Volume 34, Issue 3 (IJIEPR 2023)                   IJIEPR 2023, 34(3): 113-130 | Back to browse issues page

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Zandi S, Samizadeh R, Esmaeili M. Revenue Sharing Contracts in Coalition Loyalty Reward Supply Chain Planning: A Stochastic Programming Approach. IJIEPR 2023; 34 (3) :113-130
URL: http://ijiepr.iust.ac.ir/article-1-1775-en.html
Revenue Sharing Contracts in Coalition Loyalty Reward Supply Chain Planning: A Stochastic Programming Approach
Shahla Zandi 1, Reza Samizadeh2 , Maryam Esmaeili2
1- Department of Industrial Engineering, Faculty of Engineering, Alzahra University, Tehran, Iran , shz61mobz@gmail.com
2- Associate Professor , Alzahra University- Faculty of Engineering, Al-Zahra University, Deh Vanak St., Tehran
Abstract:   (1540 Views)
A coalition loyalty program (CLP) is a business strategy adopted by companies to increase and retain their customers. An operational challenge in this regard is to determine the coordination mechanism with business partners. This study investigated the role of revenue-sharing contracts (RSCs) considering customer satisfaction in coalition loyalty reward supply chain planning. A two-stage stochastic programming approach was considered for the solution considering the demand uncertainty. We aimed to investigate the impact of RSCs on the decision-making and profitability of the host firm of this supply chain taking into account the maximization of the profit coming from the CLP compared to the more common wholesale price contract (WPC). After the model was solved, computational experiments were performed to evaluate and compare the effects of RSCs and WPCs on the performance of the loyalty program (LP). The results revealed that RSC is an effective incentive to increase the host’s profit and reduce its cost. These findings add new insights to the management literature, which can be used by business decision makers.
 
Keywords: loyalty program, supply chain, reward, uncertainty, stochastic programming, points
Full-Text [PDF 572 kb]   (566 Downloads)    
Type of Study: Research | Subject: Optimization Techniques
Received: 2023/04/27 | Accepted: 2023/06/12 | Published: 2023/09/2
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