Two-echelon supply chain network design with trade credit
Introduction
Customer demands are increasingly satisfied through collaborative activities of connected partners in a supply chain. Substantial cost savings may be achieved through optimal design of supply chain networks. A plethora of studies have addressed this issue, most of which focus on optimizing material flows in supply chains, whereas the significance of financial flows are largely neglected. One challenge is that researchers are confronted with major difficulties in modeling multi-echelon systems at their entirety, including operational, logistical, and financial issues. Yet, integrating financial considerations into supply chain designs is necessary as simply assuming unlimited cash supply can lead to suboptimal, if not misleading, decisions. Moreover, it is also important to account for stockouts by setting up an appropriate amount of safety inventory in the system.
According to Aberdeen (2007), collaborative companies in a supply chain usually have different capital cost structures, which can be exploited to reduce system-wise capital costs by streamlining their financial flows. For instance, companies with easy access to relatively cheap capital can finance their credit-constrained partners in the form of trade credit. Trade credit is a crucial external source of company financing, which allows delayed payment for goods and services by contract or agreement between sellers and buyers. It can also be viewed as a form of short-term debt that has no interest, acting as accounts receivable on the sellers’ side and accounts payable on the buyers’ side. The length of payment delay allowed is specified by the credit term. Longer credit terms may effectively reduce prices paid by end customers. Trade credit terms could vary substantially across different industries (e.g. Yang and Birge, 2017).
Cuñat and Garcá-Appendini (2012) reported that approximately 60 percent of small businesses use trade credit financing in the United States, and outside the U.S., around 20 percent of all investment is financed externally through trade credit. For example, the leading global oil company, Shell, is engaged in oil trading in the Chinese market and operates more than 1500 gas stations in China. Most of the downstream dealers are limited in funds and lack access to credit. Shell China provides flexible business credit to these dealers, which contributes to the healthy development of its entire supply chain (Shell, 2020). On the other hand, large firms also leverage trade credit to save capital expenditure. For instance, Lenovo, one of the most renowned technology companies, adopts trade credit and establishes a sophisticated credit management mechanism that enhances the overall competitiveness of the entire supply chain (Lenovo Group Limited, 2020). JD.com, one of the top Chinese B2C online retailers, also uses trade credit from merchants to improve its cash conversion cycle (JD.com, 2020). Ries et al. (2017) observed that days payable outstanding of the world’s top 10 retailers range from 24 to 97 days.
Although trade credit is widely used in industries and arguably quite influential on a range of supply chain decisions, this financial issue has been hardly tapped in scholarly research on the design of supply chain networks. Our study attempts to fulfil this gap by integrating trade credit financing into the optimization of two-echelon supply chain networks. Furthermore, we take into account the risks associated with demand fluctuation which compel both retailers and distribution centers to stock an appropriate amount of safety inventory to meet required service levels (Li and Jiang, 2012), as well as the capital cost related to safety stock. To achieve this, we propose a modified warehouse-retailer network design (WRND) model based on the framework proposed by Teo and Shu (2004).
The main contributions of this paper are as follows:
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Our model accounts for both financial and material flows in strategic network design decisions. It integrates fixed yearly location cost, operating and handling cost at DCs, safety stock cost, transportation cost, inventory replenishment and the associated financing cost. To the best of our knowledge, this should be one of the most comprehensive supply chain network design models thus far.
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The inclusion of safety stock and trade credit may redefine the structure of supply chain networks through its inherent links with various operational decisions, thus requiring novel modeling and solution techniques. For instance, the normally fixed effective inventory holding cost (i.e., cost of storing inventory plus cost of capital) at each stage in the conventional network optimization models is now uncertain, depending on the DC-retailer assignments and trade credit terms.
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The required level of safety stock at each stage follows a nonlinear and nonseparable function of demand variance. The operating and handling cost is concave and non-decreasing to capture economies of scale whereas most existing studies simply assume that such cost is linear in demand. We devise the polymatroid cutting plane approach to solve the problem based on the submodular property of the cost terms. This approach can also be applied to models that have other concave cost functions.
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Benchmarking analysis shows that our comprehensive network design model results in lower system-wise cost. When the safety stock or financing cost increases, a more consolidated supply chain is recommended due to the economies of scale or risk-pooling effect. Interestingly, we find that as financing cost rises, the optimal reorder pattern is characterized by high volume and low frequency, but the opposite is recommended when financing gain increases.
Section snippets
Literature review
Our paper fits in the broad area of interfaces between operational and financial decisions. It essentially examines the optimization of supply chain networks, which generally focuses on the decisions of warehouse locations and the assignment of warehouses to retailers. We refer readers to Shen, 2007, Melo et al., 2009 for a comprehensive review of this research area. In general, there are two major types of supply chain network design models in the literature, one for single echelon inventory
List of notations and review of the single DC multi-retailer system
The overarching purpose of this study is to optimize the two-echelon supply chain network design through minimizing the expected total system-wise cost. It aims to determine the number of DCs to establish, the flow of products from DCs to retailers, the inventory replenishment policies, trade credit terms, and safety stock levels. Consider a two-echelon supply chain that is comprised of an outside vendor, several distribution centers, and multiple retailers. The DCs are replenished from an
Cost components and model formulation
The multi-echelon supply chain network design model is typically formulated as a set-partitioning model in the literature (Teo and Shu, 2004, Zhong et al., 2018). This type of model effectively captures the essence of the problem in a concise manner. It linearizes the essentially nonlinear objective function but dramatically increases the number of variables. To facilitate the development of solution algorithms, an alternative model formulation is proposed based on the research context of this
Solution algorithm
Different from the literature that formulate the network design problem as a set partitioning model (Teo and Shu, 2004, Zhong et al., 2018), this study devises a polymatroid cutting-plane approach to solve the problem. We will demonstrate through numerical studies that this method is efficient for solving practically sized instances. We first probe into some important mathematical properties of the model prior to detailing the solution algorithms. Definition 1 Given a finite set , a set function
Numerical study
This section presents a set of randomly generated numerical experiments to illustrate the applications of the proposed algorithm and discuss managerial insights based on the results. The polymatroid cutting-plane algorithm was coded in Java and the mixed integer programming problems were solved by the CPLEX 12.5 solver. All the instances were solved on a DELL computer with i7-9750H CPU (2.6 GHz) running the Windows 10 64-bit operating system. All computation times exclude input times.
The values
Concluding remarks
This study proposes an integrated DC-retailer network design model to jointly determines the DC locations, the product flow from the DC to the retailer, the trade credit terms, the safety stock levels, and the inventory replenishment policy. Based on the submodularity property of the cost components, we developed a polymatroid cutting-plane approach to solve the problem. Numerical experiments suggest that the algorithm is effective in solving problem instances of practical sizes.
In-depth
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
CRediT authorship contribution statement
Yi Ding: Conceptualization, Writing - original draft, Methodology. Yi Jiang: Conceptualization, Project administration. Liangping Wu: Methodology, Formal analysis. Zhanbo Zhou: Writing - review & editing, Software, Visualization.
Acknowledgement
We would like to thank the editor and four anonymous referees for their constructive comments that led to this improved version. This research was supported by the National Natural Science Foundation of China [Grant No. 71801043, 71831004, 72091213], the Social Science Fund of Jiangsu Province [No. 18GLC008], the Humanities and Social Science Fund of Ministry of Education [No. 18YJC630024], Jiangsu Provincial Six Talent Peaks Project [No. TD-RJFW-001], and Jiangsu Province "333" Project [No.
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