Skip to main content

Advertisement

Log in

Determining the price and refund of products in a supply chain with quality and advertising costs in a fuzzy environment

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

In online direct selling, three effective elements, namely price, refund and quality, affect the increment (or decrement) of demand and product return. This paper considers forward and backward (i.e., return) pricing decisions under uncertainty and develops a fuzzy mathematical model based on the Stackelberg game approach utilizing the proper action and reaction between a manufacturer and a retailer. Moreover, media advertising and manufacturer’s desire for accepting massive payments made us take into account the advertising as another factor influencing the demand. By an agreement between the manufacturer and the retailer, the costs of advertising and raising the level of the product quality are shared by two agreed rates. Two numerical examples are considered and the associated results are analyzed under fuzzy and crisp conditions when customers are sensitive or insensitive to the quality of the product. It is found that incorporation of the quality factor under a fuzzy environment has a better performance compared with the case of ignoring the quality and uncertainty in the parameters.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  • Alamdar SF, Rabbani M, Heydari J (2018) Pricing, collection, and effort decisions with coordination contracts in a fuzzy, three-level closed-loop supply chain. Expert Syst Appl 104:261–276

    Google Scholar 

  • Ali SM, Rahman MH, Tumpa TJ, Rifat AAM, Paul SK (2018) Examining price and service competition among retailers in a supply chain under potential demand disruption. J Retail Consum Serv 40:40–47

    Google Scholar 

  • Assarzadegan P, Rasti-Barzoki M (2019) A game theoretic approach for pricing under a return policy and a money back guarantee in a closed loop supply chain. Int J Prod Econ 222:107486

    Google Scholar 

  • Aust G (2015) A manufacturer-retailer supply chain with fuzzy consumer demand: logistics Management. Springer, Berlin, pp 81–93

    Google Scholar 

  • Aust G, Buscher U (2016) Game theoretic analysis of pricing and vertical cooperative advertising of a retailer-duopoly with a common manufacturer. CEJOR 24(1):127–147

    MathSciNet  MATH  Google Scholar 

  • Berger PD (1972) Vertical cooperative advertising ventures. J Market Res 9:309–312

    Google Scholar 

  • Chaab J, Rasti-Barzoki M (2016) Cooperative advertising and pricing in a manufacturer-retailer supply chain with a general demand function; A game-theoretic approach. Comput Ind Eng 99:112–123

    Google Scholar 

  • Chen B, Chen J (2017) Compete in price or service?: A study of personalized pricing and money back guarantees. J Retail 93(2):154–171

    MathSciNet  Google Scholar 

  • Chen J, Grewal R (2013) Competing in a supply chain via full-refund and no-refund customer returns policies. Int J Prod Econ 146(1):246–258

    Google Scholar 

  • Chenavaz R, Feichtinger G, Hartl RF, Kort PM (2020) Modeling the impact of product quality on dynamic pricing and advertising policies. Eur J Oper Res 284:990–1001

    MathSciNet  MATH  Google Scholar 

  • De Giovanni P, Reddy PV, Zaccour G (2016) Incentive strategies for an optimal recovery program in a closed-loop supply chain. Eur J Oper Res 249(2):605–617

    MathSciNet  MATH  Google Scholar 

  • Farshbaf-Geranmayeh A, Rabbani M, Taleizadeh AA (2018) Channel coordination with cooperative advertising considering effect of advertising on willingness to pay. J Optim Theory Appl 176(2):509–525

    MathSciNet  MATH  Google Scholar 

  • Giri B, Roy B, Maiti T (2017) Multi-manufacturer pricing and quality management strategies in the presence of brand differentiation and return policy. Comput Ind Eng 105:146–157

    Google Scholar 

  • Gupta R, Biswas I, Kumar S (2019) Pricing decisions for three-echelon supply chain with advertising and quality effort-dependent fuzzy demand. Int J Prod Res 57(9):2715–2731

    Google Scholar 

  • Huang H, Ke H (2017) Pricing decision problem for substitutable products based on uncertainty theory. J Intell Manuf 28(3):503–514

    Google Scholar 

  • Karray S, Amin SH (2015) Cooperative advertising in a supply chain with retail competition. Int J Prod Res 53(1):88–105

    Google Scholar 

  • Ketzenberg ME, Zuidwijk RA (2009) Optimal pricing, ordering, and return policies for consumer goods. Prod Oper Manag 18(3):344–360

    Google Scholar 

  • Kunter M (2012) Coordination via cost and revenue sharing in manufacturer-retailer channels. Eur J Oper Res 216:477–486

    MathSciNet  MATH  Google Scholar 

  • Li Y, Xu L, Li D (2013) Examining relationships between the return policy, the quality of product, and pricing strategy in online direct selling. Int J Prod Econ 144(2):451–460

    Google Scholar 

  • Li G, Li L, Sethi SP, Guan X (2017) Return strategy and pricing in a dual-channel supply chain. Int J Prod Econ (in Press)

  • Li G, Li L, Sethi SP, Guan X (2019) Return strategy and pricing in a dual-channel supply chain. Int J Prod Econ 215:153–164

    Google Scholar 

  • Liu B, Liu Y-K (2002) Expected value of fuzzy variable and fuzzy expected value models. IEEE Trans Fuzzy Syst 10(4):445–450

    Google Scholar 

  • Liu Y-K, Liu B (2003) Expected value operator of random fuzzy variable and random fuzzy expected value models. Int J Uncertain Fuzziness Knowl-Based Syst 11(02):195–215

    MathSciNet  MATH  Google Scholar 

  • Liu S, Xu Z (2014) Stackelberg game models between two competitive retailers in fuzzy decision environment. Fuzzy Optim Decis Making 13(1):33–48

    MathSciNet  MATH  Google Scholar 

  • Liu S, Gao J, Xu Z (2019) Fuzzy supply chain coordination mechanism with imperfect quality items. Technol Econ Dev Econ 25(2):239–257

    Google Scholar 

  • Maiti T, Giri B (2015) A closed loop supply chain under retail price and the quality of product dependent demand. J Manuf Syst 37:624–637

    Google Scholar 

  • Nahmias S (1978) Fuzzy variables. Fuzzy Sets Syst 1(2):97–110

    MathSciNet  MATH  Google Scholar 

  • Roy A, Sana SS, Chaudhuri K (2018) Optimal Pricing of competing retailers under uncertain demand-a two layer supply chain model. Ann Oper Res 260(1–2):481–500

    MathSciNet  MATH  Google Scholar 

  • Salehi H, Taleizadeh AA, Tavakkoli-Moghaddam R, Hafezalkotob A (2020) Pricing and market segmentation in an uncertain supply chain. Sadhana 45:118

    MathSciNet  Google Scholar 

  • Soleimani F (2016) Optimal pricing decisions in a fuzzy dual-channel supply chain. Soft Comput 20(2):689–696

    MATH  Google Scholar 

  • Taleizadeh AA, Noori-Daryan M, Govindan K (2016) Pricing and ordering decisions of two competing supply chains with different composite policies: a Stackelberg game-theoretic approach. Int J Prod Res 54(9):2807–2836

    Google Scholar 

  • Taleizadeh AA, Moshtagh MS, Moon I (2018a) Pricing, the quality of product, and collection optimization in a decentralized closed-loop supply chain with different channel structures: game theoretical approach. J Clean Prod 189:406–431

    Google Scholar 

  • Taleizadeh AA, Rezvan-Beydokhti S, Cárdenas-Barron LE (2018b) Joint determination of the optimal selling price, refund policy and quality level for complementary products in online purchasing. Eur J Ind Eng 12(3):332–363

    Google Scholar 

  • Wang J, Jiang H, Yu M (2020) Pricing decisions in a dual-channel green supply chain with product customization. J Clean Prod 247:119101

    Google Scholar 

  • Wei J, Zhao J (2016) Pricing decisions for substitutable products with horizontal and vertical competition in fuzzy environments. Ann Oper Res 242(2):505–528

    MathSciNet  MATH  Google Scholar 

  • Xiao T, Shi K, Yang D (2010) Coordination of a supply chain with consumer return under demand uncertainty. Int J Prod Econ 124(1):171–180

    Google Scholar 

  • Yan R, Ghose S, Bhatnagar A (2006) Cooperative advertising in a dual channel supply chain. Int J Electron Market Retail 1(2):99–114

    Google Scholar 

  • Yan R (2010) Cooperative advertising, pricing strategy and firm performance in the e-marketing age. J Acad Mark Sci 38:510–519

    Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353

    MATH  Google Scholar 

  • Zhao J, Wang L (2015) Pricing and retail service decisions in fuzzy uncertainty environments. Appl Math Comput 250:580–592

    MathSciNet  MATH  Google Scholar 

  • Zhao R, Tang W, Yun H (2006) Random fuzzy renewal process. Eur J Oper Res 169(1):189–201

    MathSciNet  MATH  Google Scholar 

  • Zhao J, Tang W, Zhao R, Wei J (2012) Pricing decisions for substitutable products with a common retailer in fuzzy environments. Eur J Oper Res 216(2):409–419

    MathSciNet  MATH  Google Scholar 

  • Zhao J, Wei J, Li Y (2018) Pricing decisions of complementary products in a two-level fuzzy supply chain. Int J Prod Res 56(5):1882–1903

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ali Husseinzadeh Kashan.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest in any matter.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Proof of Theorem 1

The first-order derivatives system of equations of retailer’s profit function (11) is as follows:

$$ \frac{{\partial E\left[ {\pi_{r} } \right]}}{\partial m} = {\kern 1pt} E\left[ {\tilde{\alpha }{\kern 1pt} {\kern 1pt} \tilde{\it K}_{r} } \right] + E\,[\tilde{\gamma }{\kern 1pt} \tilde{\it K}\tilde{\it K}_{r} ]{\kern 1pt} {\kern 1pt} r - \psi {\kern 1pt} w + E\,[\tilde{\omega }{\kern 1pt} {\kern 1pt} \tilde{\it K}_{r} ]{\kern 1pt} \,a - 2{\kern 1pt} {\kern 1pt} {\kern 1pt} \psi {\kern 1pt} m{\kern 1pt} = 0 $$
$$ \frac{{\partial E\left[ {\pi_{r} } \right]}}{\partial a} = m{\kern 1pt} {\kern 1pt} E\,[\tilde{\omega }{\kern 1pt} {\kern 1pt} \tilde{\it K}_{r} ] - 2\,(1 - t_{1} ){\kern 1pt} {\kern 1pt} \lambda {\kern 1pt} {\kern 1pt} a = 0 $$
$$ \frac{{\partial E\left[ {\pi_{r} } \right]}}{\partial r} = E[\tilde{\gamma }{\kern 1pt} \tilde{\it K}_{r} ]{\kern 1pt} {\kern 1pt} m{\kern 1pt} - E\left[ {\tilde{\it Z}} \right] - 2{\kern 1pt} {\kern 1pt} r{\kern 1pt} E\left[ {\tilde{\zeta }} \right] + w{\kern 1pt} E\left[ {\tilde{\zeta }} \right] + E\left[ {\tilde{\upsilon }} \right]{\kern 1pt} {\kern 1pt} {\kern 1pt} q = 0 $$
$$ \frac{{\partial E\left[ {\pi_{r} } \right]}}{\partial q} = E\left[ {\tilde{\upsilon }} \right]\left( {r - w} \right) - 2\left( {1 - t_{2} } \right){\kern 1pt} {\kern 1pt} \mu {\kern 1pt} {\kern 1pt} q = 0 $$

Solving the above system of equations, we get optimal values in Eqs. (13) to (16). Now we get the second-order derivatives:

$$ \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial^{2} r}} = - 2{\kern 1pt} E\left[ {\tilde{\zeta }} \right]\quad \;\frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial r{\kern 1pt} \,\partial m}} = E\,[\tilde{\gamma }{\kern 1pt} \tilde{\it K}_{r} ]\quad \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial r{\kern 1pt} \,\partial a}} = 0\quad \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{\partial r\partial q} = E\left[ {\tilde{\upsilon }} \right] $$
$$ \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial^{2} a}} = - 2{\kern 1pt} {\kern 1pt} \left( {1 - t_{1} } \right)\lambda \quad \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial a{\kern 1pt} \,\partial m}} = E\,[\tilde{\omega }{\kern 1pt} {\kern 1pt} \tilde{\it K}_{r} ]{\kern 1pt} \quad \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial a{\kern 1pt} \,\partial r}} = 0\quad \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial a{\kern 1pt} \,\partial q}} = 0 $$
$$ \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial^{2} q}} = - 2\left( {1 - t_{2} } \right)\mu \quad \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{\partial q\partial m} = 0\quad \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{\partial q\,\partial r} = E\left[ {\tilde{\upsilon }} \right]\quad \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial q{\kern 1pt} \,\partial a}} = 0 $$

Then, the Hessian Matrix of the profit function is as follows.

$$ H = \left| \begin{aligned} \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial^{2} m}}\quad \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial m{\kern 1pt} \,\partial r}}\quad \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial m{\kern 1pt} \,\partial a}}\quad \quad \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial m{\kern 1pt} \,\partial q}} \hfill \\ \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial r{\kern 1pt} \,\partial m}}\quad \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial^{2} r}}\quad \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial r{\kern 1pt} \,\partial a}}\quad \quad \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{\partial r\partial q} \hfill \\ \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial a{\kern 1pt} \,\partial m}}\quad \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial a{\kern 1pt} \,\partial r}}\quad \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial^{2} a}}\quad \quad \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial a{\kern 1pt} \,\partial q}} \hfill \\ \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{\partial q\partial m}\quad \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{\partial q\,\partial r}\quad \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial q{\kern 1pt} \,\partial a}}\quad \quad \frac{{\partial^{2} E\left[ {\pi_{r} } \right]}}{{\partial^{2} q}} \hfill \\ \end{aligned} \right| $$

To ensure that the retailer’s profit function is concave, the Hessian Matrix should be provided by the following conditions:

$$ H_{1} = \left| { - 2\,\psi } \right| < 0\quad H_{2} = \left| {\begin{array}{*{20}l} { - 2\,\psi } \hfill & {E\,[\tilde{\gamma }{\kern 1pt} {\kern 1pt} \tilde{\it K}_{r} ]} \hfill \\ {E\,[\tilde{\gamma }{\kern 1pt} {\kern 1pt} \tilde{\it K}_{r} ]} \hfill & { - 2E\left[ {\tilde{\zeta }} \right]} \hfill \\ \end{array} } \right| > 0 $$
$$ H_{3} = \left| {\begin{array}{*{20}l} { - 2\,\psi } \hfill & {E\,[\tilde{\gamma }{\kern 1pt} {\kern 1pt} \tilde{\it K}_{r} ]} \hfill & {E\,[\tilde{\omega }{\kern 1pt} {\kern 1pt} \tilde{\it K}_{r} ]} \hfill \\ {E\,[\tilde{\gamma }{\kern 1pt} {\kern 1pt} \tilde{\it K}_{r} ]} \hfill & { - 2E\left[ {\tilde{\zeta }} \right]} \hfill & 0 \hfill \\ {E\,[\tilde{\omega }{\kern 1pt} {\kern 1pt} \tilde{\it K}_{r} ]} \hfill & 0 \hfill & { - 2\left( {1 - t_{1} } \right)\lambda } \hfill \\ \end{array} } \right| < 0 $$
$$ H_{4} = \left| {\begin{array}{*{20}l} { - 2\,\psi } \hfill & {E\,[\tilde{\gamma }{\kern 1pt} {\kern 1pt} \tilde{\it K}_{r} ]} \hfill & {E\,[\tilde{\omega }{\kern 1pt} {\kern 1pt} \tilde{\it K}_{r} ]} \hfill & 0 \hfill \\ {E\,[\tilde{\gamma }{\kern 1pt} {\kern 1pt} \tilde{\it K}_{r} ]} \hfill & { - 2E\left[ {\tilde{\zeta }} \right]} \hfill & 0 \hfill & {E\left[ {\tilde{\upsilon }} \right]} \hfill \\ {E\,[\tilde{\omega }{\kern 1pt} {\kern 1pt} \tilde{\it K}_{r} ]} \hfill & 0 \hfill & { - 2\left( {1 - t_{1} } \right)\lambda } \hfill & 0 \hfill \\ 0 \hfill & {E\left[ {\tilde{\upsilon }} \right]} \hfill & 0 \hfill & { - 2\left( {1 - t_{2} } \right)\mu } \hfill \\ \end{array} } \right| > 0 $$

Therefore, we have the following conditions:

$$ - 2\,\psi < 0 $$
$$ 4\,E\left[ {\tilde{\zeta }} \right]\;\psi \, - E{\kern 1pt}^{2} {\kern 1pt} [\tilde{\gamma }{\kern 1pt} \tilde{\it K}_{r} ] > 0 $$
$$ 2\,E{\kern 1pt}^{2} \,[\tilde{\omega }{\kern 1pt} {\kern 1pt} \tilde{\it K}_{r} ]\;E\left[ {\tilde{\zeta }} \right] - 8\;E\left[ {\tilde{\zeta }} \right]\;\psi \left( {1 - t_{1} } \right)\lambda {\kern 1pt} + 2\;E{\kern 1pt}^{2} {\kern 1pt} [\tilde{\gamma }{\kern 1pt} \tilde{\it K}_{r} ]\left( {1 - t_{1} } \right)\lambda < 0 $$
$$ \begin{aligned} & E^{2} \left[ {\tilde{\upsilon }} \right]\left( {E{\kern 1pt}^{2} \,[\tilde{\omega }{\kern 1pt} {\kern 1pt} \tilde{\it K}_{r} ] - 4{\kern 1pt} {\kern 1pt} \lambda {\kern 1pt} {\kern 1pt} \psi \left( {1 - t_{1} } \right)} \right) - 4{\kern 1pt} E{\kern 1pt}^{2} \,[\tilde{\omega }{\kern 1pt} {\kern 1pt} \tilde{\it K}_{r} ]{\kern 1pt} {\kern 1pt} E\left[ {\tilde{\zeta }} \right]\mu \left( {1 - t_{2} } \right) + 4E[\tilde{\gamma }{\kern 1pt} {\kern 1pt} \tilde{\it K}_{r} ]{\kern 1pt} {\kern 1pt} \mu {\kern 1pt} \lambda \left( {t_{1} + t_{2} - t_{1} t_{2} - 1} \right) \\ & + 16{\kern 1pt} E\left[ {\tilde{\zeta }} \right]\mu {\kern 1pt} \lambda {\kern 1pt} {\kern 1pt} \psi \left( { - t_{1} - t_{2} + t_{1} t_{2} + 1} \right) > 0 \\ \end{aligned} $$

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sharanlou, H., Husseinzadeh Kashan, A. & Tavakkoli-Moghaddam, R. Determining the price and refund of products in a supply chain with quality and advertising costs in a fuzzy environment. Soft Comput 25, 2351–2370 (2021). https://doi.org/10.1007/s00500-020-05307-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-05307-7

Keywords

Navigation