Quick Response in Supply Chains with Stochastically Risk Sensitive Retailers*

Abstract

Quick response is a supply chain practice that can help improve operations by responding quickly to market changes. In particular, when retailers are perfectly rational and risk neutral, quick response is known to be a highly beneficial strategy to the retailers. However, in practice, retailers may possess different kinds of unstable risk preferences, which include risk‐averse and risk‐seeking attitudes. Thus, retailers may be stochastically risk sensitive. In this article, we consider all these factors simultaneously and explore how the retailer's stochastic risk preference affects the values of quick response to the supply chain and its members. Among various findings, we show that quick response is always beneficial to the supply chain when the retailer is stochastically risk sensitive. In most cases, we demonstrate that if the retailer is more risk averse (risk seeking) stochastically, the retailer is benefited more, whereas the manufacturer suffers a smaller profit loss (a bigger profit loss), under quick response. We prove that different commonly used supply chain contracts can achieve robust Pareto improvement in the supply chain. We also uncover that if the manufacturer ignores the retailer's stochastic risk preference, the achievability of Pareto improvement by contracts will be negatively affected.

Stochastic Production Capacity: A Bane or a Boon for Quick Response Supply Chains?

“…A growing body of literature on the QR inventory management problems with information updating has stemmed from the pioneering work of Scarf (). Many researchers have since studied various inventory problems with information update in the single‐echelon (eg, Fisher & Raman, ; Gurnani & Tang, ) and multi‐echelon supply chain settings (eg, Choi et al, ; Donohue, ; Miyaoka & Hausman, ; Zhou & Olsen, ). Our work is related to the ones focusing on the two‐echelon supply chain context.…”

“…Lin and Parlaktürk (), and Wang, Thomas, and Rudi () extended the work from monopoly to duopoly and explored the impacts of QR under different competition structures. Other QR‐related studies include Cachon and Swinney (), which considered an QR supply chain with strategic consumers; Jain, Moinzadeh, and Zhou (), which studied the retailer's optimal ordering policy with continuous ordering opportunities; Amornpetchkul, Duenyas, and Şahin (), which investigated the multiperiod procurement problem under information asymmetry and demand forecasting; Calvo and Martínez‐de‐Albéniz (), which explored the multiple sourcing problem with QR; Li and Petruzzi (), which analyzed the effects of reducing demand uncertainty in a decentralized supply chain; Chintapalli, Disney, and Tang (), which studied the coordination issues for the case where the manufacturer and the supplier have to deal with rush orders; Niu and Zou (), which examined the use of demand signal for enhancing remanufacturing channel's operations; and Choi et al (), which explored QR with a stochastically risk sensitive retailer.…”

“…Naturally, we have p > w > c > v . To model QR, we follow the classical model with Bayesian information updating (eg, Caro & Martínez‐de‐Albéniz, ; Choi et al, ; Iyer & Bergen, ), in which the retailer is allowed to order at either one of two time points, namely Stage 0 and Stage 1. Stage 0 is farther away from the selling season and Stage 1 is closer.…”

“…If the retailer orders in Stage 1, the retailer can observe the market to update its demand forecast and then make a better inventory decision, whereas the manufacturer is required to fulfill the retailer's order within a short time frame. Following the existing literature (Choi et al, ), we call the former case the “old ordering (OO)” system, that is, ordering in Stage 0, and the latter case the “quick response (QR)” system, that is, ordering in Stage 1. We formulate the information updating process of the retailer as follows: In Stage 0, the retailer forecasts that the market demand X 0 follows a normal distribution with a mean m and a variance δ 2 , that is, X 0 | m ∼Normal( m , δ 2 ).…”

“…Quick response (QR) is a common practice in many industries facing highly volatile demand such as apparel and consumer electronics (Choi, Zhang, & Cheng, ). Many fashion companies such as Uniqlo, Mango, Zara, and H&M are using the QR strategy to respond to ever‐changing customer preferences in a timely manner (Calvo & Martínez‐de‐Albéniz, ; Chen, Lee, & Moinzadeh, ).…”