Distributionally Robust Omnichannel Stocking Decisions in Quick Fulfillment Systems

Chen, Louis; Jin, Rong; Qin, Hanzhang; Simchi‐Levi, David; Zhang, Zirun

doi:10.2139/ssrn.3383881

Cited by 3 publications

(2 citation statements)

References 26 publications

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Order By: Relevance

“…Related is the open question around priority for the last item on the shelf. While current practice varies, many retailers prioritize the in‐store customers' needs over the online orders (see, e.g., AeroFulfillment, 2020; Chen et al., 2019). Given that in‐store customers would be selecting items for both themselves and the online orders, the proposed policy provides more autonomy to in‐store customers.…”

Section: Discussion Insights and Future Workmentioning

confidence: 99%

Crowdsourced order‐fulfillment policies using in‐store customers

Dayarian

Pazour

2022

Production and Operations Management

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Omni‐channel services, such as buy‐online‐pick‐up‐from‐store, transfer the in‐store logistics once completed by shoppers to retailers. To cost‐effectively meet the high demands for such pickup services, we introduce a crowdsourced order‐fulfillment policy that deploys in‐store customers to pick items for online orders while completing their own personal shopping. As opposed to existing store fulfillment policies, this new concept utilizes in‐store customers to help, not constrain, dedicated pickers. Empirical data indicate that a high percentage of surveyed in‐store shoppers would be willing to occasionally participate in such a program. In‐store customers willing to participate were observed to be heterogeneous in their efforts, with variability in how much extra time they would be willing to provide and would prefer picking tasks that had only a small deviation from their personal shopping. Motivated by these empirical results, the decision problem of how to assign picking tasks for arriving online orders with a given service commitment, to a set of arriving in‐store customers or an abundant set of dedicated pickers, was formalized to capture the uncertainty and heterogeneity of using in‐store customers for in‐store picking tasks. We propose a tractable decision‐making methodology to determine whether an order will meet both service commitment feasibility and in‐store customer availability with a probability at least equal to a target threshold. This method captures dynamic order placements and in‐store customer arrivals and stochasticity in in‐store customers' shopping baskets. Extensive computational experiments for varying operational conditions of a grocery store dynamically matching actual online orders to arriving in‐store customers helps answer open questions from practitioners. Compensating in‐store customers based on their additional efforts reduced costs of fulfillment by greater than 30%, on average, compared to a baseline that uses only dedicated pickers for store fulfillment. Using the past five shopping baskets of participating in‐store customers to estimate assignment decisions can achieve both high online order service commitments and in‐store customer availability requirements. Our results suggest that in‐store customers should be assigned smaller orders than dedicated pickers.

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Section: Discussion Insights and Future Workmentioning

confidence: 99%

Crowdsourced order‐fulfillment policies using in‐store customers

Dayarian

Pazour

2022

Production and Operations Management

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“…The survey results of omnichannel retailers show that more than 80% of retailers believe that making reasonable inventory decisions is one of the biggest challenges for omnichannel operations (Gallino et al, 2017). In an omnichannel environment, consumers are showing trends such as channel shifts, multi-line purchases, and personalized demand (Chen et al, 2019), which increases the difficulty for retailers to predict demand. Take Hema as an example.…”

Section: Introductionmentioning

confidence: 99%

An Overview on Omnichannel Inventory Strategy under Uncertain Demand

Du¹

2021

AJIBM

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With the improvement of people's living standards, there are higher requirements for product quality and personalization. At the same time, the development of science and technology, such as big data, cloud computing, VR, etc., provides technical support to meet the higher shopping needs of consumers. Promoted by these two factors, omnichannel came into being. In the omnichannel model, a single demand may exist in multiple channels, which increases the difficulty of retailer demand forecasting and brings challenges to the inventory management of omnichannel retailers. This article sorts out and analyzes the changes in omnichannel consumer demand, the relationship between channels, and the related research on multi-channel and omnichannel inventory strategies, and finds that there are few studies on omnichannel inventory strategies. At present, the research on omnichannel inventory strategy is mostly based on a specific omnichannel model inventory strategy research. The characteristics of omnichannel such as changes in demand and channel relationship have not been included in the relevant research. The characteristics of omnichannel such as changes in demand and channel relationship have not been included in the relevant research. This paper proposes that future research can pay more attention to the impact of the new characteristics of the omnichannel model on inventory management, as well as the challenges of inventory management caused by the competition and cooperation between channels, and provide a scientific basis for omnichannel inventory management.

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