In the digital economy, self-operated e-commerce platforms like JD Logistics face the challenge of balancing cost control and service quality. This study, led by Luyao Zhang and Deyin Zhang, integrates value chain theory and Analytic Hierarchy Process (AHP) to analyze JD Logistics' current logistics cost management practices and propose an optimized framework. The research highlights key areas for improvement, such as procurement, warehousing, and delivery logistics, and provides actionable strategies for enhancing efficiency and reducing costs.
Research Background
The digital economy has transformed the landscape of logistics and supply chain management, particularly for self-operated e-commerce platforms. As these platforms increasingly rely on intelligence and personalization, they need to provide more efficient and accurate services. However, traditional cost management models often fall short in addressing new challenges such as optimizing transportation routes and predicting demand accurately. This research, led by Luyao Zhang and Deyin Zhang, focuses on JD Logistics, a prominent player in the e-commerce sector, to analyze its current logistics cost management practices and propose an optimized framework.
In the context of the digital economy, the development of self-operated e-commerce platforms is increasingly dependent on advanced technologies like big data, artificial intelligence, and the Internet of Things (IoT). These technologies offer new pathways for managing logistics costs, but they also introduce new complexities. For instance, while JD Logistics has made significant strides with its “Asia No. 1” smart warehousing and big data prediction, it still faces challenges in reducing costs and improving efficiency. The integration of these technologies into logistics operations requires a robust and flexible cost management system that can adapt to dynamic market conditions.
Traditional cost management approaches are often linear and static, which makes them inadequate for the dynamic and interconnected nature of modern supply chains. For example, the conventional methods of cost control, such as manual inventory management and fixed procurement plans, cannot handle the real-time data and predictive analytics required for optimal logistics performance. This is where the combination of value chain theory and AHP comes into play. Value chain theory provides a comprehensive framework for understanding the various activities involved in logistics, while AHP offers a structured approach to prioritize and optimize these activities based on their relative importance.
By integrating these two methodologies, the study aims to provide a more nuanced and effective approach to logistics cost management. This is crucial for JD Logistics, which needs to balance cost control with maintaining high service quality. The research not only identifies the key areas where JD Logistics can improve but also provides a theoretical basis for other self-operated e-commerce enterprises facing similar challenges. The shortcomings of prior approaches, such as their inability to handle real-time data and their lack of flexibility, highlight the need for a more integrated and dynamic solution, which this study aims to address.
The digital economy has driven a paradigm shift in how businesses operate, especially in the logistics and supply chain sectors. Traditional cost management models, which were once sufficient, now struggle to keep up with the rapid changes and complexities introduced by digital technologies. For example, the need for real-time data processing, predictive analytics, and dynamic decision-making is critical in today’s fast-paced business environment. JD Logistics, as a leading e-commerce platform, has been at the forefront of adopting these technologies, but it still faces significant challenges in optimizing its logistics cost management.
One of the key issues is the high proportion of logistics costs in the total operational expenses. According to the study, logistics costs account for a substantial portion of JD Logistics’ overall expenses, making it imperative to find ways to reduce these costs without compromising service quality. The integration of value chain theory and AHP provides a structured and systematic approach to address these challenges. Value chain theory helps in identifying the critical activities and processes in the logistics chain, while AHP allows for a quantitative assessment of the relative importance of these activities, enabling better resource allocation and optimization.
The industry context is also important to consider. In the e-commerce sector, the competition is fierce, and customer expectations are high. Companies must deliver products quickly and efficiently while keeping costs under control. JD Logistics, with its extensive network and advanced technological capabilities, is well-positioned to leverage these advantages, but it needs a more refined and adaptive cost management strategy to stay ahead. The study by Luyao Zhang and Deyin Zhang aims to provide such a strategy, offering a detailed analysis of the current practices and proposing a framework for improvement.
Key Findings
Purchasing Logistics Cost Management
JD Logistics has been using digital information technology, including big data and AI, to predict demand and dynamically adjust procurement plans. This has helped reduce inventory buildup and stockout risks, and the establishment of an automated replenishment system has further lowered logistics costs.
The method principle behind purchasing logistics cost management involves leveraging digital technologies to forecast demand and optimize procurement. By using big data and AI, JD Logistics can dynamically adjust its procurement plans, ensuring that inventory levels are aligned with actual demand. This reduces the risk of overstocking or stockouts, both of which can be costly. The key design logic here is to integrate real-time data and predictive analytics into the procurement process, making it more responsive and efficient.
Experimental setup and evidence show that from 2020 to 2024, the ratio of procurement logistics costs to total revenue decreased from 85.37% to 84.13%. This trend indicates that JD Logistics is making progress in its digital transformation and the application of intelligent technologies. The consistency testing and horizontal comparison reveal that while the overall revenue increased, the proportion of procurement logistics costs decreased, suggesting that the company is becoming more efficient in managing its procurement processes.
Compared to related work, this finding aligns with the broader trend of using digital technologies to optimize logistics. For example, other studies have shown that companies that adopt advanced analytics and AI in their procurement processes see a reduction in costs and an improvement in efficiency. The specific numbers and trends observed in JD Logistics’ case provide concrete evidence of the effectiveness of this approach.
To further elaborate, the use of big data and AI in demand forecasting allows JD Logistics to make more informed decisions about when and how much to procure. This not only reduces the risk of overstocking, which can lead to increased storage costs and potential obsolescence, but also minimizes the risk of stockouts, which can result in lost sales and dissatisfied customers. The automated replenishment system, which is a key component of this approach, ensures that inventory levels are continuously adjusted based on real-time data, leading to a more efficient and responsive procurement process.
The experimental setup involved collecting and analyzing data on procurement logistics costs and total revenue over a five-year period. The data was then used to construct a hierarchical analysis model, which was validated through consistency testing. The results showed a consistent downward trend in the ratio of procurement logistics costs to total revenue, indicating that the company’s efforts to digitize and optimize its procurement processes are paying off. This is further supported by the horizontal comparison with other e-commerce platforms, which shows that JD Logistics is outperforming many of its competitors in terms of procurement efficiency.
Warehousing Logistics Cost Management
JD Logistics has invested significantly in its warehousing network, covering over 700 warehouses nationwide. While this has improved warehousing efficiency, it has also increased warehousing costs. The data shows that from 2020 to 2024, warehousing logistics expenses rose from 48,700 million yuan to 70,426 million yuan, with the warehousing cost rate increasing from 7.47% to 7.59%.
The method principle for warehousing logistics cost management involves optimizing the storage and handling of goods through a well-distributed and technologically advanced warehouse network. JD Logistics has expanded its warehousing capacity to support a large volume of orders, but this expansion has come at a cost. The key design logic is to balance the need for efficient warehousing with the associated costs, ensuring that the increase in capacity does not outpace the growth in revenue.
Experimental setup and evidence indicate that despite the increase in main operating revenue by approximately 42.3%, the warehousing logistics cost rate has not decreased proportionally. This suggests that while the company is investing in expanding its warehousing capabilities, the cost increases are not fully offset by the revenue growth. The consistency testing and horizontal comparison show that the company needs to find ways to better match its warehousing investments with its revenue growth.
Compared to related work, this finding highlights the challenge of scaling up warehousing operations without incurring disproportionate costs. Other studies have suggested that companies should focus on optimizing their warehouse layouts and adopting automation technologies to reduce costs. The specific numbers and trends in JD Logistics’ case provide a clear picture of the challenges and potential solutions in this area.
To delve deeper, the expansion of JD Logistics’ warehousing network has been a strategic move to improve service quality and delivery times. However, the increased capacity has also led to higher costs, which need to be managed carefully. The company has implemented several measures to optimize its warehousing operations, such as the use of automated systems and advanced inventory management techniques. These measures have helped to some extent, but the cost-to-revenue ratio remains a concern.
The experimental setup involved collecting data on warehousing logistics expenses and main operating revenue over the same five-year period. The data was analyzed using a hierarchical analysis model, which was validated through consistency testing. The results showed that while the main operating revenue increased by 42.3%, the warehousing logistics cost rate increased from 7.47% to 7.59%. This indicates that the cost increases are not fully offset by the revenue growth, suggesting that there is room for further optimization.
The horizontal comparison with other e-commerce platforms reveals that JD Logistics is not alone in facing this challenge. Many companies are struggling to balance the need for increased warehousing capacity with the associated costs. However, the specific numbers and trends in JD Logistics’ case provide a clear picture of the challenges and potential solutions. For example, the company could explore more advanced automation technologies, such as robotics and AI, to further reduce labor costs and improve efficiency. Additionally, optimizing the layout and design of the warehouses could help to reduce the time and effort required to handle and store goods, leading to lower costs.
Delivery Logistics Cost Management
JD Logistics has been using advanced digital information technology to manage delivery costs, aiming to improve logistics efficiency, reduce delivery costs, and enhance customer satisfaction. The company’s use of a logistics platform and intelligent scheduling system allows for real-time monitoring of delivery routes and transport processes, ensuring timely and efficient deliveries.
The method principle for delivery logistics cost management involves using digital technologies to optimize the delivery process. By leveraging a logistics platform and intelligent scheduling system, JD Logistics can monitor and adjust delivery routes in real-time, ensuring that deliveries are both efficient and on time. The key design logic is to use real-time data and predictive analytics to make the delivery process more responsive and cost-effective.
Experimental setup and evidence show that from 2020 to 2024, the coverage of JD’s same-day and next-day delivery services increased from 91% to 95%. This improvement in delivery efficiency has not only enhanced customer experience but also reduced unit delivery costs through economies of scale. The consistency testing and horizontal comparison indicate that the company’s investment in expanding its own logistics network and enhancing its warehousing and delivery capabilities has contributed to better cost management.
Compared to related work, this finding is consistent with the trend of using digital technologies to improve delivery logistics. Other studies have shown that companies that adopt advanced logistics technologies see improvements in delivery times and cost reductions. The specific numbers and trends in JD Logistics’ case provide concrete evidence of the effectiveness of this approach.
To provide more detail, the use of a logistics platform and intelligent scheduling system has been a game-changer for JD Logistics. The platform allows the company to monitor and adjust delivery routes in real-time, taking into account factors such as traffic conditions, weather, and order volumes. This ensures that deliveries are as efficient and on-time as possible, leading to higher customer satisfaction and lower costs. The intelligent scheduling system, which uses predictive analytics, helps to optimize the allocation of resources, such as vehicles and personnel, further enhancing the efficiency of the delivery process.
The experimental setup involved collecting data on the coverage of same-day and next-day delivery services over the five-year period. The data was analyzed using a hierarchical analysis model, which was validated through consistency testing. The results showed a consistent increase in the coverage of these services, from 91% in 2020 to 95% in 2024. This improvement in delivery efficiency has had a positive impact on customer experience, as evidenced by the high satisfaction rates reported in the study. Additionally, the economies of scale achieved through the expanded logistics network have led to lower unit delivery costs, contributing to better cost management.
The horizontal comparison with other e-commerce platforms shows that JD Logistics is outperforming many of its competitors in terms of delivery efficiency. For example, the coverage of same-day and next-day delivery services is higher than that of many other platforms, indicating that the company’s investment in advanced logistics technologies is paying off. The specific numbers and trends in JD Logistics’ case provide a clear picture of the benefits of using digital technologies to optimize delivery logistics.
Limitations
Limited Data on Supplier Information
One of the limitations of the study is the limited availability of detailed supplier information. This makes it challenging to fully assess the impact of supplier management on logistics costs.
The lack of detailed supplier information in JD Logistics’ annual reports means that the study had to rely on financial metrics like accounts payable turnover and days payable outstanding. While these metrics provide some insights, they do not give a complete picture of the supplier relationships and their impact on logistics costs. This limitation can affect the accuracy of the analysis and the recommendations for supplier management. To mitigate this, the study could incorporate more qualitative data, such as interviews with suppliers and internal stakeholders, to gain a deeper understanding of the supplier dynamics.
The limited data on supplier information is a significant constraint, as it limits the depth of the analysis and the ability to draw comprehensive conclusions. For example, the study relies on financial metrics such as accounts payable turnover and days payable outstanding, which, while useful, do not capture the full complexity of supplier relationships. These metrics can provide a general sense of the payment terms and the efficiency of the supply chain, but they do not reveal the nuances of supplier interactions, such as the quality of communication, the level of collaboration, and the overall reliability of the suppliers.
To address this limitation, the study could incorporate more qualitative data, such as interviews with suppliers and internal stakeholders. These interviews could provide valuable insights into the dynamics of the supplier relationships, including the challenges and opportunities for improvement. Additionally, the study could explore the use of secondary data sources, such as industry reports and case studies, to supplement the limited primary data. This would help to provide a more comprehensive and nuanced understanding of the impact of supplier management on logistics costs.
Assumptions in the AHP Model
The AHP model used in the study relies on expert judgment, which can introduce subjectivity and bias into the analysis.
The AHP model is a powerful tool for decision-making, but it is based on subjective judgments from experts. The assumptions and weights assigned to different criteria and sub-criteria can vary, leading to different outcomes. This subjectivity can affect the reliability of the results. To mitigate this, the study could use a larger and more diverse group of experts, and conduct sensitivity analyses to test the robustness of the model. Additionally, incorporating more objective data and metrics can help reduce the reliance on subjective judgments.
The AHP model, while a robust and widely used method for multi-criteria decision-making, is inherently subjective due to its reliance on expert judgment. The experts’ opinions and biases can influence the weights assigned to different criteria and sub-criteria, leading to variations in the results. For example, if the experts have different perspectives on the relative importance of procurement versus warehousing, the resulting weights and priorities will differ, potentially affecting the overall conclusions of the study.
To mitigate this limitation, the study could use a larger and more diverse group of experts, representing a wider range of perspectives and expertise. This would help to reduce the impact of individual biases and provide a more balanced and representative set of judgments. Additionally, the study could conduct sensitivity analyses to test the robustness of the model. Sensitivity analyses involve varying the weights and parameters to see how the results change, providing a more comprehensive understanding of the model’s stability and reliability.
Another way to reduce the reliance on subjective judgments is to incorporate more objective data and metrics. For example, the study could use historical data and performance metrics to validate the expert judgments and ensure that the model is grounded in empirical evidence. This would help to provide a more balanced and reliable analysis, combining the strengths of both subjective and objective approaches.
Dynamic Nature of the Digital Economy
The rapidly evolving nature of the digital economy means that the findings and recommendations may become outdated quickly.
The digital economy is characterized by rapid technological advancements and changing market conditions. The findings and recommendations in the study may not remain relevant for long, as new technologies and business models emerge. This limitation can be mitigated by conducting regular updates and follow-up studies to track the changes and adjust the recommendations accordingly. Additionally, the study could include a more forward-looking analysis, considering potential future developments and their implications for logistics cost management.
The dynamic nature of the digital economy is a significant challenge for any study, as the rapid pace of technological change and market evolution can render findings and recommendations obsolete quickly. For example, the emergence of new technologies, such as blockchain and 5G, and the adoption of new business models, such as on-demand logistics and last-mile delivery, can fundamentally alter the landscape of logistics and supply chain management. This means that the strategies and frameworks proposed in the study may need to be adapted or revised to remain relevant.
To address this limitation, the study could include a more forward-looking analysis, considering potential future developments and their implications for logistics cost management. For example, the study could explore the potential impact of emerging technologies, such as autonomous vehicles and drones, on the logistics process. It could also consider the implications of new business models, such as collaborative logistics and shared warehousing, and how they might affect the cost and efficiency of logistics operations.
Additionally, the study could recommend regular updates and follow-up studies to track the changes and adjust the recommendations accordingly. This would help to ensure that the findings and recommendations remain relevant and applicable in the rapidly evolving digital economy. By staying attuned to the latest developments and trends, the study can provide ongoing guidance and support for logistics and supply chain practitioners.
Practical Implications
Enhancing Predictive Analytics for Demand Forecasting
JD Logistics can leverage advanced predictive analytics to improve demand forecasting, thereby optimizing inventory levels and reducing costs.
By using advanced predictive analytics, JD Logistics can more accurately forecast demand, allowing for better inventory management. This can help reduce the risk of overstocking or stockouts, both of which can be costly. For example, the company can use machine learning algorithms to analyze historical sales data, market trends, and other relevant factors to predict future demand. This will enable the company to adjust its inventory levels and procurement plans in real-time, ensuring that it has the right amount of stock to meet customer demand without incurring unnecessary costs.
To implement this, JD Logistics can invest in advanced analytics tools and platforms that can handle large volumes of data and perform complex predictive modeling. The company can also train its staff in data analytics and machine learning, ensuring that they have the skills and knowledge to effectively use these tools. Additionally, the company can collaborate with external partners, such as data analytics firms and technology providers, to access the latest tools and expertise. By doing so, JD Logistics can improve its demand forecasting capabilities, leading to better inventory management and cost savings.
Optimizing Warehouse Layout and Automation
JD Logistics can optimize its warehouse layout and invest in automation technologies to improve efficiency and reduce costs.
To optimize its warehouse operations, JD Logistics can focus on improving the layout and design of its warehouses, as well as investing in automation technologies. For example, the company can use robotics and automated systems to handle tasks such as picking, packing, and sorting, which can significantly reduce labor costs and improve efficiency. Additionally, the company can use data analytics to optimize the placement of goods within the warehouse, ensuring that high-demand items are easily accessible and that the overall workflow is streamlined. This will help reduce the time and effort required to fulfill orders, leading to lower costs and faster delivery times.
To implement this, JD Logistics can conduct a thorough review of its current warehouse layouts and identify areas for improvement. The company can work with warehouse design experts to develop more efficient layouts that minimize travel distances and maximize space utilization. Additionally, the company can invest in automation technologies, such as robotic arms, automated guided vehicles (AGVs), and conveyor systems, to automate repetitive and labor-intensive tasks. By doing so, JD Logistics can improve the efficiency of its warehouse operations, leading to lower costs and faster delivery times.
Strengthening Supplier Relationships and Collaboration
JD Logistics can strengthen its relationships with suppliers and enhance collaboration to improve supply chain efficiency and reduce costs.
Building strong and collaborative relationships with suppliers is crucial for improving supply chain efficiency and reducing costs. JD Logistics can work closely with its suppliers to share data and insights, enabling better coordination and planning. For example, the company can use blockchain technology to create a transparent and secure platform for sharing data, such as inventory levels, production schedules, and delivery timelines. This will allow both parties to make more informed decisions and respond more effectively to changes in the market. Additionally, the company can explore long-term partnerships and joint ventures with key suppliers, which can lead to cost savings and improved service quality.
To implement this, JD Logistics can establish a dedicated team to manage supplier relationships and coordinate with suppliers on a regular basis. The company can also invest in digital platforms and tools that facilitate data sharing and collaboration, such as blockchain and cloud-based supply chain management systems. By doing so, JD Logistics can improve the transparency and efficiency of its supply chain, leading to better cost management and service quality.
