A Trade Credit Model with Asymmetric Competing Retailers

Abstract

W e study a supply chain of a manufacturer selling to two asymmetric retailers engaged in inventory (order quantity) competition in the presence of demand uncertainty and an exogenously given retail price. The effective demand of each retailer includes its primary demand and reallocated demand from its competitor. We model two salient features causing asymmetry: (i) the weak retailer is capital-constrained and (ii) the bargaining power of the dominant retailer implies that it enjoys a lower wholesale price. The manufacturer offers trade credit to the weak, capital-constrained retailer. We show that such trade credit can be used by the manufacturer as a strategic response to the bargaining power of its dominant retailer. Computational examples reveal that under inventory competition, the capital-constrained retailer benefits from the trade credit, leaving the dominant retailer worse off. We show that demand substitution increases the profit of the dominant retailer and the manufacturer but, somewhat surprisingly, decreases the weak retailer's profit. When both bank and trade credit are available, we show conditions under which trade credit is preferred over bank credit by the manufacturer. Compared with a trade credit with an endogenous interest rate (and an exogenously given wholesale price), a trade credit with an endogenous wholesale price (and an exogenously given interest rate) is preferred by the manufacturer, but is only preferred by the system when the weak retailer's initial working capital is small.

Trade credit insurance in a capital‐constrained supply chain

“…They indicate that the financially distressed firm may have a negative externality to his competitor because low‐cost reorganization enables the former to obtain operational advantage. Considering reallocated demand between two competing retailers with asymmetric capital, demand substitution benefits the dominant retailer and the manufacturer but harms the weak retailer (Wu et al., ).…”

“…The consumers' initial preference toward one retailer's product creates the so‐called primary demand denoted by the random variables and , where subscript d represents the dominant retailer and w represents the weak retailer. If the preferred retailer does not have any stock of the product, as a result of transportation costs and so on, the customers' unfulfilled demand is partly reallocated to the other retailer with a demand reallocation rate θ (); we refer to this as reallocated demand (Netessine and Rudi, ; Wu et al., ). In this paper, to focus on the role of insurance and competition between two differently capitalized retailers, we build a model on the basic assumption that the same demand substitution rate θ applies to both capital‐asymmetric retailers, that is, .…”

Financing strategies for a capital‐constrained manufacturer in a dual‐channel supply chain

“…Trade credit is a type of credit that the seller extends to the buyer, which enables the latter to purchase products from the former with a delay in payment by charging a credit interest. This branch of literature aims to provide a rationale for trade credit from various perspectives (Li et al., ; Peura et al., ; Tang et al., ; Xiao et al., ; Zhang et al., ; Wu et al., ). Several papers point out that trade credit has the advantage of risk sharing and consequently mitigating double marginalization compared with bank loan (Jing et al., ; Kouvelis and Zhao, ; Cai et al., ; Jing and Seidmann, ; Yang et al., ; Tang et al., ; Yang et al., ; Jin et al., ; Yang and Birge, ).…”

“…Wu et al. () demonstrate that manufacturers can use trade credit as a strategic response to the bargaining power of major retailers. Our study follows the literature on supply chain finance but differs in the following respects.…”

Service data analytics and business intelligence 2017

“…Service data analytics and business intelligence have become an important topic in today's complex, interrelated global environment, replete with threats from larger amounts of aspects, such as natural (Kiremidjian et al 2007), political (Akhtaruzzaman et al 2017;Busse and Hefeker 2007), economic (Corbett and de Groote 2000;Jin et al 2014;Wu et al 2019;Yu et al 2008), energy (Wen et al 2019;Xiao et al 2018), and technical sources (He et al 2016;Pan et al 2019). Data analytics about business intelligence can provide various reality-based examples or projects to examine the economical evaluation and risk assessment, which not only expands literature of service data analytics but also facilitates industry-academy combination.…”