Dynamic Joint Pricing and Order Fulfillment for E-Commerce Retailers

Lei, Yanzhe; Jasin, Stefanus; Sinha, Amitabh

doi:10.2139/ssrn.2753810

Cited by 23 publications

(22 citation statements)

References 47 publications

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“…The E‐retailers have to determine the order fulfillment channel for each SKU. This differentiates our work from the papers cited above, as well as those papers investigating joint pricing and fulfillment‐related decisions (Gümüş, Li, Oh, & Ray, 2013; He & Ke, 2017; Lei, Jasin, & Sinha, 2018; Leng & Becerril‐Arreola, 2010). Recently, Lai, Liu, and Xiao (2018) developed an analytical framework to study the scenario where Amazon conducts price and service competitions with a third‐party seller on a substitutable product.…”

Section: Literature Reviewmentioning

confidence: 81%

Fulfillment by Amazon versus fulfillment by seller: An interpretable risk‐adjusted fulfillment model

Sun

Lyu

et al. 2020

Naval Research Logistics

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With dual‐channel choices, E‐retailers fulfill their demands by either the inventory stored in third‐party distribution centers, or by in‐house inventory. In this article, using data from a wedding gown E‐retailer in China, we analyze the differences between two fulfillment choices—fulfillment by Amazon (FBA) and fulfillment by seller (FBS). In particular, we want to understand the impact of FBA that will bring to sales and profit, compared to FBS, and how the impact is related to product features such as sizes and colors. We develop a risk‐adjusted fulfillment model to address this problem, where the E‐retailer's risk attitude to FBA is incorporated. We denote the profit gaps between FBA and FBS as the rewards for this E‐retailer fulfilling products using FBA, our goal is to maximize the E‐retailer's total rewards using predictive analytics. We adopt the generalized linear model to predict the expected rewards, while controlling for the variability of the reward distribution. We apply our model on a set of real data, and develop an explicit decision rule that can be easily implemented in practice. The numerical experiments show that our interpretable decision rule can improve the E‐retailer's total rewards by more than 35%.

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Section: Literature Reviewmentioning

confidence: 81%

Fulfillment by Amazon versus fulfillment by seller: An interpretable risk‐adjusted fulfillment model

Sun

Lyu

et al. 2020

Naval Research Logistics

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“…Similar to Jasin and Sinha (2015) and Lei et al (2018), we take the fulfillment costs to be linear functions of the distance. Specifically we have s ij = s 0 + λ • r ij , where r ij is the distance in miles, λ = 0.005 is the distance sensitivity factor, with in-location fulfillment done at cost s 0 = $10.…”

Section: Experiments On Simulated Datamentioning

confidence: 99%

Distribution-Free Inventory Risk Pooling in a Multilocation Newsvendor

2021

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We study a multilocation newsvendor network when the only information available on the joint distribution of demands are the values of its mean vector and covariance matrix. We adopt a distributionally robust model to find inventory levels that minimize the worst-case expected cost among the distributions consistent with this information. This problem is NP-hard. We find a closed-form tight bound on the expected cost when there are only two locations. This bound is tight under a family of joint demand distributions with six support points. For the general case, we develop a computationally tractable upper bound on the worst-case expected cost if the costs of fulfilling demands have a nested structure. This upper bound is the optimal value of a semidefinite program whose dimensions are polynomial in the number of locations. We propose an algorithm that can approximate general fulfillment cost structures by nested structures, yielding a computationally tractable heuristic for distributionally robust inventory optimization on general newsvendor networks. We conduct experiments on networks resembling U.S. e-commerce distribution networks to show the value of a distributionally robust approach over a stochastic approach that assumes an incorrect demand distribution. This paper was accepted by Chung Piaw Teo, optimization.

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“…Through "demand pooling," online retailers can afford to carry a much wider assortment, including those long-tail products. This new capability generated new OM research issues related to dynamic assortment planning (Caro and Gallien 2007), customized assortment recommendations to individual online shoppers (Cachon et al 2005), and dynamic personalized pricing (Bitran and Caldentey 2003, Choudhary et al 2005, Aviv and Pazgal 2008, Lei et al 2019. To fight against the encroachment of various e-commerce companies, traditional incumbent firms (airlines, retailers, such as Macy's, Barnes and Noble, etc.)…”

Section: Changes In Om Research: E-commerce and Newmentioning

confidence: 99%

Twenty Years in the Making: The Evolution of the Journal of Manufacturing & Service Operations Management

Dai

Feng

Tang

et al. 2020

M&SOM

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Please scroll down for article-it is on subsequent pages With 12,500 members from nearly 90 countries, INFORMS is the largest international association of operations research (O.R.) and analytics professionals and students. INFORMS provides unique networking and learning opportunities for individual professionals, and organizations of all types and sizes, to better understand and use O.R. and analytics tools and methods to transform strategic visions and achieve better outcomes. For more information on INFORMS, its publications, membership, or meetings visit http://www.informs.org

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Dynamic Joint Pricing and Order Fulfillment for E-Commerce Retailers

Cited by 23 publications

References 47 publications

Fulfillment by Amazon versus fulfillment by seller: An interpretable risk‐adjusted fulfillment model

Fulfillment by Amazon versus fulfillment by seller: An interpretable risk‐adjusted fulfillment model

Distribution-Free Inventory Risk Pooling in a Multilocation Newsvendor

Twenty Years in the Making: The Evolution of the Journal of Manufacturing & Service Operations Management

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