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Supply Chain Coordination with Optimal Pricing and Logistics Service Decision in Online Retailing

  • Lingli Shu
  • Shaojian QuEmail author
  • Zhong Wu
Research Article - Systems Engineering
  • 28 Downloads

Abstract

Industry practice has shown that logistics service greatly affects the turnovers in online channel. High quality service will stimulate consumption. However, there are few studies investigating the decisions on pricing and logistics service quality simultaneously in the supply chain including one manufacturer and one online retailer. To address the gap, this paper studies the optimization of logistics service quality with pricing. To solve such decision-making challenges, firstly a basic model is applied to analyze how manufacturer decides the wholesale price. The retailing price and logistics service quality are also presented at cooperative and non-cooperative scenarios, respectively. Compared to the centralized policy, optimal price is higher in decentralized policy while the logistics service quality is lower. The expected profit of total chain decreases by 25%. Then, two contracts are proposed in a decentralized supply chain to coordinate the chain. Revenue sharing and cost sharing contract can realize the coordination, while sole cost sharing contract cannot. Finally, a numerical example is presented to verify the effectiveness of contract and the findings of models are illustrated.

Keywords

Pricing Logistics service Contract Coordination Centralized Decentralized 

References

  1. 1.
    Neslin, S.A.; Shankar, V.: Key issues in multichannel customer management: current knowledge and future directions. J. Interact. Mark. 23(1), 70–81 (2009)CrossRefGoogle Scholar
  2. 2.
    Hua, Z.S.; Hou, H.J.; Bian, Y.W.: Optimal shipping strategy and return service charge under no-reason return policy in online retailing. IEEE Trans. Syst. Man Cybern.-Syst. 47(12), 3189–3206 (2017)CrossRefGoogle Scholar
  3. 3.
    Neslin, A.; Neslin, S.A.; Heerde, H.J.V.: Promotion dynamics. Found. Trends Mark. 3(4), 177–268 (2008)CrossRefGoogle Scholar
  4. 4.
    Richards, T.J.: Dynamic model of fresh fruit promotion: a household production approach. Am. J. Agric. Econ. 81(1), 195–211 (1999)CrossRefGoogle Scholar
  5. 5.
    Monahan, J.P.: A quantity discount pricing model to increase vendor profits. Manag. Sci. 32(11), 1513–1517 (1986)CrossRefGoogle Scholar
  6. 6.
    Amblee, N.; Bui, T.: Harnessing the influence of social proof in online shopping: the effect of electronic word of mouth on sales of digital microproducts. Int. J. Electron. Commun. 16(2), 91–114 (2011)CrossRefGoogle Scholar
  7. 7.
    Ren, J.; Yeoh, W.; Ee, M.S.; Popovic, A.: Online consumer reviews and sales: examining the chicken-egg relationships. J. Assoc. Inf. Sci. Technol. 69(3), 449–460 (2018)CrossRefGoogle Scholar
  8. 8.
    Brekalo, L.; Albers, S.: Effective logistics alliance design and management. Int. J. Phys. Distrib. Log. 46(2), 212–240 (2016)CrossRefGoogle Scholar
  9. 9.
    Zhang, M.D.; Pratap, S.; Huang, G.Q.; Zhao, Z.H.: Optimal collaborative transportation service trading in B2B e-commerce logistics. Int. J. Prod. Res. 55(18), 5485–5501 (2017)CrossRefGoogle Scholar
  10. 10.
    Berger, J.; Sorensen, A.T.; Rasmussen, S.J.: Positive effects of negative publicity: when negative reviews increase sales. Mark. Sci. 29(5), 815–827 (2010)CrossRefGoogle Scholar
  11. 11.
    Voordijk, H.: Physical distribution costs in construction supply chains: a systems approach. Int. J. Logist. Syst. Manag. 7(4), 456–471 (2010)CrossRefGoogle Scholar
  12. 12.
    Marand, A.J.; Qu, T.; Li, H.Y.: Quandary of service logistics: fast or reliable? Eur. J. Oper. Res. 275(3), 983–996 (2019)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Tian, X.B.; Zhang, M.R.: Research on spatial correlations and influencing factors of logistics industry development quality. Sustain.-Basel. 11(5), 1–18 (2019)Google Scholar
  14. 14.
    Du, N.; Han, Q.L.: Pricing and service quality guarantee decisions in logistics service supply chain with fairness concern. Asia Pac. J. Oper. Res. 35(5), 1850036 (2018)MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Eghbali-Zarch, M.; Taleizadeh, A.A.; Tavakkoli-Moghaddam, R.: Pricing decisions in a multiechelon supply chain under a bundling strategy. Int. Trans. Oper. Res. 26(6), 2096–2128 (2019)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Choi, T.M.; Ma, C.; Shen, B.; Sun, Q.: Optimal pricing in mass customization supply chains with risk-averse agents and retail competition. Omega-Int. J. Manag. Sci. 88, 150–161 (2019)CrossRefGoogle Scholar
  17. 17.
    Li, R.H.; Liu, Y.P.; Teng, J.T.; Tsao, Y.C.: Optimal pricing, lot-sizing and backordering decisions when a seller demands an advance-cash-credit payment scheme. Eur. J. Oper. Res. 278(1), 283–295 (2019)MathSciNetzbMATHCrossRefGoogle Scholar
  18. 18.
    Li, R.H.; Teng, J.T.; Zheng, Y.F.: Optimal credit term, order quantity and selling price for perishable products when demand depends on selling price, expiration date, and credit period. Ann. Oper. Res. 280(1–2), 377–405 (2019)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Javadi, T.; Alizadeh-basban, N.; Asian, S.; Hafezalkotob, A.: Pricing policies in a dual-channel supply chain considering flexible return and energy-saving regulations. Comput. Ind. Eng. 135, 655–674 (2019)CrossRefGoogle Scholar
  20. 20.
    Liu, W.H.; Xie, D.: Quality decision of the logistics service supply chain with service quality guarantee. Int. J. Prod. Res. 51(5), 1618–1634 (2013)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Liu, W.; Shen, X.R.; Xie, D.: Decision method for the optimal number of logistics service providers with service quality guarantee and revenue fairness. Appl. Math. Model. 48, 53–69 (2017)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Chiu, C.H.; Choi, T.M.; Li, Y.; Xu, L.: Service competition and service war: a game-theoretic analysis. Serv. Sci. 6(1), 63–76 (2014)CrossRefGoogle Scholar
  23. 23.
    Rabinovich, E.; Rungtusanatham, M.; Laseter, T.M.: Physical distribution service performance and Internet retailer margins: the drop-shipping context. J. Oper. Manag. 26(6), 767–780 (2008)CrossRefGoogle Scholar
  24. 24.
    Rahman, S.: Quality management in logistics: an examination of industry practices. Supply Chain Manag. 11(3), 233–240 (2006)CrossRefGoogle Scholar
  25. 25.
    Hua, G.W.; Wang, S.Y.; Cheng, T.C.E.: Price and lead time decisions in dual-channel supply chains. Eur. J. Oper. Res. 205(1), 113–126 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  26. 26.
    Yang, J.C.; Yu, K.: The role of an integrated logistics and procurement service offered by a 3PL firm in supply chain. J. Manag. Anal. 6(1), 49–66 (2019)Google Scholar
  27. 27.
    Xing, Y.; Grant, D.B.; McKinnon, A.C.; Fernie, J.: The interface between retailers and logistics service providers in the online market. Eur. J. Mark. 45(3), 334–357 (2011)CrossRefGoogle Scholar
  28. 28.
    Rabinovich, E.; Bailey, J.P.: Physical distribution service quality in Internet retailing: service pricing, transaction attributes, and firm attributes. J. Oper. Manag. 21(6), 651–672 (2004)CrossRefGoogle Scholar
  29. 29.
    Wang, X.B.; Liu, Z.B.; Chen, H.R.: A composite contract for coordinating a supply chain with sales effort-dependent fuzzy demand. Int. J. Mach. Learn. Cybern. 10(5), 949–965 (2019)CrossRefGoogle Scholar
  30. 30.
    Liu, W.H.; Liu, Y.; Zhu, D.L.; Wang, Y.J.; Liang, Z.C.: The influences of demand disruption on logistics service supply chain coordination: a comparison of three coordination modes. Int. J. Prod. Econ. 179, 59–76 (2016)CrossRefGoogle Scholar
  31. 31.
    Xiao, T.J.; Qi, X.T.; Yu, G.: Coordination of supply chain after demand disruptions when retailers compete. Int. J. Prod. Econ. 109(1–2), 162–179 (2007)CrossRefGoogle Scholar
  32. 32.
    Liu, Z.M.; Qu, S.J.; Goh, M.; Huang, R.P.; Wang, S.L.: Optimization of fuzzy demand distribution supply chain using modified sequence quadratic programming approach. J. Intell. Fuzzy Syst. 36(6), 6167–6180 (2019)CrossRefGoogle Scholar
  33. 33.
    Ghosh, D.; Shah, J.: Supply chain analysis under green sensitive consumer demand and cost sharing contract. Int. J. Prod. Econ. 164, 319–329 (2015)CrossRefGoogle Scholar
  34. 34.
    Chen, H.Y.; Chen, J.; Chen, Y.H.: A coordination mechanism for a supply chain with demand information updating. Int. J. Prod. Econ. 103(1), 347–361 (2006)CrossRefGoogle Scholar
  35. 35.
    Cachon, G.P.; Lariviere, M.A.: Supply chain coordination with revenue-sharing contracts: strengths and limitations. Manag. Sci. 51(1), 30–44 (2005)zbMATHCrossRefGoogle Scholar
  36. 36.
    Kong, G.; Rajagopalan, S.; Zhang, H.: Revenue sharing and information leakage in a supply chain. Manag. Sci. 59, 556–572 (2013)CrossRefGoogle Scholar
  37. 37.
    Zhou, Y.Y.; Qu, S.J.: Optimal strategy for a green supply chain considering shipping policy and default risk. Comput. Ind. Eng. 131, 172–186 (2019)CrossRefGoogle Scholar
  38. 38.
    Yao, Z.; Leung, S.C.H.; Lai, K.K.: Manufacturer’s revenue-sharing contract and retail competition. Eur. J. Oper. Res. 186(2), 637–651 (2008)MathSciNetzbMATHCrossRefGoogle Scholar
  39. 39.
    Zhao, D.Z.; Chen, M.Y.; Gong, Y.M.: Strategic information sharing under revenue-sharing contract: explicit vs. tacit collusion in retailers. Comput. Ind. Eng. 131, 99–114 (2019)CrossRefGoogle Scholar
  40. 40.
    Liu, W.H.; Xu, X.C.; Kouhpaenejad, A.: Deterministic approach to the fairest revenue-sharing coefficient in logistics service supply chain under the stochastic demand condition. Comput. Ind. Eng. 66, 41–52 (2013)CrossRefGoogle Scholar
  41. 41.
    Han, Y.F.; Qu, S.J.; Wu, Z.; Huang, R.P.: Robust consensus models based on minimum cost with an application to marketing plan. J. Intell. Fuzzy Syst (2019).  https://doi.org/10.3233/JIFS-190863 CrossRefGoogle Scholar
  42. 42.
    Arshinder, K.; Kanda, A.; Deshmukh, S.G.: Supply chain coordination: perspectives, empirical studies and research directions. Int. J. Prod. Econ. 115(2), 316–335 (2008)CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2019

Authors and Affiliations

  1. 1.Business SchoolUniversity of Shanghai for Science and TechnologyShanghaiChina

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