1. Introduction

In the early stages of cross-border supply chain logistics management, the focus was primarily on improving the efficiency of international trade. Authors such as Christopher and Meindl (2015) emphasized the importance of strategy and planning in supply chain management. They highlighted the need for a comprehensive approach to address the challenges posed by globalization. During this period, the key objective was to reduce lead times, optimize inventory, and enhance transportation efficiency.[1] The integration of technology marked a significant turning point in the evolution of cross-border supply chain logistics management. Wang et al. (2016) explored the role of big data analytics in logistics and supply chain management. They introduced the concept of predictive analytics, which enabled organizations to forecast demand accurately and optimize inventory levels.[2] Real-time monitoring of supply chain operations became possible, providing visibility and enabling proactive issue resolution. As globalization continued to reshape supply chains, authors like Ivanov and Sokolov (2019) highlighted the increasing complexity and risks of cross-border logistics. They discussed the impact of digital technology and Industry 4.0 on supply chain risk analytics. This period saw a growing emphasis on supply chain traceability, transparency, and compliance with international regulations.[3] Globalization has transformed the landscape of modern supply chain management, leading to increased complexity and the need for innovative approaches to meet the challenges of cross-border supply chains. In recent years, the integration of big data technology has emerged as a critical driver of change in supply chain management practices. This paper overviews how globalization and big data technology have reshaped modern supply chain management, particularly in cross-border supply chains. This paper combines knowledge from big data analysis, supply chain management, and international trade to provide a new perspective for cross-border supply chain inventory management. It explores this transformation's critical implications, challenges, and opportunities.

2. Challenges in Cross-Border Supply Chain Inventory Management

The logistics and transportation uncertainties in cross-border supply chain inventory management encompass various aspects, specifically:

2.1. Delays Due to Long-Distance Transports

Long-distance and cross-border transports, which frequently encompass a combination of transportation modes such as sea, air, and land freight, contribute significantly to extended transit times and heightened unpredictability in supply chains. These complex supply chain routes often involve multiple handovers, customs clearance processes, and varying regulatory requirements in different regions or countries. As a result, the potential for delays becomes more pronounced. These delays pose significant challenges, especially for industries dealing with time-sensitive or perishable goods, such as pharmaceuticals, fresh produce, or electronics. In the case of pharmaceuticals, transportation delays can impact the availability of critical medications, affecting patient health and well-being. Likewise, fresh produce and electronics may suffer quality deterioration or obsolescence during prolonged transit, leading to inventory shortages or surpluses.

2.2. Infrastructure Disparities

The infrastructure levels vary among countries, especially in developing nations. The condition of roads, ports, and airports directly impacts the efficiency and reliability of goods transportation. Limitations in infrastructure can lead to additional transport costs and time delays. Jianying Xie has expressed concern regarding the poor targeted inventory management in China's supply chain. In her article titled “Exploring the Inventory Management Model of Cross-border E-commerce in the Supply Chain Environment,” she stated that: “due to significant differences between various inventory models, there are notable variations in the requirements for business management level, and the necessary skill level is also relatively high. This leads to less targeted inventory management, and there exist differences between several management procedures and actual requirements.” [4]

2.3. Bottlenecks and Disruptions in Transportation

Infrastructure levels exhibit significant disparities among countries, particularly pronounced differences in developing nations. The state of transportation infrastructure, including roads, ports, and airports, plays a pivotal role in determining the efficiency and dependability of goods transportation within and across borders. Inadequate infrastructure poses multifaceted challenges, such as substandard road conditions, outdated port facilities, and congested airports.

For instance, in developing nations, poorly maintained roads can result in frequent vehicle breakdowns and lengthy travel times. This escalates transport costs due to vehicle repairs and fuel consumption and contributes to shipment delays. Additionally, ports with insufficient capacity or outdated equipment may experience lengthy waiting times for cargo handling and vessel berthing, causing further delays.

2.4. Supply Chain Transparency Issues

In cross-border supply chains, monitoring the entire chain's status becomes notably challenging due to the vast geographical distances involved and the complex network of multiple parties participating in the process. This inherent complexity often results in a lack of transparency, where visibility into the movement and status of goods is limited.

This diminished transparency contributes significantly to the uncertainty surrounding inventory management. Businesses may struggle to obtain real-time information about the location, condition, and progress of their shipments as they traverse international borders. This lack of visibility hampers their ability to make informed decisions promptly.

For example, delays or disruptions at customs checkpoints or during border crossings can occur without warning. Without timely information, businesses may find it challenging to adjust inventory levels, leading to potential shortages or surpluses. Moreover, the inability to monitor the conditions of goods during transit can increase the risk of damage or spoilage, further complicating inventory management. In their book “Designing and Managing the Supply Chain: Concepts, Strategies, and Case Studies,” Simchi-Levi, Kaminsky, and Simchi-Levi discuss various aspects of supply chain management. They said: “Supply chain optimization requires a comprehensive approach combining concepts, strategies, and real-world case studies to address dynamic market demands.” [5] Therefore, it is necessary to implement new measures to address the challenges faced by supply chain logistics.

3. The Application of Big Data in Cross-Border Supply Chain Inventory Management

3.1. Data-Driven Inventory Optimization

Big data enables businesses to leverage predictive analytics to identify potential risks in cross-border supply chains. Predictive analytics involves using historical data, market trends, consumer behavior patterns, and external factors such as economic indicators, political stability, and climate change to build forecasting models. These models can predict future occurrences like supply disruptions, demand fluctuations, or price changes, thus providing valuable insights for businesses to prepare in advance. Waller et al. stated: “Big data analytics revolutionizes supply chain design, enabling predictive insights that drive operational efficiency and competitive advantage.” [6] This prediction helps businesses optimize inventory levels, avoiding surplus or shortage due to demand volatility.

Moreover, predictive analytics can also alert potential supply disruptions. For instance, by monitoring suppliers' delivery records and production statuses, businesses can identify weak links in their supply chain in advance and take measures to mitigate these risks. This might include establishing alternative sources of supply, adjusting inventory strategies, or redesigning the supply chain to increase flexibility. Predictive analytics can assist businesses in assessing the potential impact of external environmental changes on the supply chain. Cachon and Fisher consider that: “The integration of shared information in supply chain management significantly enhances inventory optimization and responsiveness to market changes.” [7] For example, big data can be used to analyze how political conflicts, changes in trade policies, or economic crises might affect the supply chain, allowing businesses to plan and develop strategies in advance.

3.2. Risk Management and Predictive Analytics

Big data is crucial for risk assessment and quantification in managing risks in cross-border supply chains. Big data analytics can reveal potential risk points within the supply chain. By analyzing historical data and performance metrics of different elements within the supply chain, businesses can identify key factors that may lead to disruptions or inefficiencies. Moreover, by utilizing statistical models and algorithms, big data can help businesses quantify these risks. For example, by calculating the frequency and extent of delayed deliveries from a supplier, a company can quantify the risk associated with working with that supplier. Big data also allows businesses to analyze the potential impact of a specific risk event on the entire supply chain. This includes assessing the impact on customer satisfaction, costs, revenue, and brand reputation. Choi, T.M. et al stated in their article that: “Big data analytics plays a pivotal role in transforming business operations and managing risks in modern supply chains.” [8]

Big data can reveal correlations between different risk factors. This is crucial for understanding how complex supply chains react under the combined effect of various risk factors. Based on historical data and market dynamics, big data can be used to build predictive models to forecast potential risk events and their possible impacts in the future.

3.3. Real-Time Tracking and Enhanced Transparency

Big data technologies can track and analyze data flows across the supply chain, from raw material procurement to final product delivery. This end-to-end visibility enables businesses to monitor inventory levels, order statuses, and goods movement in real time, enhancing awareness of supply chain activities. By collecting and analyzing data from suppliers, such as delivery times, quality control reports, and response speeds, big data can help businesses assess and manage supplier performance. Sanders said: “Big data usage in supply chains leads to significant improvements in efficiency, transparency, and decision-making processes.” [9] This aids in timely identification and resolution of potential issues within the supply chain.

Big data analysis can reveal market trends and customer demand patterns. This information is crucial for accurately forecasting market changes, developing effective inventory strategies, and optimizing product portfolios. By utilizing big data analytics, businesses can set up early warning systems to monitor risk factors that might lead to supply chain disruptions, such as extreme weather, political instability, or traffic congestion. Big data can also enhance communication and collaboration between businesses and other stakeholders in the supply chain. Manyu Li pointed out, “If a company wants to obtain inventory information from other companies, it must exchange information of equal value with its own.” [10] By sharing data and analytical results, all parties can coordinate actions and optimize supply chain decisions more effectively. Xumei Zhang pointed out that: “Under certain conditions, it is more beneficial for the OS to choose the direct-mail mode when the information is shared, as doing so can help chain members escape the prisoner's dilemma and thus lead to a ‘win-win-win’ outcome.” [11]

4. Examples

JD E-commerce and NongFu Spring demonstrate innovative applications of big data in cross-border supply chain inventory management. JD E-commerce utilizes its digitally intelligent social supply chain, employing digital intelligence technology to connect and optimize every aspect of social production, distribution, and services, thereby reducing social costs and increasing efficiency. JD boasts over 5 million SKUs of self-operated products, with inventory turnover days reduced to 34 days, a world-class level. JD employs artificial intelligence technology and big data analytics to predict future sales of merchandise and, combined with scientific modeling algorithms, automates inventory management. Apte, U.M. and Viswanathan, S. stated: “Effective cross-docking techniques can significantly improve distribution efficiencies in real-time monitoring of supply chains.” [12]

On the other hand, NongFu Spring has heavily invested in supply chain management by introducing supply chain planning systems, reshaping forecast management functions, and using big data to facilitate scenario marketing. The company has adopted supply network planning tools like LLAMASOFT to optimize the transportation data scenario from filling to shelf, thereby reducing storage and logistics costs.

In the cases of JD E-commerce and NongFu Spring, big data is innovatively employed in inventory management in the following ways.

4.1. Intelligent Replenishment Systems

In the context of JD E-commerce, the application of big data and artificial intelligence (AI) technologies for intelligent replenishment systems represents a remarkable advancement in inventory management. Li Heng and Wang Fang stated: “Artificial intelligence is leading a new round of technological revolution and industrial transformation, and accelerating intelligence in the field of supply chain logistics is an important development trend.” [13] These cutting-edge systems are engineered to revolutionize how businesses maintain and optimize inventory levels. JD's intelligent replenishment system leverages a comprehensive database of historical sales data, encompassing various product categories and market segments. This extensive data repository enables the system to perform intricate analyses, identifying patterns and trends in consumer demand. By delving into the nuances of market dynamics, including seasonal fluctuations, emerging trends, and regional preferences, the system refines its replenishment recommendations. The heart of this innovative system lies in its advanced replenishment models and meticulously calibrated replenishment parameters. These models are crafted with precision, taking into account factors such as lead times, supplier reliability, and product shelf life. The system employs state-of-the-art scientific modeling algorithms, continuously fine-tuned through machine learning, to calculate optimal reorder points and quantities.

The outcome of this sophisticated approach is twofold. Firstly, it significantly heightens replenishment accuracy, ensuring that products are available precisely when needed. This minimizes instances of stockouts, where products are unavailable, and overstocking, which can lead to excessive inventory carrying costs. Secondly, the system's insights into market dynamics allow for proactive inventory optimization. Chaffey, D claimed: “E-commerce has reshaped supply chain management by introducing digital platforms that streamline operations and customer interactions.” [14] By aligning inventory levels with actual demand patterns, JD E-commerce can reduce turnover rates and associated holding costs, further enhancing cost efficiency.

In essence, JD's intelligent replenishment system is a testament to the power of big data and AI in transforming inventory management into a data-driven, automated, and highly optimized process. It exemplifies how cutting-edge technology can revolutionize supply chain operations, providing businesses with a competitive edge in the dynamic world of e-commerce.

4.2. Predictive Analytics

In the realm of JD E-commerce, the utilization of big data analytics to predict future sales trends represents a formidable leap in demand forecasting. Bradlow, E.T. et al said: “Big data's role in retailing involves predictive analytics that enhance decision-making and customer experience management.” [15] This advanced system operates as a strategic compass, guiding the company's inventory management and control with unprecedented precision. At its core, JD's data-driven approach relies on a vast reservoir of historical sales data, encompassing a diverse array of product categories and market segments. This extensive dataset serves as the foundation for meticulous analysis, enabling the system to discern intricate patterns and trends in consumer behavior. By mining this wealth of information, JD can anticipate shifts in demand dynamics with a high degree of accuracy.

The predictive power of this system is further amplified by its integration of scientific modeling algorithms. These algorithms are intricately designed to account for a multitude of variables, including seasonality, regional preferences, and external factors such as economic conditions and emerging trends. Through a continuous process of refinement, often facilitated by machine learning, these models evolve to capture even the subtlest nuances in market responses. As a result, JD E-commerce gains the ability to offer highly accurate sales forecasts for a wide range of products. This foresight enables the company to fine-tune its inventory levels in preparation for anticipated surges or declines in demand. By aligning stock levels with projected requirements, bode stated: “Supply chain complexity and the frequency of disruptions are directly influenced by structural factors and management strategies.” [16] JD minimizes the risks of stockouts and overstocking, thereby enhancing cost efficiency and customer satisfaction.

JD's application of big data analytics in sales trend prediction signifies a pivotal transformation in inventory management, where data-driven decision-making becomes the linchpin of efficient supply chain operations. It exemplifies how harnessing the power of data can lead to not only cost savings but also improved service quality and responsiveness to the ever-changing demands of the market.

4.3. Supply Chain Transparency and Efficiency

NongFu Spring's commitment to optimizing its supply chain is exemplified by its adoption of cutting-edge supply network planning tools like LLAMASOFT. This strategic move transcends mere cost reduction; it represents a holistic transformation of the entire transportation data scenario; from when a product is filled to the point it reaches the shelf. By leveraging LLAMASOFT and similar technologies, NongFu Spring gains the ability to fine-tune every aspect of its supply chain operations. From production scheduling to distribution logistics, the company can analyze and optimize various facets of its supply network with unparalleled precision. This not only translates into reduced storage and logistics costs but also fosters a remarkable boost in transparency and efficiency throughout the supply chain.

One of the standout advantages of this approach is the heightened transparency it offers. Rivera-Rosario et al. think: “The lack of cost transparency can contribute to higher costs without improved patient outcomes.” [17] NongFu Spring can obtain real-time insights into the movement of products within its supply chain. This transparency is invaluable for monitoring inventory levels, identifying potential bottlenecks, and promptly addressing any deviations in the production or distribution processes. Furthermore, this advanced technology allows NongFu Spring to achieve a new level of supply chain efficiency. By streamlining operations and minimizing inefficiencies, the company can ensure that its products are consistently delivered to consumers promptly. This not only enhances customer satisfaction but also bolsters the company's competitive edge in a dynamic market.

NongFu Spring's adoption of supply network planning tools represents a forward-looking approach to supply chain management. It goes beyond cost savings, ushering in an era of data-driven decision-making, transparency, and operational excellence that positions the company for sustained growth and success in the beverage industry.

4.4. Supply Chain Risk Management

Both companies, JD E-commerce, and NongFu Spring, have harnessed the power of big data analysis to proactively identify and effectively manage supply chain risks. Their approach goes beyond traditional risk management by incorporating real-time monitoring and adaptive strategies.

In supply chain risk management, both companies have adopted a comprehensive strategy that involves continuous monitoring of potential disruption risk factors. These factors encompass a wide range of variables, including but not limited to extreme weather events, political instability in sourcing regions, transportation bottlenecks such as traffic congestion, labor strikes, and even geopolitical tensions. Chen, H. et al said: “The intersection of business intelligence, analytics, and big data is transforming the landscape of supply chain management.” [18] By leveraging big data analytics, they can gather and process vast amounts of data from various sources to assess the likelihood and impact of these risk factors.

The key differentiator lies in their ability to respond swiftly to emerging risks. Both JD E-commerce and NongFu Spring have developed agile inventory strategies that allow them to adapt in real time to mitigate the effects of disruptions. For instance, in the face of an impending natural disaster, they can adjust their inventory levels and distribution routes to ensure uninterrupted supply to their customers.

The integration of big data analysis into supply chain risk management has empowered JD E-commerce and NongFu Spring to not only identify potential threats but also to respond swiftly and adaptively. Ngai stated: “Technological innovations in supply chains are key to enhancing operational and management competencies, thereby ensuring agility and responsiveness.” [19] Their data-driven approach enhances their resilience in an ever-changing and unpredictable global supply chain landscape, ensuring a reliable supply of products to meet customer demands.

5. Conclusion

This paper aims to explore how big data technology drives innovation in cross-border supply chain inventory management. Through the analysis of existing literature and practical cases, we have concluded that big data technology has enormous potential in cross-border supply chain inventory management and can be used for demand forecasting, inventory optimization, risk management, and enhancing supply chain transparency. Intelligent replenishment systems and predictive analytics are key applications of big data in inventory management, improving inventory accuracy, reducing inventory costs, and enhancing supply chain agility. The application of big data not only improves inventory management but also helps businesses better address risks in the supply chain, including supplier risks, natural disasters, and political instability. Yanli Zhang believes that: “effective communication and collaboration are essential at every step of supply chain management. Enterprises need to strengthen logistics, control distribution costs, engage in trade cooperation, and focus on distribution through joint efforts.” [20] Supply chain transparency is a critical factor in achieving efficient inventory management, and big data can enable real-time monitoring and information sharing, enhancing supply chain visibility.

However, this study also has some limitations. Firstly, the application of big data technology requires a large amount of data and advanced analytical tools, which may pose challenges for some small and medium-sized enterprises. Secondly, the complexity and uncertainty of supply chains mean that it is impossible to eliminate risks, and big data can only help businesses better manage these risks.

Future research directions could include further exploration of the application of big data in different industries and different-sized enterprises, as well as the development of more advanced forecasting and optimization models. Additionally, research on the integration of emerging technologies such as supply chain digitization and blockchain with big data could further improve supply chain management efficiency and transparency.

In conclusion, big data technology has brought revolutionary changes to cross-border supply chain inventory management, providing businesses with better tools for inventory management and risk mitigation. Rathke and Santana said: “Cross-border supply chain management presents unique challenges and opportunities, necessitating a specialized conceptual framework.” [21] This field still presents challenges and opportunities, requiring ongoing research and innovation to achieve higher levels of supply chain management.