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A joint inventory–finance model for coordinating a capital-constrained supply chain with financing limitations

Abstract

Faced with economic recession, firms struggle to find ways to stay competitive and maintain market share. Effective coordination of the supply chain can solve this problem, but this may fail if existing capital constraints and financial flows are ignored. This study addresses the challenge by exploiting coordination through joint decision-making on the physical and financial flows of a capital-constrained supply chain. We also consider the capital-constrained member’s financing limitations that lead to lost sales. Two scenarios based on non-coordinated and coordinated structures are modeled in the form of bi-objective optimization problems that simultaneously optimize system costs and service levels. The models are solved using the \(\varepsilon\)-constraint method. The results indicate that the non-coordinated model cannot satisfy more than about 50% of the demand due to capital shortage and financing limitations, while the coordinated model can satisfy all of the demand via internal financing. Furthermore, the proposed coordination scheme leads to cost reduction for the members and the total system. To motivate all members to accept the proposed coordination scheme, a cost-sharing mechanism is applied to the coordination procedure. Finally, a sensitivity analysis concerning financial parameters is provided for validating the coordination model.

Introduction

Supply chain management involves coordination and cooperation among several business partners linked through flows of material, money, and information. These partners include suppliers of raw materials and basic components, manufacturers, wholesalers, distributors, transporters, retailers, banks, and financial institutes. Two key factors ensure the proper functioning of a supply chain: first is coordination between supply chain members, and second is integration of the said three flows. This study addresses both factors for enhancing supply chain profitability and effectiveness.

Supply chain coordination involves collaborative work—joint planning, joint product development, mutual information exchange, integrated information systems, cross coordination on several levels of the companies in the network, long-term cooperation, and fair sharing of risks and benefits (Skjoett-Larsen 2000). In a supply chain with a decentralized structure, each member independently optimizes its own operational and financial decisions without considering their effects on other members. Hence, a decision that is optimal for a member may cause additional costs for other members, which leads to inefficiencies in the processes of supplying, producing, distributing, and delivering goods to customers, and consequently, poor service. However, in a coordinated supply chain, all members operate to optimize the whole supply chain, leading to an increase in profitability and customer satisfaction in the supply chain.

To have an effective supply chain, proper management of upstream financial flows is as important as that of downstream physical flows (Gupta and Dutta 2011). The supply chain management literature has focused mainly on material and information flows, and financial flows have been ignored despite its great importance and inevitable effects on the physical flows (Naimi Sadigh et al. 2013; Sadeghi Rad et al. 2018; Sadeghi Rad and Nahavandi 2018). This literature stream assumes that a firm can always finance its operations at an optimal level or borrow at a constant interest rate. When financial markets are efficient, that is, external funding is plentiful and relatively inexpensive, firms have sufficient funds for daily operations, and financial and operational decisions can be made separately (Modigliani and Miller 1958). In such cases, a downstream entity in the supply chain pays the inventory ordered from an upstream entity. Consequently, financial flows become an output of logistics decisions (Luo and Shang 2015). However, capital shortage becomes a bottleneck, especially for small and medium enterprises. Financial activities complement the physical flow of materials and ensure funding of logistics operations. Thus, integrating operational and financial decisions may ensure proper implementation of operations in practice.

This study addresses both of these important issues—supply chain coordination and the integration of operational and financial decisions.

We consider an original equipment manufacturer (OEM) that faces a capital shortage and financing limitations. Specifically, financial institutions limit the OEM’s financing amount for reasons such as low credit. Because of this limitation on external financing, the OEM cannot obtain as much financing as it requires to support its operations. Therefore, it cannot satisfy a portion of the demand and thus, face lost sales (lost sales refer to selling opportunities that a firm has lost because an item was out of stock). The OEM operates in a supply chain with multiple suppliers of raw materials and components (upstream members), and an auto manufacturer (downstream member). The financial problems of the OEM affect all members in the supply chain and may lead in the loss of a portion of the market share of the supply chain’s final products. Thus, coordination among all members to solve the OEM’s financial problems is prudent so as to improve the supply chain’s performance. To understand better the proposed coordination mechanism, two scenarios are formulated: a non-coordinated supply chain (or decentralized supply chain) and a coordinated supply chain. The advantages of the second scenario for all supply chain members are illustrated using an example from a supply chain in the automotive industry.

The automotive industry is one of the world's most important economic sectors in terms of revenue and employment, and any kind of disruption in the production and profitability of a key member can be detrimental to the whole industry. Therefore, it is necessary to investigate issues such as the financial challenges and coordination of operational and financial decisions in this industry. The OEMs in automotive supply chains commonly try to establish long-term relationships with their suppliers and/or customers. Proper coordination mechanisms that align the parties' objectives can help build such relationships. This study finds ways to build these relationships in light of an OEM’s financial constraints (capital shortage and financing limitations). Nevertheless, the proposed coordination mechanism can be adopted by any manufacturing business that faces such financial challenges.

This study looks for effective ways to solve the financial problems of a supply chain and improve its performance through coordination of all members in light of a difficult financial situation. To this end, this study proposes a bi-objective nonlinear optimization model that minimizes system-wide costs (including operational and financial costs) and maximizes the satisfied demand, which is equal to the optimal customer service level.

In the supply chain coordination literature, it is a novel idea to consider financing limitations (which is a real-world problem for capital-constrained firms) and the consequent lost sales. Moreover, we believe that our study is the first to address supply chain coordination together with financing issues for maximizing service level and minimizing operational and financing costs.

The rest of this paper is organized as follows. Section “Literature review” provides a brief review of the related literature. Section “Model description and solution approach” presents the proposed model, including the model’s assumptions and notations, and the mathematical formulations of scenarios 1 and 2. Section “Numerical analysis” discusses the numerical results, along with a cost-sharing mechanism and sensitivity analysis of the financial parameters. Section “Conclusion” presents the conclusion and suggestions for further research.

Literature review

Our study relates to two literature streams: supply chain coordination and integration of the physical and financial flows of a supply chain (also known as operations–finance interface).

Supply chain coordination (SCC)

Coordination mechanisms in supply chains are generally based on centralized and decentralized decision-making processes (Jaber and Osman 2006). In a centralized decision-making process, a unique decision maker manages the whole supply chain, with the main objectives of minimizing (maximizing) the total supply chain cost (profit). By contrast, in a decentralized decision-making process with multiple decision-makers, each member focuses on its own objectives. Li and Wang (2007) conducted a review of coordination mechanisms of supply chain systems in a framework that is based on the supply chain’s decision structure (centralized or decentralized) and the nature of demand (deterministic or stochastic).

Abundant research exists regarding the coordination between supply chain members. Several review papers have focused on various aspects of supply chain coordination (Fugate et al. 2006; Li and Wang 2007; Arshinder et al. 2008, 2011; Govindan et al. 2013). Accordingly, various classifications of coordination mechanisms exist in the literature. Arshinder et al. (2008), in their review of the literature, grouped coordination mechanisms into four categories: supply chain contracts, information technology, information sharing, and joint decision-making. As major categories of coordination mechanisms, Sahin and Robinson (2002) proposed price, non-price, buy-back and returns policies, quantity flexibility, and allocation rules. Subsequently, Fugate et al. (2006) adapted their classification into three major categories: price, non-price, and flow coordination mechanisms. Bernstein and Federgruen (2007) showed the types of coordination mechanisms that allow a decentralized supply chain to generate aggregated expected profits equal to the optimal profits in a centralized system, and how the parameters of these (perfect) coordination schemes can be determined.

One common coordination mechanism is coordinating contracts, which are formulated using game theory in a decentralized structure. This stream of SCC literature uses some incentives in the form of contracts to achieve coordination among supply chain members. Some of these incentives include buy-back contracts (Padmanabhan and Png 1997; Hou et al. 2010; Shen and Zhang 2012; Tibrewala et al. 2018), revenue sharing contract (Giannoccaro and Pontrandolfo 2004; Cachon and Lariviere 2005), service level-based contracts (Sieke et al. 2012), discount policy (Xu et al. 2018a; Nouri et al. 2018), credit period (delay in payment) (Ebrahimi et al. 2019), bi-level credit period (Johari et al. 2018). The readers may refer to Cachon (2003) for comprehensive information about the structure of coordinating contracts.

Some researchers have shown that coordination can be achieved by integrating lot-sizing models (Goyal and Gupta 1989). Joint economic lot size models are especially useful planning tools in situations where companies have established long-term relationships with their suppliers or customers, which is common in the automotive industry (Glock 2012). Glock (2012) focused on coordinating inventory replenishment decisions between buyer and vendor and their impact on the performance of the supply chain. He reviewed joint economic lot size models aimed at minimizing total system costs. Some of the first studies on the coordination of inventory replenishment and production decisions between supply chain members include Goyal (1977), Goyal and Gupta (1989), and Goyal et al. (2003). Sarmah et al. (2006) classified buyer–vendor coordination models that use quantity discount as a coordination mechanism under a deterministic environment. In a recent study, AlDurgam et al. (2017) considered a single-vendor, single-manufacturer joint economic lot size problem under demand uncertainty. They developed an integrated mathematical model that investigates the impact of a variable production rate on the system. In most of these works, coordination is achieved through joint consideration of the system-wide costs. This coordination scheme is based on a centralized decision-making process. Some other works that adopted coordination by joint consideration of system-wide costs include Chen and Chen (2005), Jaber and Osman (2006), Chiadamrong et al. (2007), Jayaraman and Pirkul (2001), Kim et al. (2005), and Moussawi-Haidar et al. (2014). In this study, coordination among members is adopted through this coordination scheme.

Operations–finance interface

Supply chain management literature mostly focuses on the flow of goods and services without considering the effects of capital shortage and financial flows on operations management. Researchers in this field assume that there is always adequate working capital to supply the required inventory. However, operational decisions are constrained by limited working capital and often critically depend on external financing (Xu and Birge 2006). These constraints influence supply decisions. Nevertheless, the interaction of physical and financial flows has been ignored by many researchers in the field of operations management. The interface of finance, operations, and risk management is a relatively new research area, which deals with timely, complex, and boundary-spanning issues in a variety of settings from startups to global enterprises (Babich and Kouvelis 2018). Zhao and Huchzermeier (2015) identified three types of interdependence between operations management and corporate finance in their review of the operations–finance models: (i) financial constraints on operations, (ii) correlation between operational and financial risks, and (iii) alternative risk mitigation. Financial risks arise when there are uncertainties related to any form of financing, including credit, business, investment, and operational risks (Kou et al. 2014).

There is no specific framework for the works on the operation–finance interface in the literature. However, some classifications of existing works on supply chain finance (SCF) can be found in the review papers of Gelsomino et al. (2016) and Xu et al. (2018b).

To address the financial flows of the supply chain, some works in this field have focused on the management of accounts payables/receivables of a firm in the supply chain. Gupta et al. (1987) and Gupta and Dutta (2011) extended the application of scheduling theory to the financial decisions of the supply chain. Longinidis and Georgiadis (2011) proposed a mixed integer linear programming model that integrates financial issues with a supply chain design under stochastic demand. Jahangiri and Cecelja (2014) developed a stochastic model to maximize the profit of a firm and find the optimal strategy for paying its accounts payables. Luo and Shang (2015) studied a centralized two-division supply chain and integrated inventory decisions and cash management in the mentioned supply chain. Miloudi et al. (2016) proposed a model to optimize the working capital of a firm in a three-level supply chain by scheduling its accounts payables and receivables.

Some researchers have considered capital shortage and financing decisions. In this group, two financing methods have been proposed: (1) external financing and (2) internal financing. In the first method, the capital-constrained member gets direct financing from financial institutions such as banks (Xu and Birge 2006; Guillén et al. 2007). We note that any kind of financing from a source outside the supply chain can be considered as external financing such as bank financing, peer-to-peer lending, and so on (Interested readers are referred to Wang et al. (2020) for further information regarding peer-to-peer lending and the associated credit ratings.). The second method deals with financing from other members of the supply chain through various options such as trade credit (Jaber and Osman 2006; Lee and Rhee 2011; Lee et al. 2018).

Trade credit refers to a seller’s short-term loan to a buyer, allowing the buyer to delay payment of an invoice; this has been the largest source of working capital for a majority of business-to-business firms in the United States (Lee and Rhee 2011). Aggarwal and Jaggi (1995) developed a mathematical economic order quantity model for perishable goods using delayed payments. Teng (2009) established an economic order quantity (EOQ)-based model for a retailer who receives full trade credit from its supplier and offers full or partial trade credit to its good or bad customers, respectively. Using delayed payments, Moussawi-Haidar and Jaber (2013) developed a joint model for cash and inventory management for a retailer. Taleizadeh et al. (2016) proposed an economic production quantity model (with defective items and rework processes) that considers both upstream and downstream trade credit. Yang and Birge (2018) adopted a trade credit contract as a risk-sharing mechanism for a two-level supply chain with limited working capital. Babich and Kouvelis (2018) discussed recent contributions and future directions on the interface of finance, operations, and risk management. Kouvelis and Zhao (2018) studied the impact of credit ratings on operational and financial decisions of a supply chain with a supplier and a retailer interacting via an early payment discount contract. Tang et al. (2018) compared two schemes—purchase order financing and buyer direct financing—for financing a capital-constrained supplier. Shin et al. (2018) found it beneficial to choose a trade-credit-financing policy in a centralized two-echelon supply chain model with imperfect quality items. They did not consider budget constraints in their study. Li et al. (2019) optimized dynamic credit term decisions, along with inventory production decisions in a two-level supply chain. They ignored financial constraints in their study as well. Mashud et al. (2019) extended the EOQ inventory model for deteriorating items with price-dependent demand under a two-level trade credit policy. In their study, budget constraints and external financing were not addressed. Krommyda et al. (2019) explored some effects of the global financial crisis on inventory management decisions within the general EOQ that has a partial backorder paradigm. In this study, an EOQ inventory policy is used to manage inventories in the whole supply chain. An EOQ inventory policy has many applications in real-world problems. (See e.g. Khalilpourazari and Pasandideh 2019; Khalilpourazari et al. 2019a, b, and Khalilpourazari et al. 2019b for more information about the application of an EOQ policy in different problems.) Pramanik and Maiti (2019) considered the time value of money and inflation in an inventory model with two-level partial trade credit. They did not consider financial problems such as budget shortage and limitations in financing. Chakuu et al. (2020) conducted an empirical study to explore the conditions in which logistic service providers offer inventory financing as an SCF service. Yan et al. (2020a, b) examined two financing strategies—loan and investment—for the financing of a capital-constrained supplier by a risk-averse retailer. In another study, Yan et al. (2020b) analyzed the pricing competition in a dual-channel supply chain consisting of one e-retailer that provides financing to a capital-constrained supplier. Huang et al. (2020) compared trade credit financing (TCF), credit guarantee financing, and buy-back guarantee financing as three types of SCF modes. They provided managerial insights for the application of these financing modes.

Combining supply chain coordination and operations–finance interface

A few studies in the literature have considered the coordination of supply chain members together with the integration of financial and operational decisions. Jaber and Osman (2006) proposed a centralized model where players in a two-level (supplier–retailer) supply chain coordinate their orders to minimize supply chain costs. In their model, permissible delay in payment was considered a decision variable and was adopted as a trade credit scenario to coordinate the order quantity between the two levels. They did not address budget constraints for members in their study. Dada and Hu (2008) applied a nonlinear loan schedule to coordinate the decisions of a profit-maximizing bank and a capital-constrained retailer. Yin and Xu (2010) proposed a two-level supply chain with delay in payments and inventory-in-pawn financing policy to minimize supply chain costs by coordinating material and capital flows. Lee and Rhee (2011) studied trade credit from a supplier’s perspective and presented it as a tool for supply chain coordination. Chen and Wang (2012) studied the interactions between a capital-constrained retailer and its supplier under TCF. They found that trade credit can create value in a capital-constrained supply chain and partly achieve coordination. Moussawi-Haidar et al. (2014) coordinated a three-level supply chain consisting of a capital-constrained supplier, a retailer, and a financial intermediary (bank) by considering operational and financial decisions. Kouvelis and Zhao (2016) addressed two coordinating contracts (revenue sharing and buy-back contracts) in the presence of capital constraint and default risk. Feng et al. (2015) developed a revenue-sharing-and-buy-back contract to coordinate a capital-constrained supply chain. Yan et al. (2016) proposed a partial credit guarantee contract for SCF and analyzed coordination conditions for this contract. Xiao et al. (2017) designed a generalized revenue sharing contract to coordinate a supply chain with financial constraints. Johari et al. (2018) proposed a bi-level credit period (delay in payment) for a periodic review inventory system that coordinates the inventory and pricing decisions of a two-level supply chain. Ebrahimi et al. (2019) used the delay in payment contract as an incentive to coordinate a supply chain with stochastic, promotional-effort-dependent demand. Devalkar and Krishnan (2019) examined the use of trade credit in decreasing moral hazard and adopting supply chain coordination in the presence of information asymmetry and financial friction. Ding and Wan (2020) studied supply chain coordination in the presence of capital constraints and production yield uncertainty. In contrast to our research, in their study, the upstream member (supplier) is capital-constrained and needs financing from the downstream member (manufacturer).

Literature that simultaneously considers operational and financial decisions is novel, and many assumptions about the real world have not yet been considered. For example, some works have not addressed budget shortage and financing options. The primary purpose has only been to improve supply chain performance by integrating physical and financial flows. Meanwhile, other works that have considered budget constraints and financing decisions have ignored the limitation on the amount of borrowing. However, in real-world situations, some firms cannot borrow an unlimited amount of cash from financial institutions, thereby leading to lost sales due to insufficient working capital for supporting the operations.

In this study, we address both of these important issues: coordination of inventory replenishment and production decisions among all members, and the integration of operational and financial decisions. Specifically, a coordination scheme based on joint decision-making on inventory replenishment, production decisions, and financial decisions is applied to a capital-constrained supply chain with financing limitations in the automotive industry. This coordination mechanism integrates the operational and financial decisions of all members and prevents lost sales. For a better understanding of the coordination model, two scenarios are formulated: i) a non-coordinated supply chain used as a basis for the coordinated model and ii) a coordinated supply chain. The advantages of the coordinated model for all members are illustrated by solving the model using data from a supply chain in the automotive industry.

Table 1 compares previous literature against the current paper, with the contributions of this study shown in bold and italic font. Our study, with some differences, is most related to Jaber and Osman (2006) and Moussawi-Haidar et al. (2014), which adopted the centralized decision-making process as a scheme for coordination (joint consideration of system-wide costs) in their supply chain models. They considered both inventory and financial decisions in their coordination schemes. Jaber and Osman (2006) did not consider budget constraints and external financing in their study. They applied coordination and integration of operations and finance to improve supply chain profitability. Moussawi-Haidar et al. (2014) addressed budget constraints, but they assumed that the firms could borrow an unlimited amount of cash. Moreover, internal financing in their study was limited to the trade credit granted by the supplier. We consider a three-level supply chain with multiple suppliers and multiple products. The OEM is capital constrained and faces financing limitations, leading to lost sales. To solve these difficulties, in addition to external financing, the OEM can use (1) trade credit granted by the suppliers and (2) prepayment of the auto manufacturer where needed, as two sources of internal financing in the coordination scheme proposed here.

Table 1 Comparison of the proposed model and the literature on incorporating SCC and operations-finance interface

This work contributes to the existing literature in the following ways:

  • We consider financing limitations in addition to budget constraints, highlighting that there is a maximum limit on borrowing from financial institutions, which means limited financing capacity. Furthermore, we consider lost sales as a consequence of financing limitations. Previous related works used a capital-constrained firm financed from an unlimited external source, and the shortage of financing was not considered.

  • In the current study, the prepayment of the downstream partner is suggested as an internal source of financing, in addition to TCF. This option leads to new decision-making regarding different financing options (loan and prepayment of the downstream partner), specifically, which option to choose and how much to finance from each source. As such, the loan amount is a decision variable in this study, whereas in previous works, the loan amount is equal to the production or inventory costs minus initial capital.

  • We consider the service level to the customers as a decision variable of the coordinated supply chain. Moreover, we formulate the problem as a bi-objective optimization problem, where both the costs and the satisfied demand are optimized. To the best of our knowledge, considering this issue is novel in the related literature.

  • We model a real-world supply chain in the automotive industry that has three levels and multiple products, while most previous works have been limited to the relationships in a single buyer–single vendor with a single product scenario.

Model description and solution approach

In this section, the proposed model and solution approach are explained in detail based on two scenarios. First is the decentralized non-coordinated structure, in which each member tries to optimize its own objectives. We show that under this setting, the supply chain performance is not satisfactory, and lost sales are inevitable. To improve the supply chain performance, a second scenario is proposed in which all members operate in a way that optimizes the financial and operational decisions of the entire system by adopting a coordination scheme. In the end, we apply a cost-sharing mechanism for sharing the benefits of coordination among all members. This can be a motivation tool for all members to accept the proposed coordination scheme.

Assumptions and notations

We consider a three-level multi-product supply chain in the automotive industry. Figure 1 shows the structure of the mentioned supply chain. This supply chain includes several suppliers of raw materials and the components of automobile original parts, a capital-constrained OEM that manufactures original parts of automobiles (powertrain components including engines, gearboxes, and axles), and an auto manufacturer.

Fig. 1
figure1

The structure of the supply chain considered in this study

In the said supply chain, the necessary components and materials (shown by index k) are shipped from outside sources to the suppliers (\(S_{1}, S_{2}, \ldots ,S_{K}\)). These components are purchased by the OEM to produce the original parts of automobiles (shown by index n) according to the deterministic demand of the auto manufacturer. Afterward, these original parts are shipped to the auto manufacturer for assembling and producing final products (shown by index j). All of these activities take place continuously during the planning horizon (T).

The auto manufacturer has a predetermined production capacity for each final product j, which can be considered as the deterministic demand (shown by \(D_{j}\)) in the specified planning horizon (T). The OEM manufactures original parts of the final products (shown by index n) according to the auto manufacturer’s demand. The demand for the original parts manufactured by the OEM (\(D_{n}\)) can be calculated simply according to \(D_{j}\), that is, \(D_{n} = \mathop \sum \limits_{j} m_{jn} D_{j}\), where \(m_{jn}\) represents the quantity of part n needed for producing each unit of product j. The ordering and replenishment of required components and materials by the OEM take place in multiple periods during the planning horizon. Note that each component k is purchased from one supplier (\(S_{k}\)).

The OEM faces liquidity constraints and limitations in short-term financing, leading to lost sales and consequently, an unsatisfactory service level. In this study, we define service level as the satisfied demand of original parts divided by the total demand of original parts, which is shown by \(\alpha_{n} \,(\alpha_{n} = \frac{Satisfied\,demand\,of\,part\,n}{{Total\,demand\,of\,part\,n}})\). It is a decision variable for the proposed model.

The inventory policy adopted in the proposed problem is based on the EOQ inventory policy, with some different and additional assumptions. Table 2 provides a comparison between the classical EOQ assumptions and the assumptions of the proposed model.

Table 2 Comparison between EOQ policy and inventory policy of the current paper

The notations of the mathematical formulation are given in Table 3.

Table 3 Summary of notations

The mathematical formulation of the non-coordinated supply chain

In this section, we model the decentralized non-coordinated supply chain in which each member optimizes its own problem without considering other members' costs and benefits. As such, three models are discussed, namely, the OEM's, the suppliers', and the auto manufacturer's models.

The OEM's model

As previously mentioned the OEM is faced with capital shortage and financing limitations. Since the OEM does not have sufficient initial budget to carry out its operations and the amount of external financing is limited, it might not be able to satisfy all of the auto manufacturer's demand, which leads to lost sales. In this case, the OEM should decide on the fraction of satisfied demand for original parts (\(\alpha_{n}\)) while minimizing cost. To deal with this situation, we propose a bi-objective model that simultaneously minimizes the operational–financial costs of the OEM (\({\Pi }_{OEM}\)) and maximizes satisfied demand of the original parts (\(\mathop \sum \limits_{n} \alpha_{n} D_{n}\)).

The first objective function includes inventory replenishment costs (ordering, purchasing, and inventory carrying costs), fixed and variable production costs, and the cost of capital for borrowing the required capital. Inventory carrying cost or holding cost is the opportunity cost of the capital tied up in the inventories plus other costs (storage, obsolescence, damage, deterioration, insurance, etc.). Therefore, in this study, the parameter \(h_{k}\) only represents the “cost of capital,” and \(s_{k}\) represents the “other costs,” which we refer to as the storage cost of component k for the OEM.

Lemma 1

The opportunity cost of the capital tied up in the inventories is calculated as follows:

$$H\left( k \right) = h_{k} \frac{{\left( {\left( {Q_{k} - t_{k} D_{k} } \right)^{ + } } \right)^{2} }}{{2D_{k} }},{\text{where}}\quad \left( x \right)^{ + } = \max \left( {x, 0} \right)\,{\text{and}}\,k = 1,2, \, \ldots , \, K.$$
(1)

Proof

See “Appendix 1”.

The OEM replenishes its inventory from suppliers in the shipments of size \(Q_{k}\) every \(T_{{OEM_{k} }} = Q_{k} /D_{k}\) units of time where \(D_{k} = \left( {\mathop \sum \limits_{n} N_{nk} \alpha_{n} D_{n} } \right)\). We model the OEM's problem as a bi-objective optimization problem that minimizes the total costs of inventories, production, and finance per unit time, and maximizes the satisfied demand:

$$\min TC_{OEM} = \mathop \sum \limits_{k} A_{k} \frac{{\mathop \sum \nolimits_{n} N_{nk} \alpha_{n} D_{n} }}{{Q_{k} }} + \mathop \sum \limits_{k} w_{k} *\left( {\mathop \sum \limits_{n} N_{nk} \alpha_{n} D_{n} } \right) + \mathop \sum \limits_{k} s_{k} \frac{{Q_{k} }}{2} + \mathop \sum \limits_{k} H\left( k \right)\frac{{\mathop \sum \nolimits_{n} N_{nk} \alpha_{n} D_{n} }}{{Q_{k} }} + \mathop \sum \limits_{n} \left( {F_{n} + v_{n} \alpha_{n} D_{n} } \right) + Dbt\left( {\left( {1 + i} \right)^{T} - 1} \right)$$
(2)
$${\text{Max}} \omega = \mathop \sum \limits_{n} \alpha_{n} D_{n}$$
(3)
$$\begin{aligned} & {\text{s.t}} \\ & Dbt \le M \\ \end{aligned}$$
(4)
$$\mathop \sum \limits_{k} w_{k} *\left( {\mathop \sum \limits_{n} N_{nk} \alpha_{n} D_{n} } \right) + \mathop \sum \limits_{n} \left( {F_{n} + v_{n} \alpha_{n} D_{n} } \right) + \mathop \sum \limits_{k} s_{k} \frac{{Q_{k} }}{2} + \mathop \sum \limits_{k} A_{k} \frac{{\mathop \sum \nolimits_{n} N_{nk} \alpha_{n} D_{n} }}{{Q_{k} }} \le B + Dbt$$
(5)
$$B + Dbt + \mathop \sum \limits_{n} p_{n} \alpha_{n} D_{n} - \mathop \sum \limits_{k} w_{k} *\left( {\mathop \sum \limits_{n} N_{nk} \alpha_{n} D_{n} } \right) - \mathop \sum \limits_{n} \left( {F_{n} + v_{n} \alpha_{n} D_{n} } \right) - \mathop \sum \limits_{k} s_{k} \frac{{Q_{k} }}{2} - \mathop \sum \limits_{k} A_{k} \frac{{\mathop \sum \nolimits_{n} N_{nk} \alpha_{n} D_{n} }}{{Q_{k} }} - Dbt\left( {1 + i} \right)^{T} \ge L$$
(6)

The first objective (2) is to minimize the OEM's total costs, which include ordering cost (first term), purchasing cost (second term), storage cost (third term), the opportunity cost of the capital tied up in inventories (fourth term), fixed and variable production costs (fifth term), and loan repayment at the end of the time horizon (sixth term). The first constraint (4) represents the limitation of external financing (debt). The second (5), demonstrates the OEM’s budget constraint, and the third constraint (6) implies that the amount of cash by the end of the time horizon should not be less than the lower limit (L) determined by the manager.

The suppliers' model

Each supplier supplies one component for the OEM. They replenish their inventory from outside vendors in the shipments of size \(\lambda_{k} Q_{k}\) every \(T_{{S_{k} }} = \lambda_{k} Q_{k} /D_{k}\) units of time where \(D_{k} = \left( {\mathop \sum \limits_{n} N_{nk} \alpha_{n} D_{n} } \right)\) and \(\lambda_{k} = 1, 2, \ldots\) and deliver \(Q_{k}\) to the OEM every \(T_{{OEM_{k} }} = Q_{k} /D_{k}\) units of time (Fig. 2). Note that if \(\lambda_{k} = 1\), the suppliers follow the lot-for-lot policy. Figure 2 shows the inventory levels of each supplier and the OEM. According to Fig. 2, the average of each supplier's inventory per cycle can be calculated as the area under the inventory level, that is, \(\left( {\lambda_{k} - 1} \right)Q_{k} T_{{OEM_{k} }} + \left( {\lambda_{k} - 2} \right)Q_{k} T_{{OEM_{k} }} + \cdots + Q_{k} T_{{OEM_{k} }} = Q_{k} T_{{OEM_{k} }} \mathop \sum \limits_{i = 1}^{{\lambda_{k} }} \left( {\lambda_{k} - i} \right)\), which is equal to \(\frac{{Q_{k}^{2} \lambda_{k} \left( {\lambda_{k} - 1} \right)}}{{2D_{k} }}\).

Fig. 2
figure2

Inventory levels for each supplier and the OEM

Similar to the OEM's problem, two elements are considered for inventory holding cost—the storage cost and cost of capital. Since the suppliers pay immediately to their vendors, the amount of inventory that is included in the cost of capital is the same as the one included in the storage cost. Thus, we calculate the total inventory holding cost for supplier \(k\) in the planning horizon as \(\left( {s_{k}^{{\prime }} + h_{k}^{{\prime }} } \right)\frac{{Q_{k} \left( {\lambda_{k} - 1} \right)}}{2}\) by dividing the holding cost per period, \((\left( {s_{k}^{{\prime }} + h_{k}^{{\prime }} } \right)\frac{{Q_{k}^{2} \lambda_{k} \left( {\lambda_{k} - 1} \right)}}{{2D_{k} }})\), by \(T_{{s_{k} }}\).

It is supposed that the suppliers have sufficient budget. Thus, we just considered inventory replenishment costs for the suppliers such as procurement, ordering, and holding costs. The cost function of each supplier k in the planning horizon (1 year) is modeled as follows:

$${\text{TC}}_{{S_{k} }} = c_{k} \left( {\mathop \sum \limits_{n} N_{nk} \alpha_{n} D_{n} } \right) + A_{k}^{{\prime }} \frac{{\mathop \sum \nolimits_{n} N_{nk} \alpha_{n} D_{n} }}{{\lambda_{k} Q_{k} }} + \left( {s_{k}^{{\prime }} + h_{k}^{{\prime }} } \right)\frac{{Q_{k} \left( {\lambda_{k} - 1} \right)}}{2}$$
(7)

The auto manufacturer’s model

The auto manufacturer produces the final products of the supply chain. It supplies the required original parts from the OEM. It is assumed that the required parts are shipped every day from the OEM to the auto manufacturer to be included in the production line of automobiles. The auto manufacturer pays for these parts at the end of the planning horizon. The majority of the auto manufacturer's inventory costs are related to the procurement of original parts. Therefore, in this study, the auto manufacturer's cost function is modeled only as the sum of the procurement cost of parts from the OEM, since inventory holding and ordering costs are negligible as compared with the procurement cost.

$${\text{TC}}_{AM} = \mathop \sum \limits_{n} p_{n} \alpha_{n} D_{n}$$
(8)

(Note that AM refers to the auto manufacturer in the mathematical terms and tables in the rest of this paper.)

Supply chain coordination model

In the coordination scheme, the suppliers offer trade credit to the OEM as a financing source in exchange for an increase in the OEM's orders. Having delayed (extended) its payments, the OEM invests its cash, if any, until \(t_{k}\) and gains interest equal to \(\mathop \sum \limits_{k} w_{k} *Q_{k} *\left[ {\left( {1 + r_{OEM} } \right)^{{t_{k} }} - \left( {1 + \gamma_{k} } \right)^{{\left( {t_{k} - d_{k} } \right)^{ + } }} } \right]\) units of cash each period, where \(\left( x \right)^{ + } = \max \left( {x ,0} \right)\). Meanwhile, the auto manufacturer pays in advance a fraction of payables to help the OEM improve its service level by increasing its production rate. Consequently, the auto manufacturer incurs an opportunity cost by prepaying. The members' cost functions are rewritten based on the aforementioned assumptions (\({\text{TC}}_{.}^{c}\) refers to the cost function of the members and supply chain under the coordination scheme.). The OEM's cost function in the coordinated model becomes:

$$TC_{OEM}^{c} = \mathop \sum \limits_{k} A_{k} \frac{{\mathop \sum \nolimits_{n} N_{nk} \alpha_{n} D_{n} }}{{Q_{k} }} + \mathop \sum \limits_{k} s_{k} \frac{{Q_{k} }}{2} + \mathop \sum \limits_{k} H\left( k \right)\frac{{\mathop \sum \nolimits_{n} N_{nk} \alpha_{n} D_{n} }}{{Q_{k} }} + \mathop \sum \limits_{n} \left( {F_{n} + v_{n} \alpha_{n} D_{n} } \right) + \mathop \sum \limits_{k} w_{k} *\left( {\mathop \sum \limits_{n} N_{nk} \alpha_{n} D_{n} } \right) + Dbt\left( {\left( {1 + i} \right)^{T} - 1} \right) - \mathop \sum \limits_{k} w_{k} *\left( {\mathop \sum \limits_{n} N_{nk} \alpha_{n} D_{n} } \right)*\left[ {\left( {1 + r_{OEM} } \right)^{{t_{k} }} - \left( {1 + \gamma_{k} } \right)^{{\left( {t_{k} - d_{k} } \right)^{ + } }} } \right]$$
(9)

The last term in the objective function is the capital gain of the OEM from extending its payables. There are upper bounds on the OEM's payment time to the suppliers, which are considered as constraints for the coordination problem:

$$t_{k} \le t\max{k} \forall k = 1, 2, \ldots ,K$$
(10)

The budget constraint in (5) will change based on the coordination settings as:

$$\mathop \sum \limits_{k} w_{k} *\left( {\mathop \sum \limits_{n} N_{nk} \alpha_{n} D_{n} } \right)*\left( {1 + \gamma_{k} } \right)^{{\left( {t_{k} - d_{k} } \right)^{ + } }} + \mathop \sum \limits_{n} \left( {F_{n} + v_{n} \alpha_{n} D_{n} } \right) + \mathop \sum \limits_{k} s_{k} \frac{{Q_{k} }}{2} + \mathop \sum \limits_{k} A_{k} \frac{{\mathop \sum \nolimits_{n} N_{nk} \alpha_{n} D_{n} }}{{Q_{k} }} \le B + Dbt + \beta *\left( {\mathop \sum \limits_{n} p_{n} \alpha_{n} D_{n} } \right)$$
(11)

The balance of the cash at the end of the time horizon in (6) is rewritten as follows:

$$B + Dbt + \mathop \sum \limits_{n} p_{n} \alpha_{n} D_{n} + \mathop \sum \limits_{k} w_{k} \left( {\mathop \sum \limits_{n} N_{nk} \alpha_{n} D_{n} } \right)\left( {\left( {1 + r_{OEM} } \right)^{{t_{k} }} - 1} \right) - \mathop \sum \limits_{n} \left( {F_{n} + v_{n} \alpha_{n} D_{n} } \right) - \mathop \sum \limits_{k} w_{k} *\left( {\mathop \sum \limits_{n} N_{nk} \alpha_{n} D_{n} } \right)*\left( {1 + \gamma_{k} } \right)^{{\left( {t_{k} - d_{k} } \right)^{ + } }} - Dbt\left( {1 + i} \right)^{T} \ge L$$
(12)

In the coordination scheme, the members should decide on the due date of the interest-free period (\(d_{k}\)). According to this setting, as the OEM delays its payments, an opportunity cost is incurred by the suppliers, which is added to their cost function. The cost function for each supplier k is rewritten as:

$${\text{TC}}_{{S_{k} }}^{c} = c_{k} \left( {\mathop \sum \limits_{n} N_{nk} \alpha_{n} D_{n} } \right) + A_{k}^{\prime} \frac{{\mathop \sum \nolimits_{n} N_{nk} \alpha_{n} D_{n} }}{{\lambda_{k} Q_{k} }} + \left( {s_{k}^{\prime} + h_{k}^{\prime} } \right)\frac{{Q_{k} \left( {\lambda_{k} - 1} \right)}}{2} + w_{k} \left( {\mathop \sum \limits_{n} N_{nk} \alpha_{n} D_{n} } \right)\left[ {\left( {1 + r_{k} } \right)^{{t_{k} }} - \left( {1 + \gamma_{k} } \right)^{{\left( {t_{k} - d_{k} } \right)^{ + } }} } \right]$$
(13)

By adding the opportunity cost due to prepayment to the auto manufacturer's objective function, it becomes:

$${\text{TC}}_{AM}^{c} = \mathop \sum \limits_{n} p_{n} \alpha_{n} D_{n} + \beta \left( {\mathop \sum \limits_{n} p_{n} \alpha_{n} D_{n} } \right)\left( {\left( {1 + r_{AM} } \right)^{T} - 1} \right)$$
(14)

where \(\beta\) is the prepayment rate that is a decision variable in the coordinated scenario.

According to the members' cost functions, we can write the coordinated nonlinear bi-objective supply chain model as follows:

$$\min Z = {\text{TC}}_{OEM}^{c} + \mathop \sum \limits_{k} {\text{TC}}_{{S_{k} }}^{c} + {\text{TC}}_{AM}^{c}$$
(15)
$$\max \omega = \mathop \sum \limits_{n} \alpha_{n} D_{n}$$
(16)

subject to (4), (10), (11), and (12).

All of the variables are assumed to be positive to decrease the problem’s complexity, that is, the variables of time, \(t_{k}\) and \(d_{k}\), and the coefficient \(\lambda_{k}\) are considered as positive continuous variables. Moreover, \(\lambda_{k}\) has a lower bound which is equal to 1.

Solution approach

To solve the bi-objective optimization models in both scenarios, we use the ε-constraint method by considering the cost function as the main objective and the other objective as a constraint of the problem. It is assumed that \(\alpha_{1} = \alpha_{2} = \cdots = \alpha_{n} = \alpha\) (here \(n = 3)\), that is, all of the n parts should be produced in equal quantities. In other words, the demand satisfaction level for all parts should be the same. Based on this assumption, maximizing the satisfied demand for original parts (\(\mathop \sum \limits_{n} \alpha D_{n}\)) is equal to the maximization of the service level for original parts (\(\alpha )\). As such, for simplicity, in the ε-constraint method, we use \(\alpha \ge x \left( {where \,\varepsilon = 0.1, 0.2, \ldots ,1} \right)\) in the constraints of the models instead of \(\mathop \sum \limits_{n} \alpha_{n} D_{n} \ge y\) (where y is the lower bounds of the satisfied demand). In this method, the non-dominated (Pareto) solutions are found according to different service levels. The best solution among these non-dominated solutions is chosen according to the experts' opinions. In the optimal solution of the proposed model, it can be proved that \(t_{k}\) is less than or equal to \(d_{k}\). This reduces the problem’s complexity.

Lemma 2

\(t_{k}\) is less than or equal to \(d_{k}\) in the optimal solution of the proposed model.

Proof

See “Appendix 2”.

After simplifying the model according to the above-mentioned lemma and assumptions, the models are solved by \(the \ \varepsilon\)-constraint method in the general algebraic modeling system (GAMS).

Numerical analysis

In this section, we compare the two scenarios explained in the previous section. We show the effects of coordinating a budget-constrained supply chain on the financial and operational decisions of the members. Furthermore, a cost-sharing mechanism is developed to share coordination benefits among all members. In the end, we provide a sensitivity analysis to show the sensitivity of the model variables and cost function to some critical financial parameter fluctuations.

Data

The models are solved using data from an OEM of an automotive supply chain in Iran (Table 4). This supply chain includes 10 suppliers of strategic components and materials for the OEM (Each component is supplied by only one supplier; thus, K = 10), which produces three kinds of original parts (powertrain components including engines, gearboxes, and axles) for the auto manufacturer (N = 3), and an auto manufacturer with 2 kinds of final products (J = 2; Fig. 1). The required data are collected from the company’s archives and analyzed under the supervision of the OEM’s supply chain manager. In solving the model, the initial budget (B) and minimum cash level at the OEM (L) are assumed to be zero.

Table 4 Data from an OEM in the automotive supply chain for the model parameters (Prices are in terms of 1000 units of cash)

Solution results

This section presents the results of solving the proposed models in Section “Model description and solution approach”. The results are reported in two tables in the form of non-dominated solutions. Table 5(a) and (b) show the costs of the members and the total cost of the supply chain according to different service levels in both scenarios, and Table 6 presents the quantities of the problem’s decision variables.

Table 5 (a) Supply chain costs in the non-coordinated scenario (italics shows the infeasible solutions for the main problem). (b) Supply chain costs in the coordinated scenario
Table 6 The value of decision variables in both scenarios; italics shows the infeasible solutions for the main problem

The OEM is faced with capital shortage and financing limitations, that is, it cannot obtain more than M units of cash from external financial institutions such as banks (M is the maximum limit for external financing). As explained earlier, this limitation may have some consequences such as the inability to satisfy all of the auto manufacturer’s demand and losing part of the OEM’s market share.

According to the numerical results, in the non-coordinated scenario with this circumstance (Table 5(a)), the OEM is not able to satisfy more than about 50% of the demand, as the quantity of required external financing for 60% of the auto manufacturer's demand is 55,479,000 (Table 6), which is more than the maximum limit of external financing (M = 50,000,000). Therefore, the problem is infeasible for \(\alpha \ge 0.6\) (italics in Tables 5(a) and 6) for the non-coordinated structure in accordance with the financing limitations (For \(\alpha \ge 0.6\), the problem is solved by eliminating constraint (5), which is related to the financing limitations, to track the amount of required loan from financial institutions.). The proposed coordination mechanism is developed to deal with these kinds of difficulties. Table 5(b) shows that in the coordinated scenario, all of the demand can be satisfied despite the financing limitations because of internal financing. Figure 3 illustrates the differences between the costs of the OEM, auto manufacturer, and the supply chain for the two scenarios for different service levels.

Fig. 3
figure3

The differences between the costs of OEM, AM, and the supply chain in two scenarios for different service levels. Note: For the service levels more than 0.5, the problem is infeasible for the non-coordinated scenario, that is, infinity costs

The numerical results indicate that the proposed coordination mechanism leads to cost reduction for the whole supply chain as compared with the non-coordinated scenario. As seen in Table 5(b), the supply chain cost is reduced in the coordinated scenario, as compared with the non-coordinated one, for all service levels (According to the results, for \(\alpha > 0.5\), the problem is infeasible for the non-coordinated scenario, i.e., the costs become infinity.). Moreover, through coordination, the OEM can order in larger quantities and larger periods than in the non-coordinated case (Table 6). In the coordinated scheme, the OEM takes advantage of the prepayment option and avoids external financing for all service levels in this example. As explained later in Lemma 3 in section “Sensitivity analysis”, the reason is that in this example, the interest rate of external financing is larger than the opportunity cost of the auto manufacturer. Consequently, the auto manufacturer's loss due to the prepayment to the OEM is less than the interest paid to the external institution for the loan at the end of the time horizon. It is also observed that for service levels of 0.9 and 1, the OEM takes advantage of both options: trade credit and prepayment of the auto manufacturer.

Cost-sharing mechanism

As previously mentioned, the supply chain benefits from the proposed coordination scheme through cost reduction. In the process of coordination, it is normal that some of the members face increased costs and others, reduced costs. As seen in Fig. 3, through coordination, the OEM’s costs decrease but those of the auto manufacturer increase.

In such cases, to persuade members whose costs increased to accept the coordination scheme, the last step is sharing the gains and losses among all members of the supply chain so that all the parties benefit equally from the coordination. In this regard, we apply a cost-sharing mechanism to the coordination scheme, as adopted by Moussawi-Haidar et al. (2014), wherein the gains and losses are shared according to the ratio of the individual cost per unit time with respect to the chain’s non-coordinated cost per unit time. Let \(C_{chain}\) and \(C_{chain}^{0}\) be the total unit cost of a coordinated supply chain and the total unit cost of a non-coordinated supply chain, respectively, and \(C_{x}^{0}\) the cost per unit time of member x of the non-coordinated supply chain. x is a member of set \(M_{x}\) = OEM, auto manufacturer, \(S_{1}\), \(S_{2}\), …, \(S_{K}\). We get the following ratios:

$$Z_{x} = \frac{{C_{x}^{0} }}{{C_{chain}^{0} }}\,with\, \mathop \sum \limits_{{M_{x} }} Z_{x} = 1$$
(17)

We can calculate the cost of each member by:

$${\text{Cost}}\,{\text{of}}\,{\text{member}}\,{\text{x}} = Z_{x} C_{chain}$$
(18)

Table 7 shows the results of sharing the costs among members. As seen in Table 7, compared with the non-coordinated case, the costs of all members in the coordinated scenario are reduced for all service levels (Table 5(a)) after applying the cost-sharing mechanism.

Table 7 Results of applying the proposed cost-sharing mechanism

Figure 4 shows the difference between the costs of the OEM, auto manufacturer, and supply chain in the non-coordinated and coordinated scenarios after applying the cost-sharing mechanism. A comparison between Fig. 3 and Fig. 4 indicates the fair distribution of gains and losses among supply chain members as a result of applying the cost-sharing mechanism.

Fig. 4
figure4

The differences between the costs of OEM, AM, and the supply chain in the non-coordinated scenario and the coordinated scenario after applying the cost-sharing mechanism. Note: For the service levels more than 0.5, the problem is infeasible for the non-coordinated scenario, that is, infinity costs)

Figure 5 shows the difference between the costs of the OEM and auto manufacturer before and after applying the cost-sharing mechanism in the coordinated scenario.

Fig. 5
figure5

The differences between the costs of OEM and AM before and after applying the cost-sharing mechanism in the coordinated scenario

Sensitivity analysis

In this section, we analyze the sensitivity of the system’s costs and model variables to the fluctuations in both the operational and financial parameters. To avoid excessive calculations, the analysis is only performed for the case with \(\alpha = 1\).

Sensitivity analysis for the operational parameters

A sensitivity analysis regarding inventory-related parameters, \(A_{k}\), \(S_{k}\), \(A_{k}^{\prime}\), and \(S_{k}^{\prime}\), is provided. To this end, for simplicity, we define a coefficient denoted by \(\omega\) to make changes in the parameters. For instance, for parameter \(A_{k}\), if \(\omega = 2\), this means that for all k, \(A_{k}\) is multiplied by 2. The results are illustrated in Fig. 6. As expected, the results indicate that the impact of changes in \(A_{k}\) and \(S_{k}\) on the order quantities and the total cost is similar to the classical EOQ model (see Fig. 6a, b). Moreover, by decreasing \(A_{k}\) or increasing \(S_{k}\), the average amount of \({\uplambda }_{k}\) increases (see Fig. 6c) because of the reduction in the OEM’s order quantities (Fig. 6a).

Fig. 6
figure6

The results of the sensitivity analysis for the operational parameters. Note: In the vertical axis, the average amount of each variable is defined on k, where k = 1,2, …, K. For instance, for the order quantity, it is defined as \(\frac{{\mathop \sum \nolimits_{k} Q_{k} }}{K}\)

Any changes (increase or decrease) in \(A_{k}^{{\prime }}\) has a direct effect on the order quantities at the OEM (see Fig. 6a). Furthermore, by increasing \(A_{k}^{\prime}\) or decreasing \(S_{k}^{{\prime }}\) the average amount of \({\uplambda }_{k}\) increases. In this case, the suppliers tend to order in larger quantities to support more than one order at the OEM every time and store the remaining inventory for the next orders. According to the results shown in Fig. 6a, the average order quantity at the OEM decreases by decreasing \(S_{k}^{{\prime }}\). This can be justified according to the model. As seen in Fig. 6c, d, by increasing \(S_{k}^{\prime}\) (for \(\omega < 1\)) the average amount of \({\uplambda }_{k}\) decreases until it equals 1 (the lot-for-lot policy). In this situation, if the average order quantity also decreases, the average amount of \({\uplambda }_{k} *{\text{Q}}_{k}\) will largely decrease and cause a large increase in the total cost. Therefore, to avoid this large loss, the OEM increases the order quantities. (The order quantities of the OEM and the total cost do not change by increasing \(S_{k}^{\prime }\) from its values in the main example, that is, where \(\omega = 1\) in the horizontal axis, because of the lot-for-lot policy of suppliers).

Sensitivity analysis for financial parameters

A sensitivity analysis is provided with respect to some important financial parameters namely, \(i, r_{OEM}, r_{AM} \ ,and\,r_{k}\). The results are presented in Tables 8, 9, 10 and 11. Note that the rates in the tables are daily rates. The analysis of the results is summarized in the form of some lemmas.

Table 8 The results of sensitivity analysis for various quantities of i
Table 9 The results of sensitivity analysis for various quantities of \(r_{OEM}\)
Table 10 The results of sensitivity analysis for various quantities of \(r_{j}\)
Table 11 The results of sensitivity analysis for various quantities of \(r_{k}\)

Lemma 3

When \(i < r_{AM}\), the OEM borrows from the external financial institution as much as possible, that is, the maximum debt limit (Table 8(a)). Conversely, when \(i \ge r_{AM}\), the OEM obtains financing from its internal source, the auto manufacturer’s prepayment (Table 8(b), (c) and (d)).

Proof

\(i < r_{AM}\) means that the interest rate of the loan is less than the opportunity cost of the auto manufacturer, such that the interest paid to the external institution for the loan at the end of the time horizon is less than the auto manufacturer's loss arising from its prepayment to the OEM. This is why the OEM uses the maximum limit of the loan in this situation. By contrast, when \(i > r_{AM}\), the OEM uses internal financing from the auto manufacturer (Table 8(c), (d)). For the equality mode, \(i = r_{AM}\), no difference is expected between external and internal financing, but by making the debt equal to zero, constraint (12) becomes less constrictive. Therefore, in this case, internal financing is preferred (Table 8(b)).□

Lemma 4

If \(r_{k} < r_{OEM}\) then the OEM delays its payments until the maximum limit (Table 9(c), (d)) is reached, and if \(r_{k} > r_{OEM}\) the OEM pays at the time of ordering (\(t_{k} = 0;\) Table 9(a)).

Proof

To prove this lemma, we consider \(Z^{{\prime }}\) (Eq. 25 in “Appendix 2”) because the rest of the cost function does not affect the process of proving. According to \(Z^{{\prime }}\), we get the following rule:

The smaller the \(Z^{{\prime }}\) the better is the objective function. Therefore, if \(r_{k} > r_{OEM}\), the greater the \(t_{k}\), the greater is the \(Z^{{\prime }}\) and consequently, the worse is the objective function. From a mathematical viewpoint, we prove the proposition below:

Proposition 1: If \(r_{k} > r_{OEM} > 0\) and assuming we have \(t_{k}^{{\prime }}\) such that \(t_{k}^{{\prime }} > t_{k} > 0\); then:

$$\left( {1 + r_{k} } \right)^{{t_{k}^{{\prime }} }} - \left( {1 + r_{OEM} } \right)^{{t_{k}^{{\prime }} }} > \left( {1 + r_{k} } \right)^{{t_{k} }} - \left( {1 + r_{OEM} } \right)^{{t_{k} }}$$
(19)

Proof of proposition 1

This inequality implies that under this circumstance, the lower the \(t_{k}\), the better is the objective function. To prove this, we write the inequality below:

$$\left[ {\left( {1 + r_{k} } \right)^{{t_{k} }} - \left( {1 + r_{OEM} } \right)^{{t_{k} }} } \right]\left( {1 + r_{OEM} } \right)^{{t_{k}^{{\prime }} - t_{k} }} > \left[ {\left( {1 + r_{k} } \right)^{{t_{k} }} - \left( {1 + r_{OEM} } \right)^{{t_{k} }} } \right]$$
(20)

The inequality above is true because \(r_{OEM} > 0\) and \(t_{k}^{{\prime }} - t_{k} > 0;\) consequently, \(\left( {1 + r_{OEM} } \right)^{{t_{k}^{{\prime }} - t_{k} }} > 1\); therefore, we reach:

$$\left( {1 + r_{k} } \right)^{{t_{k} }} \left( {1 + r_{OEM} } \right)^{{t_{k}^{{\prime }} - t_{k} }} - \left( {1 + r_{OEM} } \right)^{{t_{k}^{{^{{\prime }} }} }} > \left[ {\left( {1 + r_{k} } \right)^{{t_{k} }} - \left( {1 + r_{OEM} } \right)^{{t_{k} }} } \right]$$
(21)

If \(r_{k} > r_{OEM} > 0\), then \(\left( {1 + r_{k} } \right)^{{t_{k}^{{\prime }} - t_{k} }} > \left( {1 + r_{OEM} } \right)^{{t_{k}^{{\prime }} - t_{k} }}\); thus, we can substitute \(\left( {1 + r_{k} } \right)^{{t_{k}^{{\prime }} - t_{k} }}\) with \(\left( {1 + r_{OEM} } \right)^{{t_{k}^{\prime } - t_{k} }}\) in the inequality (22). Then we get:

$$\left( {1 + r_{k} } \right)^{{t_{k} }} \left( {1 + r_{k} } \right)^{{t_{k}^{{\prime }} - t_{k} }} - \left( {1 + r_{OEM} } \right)^{{t_{k}^{{\prime }} }} > \left[ {\left( {1 + r_{k} } \right)^{{t_{k} }} - \left( {1 + r_{OEM} } \right)^{{t_{k} }} } \right]$$
(22)

which is equal to \(\left( {1 + r_{k} } \right)^{{t_{k}^{{\prime }} }} - \left( {1 + r_{OEM} } \right)^{{t_{k}^{{\prime }} }} > \left( {1 + r_{k} } \right)^{{t_{k} }} - \left( {1 + r_{OEM} } \right)^{{t_{k} }}\).

This is why the OEM should pay at the beginning of the period when \(r_{k} > r_{OEM}\).

Lemma 5

The lower the \(r_{AM}\), the lower is the total cost of the supply chain (Table 10).

Proof

Taking the derivative of the cost function with respect to \(r_{AM}\), we get \(T\beta \left( {\mathop \sum \limits_{n} p_{n} \alpha_{n} D_{n} } \right)\left( {1 + r_{AM} } \right)^{T - 1}\), which is greater than or equal to zero. Thus, the total cost function is increasing in \(r_{AM}\).

Lemma 6

If \(r_{k} \ge r_{OEM}\) (Table 11(c), (d)), then the total cost of the supply chain becomes greater than the total cost in the case where \(r_{k} < r_{OEM}\) (Table 11(a), (b)), that is, \(Z_{{r_{k} \ge r_{OEM} }} > Z_{{r_{k} < r_{OEM} }}\).

Proof

Again, we consider only \(Z^{\prime }\) (Eq. 25 in “Appendix 2”) because the other parts do not have any effect on the proof of this theorem. According to \(Z^{\prime }\), it is obvious that the greater \(the \ r_{OEM}\) from \(r_{k}\), the lesser is the \(Z^{\prime }\), which means the lesser is the cost function of the supply chain.

Conclusion

In this study, we exploited two vital issues in supply chain management—coordination between supply chain members and integration of the supply chain’s physical and financial flows—to improve system performance and service level.

As previously mentioned, combining supply chain coordination and financial decisions is a relatively new research area and needs more attention and extensions to real-world assumptions. In this stream of literature, some important financial issues, such as budget constraints and different financing options, have not been addressed in many of the existing works. Moreover, the idea of considering financing limitations and lost sales as a consequence of financing limitations is novel in the supply chain coordination literature. Meanwhile, most of the previous works have been limited to the relationships in a single buyer–single vendor with a single product scenario. To fill these gaps in the literature, we proposed a coordination scheme that is based on joint decision-making and that coordinates the operational (inventory–production) and financial decisions of the members of a capital-constrained supply chain (with financing limitations) in the automotive industry. To the best of our knowledge, this is the first work to address supply chain coordination together with financing issues for maximizing service level and minimizing operational–financial costs.

The proposed supply chain model consists of three levels and multiple products. Two scenarios were developed according to the different structures of the supply chain: i) non-coordinated supply chain and ii) coordinated supply chain. In the first scenario, the members optimize their own costs without considering the benefits and costs of others, but in the second one, all the members work in a way that optimizes the supply chain costs.

The models were solved using the ε-constraint method, applying GAMS based on data from an automotive supply chain in Iran. The results indicate that in the non-coordinated scenario, the OEM cannot satisfy all of the auto manufacturer’s demand and faces lost sales. This happens because of the capital shortage and limitations in external financing. As observed in Table 5(a) (italics), the OEM is not able to satisfy more than about 50% of the auto manufacturer's demand in the non-coordinated scenario. However, in the coordinated scenario, all of the auto manufacturer's demand can be satisfied through the OEM’s internal financing.

The advantages of this coordination scheme are twofold: (1) By applying the proposed coordination scheme, the total cost of the supply chain decreases compared with the non-coordinated scenario; and (2) The scheme makes it possible to satisfy all of the auto manufacturer's demand despite the capital shortage and financing limitations of the OEM. In other words, by adopting this coordination scheme, the supply chain manager can finance the supply chain operations and still ensure the service level for the auto manufacturer.

In such coordination methods, some of the members take advantage of the coordination more than others, and some of them may even lose a fraction of their profit. To avoid this inequity and motivate the members that lose a portion of their profits because of the coordination, a cost-sharing mechanism was applied to the coordination scheme. The results demonstrate that after applying the cost-sharing mechanism, the costs of all members are lower than their costs in the non-coordinated scenario. Thus, the proposed coordination scheme is beneficial to all members of the supply chain. Finally, to validate the proposed coordination model, a sensitivity analysis for the financial parameters was conducted to measure the sensitivity of the model variables and cost function to the fluctuations of these parameters.

Our results provide the following managerial insights for manufacturers that face financial constraints such as insufficient capital and limitations on external financing. First, the supply chain manager should integrate the operational and financial decisions of the firm. Second, he/she should entice the upstream and downstream partners to adopt operational–financial coordination. Furthermore, some detailed managerial insights after adopting the proposed coordination scheme are listed below.

  • If the interest rate of the loan is more than the downstream partner’s rate of return (ROR), the manufacturer should finance all the required working capital from the downstream partner. Conversely, it should borrow from the external source as much as it needs until the maximum limit of the external financing is reached.

  • If supplier k’s ROR is less than the manufacturer’s ROR, supplier k should offer a credit period (interest-free period) to the manufacturer for its payment, and the manufacturer should delay its payment to supplier k until the maximum deadline. Conversely, it should pay at the time of ordering.

We end this paper with two remarks. First, we have modeled the deterministic state of the problem based on an EOQ policy. However, sometimes in the real world, firms face fluctuations in demand. Thus, this research can be extended to the case with stochastic demand, which calls for a new coordination scheme. Second, we used trade credit and prepayment of the auto manufacturer as two options for internal financing. Adopting other options to finance the capital-constrained member is another avenue for further research.

Availability of data and materials

Data are included in this published article.

Abbreviations

AM:

Auto manufacturer

EOQ:

Economic order quantity

GAMS:

General algebraic modeling system

OEM:

Original equipment manufacturer

ROR:

Rate of return

SC:

Supply chain

SCC:

Supply chain coordination

SCF:

Supply chain finance

TCF:

Trade credit financing

References

  1. Aggarwal SP, Jaggi CK (1995) Ordering policies of deteriorating items under permissible delay in payments. J Oper Res Soc 46:658–662. https://doi.org/10.1057/jors.1995.90

    Article  Google Scholar 

  2. AlDurgam M, Adegbola K, Glock CH (2017) A single-vendor single-manufacturer integrated inventory model with stochastic demand and variable production rate. Int J Prod Econ 191:335–350. https://doi.org/10.1016/j.ijpe.2017.05.017

    Article  Google Scholar 

  3. Arshinder K, Kanda A, Deshmukh SG (2008) Supply chain coordination: perspectives, empirical studies and research directions. Int J Prod Econ 115:316–335

    Article  Google Scholar 

  4. Arshinder K, Kanda A, Deshmukh SG (2011) A review on supply chain coordination: coordination mechanisms, managing uncertainty and research directions. In: Supply chain coordination under uncertainty. Springer, Berlin, pp 39–82

  5. Babich V, Kouvelis P (2018) Introduction to the special issue on research at the interface of finance, operations, and risk management (iFORM): Recent contributions and future directions. Manuf Serv Oper Manag 20:1–18

    Article  Google Scholar 

  6. Bernstein F, Federgruen A (2007) Coordination mechanisms for supply chains under price and service competition. Manuf Serv Oper Manag 9:242–262. https://doi.org/10.1287/msom.1070.0159

    Article  Google Scholar 

  7. Cachon GP (2003) Supply chain coordination with contracts. Handbooks Oper Res Manag Sci 11:227–339

    Article  Google Scholar 

  8. Cachon GP, Lariviere MA (2005) Supply chain coordination with revenue-sharing contracts: Strengths and limitations. Manag Sci 51:30–44. https://doi.org/10.1287/mnsc.1040.0215

    Article  Google Scholar 

  9. Chakuu S, Masi D, Godsell J (2020) Towards a framework on the factors conditioning the role of logistics service providers in the provision of inventory financing. Int J Oper Prod Manag. https://doi.org/10.1108/IJOPM-06-2019-0502

    Article  Google Scholar 

  10. Chen TH, Chen JM (2005) Optimizing supply chain collaboration based on joint replenishment and channel coordination. Transp Res Part E Logist Transp Rev 41:261–285. https://doi.org/10.1016/j.tre.2004.06.003

    Article  Google Scholar 

  11. Chen X, Wang A (2012) Trade credit contract with limited liability in the supply chain with budget constraints. Ann Oper Res 196:153–165. https://doi.org/10.1007/s10479-012-1119-0

    Article  Google Scholar 

  12. Chiadamrong N, Prasertwattana K, Yoshiaki S (2007) Using financial incentives as a coordinating mechanism to improve the supply chain network integration. Eur J Ind Eng 1:280–300. https://doi.org/10.1504/EJIE.2007.014688

    Article  Google Scholar 

  13. Dada M, Hu Q (2008) Financing newsvendor inventory. Oper Res Lett 36:569–573. https://doi.org/10.1016/j.orl.2008.06.004

    Article  Google Scholar 

  14. Devalkar SK, Krishnan H (2019) The impact of working capital financing costs on the efficiency of trade credit. Prod Oper Manag 28:878–889. https://doi.org/10.1111/poms.12954

    Article  Google Scholar 

  15. Ding W, Wan G (2020) Financing and coordinating the supply chain with a capital-constrained supplier under yield uncertainty. Int J Prod Econ. https://doi.org/10.1016/j.ijpe.2020.107813

    Article  Google Scholar 

  16. Ebrahimi S, Hosseini-Motlagh SM, Nematollahi M (2019) Proposing a delay in payment contract for coordinating a two-echelon periodic review supply chain with stochastic promotional effort dependent demand. Int J Mach Learn Cybern 10:1037–1050. https://doi.org/10.1007/s13042-017-0781-6

    Article  Google Scholar 

  17. Feng X, Moon I, Ryu K (2015) Supply chain coordination under budget constraints. Comput Ind Eng 88:487–500. https://doi.org/10.1016/j.cie.2015.08.005

    Article  Google Scholar 

  18. Fugate B, Sahin F, Mentzer JT (2006) Supply chain management coordination mechanisms. J Bus Logist 27:129–161. https://doi.org/10.1002/j.2158-1592.2006.tb00220.x

    Article  Google Scholar 

  19. Gelsomino LM, Mangiaracina R, Perego A, Tumino A (2016) Supply chain finance: a literature review. Int J Phys Distrib Logist Manag 46:348–366

  20. Giannoccaro I, Pontrandolfo P (2004) Supply chain coordination by revenue sharing contracts. Int J Prod Econ 89:131–139. https://doi.org/10.1016/S0925-5273(03)00047-1

    Article  Google Scholar 

  21. Glock CH (2012) The joint economic lot size problem: a review. Int J Prod Econ 135:671–686

    Article  Google Scholar 

  22. Govindan K, Popiuc MN, Diabat A (2013) Overview of coordination contracts within forward and reverse supply chains. J Clean Prod 47:319–334. https://doi.org/10.1016/j.jclepro.2013.02.001

    Article  Google Scholar 

  23. Goyal SK, Gupta YP (1989) Integrated inventory models: the buyer-vendor coordination. Eur J Oper Res 41:261–269

    Article  Google Scholar 

  24. Goyal SK (1977) An integrated inventory model for a single supplier-single customer problem. Int J Prod Res 15:107–111. https://doi.org/10.1080/00207547708943107

    Article  Google Scholar 

  25. Goyal SK, Huang CK, Chen KC (2003) A simple integrated production policy of an imperfect item for vendor and buyer. Prod Plan Control 14:596–602. https://doi.org/10.1080/09537280310001626188

    Article  Google Scholar 

  26. Guillén G, Badell M, Puigjaner L (2007) A holistic framework for short-term supply chain management integrating production and corporate financial planning. Int J Prod Econ 106:288–306. https://doi.org/10.1016/j.ijpe.2006.06.008

    Article  Google Scholar 

  27. Gupta S, Dutta K (2011) Modeling of financial supply chain. Eur J Oper Res 211:47–56. https://doi.org/10.1016/j.ejor.2010.11.005

    Article  Google Scholar 

  28. Gupta SK, Kunnathur AS, Dandapani K (1987) Optimal repayment policies for multiple loans. Omega 15:323–330. https://doi.org/10.1016/0305-0483(87)90020-X

    Article  Google Scholar 

  29. Hou J, Zeng AZ, Zhao L (2010) Coordination with a backup supplier through buy-back contract under supply disruption. Transp Res Part E Logist Transp Rev 46:881–895. https://doi.org/10.1016/j.tre.2010.03.004

    Article  Google Scholar 

  30. Huang J, Yang W, Tu Y (2020) Financing mode decision in a supply chain with financial constraint. Int J Prod Econ 220:107441. https://doi.org/10.1016/j.ijpe.2019.07.014

    Article  Google Scholar 

  31. Jaber MY, Osman IH (2006) Coordinating a two-level supply chain with delay in payments and profit sharing. Comput Ind Eng 50:385–400. https://doi.org/10.1016/j.cie.2005.08.004

    Article  Google Scholar 

  32. Jahangiri MH, Cecelja F (2014) Modelling financial flow of the supply chain. In: IEEE international conference on industrial engineering and engineering management, pp 1071–1075

  33. Jayaraman V, Pirkul H (2001) Planning and coordination of production and distribution facilities for multiple commodities. Eur J Oper Res 133:394–408. https://doi.org/10.1016/S0377-2217(00)00033-3

    Article  Google Scholar 

  34. Johari M, Hosseini-Motlagh SM, Nematollahi M et al (2018) Bi-level credit period coordination for periodic review inventory system with price-credit dependent demand under time value of money. Transp Res Part E Logist Transp Rev 114:270–291. https://doi.org/10.1016/j.tre.2018.04.008

    Article  Google Scholar 

  35. Khalilpourazari S, Pasandideh SHR (2019) Modeling and optimization of multi-item multi-constrained EOQ model for growing items. Knowl Based Syst 164:150–162. https://doi.org/10.1016/j.knosys.2018.10.032

    Article  Google Scholar 

  36. Khalilpourazari S, Pasandideh SHR, Niaki STA (2019) Optimizing a multi-item economic order quantity problem with imperfect items, inspection errors, and backorders. Soft Comput 23:11671–11698. https://doi.org/10.1007/s00500-018-03718-1

    Article  Google Scholar 

  37. Khalilpourazari S, Teimoori S, Mirzazadeh A et al (2019) Robust Fuzzy chance constraint programming for multi-item EOQ model with random disruption and partial backordering under uncertainty. J Ind Prod Eng 36:276–285. https://doi.org/10.1080/21681015.2019.1646328

    Article  Google Scholar 

  38. Kim T, Hong Y, Lee J (2005) Joint economic production allocation and ordering policies in a supply chain consisting of multiple plants and a single retailer. Int J Prod Res 43:3619–3632. https://doi.org/10.1080/00207540500140831

    Article  Google Scholar 

  39. Kou G, Peng Y, Wang G (2014) Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Inf Sci (Ny) 275:1–12. https://doi.org/10.1016/j.ins.2014.02.137

    Article  Google Scholar 

  40. Kouvelis P, Zhao W (2018) Who should finance the supply chain? Impact of credit ratings on supply chain decisions. Manuf Serv Oper Manag 20:19–35. https://doi.org/10.1287/msom.2017.0669

    Article  Google Scholar 

  41. Kouvelis P, Zhao W (2016) Supply chain contract design under financial constraints and bankruptcy costs. Manage Sci 62:2341–2357. https://doi.org/10.1287/mnsc.2015.2248

    Article  Google Scholar 

  42. Krommyda IP, Skouri K, Lagodimos AG (2019) A unified EOQ model with financial constraints and market tolerance. Appl Math Model 65:89–105. https://doi.org/10.1016/j.apm.2018.08.002

    Article  Google Scholar 

  43. Lee CH, Rhee BD (2011) Trade credit for supply chain coordination. Eur J Oper Res 214:136–146. https://doi.org/10.1016/j.ejor.2011.04.004

    Article  Google Scholar 

  44. Lee HH, Zhou J, Wang J (2018) Trade credit financing under competition and its impact on firm performance in supply chains. Manuf Serv Oper Manag 20:36–52. https://doi.org/10.1287/msom.2017.0640

    Article  Google Scholar 

  45. Li H, Mai L, Zhang W, Tian X (2019) Optimizing the credit term decisions in supply chain finance. J Purch Supply Manag 25:146–156. https://doi.org/10.1016/j.pursup.2018.07.006

    Article  Google Scholar 

  46. Li X, Wang Q (2007) Coordination mechanisms of supply chain systems. Eur J Oper Res 179:1–16. https://doi.org/10.1016/j.ejor.2006.06.023

    Article  Google Scholar 

  47. Longinidis P, Georgiadis MC (2011) Integration of financial statement analysis in the optimal design of supply chain networks under demand uncertainty. Int J Prod Econ 129:262–276. https://doi.org/10.1016/j.ijpe.2010.10.018

    Article  Google Scholar 

  48. Luo W, Shang K (2015) Joint inventory and cash management for multidivisional supply chains. Oper Res 63:1098–1116. https://doi.org/10.1287/opre.2015.1409

    Article  Google Scholar 

  49. Mashud AHM, Uddin MS, Sana SS (2019) A two-level trade-credit approach to an integrated price-sensitive inventory model with shortages. Int J Appl Comput Math 5:1–28. https://doi.org/10.1007/s40819-019-0703-2

    Article  Google Scholar 

  50. Miloudi F El, Tchernev N, Rian F (2016) Scheduling payments optimization to drive working capital performance within a supply chain. In: ILS 2016—6th international conference on information systems, logistics and supply chain

  51. Modigliani F, Miller MH (1958) The cost of capital, corporation finance and the theory of investment. Am Econ Rev 48:261–297

    Google Scholar 

  52. Moussawi-Haidar L, Dbouk W, Jaber MY, Osman IH (2014) Coordinating a three-level supply chain with delay in payments and a discounted interest rate. Comput Ind Eng 69:29–42. https://doi.org/10.1016/j.cie.2013.12.007

    Article  Google Scholar 

  53. Moussawi-Haidar L, Jaber MY (2013) A joint model for cash and inventory management for a retailer under delay in payments. Comput Ind Eng 66:758–767. https://doi.org/10.1016/j.cie.2013.09.021

    Article  Google Scholar 

  54. Naimi Sadigh A, Fallah H, Nahavandi N (2013) A multi-objective supply chain model integrated with location of distribution centers and supplier selection decisions. Int J Adv Manuf Technol 69:225–235. https://doi.org/10.1007/s00170-013-5013-y

    Article  Google Scholar 

  55. Nouri M, Hosseini-Motlagh SM, Nematollahi M (2018) Proposing a discount policy for two-level supply chain coordination with periodic review replenishment and promotional efforts decisions. Oper Res. https://doi.org/10.1007/s12351-018-0434-x

    Article  Google Scholar 

  56. Padmanabhan V, Png IPL (1997) Manufacturer’s returns policies and retail competition. Mark Sci 16:81–94. https://doi.org/10.1287/mksc.16.1.81

    Article  Google Scholar 

  57. Pramanik P, Maiti MK (2019) An inventory model for deteriorating items with inflation induced variable demand under two level partial trade credit: a hybrid ABC-GA approach. Eng Appl Artif Intell 85:194–207. https://doi.org/10.1016/j.engappai.2019.06.013

    Article  Google Scholar 

  58. Sadeghi Rad R, Nahavandi N (2018) A novel multi-objective optimization model for integrated problem of green closed loop supply chain network design and quantity discount. J Clean Prod 196:1549–1565. https://doi.org/10.1016/j.jclepro.2018.06.034

    Article  Google Scholar 

  59. Sadeghi Rad R, Nahavandi N, Husseinzadeh Kashan A, Zegordi S (2018) An integrated closed-loop supply chain configuration model and supplier selection based on offered discount policies. Int J Eng 31:440–449

    Google Scholar 

  60. Sahin F, Robinson EP (2002) Flow coordination and information sharing in supply chains: review, implications, and directions for future research. Decis Sci 33:505–536. https://doi.org/10.1111/j.1540-5915.2002.tb01654.x

    Article  Google Scholar 

  61. Sarmah SP, Acharya D, Goyal SK (2006) Buyer vendor coordination models in supply chain management. Eur J Oper Res 175:1–15. https://doi.org/10.1016/j.ejor.2005.08.006

    Article  Google Scholar 

  62. Shen C, Zhang X (2012) Supply chain coordination based on buy-back contract under price-sensitive and time-sensitive demand. Springer, Berlin, pp 345–353

    Google Scholar 

  63. Shin D, Mittal M, Sarkar B (2018) Effects of human errors and trade-credit financing in two-echelon supply chain models. Eur J Ind Eng 12:465–503. https://doi.org/10.1504/EJIE.2018.093574

    Article  Google Scholar 

  64. Sieke MA, Seifert RW, Thonemann UW (2012) Designing service level contracts for supply chain coordination. Prod Oper Manag 21:698–714. https://doi.org/10.1111/j.1937-5956.2011.01301.x

    Article  Google Scholar 

  65. Skjoett-Larsen T (2000) European logistics beyond 2000. Int J Phys Distrib Logist Manag 30:377–387

    Article  Google Scholar 

  66. Taleizadeh AA, Lashgari M, Akram R, Heydari J (2016) Imperfect economic production quantity model with upstream trade credit periods linked to raw material order quantity and downstream trade credit periods. Appl Math Model 40:8777–8793. https://doi.org/10.1016/j.apm.2016.05.008

    Article  Google Scholar 

  67. Tang CS, Alex Yang S, Wu J (2018) Sourcing from suppliers with financial constraints and performance risk. Manuf Serv Oper Manag 20:70–84. https://doi.org/10.1287/msom.2017.0638

    Article  Google Scholar 

  68. Teng JT (2009) Optimal ordering policies for a retailer who offers distinct trade credits to its good and bad credit customers. Int J Prod Econ 119:415–423. https://doi.org/10.1016/j.ijpe.2009.04.004

    Article  Google Scholar 

  69. Tibrewala R, Tibrewala R, Meena PL (2018) Buy-back policy for supply chain coordination: a simple rule. Int J Oper Res 31:545–572. https://doi.org/10.1504/IJOR.2018.090437

    Article  Google Scholar 

  70. Wang H, Kou G, Peng Y (2020) Multi-class misclassification cost matrix for credit ratings in peer-to-peer lending. J Oper Res Soc. https://doi.org/10.1080/01605682.2019.1705193

    Article  Google Scholar 

  71. Xiao S, Sethi SP, Liu M, Ma S (2017) Coordinating contracts for a financially constrained supply chain. Omega (United Kingdom) 72:71–86. https://doi.org/10.1016/j.omega.2016.11.005

    Article  Google Scholar 

  72. Xu J, Qi Q, Bai Q (2018) Coordinating a dual-channel supply chain with price discount contracts under carbon emission capacity regulation. Appl Math Model 56:449–468. https://doi.org/10.1016/j.apm.2017.12.018

    Article  Google Scholar 

  73. Xu X, Birge JR (2006) Equity valuation, production, and financial planning: A stochastic programming approach. Nav Res Logist 53:641–655. https://doi.org/10.1002/nav.20182

    Article  Google Scholar 

  74. Xu X, Chen X, Jia F et al (2018) Supply chain finance: A systematic literature review and bibliometric analysis. Int J Prod Econ 204:160–173

    Article  Google Scholar 

  75. Yan N, Jin X, Zhong H, Xu X (2020) Loss-averse retailers’ financial offerings to capital-constrained suppliers: loan vs. investment. Int J Prod Econ 227:107665. https://doi.org/10.1016/j.ijpe.2020.107665

    Article  Google Scholar 

  76. Yan N, Liu Y, Xu X, He X (2020) Strategic dual-channel pricing games with e-retailer finance. Eur J Oper Res 283:138–151. https://doi.org/10.1016/j.ejor.2019.10.046

    Article  Google Scholar 

  77. Yan N, Sun B, Zhang H, Liu C (2016) A partial credit guarantee contract in a capital-constrained supply chain: Financing equilibrium and coordinating strategy. Int J Prod Econ 173:122–133. https://doi.org/10.1016/j.ijpe.2015.12.005

    Article  Google Scholar 

  78. Yang SA, Birge JR (2018) Trade credit, risk sharing, and inventory financing portfolios. Manag Sci 64:3667–3689. https://doi.org/10.1287/mnsc.2017.2799

    Article  Google Scholar 

  79. Yin LN, Xu Q (2010) Coordinating a supply chain with material flow and capital flow integrated. In: 2010 international conference on logistics systems and intelligent management, ICLSIM 2010. pp 1271–1274

  80. Zhao L, Huchzermeier A (2015) Operations-finance interface models: a literature review and framework. Eur J Oper Res 244:905–917. https://doi.org/10.1016/j.ejor.2015.02.015

    Article  Google Scholar 

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Appendices

Appendix 1

The proof of lemma 1

As mentioned in section “The mathematical formulation of the non-coordinated supply chain”, this paper has assumed that the total holding cost is a composite of two cost components, one associated with the opportunity cost of capital, while the other is the cost of stocking one unit of an item. Since the suppliers offer the OEM to settle his/her accounts by the time \(t_{k}\) (\(t_{k}\) is in terms of days) after the shipment receives, the OEM's holding cost will be different from the case that the OEM settles his/her accounts at the time of receiving the shipments. We have considered two cases: 1) \(t_{k} \le T_{{OEM_{k} }}\) and 2) \(t_{k} > T_{{OEM_{k} }}\). In case 1, the holding cost of the OEM with respect to the cost of capital (\(H\left( k \right)\)) is calculated as \(h_{k} \frac{{\left( {Q_{k} - t_{k} D_{k} } \right)\left( {T_{{OEM_{k} }} - t_{k} } \right)}}{2}\) according to Fig. 

Fig. 7
figure7

The behavior of the inventory in an OEM's cycle

7. Where \(\frac{{\left( {Q_{k} - t_{k} D_{k} } \right)\left( {T_{{OEM_{k} }} - t_{k} } \right)}}{2}\), is the area of shaded triangle and \(h_{k}\) is the unit holding cost of component k for the OEM, representing the cost of capital, excluding the storage cost. We substitute \(T_{{OEM_{k} }} = Q_{k} /D_{k}\) in the above statement and reach \(H\left( k \right) = h_{k} \frac{{\left( {Q_{k} - t_{k} D_{k} } \right)^{2} }}{{2D_{k} }}\).

In case 2, since the OEM settles his/her account for component k after \(T_{{OEM_{k} }}\), i.e., \(t_{k} > T_{{OEM_{k} }}\), his/her inventory level of component k will be zero and subsequently \(H\left( k \right) = 0\). We have merged these two cases as bellows:

$$H\left( k \right) = h_{k} \frac{{\left( {\left( {Q_{k} - t_{k} D_{k} } \right)^{ + } } \right)^{2} }}{{2D_{k} }}\quad {\text{where}}\quad \left( x \right)^{ + } = \max \left( {x,0} \right).$$

Appendix 2

The proof of lemma 2

The total system cost function (23) is as follows:

$$\begin{aligned} Z & = \mathop \sum \limits_{k} A_{k} \frac{{\mathop \sum \nolimits_{n} N_{nk} \alpha_{n} D_{n} }}{{Q_{k} }} + \mathop \sum \limits_{k} s_{k} \frac{{Q_{k} }}{2} + \mathop \sum \limits_{k} H\left( k \right)\frac{{\mathop \sum \nolimits_{n} N_{nk} \alpha_{n} D_{n} }}{{Q_{k} }} + \mathop \sum \limits_{n} \left( {F_{n} + v_{n} \alpha_{n} D_{n} } \right) \\ & \quad + \mathop \sum \limits_{k} w_{k} *\left( {\mathop \sum \limits_{n} N_{nk} \alpha_{n} D_{n} } \right) + Dbt\left( {\left( {1 + i} \right)^{T} - 1} \right) \\ & \quad - \mathop \sum \limits_{k} w_{k} *\left( {\mathop \sum \limits_{n} N_{nk} \alpha_{n} D_{n} } \right)*\left[ {\left( {1 + r_{OEM} } \right)^{{t_{k} }} - \left( {1 + \gamma_{k} } \right)^{{\left( {t_{k} - d_{k} } \right)^{ + } }} } \right] \\ & \quad + \mathop \sum \limits_{k} \left[ {c_{k} \left( {\mathop \sum \limits_{n} N_{nk} \alpha_{n} D_{n} } \right) + A_{k}^{\prime} \frac{{\mathop \sum \nolimits_{n} N_{nk} \alpha_{n} D_{n} }}{{\lambda_{k} Q_{k} }} + \left( {s_{k}^{{\prime }} + h_{k}^{{\prime }} } \right)\frac{{Q_{k} \left( {\lambda_{k} - 1} \right)}}{2}} \right] \\ & \quad + \mathop \sum \limits_{k} w_{k} \left( {\mathop \sum \limits_{n} N_{nk} \alpha_{n} D_{n} } \right)\left[ {\left( {1 + r_{k} } \right)^{{t_{k} }} - \left( {1 + \gamma_{k} } \right)^{{\left( {t_{k} - d_{k} } \right)^{ + } }} } \right] \\ & \quad + \mathop \sum \limits_{n} p_{n} \alpha_{n} D_{n} + \beta \left( {\mathop \sum \limits_{n} p_{n} \alpha_{n} D_{n} } \right)\left( {\left( {1 + r_{j} } \right)^{T} - 1} \right) \\ \end{aligned}$$
(23)

In the above cost function, we consider only the parts containing \(t_{k}\) and \(d_{k}\) as the other parts do not have any influence on our process of proving. We have shown this part by \(Z^{\prime}\):

$$Z^{{\prime }} = - \mathop \sum \limits_{k} w_{k} *\left( {\mathop \sum \limits_{n} N_{nk} \alpha_{n} D_{n} } \right)*\left[ {\left( {1 + r_{OEM} } \right)^{{t_{k} }} - \left( {1 + \gamma_{k} } \right)^{{\left( {t_{k} - d_{k} } \right)^{ + } }} } \right] + \mathop \sum \limits_{k} w_{k} \left( {\mathop \sum \limits_{n} N_{nk} \alpha_{n} D_{n} } \right)\left[ {\left( {1 + r_{k} } \right)^{{t_{k} }} - \left( {1 + \gamma_{k} } \right)^{{\left( {t_{k} - d_{k} } \right)^{ + } }} } \right]$$
(24)

By simplification of (Eq. 24) we have:

$$Z^{{\prime }} = \mathop \sum \limits_{k} w_{k} \left( {\mathop \sum \limits_{n} N_{nk} \alpha_{n} D_{n} } \right)\left[ {\left( {1 + r_{k} } \right)^{{t_{k} }} - \left( {1 + r_{OEM} } \right)^{{t_{k} }} } \right]$$
(25)

We can see that \(\left( {t_{k} - d_{k} } \right)^{ + }\) is omitted from the objective function. Therefore, we need to check these two variables only in the constraints (11) and (12).

If \(t_{k} > d_{k}\) then it exacerbates these two constraints, on the contrary, putting \(t_{k} \le d_{k}\) meliorates the two constraints. So in the optimal solution, we will have \(t_{k} \le d_{k}\).

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Emtehani, F., Nahavandi, N. & Rafiei, F.M. A joint inventory–finance model for coordinating a capital-constrained supply chain with financing limitations. Financ Innov 7, 6 (2021). https://doi.org/10.1186/s40854-020-00223-z

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Keywords

  • Supply chain coordination
  • Inventory–finance model
  • Capital shortage
  • Financing limitation
  • Service level
  • Bi-objective optimization
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