Abstract
Reverse factoring, a financial scheme in which established retailers facilitate financing for suppliers, is becoming an increasingly important tool in the industry. Normally, an SME supplier, a core retailer and a bank participate in the reverse factoring scheme. A three-level Stackelberg game is proposed in this study to investigate the interaction of the participants. The closed-form equilibria of the retailer’s replenishment decision, the supplier’s payment term decision and the bank’s financing decision are derived from the theoretical model. To our knowledge, this study is the first attempt which takes banks into account and endogenises their interest rates in the modelling of reverse factoring. The reverse factoring scheme is compared with commercial loans and traditional factoring. Compared to commercial loans, the introduction of factoring can lower credit risk, but fraud risk still exists. Reverse factoring solves this fraud problem and further decreases the financing cost for the supplier. Consequently, reverse factoring benefits the retailer through a significantly increased payment extension granted by the supplier. The numerical results also indicate that the utility of the bank significantly improves by 8–50% under varying levels of default risk compared with traditional factoring. Our study provides incentives and guidelines for supply chain participants to adopt such schemes when faced with capital constraints and the credit risk of the supplier.
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http://www.stats.gov.cn/english/PressRelease/202002/t20200204_1725014.html (accessed 30 August 2020).
https://fci.nl/en/annual-review (accessed 30 August 2020).
https://www.worldbank.org/en/topic/smefinance (accessed 15 May 2021).
Subscript BU/BL/BLL here represents the three circumstances of the financial constraints: without financial constraints, with financial constraints but can meet the minimum ordering request and with financial constraints and fails to meet the minimum ordering request
http://bank.pingan.com/gongsi/rongzi/guonei/fanxiangbaoli.shtml (accessed 30 August 2020).
http://english.www.gov.cn/policies/policywatch/202002/21/content_WS5e4f4bcbc6d0595e03c21296.html (accessed 30 August 2020).
The equilibrium in the benchmark model here is under the condition of no capital constraint. The interest rate α in the benchmark model is an assumption instead of the equilibrium result. To provide a clear comparison, it is presented here together with the equilibrium results of the other two models.
With the current parameter settings, the capital of the supplier to support the optimal order quantity is equal to (\(c{q}_{BU}+w{q}_{BU}{T}_{BU}\) α) = 426.5625. When the supplier’s controlling capital L is lower than 426.5625, the supplier cannot support the optimal decisions and a capital constraint exists. The minimum order quantity of the retailer would be = b-w(b-a) = 750. To support the production, the funds of the supplier need to be above 375. If L is lower than 375, the supplier fails to afford the minimum order quantity from retailers.
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Acknowledgements
The authors would like to express their gratitude to the guest editor and two anonymous referees for their valuable comments and suggestions which contributed to a significant improvement of the original version of this paper. The authors would also like to thank Xiaoyi Mu for his helpful comments and suggestions regarding this work. Financial support from the National Natural Science Foundation of China (Grant No. 71804188) and the Science Foundation of China University of Petroleum, Beijing (Grant No. 2462020YXZZ038) are gratefully acknowledged.
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Appendices
Appendices
Appendix A1: Proof of Proposition 1.
We use the method of backward induction to obtain the optimal solution. First, the retailer decides its own order quantity \({q}_{B}\) based on the existing payment term \({T}_{B}\). As f(x) follows a uniform distribution U[a, b], \(f(x)=\frac{1}{b-a}\). At the same time, the retail price per unit p is normalised to 1, that is, p = 1.
Equation (1) can be rewritten as
Taking the first derivative of \({q}_{B}\) in Eq. (1), we have:
Taking the second derivative of \({q}_{B}\) in Eq. (1), we have:
As \(\frac{{d}^{2}{\pi }_{B}^{r}}{d{q}_{B}^{2}}=-\frac{1}{b-a}\) <0, the objective function \({\pi }_{B}^{r}\) is concave in \({q}_{B}\).
-
A.
When a supplier has no financial constraint, we can obtain the optimal solution of \({q}_{B}\)
by solving \(\frac{{d\pi }_{B}^{r}}{{dq}_{B}}=0\):
To make the problem nontrivial, we suppose T≧ 0. Under this condition, the minimum order quantity of the retailer is qbmin=b–w(b–a)≦ b–w(1–TBθ)(b–a). The retailer will choose the minimum order quantity when the supplier does not grant it any payment term, that is, \({T}_{B}\)=0.
-
B.
When the supplier has a financial constraint but \({cq}_{B}^{min}\)<L, i.e., c(b–w(b–a)) < L, the supplier is able to meet the minimum order quantity of the retailer. The supplier can still set up a \({T}_{B}\)>0 to stimulate the demand from the retailer. The optimal solution is \({q}_{B}^{*}\)=\({q}_{BL}=\) b–w(1–\({T}_{B}\) θ)(b–a), obtained by solving \(\frac{{d\pi }_{B}^{r}}{{dq}_{B}}=0\).
-
C.
When a supplier has a financial constraint and \({cq}_{B}^{min}\)>L, that is, c(b–w(b–a)) > L, the supplier is unable to meet the minimum order quantity \({q}_{B}^{min}\) of the retailer. The order quantity that the supplier can meet is no more than L/c. It is obvious that the retailer’s profit is increasing in [0, L/c]. Hence, the order quantity of the retailer in this circumstance is \({q}_{B}^{*}\)=\({q}_{BLL}\)=L/c, and \({q}_{BLL}\)<\({q}_{B}^{min}\)<\({q}_{BL}\).
Second, the supplier decides its payment term for the retailer while considering the impact of its decision on the retailer.
Taking the first derivative of \({T}_{B}\) in Eq. (2), we have:
Taking the second derivative of \({T}_{B}\) in Eq. (2), we have:
As \(\frac{{d}^{2}{\pi }_{B}^{s}}{d{T}_{B}^{2}}=\frac{{dq}_{B}}{{dT}_{B}}*(-2w\alpha )<0\), the objective function \({\pi }_{B}^{s}\) is concave in \({T}_{B}\).
-
A.
When there is no financial constraint, solving \(\frac{{d\pi }_{B}^{s}}{{dT}_{B}}=0\) gives us:
At the same time, the order quantity of the retailer is \(q_{B}^{*} = q_{BU} = b - w\left( {1 - T_{BU} \theta } \right)\left( {b - a} \right)\).
For \({T}_{B}^{*}\geqq 0\), the condition \(\alpha \leqq \frac{(b-a)\theta (w-c)}{b-(b-a)w}\) should be met. This implies that the interest rate on commercial loans cannot be too high; otherwise, it will be too costly for the supplier to set up any credit term.
-
B.
When there is a financial constraint but c \({q}_{B}^{min}\)<L, i.e., c(b–w(b–a)) < L, the optimal solution remains as \({q}_{B}^{*}\)=\({q}_{BL}\)=b–w(1–\({T}_{B}\) θ)(b–a); however, the supplier does not have enough funds to afford the original optimal payment term \({T}_{BU}=\frac{w-c}{2w\alpha }+\frac{1}{2\theta }-\frac{b}{(b-a)*2w\theta }\). We denote the maximum payment term that the supplier can afford in this circumstance as TBL. In this case, \({T}_{BL}<{T}_{BU}\). It is obvious that the supplier’s profit is increasing in [0, TBL]. Hence, the optimal payment term \({T}_{B}^{*}\) in this circumstance would be TBL.
The production cost of the supplier is equal to cqBL + wqBLTBLα. By solving cqBL + wqBLTBLα = L, we have:
At the same time, the order quantity of the retailer is \({q}_{B}^{*}\)=qBL=b–w(1–\({T}_{BL}\) θ)(b–a).
In addition, \({q}_{B}^{*}\)=qBL=b–w(1–\({T}_{BL}\) θ)(b–a) < b–w(1–\({T}_{BU}\) θ)(b–a) = qBU.
-
C.
When there is a financial constraint and \({cq}_{B}^{min}\)>L, i.e., c(b–w(b–a)) > L, the supplier is unable to meet the minimum order quantity of the retailer and \({q}_{B}^{*}\)=qBLL < \({q}_{B}^{min}\)<qBL<qBU. There is no reason for the supplier to set up any payment term to stimulate the order quantity. Hence, we have \(T_{B}^{*} = T_{BLL} = 0,\) and \({T}_{BLL}\)<\({T}_{BL}\)<\({T}_{BU}\).
Appendix A2: Proof of Proposition 2
We use the method of backward induction to obtain the optimal solution. First, the retailer decides on its order quantity based on the payment term \({T}_{TF}\). In this circumstance, there is no financial constraint. Similar to the proof of proposition 1, by solving \(\frac{{d\pi }_{TF}^{r}}{{dq}_{TF}}\)=0, we obtain the optimal solution q*TF(TTF)=b–w(1–TTFθ)(b–a).
Next, by solving \(\frac{{d\pi }_{TF}^{s}}{{dT}_{TF}}=0\), we have: \(T_{TF}^{*} = \frac{w - c}{{2wr_{TF}^{*} }} + \frac{1}{2\theta } - \frac{b}{{\left( {b - a} \right)*2w\theta }}\).
Since we assume \(T_{TF}^{*}\)≧0, the condition \(r_{TF} { \leqq }\frac{{\left( {b - a} \right)\theta \left( {w - c} \right)}}{{b - \left( {b - a} \right)w}}\) should be met.
Finally, the bank decides on \({r}_{TF}\). Taking the first derivative of Eq. (3), we have:
It can be proved that there exists one point x. \(\frac{d{ lnU( \pi }_{TF}^{b})}{dr}\)>0 when \({r}_{TF}\) is inside [0, x], \(\frac{d{ lnU( \pi }_{TF}^{b})}{dr}\)<0 when \({r}_{TF}\) is inside [x,\(\frac{(b-a)\theta (w-c)}{b-(b-a)w}\)], under the condition of \(\frac{(b-a)(w-c){/[\eta /(\eta +\lambda -\lambda \eta )]}^\frac{1}{2}}{b-(b-a)w}>1\).
By solving \(\frac{d{ lnU( \pi }_{TF}^{b})}{dr}=\) 0, we have
where.
It can be proved that \({0\leqq r}_{TF}^{*}\leqq \frac{(b-a)\theta (w-c)}{b-(b-a)w}\) at the current condition. Therefore, \({r}_{TF}^{*}\) is the equilibrium result.
Similar to the proof of proposition 1, we can obtain:
“Appendix A3”: Proof of Proposition 4
As \(\eta \)<1 and \(\lambda \)<1, it is obvious that \(\eta /(\eta +\lambda -\lambda \eta )\) <1.
As \(\eta /(\eta +\lambda -\lambda \eta )\)<1, it is obvious that \({r}_{TF}^{*}\)>\({r}_{RF}^{*}\).
Appendix A4: Proof of Proposition 5
qBLL < qBL < qBU and TBLL < TBL < TBU is already proved in proposition 1.
B. As TBU < TRF*, qBU = b–w(1–TBUθ)(b–a) < b–w(1–TRF*θ)(b–a) = qRF*.
As α>\({r}_{RF}^{*},\) TBU < \({T}_{RF}^{*}\), and qBU\({<q}_{RF}^{*}\), we have.
It is obvious that \({\pi }_{B}^{s}\) is increasing in [0, \({T}_{BU}\)]. Hence, \({\pi }_{BU}^{s*}\)>\({\pi }_{BL}^{s*}\)>\({\pi }_{BLL}^{s*}\).
It is obvious that \({\pi }_{B}^{r}\) is increasing in [0, \({q}_{BU}\)]. Hence, \({\pi }_{BU}^{r*}\)>\({\pi }_{BL}^{r*}\)>\({\pi }_{BLL}^{r*}\).
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Zhu, L., Ou, Y. Enhance financing for small- and medium-sized suppliers with reverse factoring: a game theoretical analysis. Ann Oper Res 331, 159–187 (2023). https://doi.org/10.1007/s10479-021-04361-0
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DOI: https://doi.org/10.1007/s10479-021-04361-0