While supplier encroachment has been extensively studied, existing research has largely overlooked two important market elements: the segment of used products owners and the peer-to-peer (P2P) resale market. We bridge this gap by building game-theoretic models featuring a retailer who distributes its supplier’s products to both new customers and existing owners through a trade-in program. Additionally, we incorporate a P2P resale platform that permits direct used-good exchanges between new consumers and owners. The supplier’s encroachment strategies and the P2P resale market’s effects are investigated. Our research indicates that the supplier’s optimal strategy can be either partial encroachment on new customers (Strategy N) or full encroachment on both segments (Strategy B). Furthermore, the supplier systematically favors Strategy B over Strategy N. The owner ratio, new product durability and the P2P platform’s commission rate serve as key moderators, capable of either amplifying or diminishing the supplier’s motivation to encroach and the resulting positive spillovers. Intriguingly, when the platform commission rate meets certain thresholds, Strategy N invariably enhances the retailer’ profitability. The emergence of the P2P resale market consistently reduces the supplier’s profitability and dampens encroachment incentives. Yet, it can mitigate the supply chain double marginalization effects and enhance the retailer's sales, thereby benefiting both the retailer and the overall supply chain when encroachment occurs.

An accurate demand forecast by artificial intelligence (AI) reshapes information asymmetry and strategic interactions in a decentralized supply chain, where the observability of real-time market fluctuation originates from the AI-driven demand forecast. This paper constructs an analytical model of the supply chain to examine the retailer's demand prediction strategy and the manufacturer's information sharing strategy. Research shows that the retailer prefers AI-driven demand prediction when the unit computing power cost is low, while achieving information sharing can lead to a win-win situation. Although the accurate demand forecast improves the manufacturer's payoff, higher cost allocation of AI adoption may also deter information sharing as the unit computing power cost increases. In contrast, if the retailer adopts traditional demand prediction, they can only reach an information-sharing agreement through a subsidy contract. Once voluntary sharing is reached, such an agreement can constantly be reached under AI-driven demand prediction but fails under traditional demand prediction due to the absence of the subsidy cannibalization effect. Furthermore, it is through the subsidy contract that the demand distortion can be prevented from harming the manufacturer's payoff when the retailer inflates forecasting demand. This study provides enterprise managers with a better understanding of tradeoffs between the costs and benefits of AI adoption and makes prescriptions regarding how information sharing can be achieved for supply chain coordination.

Besides the existing traditional reselling channel, the manufacturer is increasingly adding the direct self-build channel (i.e., direct encroachment) or agency channel (i.e., agency encroachment) to reap more profits in an e-commerce supply chain. In response to such manufacturer encroachment, platforms may introduce their store brand products. However, the existing literature ignores the impacts of manufacturer encroachment mode choices on store brand introduction. In this article, we develop a game model to investigate the interaction between manufacturer encroachment channel selection and the platform's store brand introduction. The results show that the decision to introduce store brands is quite different under different encroachment modes. More specifically, when the manufacturer does not encroach or adopts direct encroachment, the platform always introduces her store brand. However, the adoption of agency encroachment may prevent the platform from introducing the store brand when the agency fee and the cost of direct encroachment are moderate and the perceived value of the store brand is sufficiently large. Furthermore, we show that the manufacturer could always choose a suitable method of encroachment to raise his profit. Specifically, when the direct encroachment's cost is low, the manufacturer is more inclined to use direct encroachment, whereas when this cost is medium, the manufacturer prefers to adopt agency encroachment. In addition, we show that both manufacturer encroachment and store brand introduction could increase social welfare and consumer surplus. These findings have important practical significance and can provide certain guidance for the government and enterprises.

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.