1. Introduction

The global economy is currently in a complex and dynamic phase, driven by both emerging markets and developed countries. In this context, the logistics industry, a key pillar of global trade, has seen China’s total logistics volume grow by 5.2% in 2023, with a declining proportion of logistics costs to GDP, indicating improved efficiency. However, further optimization of logistics costs remains a pressing issue.[1] In supply chain management, two key strategies for reducing logistics costs are VMI and WDI. It is shown that VMI can enhance supply chain coordination and reduce inventory risks, though its implementation requires balanced interests among stakeholders, and WDI, an emerging service model, can shorten response times and lower inventory costs but also faces challenges that need to be addressed[2]. Improvements in these management methods allow enterprises better adapt to market changes and achieve sustainable growth[3]. VMI can significantly reduce inventory costs and improve supply chain efficiency, while WDI can speed up delivery to enhance customer experience[4]. This paper explores the role of integrated warehousing and distribution within the VMI strategy in reducing logistics costs, with the aim of helping enterprises build a sustainable supply chain system and strengthen industry competitiveness. The research show that VMI model and WDI strategy can serve to reduce logistics costs, while simultaneously building a robust and sustainable supply chain management system to ensure the efficient and stable operation of the supply chain.

2. Overview of Vendor Managed Inventory and Warehouse-Distribution Integration

VMI and WDI are two complementary supply chain management strategies designed to enhance efficiency, reduce costs, and mitigate risks such as the bullwhip effect[5]. VMI means that suppliers are responsible for inventory management, using real-time data sharing and demand forecasting to optimize inventory planning and replenishment strategies, which minimizes inventory backlogs and stock-outs, improves order processing speeds, and boosts customer satisfaction by giving suppliers a better understanding of customer needs and more timely responses[6]. At the same time, WDI coordinates warehouse management and logistics to meet customer demand more effectively, including demand forecasting, inventory management, order processing, and distribution management, all of which supported by a unified information system that enables data sharing and collaborative operation. The alignment of inventory levels with actual demand enables enterprises to reduce the capital tied up and ensure the delivery of goods in a more accurate and timely manner[7]. When VMI and WDI are combined, powerful synergies are created. Such integration helps companies further reduce logistics costs, enhances supply chain efficiency, and builds stronger relationships between suppliers and customers. Effective information transmission, optimized replenishment processes, and coordinated warehousing and logistics operations are essential at the operational level to achieve these benefits.

3. Yonghui Superstore’s Sales and Inventory Management: Large Appliances Case Study

3.1. Yonghui Superstore: Operational Overview and Large Appliance Inventory Management

Founded in 2001, Yonghui Superstore has grown to become a major player in China’s retail sector, with over 1,000 stores across 600 cities in 29 provinces. Its strategic expansion plan included the construction of the Yonghui West Logistics Park (Chongqing) and Yonghui (Pengzhou) Agricultural Product Processing and Distribution Center, enhancing its distribution and warehousing capabilities in the Sichuan-Chongqing region. Chengdu’s role as a central hub makes it crucial to understand Yonghui’s operational strategies in this market.

Yonghui’s approach to managing inventory for large appliances utilizes a sophisticated indicator model. This model estimates monthly sales volumes by considering factors such as population size, population growth, average income, and consumption level. These factors are weighted to reflect their impact on sales: population size (weight 3), population growth (weight 2), average income (weight 4), and consumption level (weight 5). For example, the model projects an average monthly sales volume of 357 refrigerators for Longquanyi District. This forecasting model allows Yonghui to align inventory levels with predicted sales, optimizing stock management. By reducing both excess inventory and the risk of stockouts, Yonghui enhances inventory turnover and reduces carrying costs. The model supports effective inventory management by integrating sales forecasts with supply chain operations, ensuring timely stock replenishment and efficient use of warehouse space. This data-driven approach underscores Yonghui’s commitment to improving operational efficiency and maintaining a competitive edge in the retail market.

3.2. Inventory Management Models

Yonghui Superstore uses the reorder point model for inventory management, which helps maintain optimal inventory levels and prevent stock-outs[8]. The reorder point is calculated using the formula:

\( R=d×L+S \) (1)

where d is the sales volume, L is the lead time, and S is the safety stock. For constant lead times, the safety stock formula simplifies to[9]:

\( S=Z×σ×\sqrt[]{L} \) (2)

where Z represents the standard deviation of demand and \( σ \) represents the standard deviation of demand.This approach helps in maintaining a balance between sufficient inventory to meet demand and minimizing excess stock.

In addition, Yonghui optimizes inventory management by calculating the EOQ, which helps in determining the optimal order size to minimize total inventory costs. However, challenges arise when demand forecasts are inaccurate. Such inaccuracies can lead to poor inventory and capacity planning, potentially resulting in either stock-outs or excess inventory. Overreliance on forecasted demand may also misrepresent true demand, particularly for customized products, further complicating inventory management[10]. Thus, while the reorder point model and EOQ provide valuable frameworks, their effectiveness depends on the accuracy of demand forecasts and the ability to adapt to variability in actual demand.

3.3. Supplier Selection

In implementing the integrated warehousing and distribution model, the quality control and procurement departments must refine supplier development and evaluation processes to enhance delivery timeliness, improve product quality, and ensure supply chain stability[11]. Effective supplier selection and cooperation are crucial. Enterprises should evaluate suppliers based on brand management by considering market reputation and profitability, which can guide the choice of brands to prioritize. Additionally, assessing supplier stability involves not only examining production capabilities and technical expertise but also focusing on personnel stability and requiring timely reporting of significant personnel changes to mitigate potential disruptions. Financial stability is equally important, particularly under the VMI model, where suppliers may face capital pressures and bankruptcy risks. Procurement staff must strengthen financial oversight, scrutinize upstream suppliers, and address capital chain risks promptly. Weber’s review of 74 studies since 1966 highlights the importance of supplier selection as a fundamental aspect of procurement, emphasizing that maintaining a reliable supplier base is crucial for acquiring necessary materials, services, and equipment[12].

4. Application of VMI Strategy in Yonghui Superstore

4.1. Implementation Process of VMI Strategy

The implementation of the VMI strategy at Yonghui Superstore involves adapting the EOQ model to the Economic Production Quantity (EPQ) model to align with a production environment[13]. The EPQ model modifies the EOQ model by replacing the ordering cost C with the production fixed cost \( {C_{SC}} \) , and substituting the holding cost with variable costs. The fixed production cost \( {C_{SC}} \) is calculated as:

\( {C_{SC}}={C_{Z}}+{C_{G1}}+{C_{J}} \) (3)

where, \( {C_{Z}} \) represents equipment depreciation cost, \( {C_{G1}} \) denotes management salaries, and \( {C_{J}} \) is maintenance cost per equipment operation. The variable cost \( {C_{B}} \) is calculated as:

\( {C_{B}}={C_{CL}}+{C_{G2}}+{C_{E}} \) (4)

where \( {C_{CL}} \) is the cost of raw materials, \( {C_{G2}} \) is wages of workers, and \( {C_{E}} \) represents the cost of electricity consumption. Based on the EOQ model, the EPQ model can be derived as:

\( {Q_{SC}}=\sqrt[]{\frac{2D{C_{SC}}}{{C_{B}}}} \) (5)

The feasibility of this formula can be verified using dimensional analysis, ensuring consistency in units and confirming the model’s applicability. The unit of \( D \) is pieces, the unit of \( {C_{SC}} \) is yuan, and the unit of \( {C_{B}} \) is yuan/piece, which can be obtained by substituting them into the formula:

\( \sqrt[]{\frac{2∙piece∙yuan}{\frac{yuan}{piece}}}=\sqrt[]{2{piece^{2}}}=\sqrt[]{2}piece \) (6)

The calculated result in terms of pieces proves that the analogized EPQ model is feasible.

In addition, selecting the optimal location for the distribution center is crucial. The initial step is to define \( {C_{j}} \) as the transportation cost from the logistics center to each store. This can be expressed as:

\( H=\sum _{j=1}^{n}{C_{j}} \) (7)

\( {C_{j}} \) can be represented by the following formula:

\( {C_{j}}={h_{j}}×{w_{j}}×{d_{j}} \)

\( {d_{j}}=\sqrt[]{{({x_{i}}-{x_{j}})^{2}}+{({y_{i}}-{y_{j}})^{2}}} \) (8)

To find the \( x \) and \( y \) that minimize \( H \) , the following conditions must be satisfied:

\( \frac{∂H}{∂x}=\sum _{j=1}^{n}{h_{j}}{w_{j}}(x-{x_{j}})/{d_{j}}=0 \)

\( \frac{∂H}{∂y}=\sum _{j=1}^{n}{h_{j}}{w_{j}}(y-{y_{j}})/{d_{j}}=0 \) (9)

Through calculation, the most suitable \( {x^{*}} \) and \( {y^{*}} \) can be obtained as follows:

\( {x^{*}}=\frac{\sum _{j=1}^{n}{h_{j}}{w_{j}}{x_{j}}/{d_{j}}}{\sum _{j=1}^{n}{h_{j}}{w_{j}}/{d_{j}}} \)

\( {y^{*}}=\frac{\sum _{j=1}^{n}{h_{j}}{w_{j}}{y_{j}}/{d_{j}}}{\sum _{j=1}^{n}{h_{j}}{w_{j}}/{d_{j}}} \) (10)

When determining the best distribution center location, an iterative calculation method can be used to optimize the location selection. An initial logistics center location is selected and the total transportation cost from that location to all stores is calculated. This initial total transportation cost can be used as a baseline. Then, the location of the logistics center is adjusted using the centroid method formula.

4.2. Profit Model Development

In traditional inventory management, the profit model for suppliers is based on calculating annual revenue and subtracting distribution, production, and inventory costs[14]. The total cost in this model is determined by the EOQ formula, which includes both inventory and ordering costs:

\( {Ic^{0}}=PQ-\frac{EOQ}{2}{C_{s}}-{M_{s}}Q-{QC_{s}} \) (11)

where \( {Ic^{0}} \) means supplier’s profit under the traditional model, \( P \) is selling price of refrigerators at Yonghui Superstore, Q is arket demand, \( {M_{s}} \) is cost for the supplier to produce each refrigerator, and \( {C_{s}} \) is unit storage cost for the supplier.

To optimize inventory management, it is essential to balance inventory and ordering costs:

\( \frac{EOQ}{2}{C_{r}}+\frac{Q∙{C_{d}}}{EOQ}≥2\sqrt[]{\frac{EOQ}{2}{C_{r}}×\frac{Q{∙C_{d}}}{EOQ}} \) (12)

\( \frac{EOQ}{2}{C_{r}}=\frac{Q∙{C_{d}}}{EOQ}→EOQ=\sqrt[]{\frac{2Q{∙C_{d}}}{{C_{r}}}} \) (13)

where \( {C_{d}} \) is ordering cost for Yonghui Superstore, and \( {C_{r}} \) is the storage cost for Yonghui superstore.

For Yonghui Superstore, the profit calculation involves subtracting ordering, storage, and raw material procurement costs from total revenue. The formula is:

\( {Ict^{0}}=PQ-{P_{1}}Q-\frac{EOQ}{2}∙{C_{r}}-\sqrt[]{\frac{Q{C_{r}}{C_{d}}}{2}} \) (14)

where \( Ic \) is supplier's profit under the VMI model, and \( {P_{1}} \) is the rice of goods supplied by the supplier to Yonghui Supermarket after implementing VMI. After adopting the VMI strategy, the profit model is adjusted:

\( {EOQ_{After}}=\sqrt[]{\frac{2CQ}{\frac{{C_{r}}+{C_{S}}}{2}}}=\sqrt[]{\frac{4CQ}{{C_{r}}+{C_{S}}}} \) (15)

Therefore, the profit calculation formula for Yonghui after adopting the VMI strategy is:

\( Ict=PQ-{P_{1}}Q \) (16)

The supplier’s profit is the sales revenue for the year minus the costs of storage, distribution, and production. The correct calculation of the annual storage cost needs to consider the product of the economic production quantity and the annual storage cost, divided by 2.｡

\( \frac{{EOQ_{After}}}{2}×\frac{{C_{r}}+{C_{S}}}{2} \) (17)

Substituting into the profit formula yields:

\( {I_{C}}={P_{1}}Q-{M_{s}}Q-{C_{p}}Q-\frac{{EOQ_{After}}}{2}×\frac{{C_{r}}+{C_{S}}}{2} \) (18)

where \( {C_{p}} \) is unit distribution cost for the supplier.

Comparing the profit models for traditional inventory management and VMI strategies reveals that VMI typically reduces costs and improves supplier profits[15], demonstrating the benefits of optimized inventory management.

4.3. Challenges and Issues in Implementation

It is found that implementing VMI strategies can substantially enhance inventory turnover rates and lower inventory costs. By proactively managing suppliers, companies can effectively reduce stockouts and excess inventory, which leads to improved operational efficiency. This model optimizes inventory levels and boosts the responsiveness of the supply chain, helping companies maintain a competitive edge in dynamic market conditions. However, the application of VMI strategies involves challenges. Establishing and maintaining trust between suppliers and retailers is crucial, as it influences the effectiveness of information sharing and collaboration. Additionally, VMI strategies depend heavily on advanced technology, requiring reliable IT systems for real-time data sharing and inventory management. This technological dependence can introduce risks if systems fail or are inadequately maintained. Moreover, cost concerns related to implementing and sustaining VMI systems can impact the overall feasibility and effectiveness of the model. Addressing these challenges is essential for maximizing the benefits of VMI and ensuring its successful integration into business operations.

4.4. Improvement and Optimization Measures

To address the challenges in implementing the VMI model, companies should prioritize establishing a trust-based partnership with suppliers. This involves formalizing agreements through clear contracts and data-sharing protocols, and maintaining open communication channels through regular meetings. Such measures foster a cooperative relationship, ensuring smooth information flow and enhancing decision-making efficiency. Additionally, investing in a robust IT infrastructure is fundamental. Companies need to select reliable technology providers and conduct ongoing system maintenance to minimize technical disruptions. A stable IT environment supports the effective operation of the VMI system, reducing the risk of failures. Finally, Yonghui Supermarket should focus on strengthening its cooperative relationship with suppliers to achieve long-term mutual benefits. Improved information utilization will lead to better economic outcomes for both parties. The stability and effectiveness of this cooperation will significantly influence the commitment to and success of the VMI model.

5. Conclusion

In this paper, the challenges faced by Yonghui Superstore in inventory management are studied by selecting a large home appliance sales in Longquanyi District as a specific case study, and a comprehensive optimization plan is developed, including model formulation, theoretical analysis, production model conversion and supplier evaluation index design. The results demonstrate that Vendor Managed Inventory (VMI) is a highly effective inventory management strategy. VMI, when compared to traditional models, excels in mitigating the bullwhip effect, reducing overall supply chain costs, and fostering a mutually beneficial relationship between suppliers and customers. By leveraging real-time data and enabling suppliers to better control inventory levels, VMI enhances supply chain coordination and operational efficiency. While VMI presents certain risks, including the potential for data inaccuracy and an increased reliance on supplier performance, these risks can be mitigated through proactive forecasting, rigorous monitoring, and timely adjustments. The adaptive and forward-thinking nature of the VMI model significantly enhances distribution and productivity while reducing non-value-added activities, making it a valuable strategy that is widely adopted by companies seeking to optimize supply chain management. The VMI model has gained considerable traction among companies striving to boost their supply chain management practices.