Perspectives of two competing manufacturers: customer rebate vs. contract mechanism

IF 1.8 Q3 MANAGEMENT Journal of Modelling in Management Pub Date : 2023-08-14 DOI:10.1108/jm2-11-2022-0260
Sani Majumder, I. Nielsen, Susanta Maity, Subrata Saha
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Abstract

Purpose This paper aims to analyze the potentials of dynamic, commitment and revenue-sharing contracts; that a nonrebate offering manufacturer can use to safeguard his profit while his competitor offers customer rebates in a supply chain consisting of two manufacturers and a common retailer. Design/methodology/approach We consider a two-period supply chain model to explore optimal decisions under eight possible scenarios based on the contract and rebate offering decisions. Because the manufacturers are selling substitutable products, therefore, a customer rebate on one of the products negatively impacts the selling quantity of other. Optimal price, rebate, and quantities are examined and compared to explore the strategic choice for both the rebate offering and non-rebate offering manufacturer. Comparative evaluation is conducted to pinpoint how the parameters such as contract parameters and its nature affect the members. Findings The results demonstrate that all these contracts instigate the rebate offering manufacturer to provide a higher rebate, but do not ensure a higher profit. If the revenue sharing contract is offered to the common retailer, the effectiveness of the rebate program might reduce significantly, and the rebate offering manufacturer might receives lower profits. A non-rebate offering manufacturer might use a commitment contract to ensure higher profits for all the members and make sure the common retailer continues the product. Originality/value The effect of customer rebate vs. supply chain contract under competition has not yet been explored comprehensively. Therefore, the study contributes to the literature regarding interplay among pricing decision, contract choice and rebate promotion in a two-period setting. The conceptual and managerial insights contribute to a better understanding of strategic decision-making for both competing manufacturers under consumer rebates.
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两个竞争制造商的视角:客户回扣与合同机制
目的分析动态契约、承诺契约和收益分享契约的潜力;在由两个制造商和一个普通零售商组成的供应链中,当竞争对手向客户提供回扣时,非折扣产品制造商可以用来保障其利润。设计/方法论/方法我们考虑了一个两阶段的供应链模型,以探索基于合同和回扣决策的八种可能情况下的最优决策。由于制造商销售的是可替代的产品,因此,其中一种产品的客户回扣会对另一种的销售数量产生负面影响。对最优价格、折扣和数量进行了检验和比较,以探索提供折扣和不提供折扣的制造商的战略选择。进行比较评估,以确定合同参数及其性质等参数对成员的影响。结果表明,所有这些合同都促使提供回扣的制造商提供更高的回扣,但不能确保更高的利润。如果向普通零售商提供收入共享合同,则回扣计划的有效性可能会显著降低,提供回扣的制造商可能会获得更低的利润。不提供回扣的制造商可能会使用承诺合同来确保所有成员获得更高的利润,并确保普通零售商继续销售产品。独创性/价值竞争条件下客户回扣与供应链合同的影响尚未得到全面探讨。因此,本研究有助于研究两个时期内定价决策、合同选择和回扣促销之间的相互作用。这些概念和管理见解有助于更好地理解消费者回扣下两家竞争制造商的战略决策。
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来源期刊
CiteScore
5.50
自引率
12.50%
发文量
52
期刊介绍: Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling. The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications. JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between ''antecedents and modelling'' (how to tackle certain problems) and ''modelling and consequences'' (how to apply the models and draw appropriate conclusions). The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions. JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.
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