Article Contents
Article Contents

# Collection decisions and coordination in a closed-loop supply chain under recovery price and service competition

The first author is supported by the Natural Science Foundation of Zhejiang Province, China (No. LY18G010019) and Zhejiang Province Public Welfare Technology Application Research Project (No. 2015C33014)

• With the increasing growth of consumers' request for recovery channels, in addition to collecting price, the collecting service has gradually become a competitive point for collectors to collect used products. Focusing on a closed-loop supply chain (CLSC) with recovery competition (on collecting price and collecting service) and distinguishing collecting quality, we propose two models (decentralized and centralized models) to study the collection strategies and profits of the CLSC. Moreover, we analyze the impact of the collecting competition and quality on the CLSC. Finally, a revenue-cost sharing contract (RCSC) is introduced to coordinate the supply chain. And a numerical example is illustrated to verify the contract's efficiency. It is found that the collected quantities and profits of the CLSC members are positively correlated with the remanufacturable ratio. The collecting competition dampens consumers' enthusiasm for recycling, which is not conducive to collectors to carry out collecting activity, resulting in the reduction of the CLSC's profit. The collectors appropriately improving collecting prices and service levels can increase the collected quantities, but to cope with the increasing competition, increasing collecting price is the main means for collectors to attract consumers to recycle. In addition, the designed RCSC can effectively improve the CLSC's efficiency and increase the profits of each party.

Mathematics Subject Classification: Primary: 90B06; Secondary: 91A10, 91A25.

 Citation:

• Figure 1.  Decentralized model (DM)

Figure 2.  Centralized model (CM)

Figure 3.  Influence of $\rho$ on the CLSC members' optimal collection decisions and profits

Figure 4.  Influence of $n_b$ on the CLSC members' optimal collection decisions and profits

Figure 5.  Influence of $n_S$ on the CLSC members' optimal collection decisions and profits

Figure 6.  Influence of $m_b$ on the CLSC members' optimal collected quantities and profits

Figure 7.  Influence of $m_S$ on the CLSC members' optimal collected quantities and profits

Figure 8.  Influence of sharing ratio $\theta$ on chain members' profits

Table 1.  Comparison between our research and related literatures

 References Collecting channel Pricing on Service on Collecting competition on Collecting quality Coordination mechanism Single channel dual-channel Sales Collection Sales Collection Price Service Ferrer and Swaminathan (2010) √ √ Wei et al. (2015) √ √ Gao et al. (2016) √ √ LPSS Gan et al. (2017) √ √ P. He et al. (2019) √ √ Modak et al. (2019) √ √ TPTC Wen et al. (2020) √ √ Savaskan et al. (2004) √ √ WPC Hong and Yeh (2012) √ √ Atasu et al. (2013) √ √ Shi et al. (2015) √ √ Liu and Xiao (2020) √ √ Huang et al. (2013) √ √ √ L. Liu et al. (2017) √ √ √ Liu et al. (2016) √ √ √ √ Wang et al. (2018) √ √ √ √ RPM Q. He et al. (2019) √ √ √ √ TPTC, AM Wang et al. (2019) √ √ √ √ Wu (2012) √ √ Hong et al. (2015) √ √ √ CA, TPTC Zhang et al. (2015) √ √ √ Jena and Sarmah (2016) √ √ √ QDC Kong et al. (2017) √ √ √ Zhao et al. (2019) √ √ √ Li et al. (2019) √ √ √ Huang et al. (2020) √ √ √ De Giovanni (2014) √ √ WPC, RRSC Panda et al. (2017) √ √ RSC Liang et al. (2017) √ √ √ RSC Xie et al. (2017) √ √ √ √ RSC Xie et al. (2018) √ √ √ √ √ RSC, RCSC Zheng et al. (2019) √ √ SVM, NSM, ESM Wang et al. (2020) √ √ √ √ RSC, TPTC This research √ √ √ √ √ √ √ RCSC LPPS: Low Price Promotion Strategy, TPTC: Two Part Tariff Contract, WPC: Wholesale Price Contract, RPM: Reward-Penalty Mechanism, CA: Cooperative advertising, QDC: Quantity Discount Contract, RSC: Revenue Sharing Contract, RRSC: Reverse Revenue Sharing Contract, RCSC: Revenue and Cost Sharing Contract, AM: Authorization Mechanism, SVM: Shapley Value Mechanism, NSM: Nucleolus Solution Mechanism, ESM: Equal Satisfaction Mechanism.

Table 2.  Notations

 Symbol Description Parameters $c_m$ Production costs via new raw materials $c_r$ Production costs via collected products $\Delta$ The cost-savings provided by remanufacturing. Note that $\Delta={c_m}-{c_r}$ $\rho$ Remanufacturable ratio of the collected products $r$ The revenue by disassembling collected products $q_R$ Quantity of used product collected by the retailer $q_T$ Quantity of used product collected by the third-party collector $m_b$ Collecting price sensitivity coefficient $n_b$ Collecting price competition coefficient $m_S$ Collecting service sensitivity coefficient $n_S$ Collecting service competition coefficient $A$ Cost index of reverse channel investment $d$ Product demand. Note that $d=\alpha-\ \beta p$ Decision variables $p$ Selling price of products $w$ Wholesale price $B$ The transfer price that the manufacturer pays to the retailer or the third-party collector $S_R$ The retailer's collecting service level $S_T$ The third-party collector's collecting service level $b_R$ Collecting price to market given by the retailer $b_T$ Collecting price to market given by the third-party collector Other notations $M$ The manufacturer $R$ The retailer $T$ The third-party collector $\pi_C$ The total profit of the supply chain $\pi_M$ The profit of the manufacturer $\pi_R$ The profit of the retailer $\pi_T$ The profit of the third-party collector CM The centralized model DM The decentralized model Note that $p>w>{c_m}>{c_r}>0$, $\Delta>B>{b_i}>0$, ${S_i}>0$, $i=\{R,T\}$.

Table 3.  The comparisons between the optimal collection decisions and profits of the two models

 $p$ $b_R$ $b_T$ $S_R$ $S_T$ $q_R$ $q_T$ $q_C$ $\pi_C$ DM 39.5 0.76 0.76 0.38 0.38 9.44 9.44 18.89 707.17 CM 29 1.35 1.35 0.61 0.61 16.36 16.36 32.72 937.63

Table 4.  The comparison between the profits of the CLSC with coordination and that without coordination

 Profit $\pi_T$ $\pi_R$ $\pi_M$ $\pi$ Supply chain efficiency Without coordination 6.78 227.28 473.11 707.17 75.42% With coordination 8.86 367.21 561.44 937.51 99.99%

Figures(8)

Tables(4)