Technical note - operational statistics: Properties and the risk-averse case

Lu, Mengshi; Shanthikumar, J. George; Shen, Zuo‐Jun Max

doi:10.1002/nav.21623

Cited by 13 publications

(4 citation statements)

References 21 publications

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“…In the first group, the Bayesian approach corresponds to the earliest solutions that were developed [22], which assumes that the unknown demand belongs to a parametric distribution family to reach the newsvendor solution in two separate steps for parameter estimation and inventory optimization. Operational statistics is another parametric approach that was designed to perform demand estimation and optimization simultaneously, as in previous literature [4,[23][24][25]. The assumption that the decision-maker knows which distributional family the demand belongs to is still a limitation of this method.…”

Section: Related Literaturementioning

confidence: 99%

Evolving Hybrid Deep Neural Network Models for End-to-End Inventory Ordering Decisions

de Castro Moraes,

Qin,

Yuan

et al. 2023

Logistics

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Background: Over the past decade, the potential advantages of employing deep learning models and leveraging auxiliary data in data-driven end-to-end (E2E) frameworks to enhance inventory decision-making have gained recognition. However, current approaches predominantly rely on feed-forward networks, which may have difficulty capturing temporal correlations in time series data and identifying relevant features, resulting in less accurate predictions. Methods: Addressing this gap, we introduce novel E2E deep learning frameworks that combine Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for resolving single-period inventory ordering decisions, also termed the Newsvendor Problem (NVP). This study investigates the performance drivers of hybrid CNN-LSTM architectures, coupled with an evolving algorithm for optimizing network configuration. Results: Empirical evaluation of real-world retail data demonstrates that our proposed models proficiently extract pertinent features and interpret sequential data characteristics, leading to more accurate and informed ordering decisions. Notably, results showcase substantial benefits, yielding up to an 85% reduction in costs compared to a univariate benchmark and up to 40% savings compared to a feed-forward E2E deep learning architecture. Conclusions: This confirms that, in practical scenarios, understanding the impact of features on demand empowers decision-makers to derive tailored, cost-effective ordering decisions for each store or product category.

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Section: Related Literaturementioning

confidence: 99%

Evolving Hybrid Deep Neural Network Models for End-to-End Inventory Ordering Decisions

de Castro Moraes,

Qin,

Yuan

et al. 2023

Logistics

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“…There is no estimation of the demand distribution, and the order decision is only a function of past demand data. The work has been followed and extended by Chu et al (2008), Ramamurthy et al (2012), andLu et al (2015). Levi et al (2007Levi et al ( , 2015 use a sample average approximation (SAA) approach for the newsvendor problem and ensure out-of-sample performance guarantees.…”

Section: Related Literaturementioning

confidence: 99%

Data‐Driven Newsvendor Problems Regularized by a Profit Risk Constraint

Lin

Chen

et al. 2022

Production and Operations Management

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W e study a risk-averse newsvendor problem where demand distribution is unknown. The focal product is new, and only the historical demand information of related products is available. The newsvendor aims to maximize its expected profit subject to a profit risk constraint. We develop a model with a value-at-risk constraint and propose a datadriven approximation to the theoretical risk-averse newsvendor model. Specifically, we use machine learning methods to weight the similarity between the new product and the previous ones based on covariates. The sample-dependent weights are then embedded to approximate the expected profit and the profit risk constraint. We show that the data-driven riskaverse newsvendor solution entails a closed-form quantile structure and can be efficiently computed. Finally, we prove that this data-driven solution is asymptotically optimal. Experiments based on real data and synthetic data demonstrate the effectiveness of our approach. We observe that under data-driven decision-making, the average realized profit may benefit from a stronger risk aversion, contrary to that in the theoretical risk-averse newsvendor model. In fact, even a risk-neutral newsvendor can benefit from incorporating a risk constraint under data-driven decision-making. This situation is due to the value-at-risk constraint that effectively plays a regularizing role (via reducing the variance of order quantities) in mitigating issues of data-driven decision-making, such as sampling error and model misspecification. However, the above-mentioned effects diminish with the increase in the size of the training data set, as the asymptotic optimality result implies.

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“…Ramamurthy, George Shanthikumar, and Shen (2012) study the operational statistics approach when the demand distribution has an unknown shape parameter. Lu, Shanthikumar, and Shen (2015) then generalize operational statistics to the risk‐averse case under the conditional value‐at‐risk (CVaR) criterion.…”

Section: Data‐driven Inventory Managementmentioning

confidence: 99%

Data‐driven research in retail operations—A review

Meng

Mak

Shen

2020

Naval Research Logistics

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We review the operations research/management science literature on data‐driven methods in retail operations. This line of work has grown rapidly in recent years, thanks to the availability of high‐quality data, improvements in computing hardware, and parallel developments in machine learning methodologies. We survey state‐of‐the‐art studies in three core aspects of retail operations—assortment optimization, order fulfillment, and inventory management. We then conclude the paper by pointing out some interesting future research possibilities for our community.

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Technical note - operational statistics: Properties and the risk-averse case

Cited by 13 publications

References 21 publications

Evolving Hybrid Deep Neural Network Models for End-to-End Inventory Ordering Decisions

Evolving Hybrid Deep Neural Network Models for End-to-End Inventory Ordering Decisions

Data‐Driven Newsvendor Problems Regularized by a Profit Risk Constraint

Data‐driven research in retail operations—A review

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