Dynamic Pricing, Production, and Channel Coordination with Stochastic Learning

Li, Tao; Sethi, Suresh P.; He, Xiuli

doi:10.1111/poms.12320

Cited by 62 publications

(38 citation statements)

References 44 publications

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“…This means at least formally that the manufacturer sets the product quality and the wholesale price as functions of reference quality at each instant, and then the retailer determines the retail price as its optimal response to the manufacturer's decisions at that instant. For further details, see Sethi (2014, 2015) and Li, Sethi, and He (2015). We take up the myopic case in Section 4.2 along the lines followed, e.g.…”

Section: Feedback Stackelberg Equilibrium and Myopic Solutionmentioning

confidence: 99%

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Myopic vs. far-sighted behaviours in a revenue-sharing supply chain with reference quality effects

Liu

Sethi

Zhang

2015

International Journal of Production Research

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In this paper, we consider a dynamic supply chain where a manufacturer produces and distributes a featured product through an exclusive retailer to end consumers. The manufacturer decides the product quality and wholesale price, while the retailer sets the retail price in the presence of a revenue-sharing contract and consumers' reference quality effects. The objective of this study is to investigate the impacts of myopic behaviour regarding the reference quality on the product quality and pricing strategies and profits of both members. Our results suggest that the manufacturer's myopia leads to a higher quality, higher price strategy and a higher quality-price ratio which benefits consumers. Meanwhile, relative to the far-sighted behaviour, myopia results in a more quality-sensitive but less price-sensitive market demand. What's more, we find that the manufacturer is apt to act in a far-sighted way, but the retailer isn't always willing to cooperate with a far-sighted manufacturer. Taking myopic strategies for both members is likely to gain a high profit of the whole supply chain for a relatively high marginal contribution of product quality on demand and a relatively low revenue-sharing proportion.

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Section: Feedback Stackelberg Equilibrium and Myopic Solutionmentioning

confidence: 99%

“…Dana and Spier 2001;Cachon 2003;Cachon and Lariviere 2005;Yao, Leung, and Lai 2008;De Giovanni 2011a;Gutierrez and He 2011;De Giovanni and Roselli 2012;Li, Sethi, and He 2015), or to compare with a wholesale price contract (e.g. Gerchak and Wang 2004;Pan et al 2010;El Ouardighi and Kim 2010;El Ouardighi and Kogan 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

Myopic vs. far-sighted behaviours in a revenue-sharing supply chain with reference quality effects

Liu

Sethi

Zhang

2015

International Journal of Production Research

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“…In this paper, we assume that the market information can be observed in full but in some cases, we may only have partially observable market signal (Bensoussan, Çakanyildirim, Minjárez-Sosa, Sethi, & Shi, 2008). One natural extension is to consider different kinds of observation and learning (Li, Sethi, & He, 2015). Moreover, one may consider extending our model by including behavioral factors such as risk sensitivity, fairness, and bounded rationality concerns.…”

Section: Effects Of Manufacturer's Ability To Expand Capacitymentioning

confidence: 99%

Stochastic production capacity: A bane or a boon for quick response supply chains?

Zhang

Choi

Cheng

2020

Naval Research Logistics

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The quick response (QR) system that can cope with demand volatility by shortening lead time has been well studied in the literature. Much of the existing literature assumes implicitly or explicitly that the manufacturers under QR can always meet the demand because the production capacity is always sufficient. However, when the order comes with a short lead time under QR, availability of the manufacturer's production capacity is not guaranteed. This motivates us to explore QR in supply chains with stochastic production capacity. Specifically, we study QR in a two-echelon supply chain with Bayesian demand information updating. We consider the situation where the manufacturer's production capacity under QR is uncertain. We first explore how stochastic production capacity affects supply chain decisions and QR implementation. We then incorporate the manufacturer's ability to expand capacity into the model. We explore how the manufacturer determines the optimal capacity expansion decision, and the value of such an ability to the supply chain and its agents. Finally, we extend the model to the two-stage two-ordering case and derive the optimal ordering policy by dynamic programming. We compare the single-ordering and two-ordering cases to generate additional managerial insights about how ordering flexibility affects QR when production capacity is stochastic. We also explore the transparent supply chain and find that our main results still hold. KEYWORDSBayesian information updating, flexible capacity, quick response supply chain, random demand and capacity 1 126

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“…Ifrach et al [24] suggests that when strategic consumers exist in the market, social learning helps firms to make more profit. Considering the impact of social learning on pricing, Li et al [25] shows that social learning can reduce a firm's production costs. e work aforementioned analyzes the impacts of social learning on consumers' purchasing decision, yet little attention has been paid to consumers' environmental awareness when purchasing.…”

Section: Literature Reviewmentioning

confidence: 99%

Pricing Strategy and Carbon Emission Abatement under Cap-and-Trade Regulation Considering Social Learning

Chen

2019

Discrete Dynamics in Nature and Society

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In an uncertainty market, social learning plays a signi cant role in obtaining information to make better decisions. Under cap-and-trade regulation, this paper aims to investigate rms' pricing and carbon emission abatement issues considering the impact of social learning. is paper establishes a two-period model in a market consisting of a manufacturer and heterogeneous consumers. e manufacturer produces two alternatives (ordinary product and low-carbon product) and makes decisions on sales prices and carbon emission abatement levels. Consumers make decisions on whether and which product to buy. Consumers are not sure about their valuations of products and have the opportunity to discover their true valuation by social learning. e results show that the emission abatement level on ordinary product is a ected by the pricing strategy for both types of products. However, the emission abatement level on low-carbon product is only a ected by its own pricing strategy. It also shows that social learning lowers the emission abatement level on ordinary product, whereas it improves the emission abatement level on low-carbon product when charging a high price for low-carbon product. Moreover, the price of ordinary product in period 1 is no less than that in period 2. In contrast, the price of low-carbon product in period 2 is higher than that in period 1.Hindawi

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Dynamic Pricing, Production, and Channel Coordination with Stochastic Learning

Cited by 62 publications

References 44 publications

Myopic vs. far-sighted behaviours in a revenue-sharing supply chain with reference quality effects

Myopic vs. far-sighted behaviours in a revenue-sharing supply chain with reference quality effects

Stochastic production capacity: A bane or a boon for quick response supply chains?

Pricing Strategy and Carbon Emission Abatement under Cap-and-Trade Regulation Considering Social Learning

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