's marketplace channel introduction strategy. Our results show that the factory’s entry via the resale channel always benefits both the platform and himself. By contrast, the factory entering via the marketplace channel may hurt the platform or himself. Furthermore, we show that when the PL's perceived value is low, the OEM’s marketplace channel introduction reduces the probability of the factory’s entry through the marketplace channel; otherwise, such marketplace channel introduction raises the probability that the factory enters the market via the marketplace channel. Surprisingly, we find that the OEM's introduction of the marketplace channel may worsen her profit reduction caused by factory entry. Finally, we derive the equilibrium result and show that in response to factory entry, the OEM chooses not to introduce a marketplace channel to guide the factory to enter the market through the resale channel.

This study proposes a dynamic inventory optimization model based on deep reinforcement learning (DRL) to improve the intelligence of inventory management in multi-node supply chain systems, aiming to mitigate stockout risks and reduce inventory holding costs. The model is built upon three key components. First, a simulation environment is developed to mirror real-world supply chain operations, incorporating multiple products, warehouses, and sales channels. The simulator takes into account critical factors such as lead times, demand variability, and order fulfillment constraints. Second, the Soft Actor-Critic (SAC) algorithm is employed as the core of the learning strategy. To enhance the model’s adaptability to dynamic inventory changes, a dual-stage state representation and an attention-enhanced perception mechanism are introduced. Third, the model is trained and validated using the publicly available Instacart Market Basket Analysis dataset, which serves as a benchmark platform for testing retail replenishment strategies. To assess its effectiveness, the proposed SAC-based model is compared with several baseline methods, including Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO), and an LSTM-based Model Predictive Control approach (LSTM-MPC). Experimental results across six representative scenarios show that the SAC model consistently outperforms competing approaches. Specifically, in high-demand volatility and limited-capacity scenarios, the SAC model reduces average inventory costs to ¥69,900 and ¥97,200, respectively, substantially lower than those achieved by DDPG (¥120,700 and ¥136,300). Under compound disturbance conditions, SAC limits the number of stockout events to 59, which is 50% fewer than the rule-based benchmark, significantly improving service level performance. Moreover, in high-frequency, short-cycle environments, SAC achieves the lowest inventory variability, with a standard deviation of just 4.12, indicating superior policy stability. Overall, the proposed DRL-based model exhibits strong robustness and adaptability, offering a practical solution for intelligent and resilient inventory control. This study highlights the potential of advanced reinforcement learning methods in addressing complex scheduling problems in real-world supply chain management.

Utilizing a game-theoretic model, this study distinguishes between privacy-protecting and privacy-disclosing consumers, examining the impact of digital privacy on manufacturers’ channel strategies, including no encroachment, encroachment, and shifting. The results indicate that when consumers prefer privacy disclosure, manufacturers consistently opt for the shifting strategy. In contrast, when privacy protection is prioritized, the outcome depends on the intensity of consumer preferences. Specifically, weak preferences lead to encroachment, whereas strong preferences favor no-encroachment. This contingency framework illustrates how privacy preference thresholds shape channel strategy selection. From a practical perspective, the findings offer actionable guidance for manufacturers. First, a win-win outcome depends on the manufacturer’s cost-efficiency in channel-shifting operations, suggesting that firms should assess their operational capabilities before implementation. Second, consumer benefits under encroachment imply manufacturers can adopt this strategy for competitive advantage while addressing privacy concerns. These insights equip practitioners with a decision-making toolkit for balancing privacy compliance, channel efficiency, and stakeholder value creation in digital transformation initiatives.

With the rapid growth of e-commerce, platform-based financing in electronic business (EB) has emerged as an innovative solution for online retailers facing capital constraints. This study develops a Stackelberg game-theoretic framework to analyze strategic financing decisions in a two-tier e-commerce supply chain, where an electronic business platform (EBP) , as the leader, assumes leadership by setting financing interest rates, while a capital-constrained, loss-averse online retailer (LOR), as the follower, optimizes order quantities and financing participation under behavioral risk preferences. A hierarchical game-theoretic framework is established to examine strategic interactions between an EBP and a LOR, and the equilibrium outcomes are given. The model derives optimal decisions for both financing rates and ordering strategies. Results demonstrate that when the retailer's initial capital grows, their necessity for external financing diminishes correspondingly, leading to smaller order quantities due to reduced bankruptcy risk. Moreover, higher levels of loss aversion cause retailers to order less and avoid financing, reflecting risk-sensitive behavior. The study also presents comprehensive numerical analyses to explore additional managerial implications, offering insights into how capital availability and behavioral factors like loss aversion shape decision-making in EB financing environments.

To alleviate the inventory pressure and improve operational performance, the e-tailer that develops a store brand (SB) may make a service investment in her self-operated stores. However, the existing literature rarely considers such a service investment strategy and its impact on national brand (NB) suppliers, especially on their selling model selection. We employ a theoretical model to explore the interactions of the NB supplier’s selling model selection and the service investment strategy of the e-tailer developing an SB. Our findings show that under the reselling model, the e-tailer always benefits from her service investment. Interestingly, however, the e-tailer may suffer from her service investment under the agency model. Meanwhile, the likelihood of the e-tailer adopting service investment decreases as the consumer service sensitivity increases. Furthermore, we find that the service investment increases the scope wherein both firms prefer the reselling model. In addition, we show that the supplier may adopt the agency model rather than the reselling model to counteract the service investment strategy of the e-tailer. These findings provide actionable insights to help suppliers and e-tailers make strategic operational decisions.

Deferred payment and revenue-sharing contracts are significantly important for promoting the collaboration and the management of retail export supply chains for cross-border e-commerce. This research addresses the real-world challenges faced by managers in this domain by using a joint optimization model to investigate the best ordering and pricing tactics within cross-border e-commerce retail export supply chains, particularly taking into account export tax rebates and import tariffs. Our findings reveal that while revenue-sharing contracts and deferred payment mechanisms can significantly enhance supply chain profitability, their effectiveness is contingent on variables such as export rebate rates, tariffs, and tariff transfer factors. The practical implications of this study suggest that business administrators should carefully assess these factors when designing contracts to ensure robust supply chain coordination. When traditional contract mechanisms fail, hybrid approaches combining revenue-sharing and deferred payment can offer superior outcomes, thus providing a strategic advantage in volatile markets. These insights are crucial for managers seeking to navigate the complexities of international trade and optimize their supply chain performance.

This systematic literature review critically examines the application of the Resource-Based View (RBV) theory within Supply Chain Management (SCM) across various industries. Developed by Jay B. Barney in 1991, RBV posits that a firm's sustained competitive advantage is driven by its unique resources and capabilities that are valuable, rare, inimitable, and non-substitutable. RBV suggests that a company’s long-term competitive advantage stems from its distinct resources and capabilities, which are valuable, rare, difficult to imitate, and not easily substitutable. Despite extensive utilization in strategic management, the direct application of RBV in SCM has been less explored, particularly in understanding how specific internal resources enhance SCM capabilities and outcomes. The review adopts a systematic approach, analyzing 97 peer-reviewed articles from diverse journals. This method includes a comprehensive search and evaluation process, ensuring the inclusion of significant studies that discuss the application of RBV in SCM across various industries. The articles were sourced from Scopus databases, with keywords related to RBV and SCM to ensure thorough topic coverage. The findings indicate a pronounced increase in related publications since 2010, reflecting a growing scholarly and practical interest in RBV’s application to SCM. The findings revealed that emerging trends like integrating advanced technologies like Blockchain, Artificial Intelligence and the Internet of Things are identified as strategic resources that redefine competitive landscapes by enhancing transparency, responsiveness and responsiveness within supply chains. The review also highlights the increasing importance of sustainability practices within SCM, aligning with RBV to potentially offer a sustainable competitive advantage. Conclusively, this review contributes to both academic knowledge and guides practitioners toward leveraging internal resources strategically to navigate contemporary challenges, setting a foundation for future inquiries into global supply chain resilience and dynamic capabilities.

The escalating demand for items and their consumption has exerted immense pressure on industrial production and supply chain systems, leading to adverse environmental and societal consequences. The rise in pollution rates and ecological disasters resulting from industrial production has prompted numerous scholars and industry specialists to focus on Sustainable Production and Consumption matters within the framework of Sustainable Supply Chain Management (SSCM). The interest in sustainable supply chain management has significantly increased in recent years, spanning both business and academic sectors. This phenomenon is seen in the growing prevalence of papers, conferences, specialized periodicals, and websites exclusively focused on the subject matter. Nevertheless, the importance of sustainable development in developing economies has only just started to be acknowledged. This literature review aims to assess existing research on sustainable supply chain management (SSCM) in developing nations while considering a worldwide perspective. This paper thoroughly examines the rapid expansion of the subject from an evolutionary perspective; aimed at comprehending the progression of sustainability concerns by examining patterns across different industries, and economies, and employing diverse approaches. An extensive thematic analysis was conducted on 56 selected publications published between 2010 and 2023, emphasizing the growth and significance of the knowledge base. The analysis is conducted utilizing a descriptive and content-oriented methodology. Subsequently, the results are presented, demonstrating an increasing interest in Sustainable Supply Chain Management (SSCM). Nevertheless, there is a conspicuous discrepancy in the extent of research carried out on this subject in emerging economies as opposed to industrialized ones. The findings indicate that the context in less developed countries plays a crucial role in carrying out empirical or case study research. Moreover, it is crucial to analyze how the integration of the three dimensions of sustainability impacts the efficiency of the supply chain, especially when considering the perspective of an emerging economy. Hence, the limitations of this investigation are delineated. Ultimately, it is crucial to do further research from multiple angles within the supply chain, encompassing collaboration, sustainable practices, innovation, sourcing, and supplier growth, with a special emphasis on the position and background of rising countries.

A consideration of the integral variables of customer location, traffic flow, and road conditions to determine the best feasible delivery routes is a big challenge in Logistical operations. A poor routing strategy that delivers products places an ineffective gloss and eventually converts into high operating expenses, over-consumption of fuel, and shipment delays. The paper’s goal is to build a model for the logistics management of the company which aims for effective management of the truck allocation and vehicle routing using K-means clustering and TSP. K-means clustering is often used to classify the sites of delivery based on their closeness in space, hence simplifying the problem by reducing its dimensionality. The proposed algorithm considered customer location prioritization in deliveries, delivery task allocation, and truck allocation to enable timely delivery. Therefore, this paper presented a solution to enhance the logistics operations of beverage brand “KINZA” by optimizing its truck loading and delivery route. The model would ensure that each truck is able to travel optimally, with vehicle-routing algorithms applied in a way to avoid all unnecessary waste of time and distance. Finally, the main scope of this paper is to develop and design a dynamic logistics platform for the KINZA Company distribution network.

This paper studies the marketplace channel introduction of contract manufacturers and the response of the platform with an option to introduce a private brand. We develop a game-theoretical model to examine a three-tier e-commerce supply chain including a contract manufacturer (CM), an original equipment manufacturer (OEM) and a platform and derive the equilibrium results. We find that the marketplace channel introduction of the CM and the platform's private brand introduction influence each other. More specifically, marketplace channel encroachment may discourage the platform from introducing a private brand, and this preference is reinforced as the referral fee increases. Interestingly, the introduction of the platform's private brand increases the likelihood of contract manufacturer encroachment, which is mediated by the difference between the two private brands of the CM and platform--as the difference increases, the CM prefers to enter the marketplace channel. Furthermore, only contract manufacturer encroachment (or private brand introduction for the platform) can always benefit the whole supply chain, but the supply chain may be hurt when the platform and the CM perform their strategies simultaneously. In the extension section, in addition to demonstrating the validity of our main results when the CM and the OEM act as a single entity, we also find that the first-mover advantage of the platform may reduce the possibility of the contract manufacturer encroachment.