The data-driven newsvendor with censored demand observations

Sachs, Anna‐Lena; Minner, Stefan

doi:10.1016/j.ijpe.2013.04.039

Cited by 35 publications

(4 citation statements)

References 23 publications

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“…Overall, we identify that the nonparametric models, QR and QRF, are closer to the service level targets than the parametric models, which would seem to corroborate the results of Sachs & Minner (2014). The models GAMLSS PO, RF GA, RF NO q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q q…”

Section: Out-of-sample Prediction Of Demandsupporting

confidence: 78%

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Distributional regression for demand forecasting in e-grocery

Ulrich

Jahnke

Langrock

et al. 2021

European Journal of Operational Research

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E-grocery offers customers an alternative to traditional brick-and-mortar grocery retailing. Customers select e-grocery for convenience, making use of the home delivery at a selected time slot. In contrast to brick-and-mortar retailing, in e-grocery on-stock information for stock keeping units (SKUs) becomes transparent to the customer before substantial shopping effort has been invested, thus reducing the personal cost of switching to another supplier. As a consequence, compared to brick-and-mortar retailing, on-stock availability of SKUs has a strong impact on the customer's order decision, resulting in higher strategic service level targets for the e-grocery retailer. To account for these high service level targets, we propose a suitable model for accurately predicting the extreme right tail of the demand distribution, rather than providing point forecasts of its mean. Specifically, we propose the application of distributional regression methods -so-called Generalised Additive Models for Location, Scale and Shape (GAMLSS) -to arrive at the cost-minimising solution according to the newsvendor model. As benchmark models we consider linear regression, quantile regression, and some popular methods from machine learning. The models are evaluated in a case study, where we compare their out-of-sample predictive performance with regard to the service level selected by the e-grocery retailer considered.

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Section: Out-of-sample Prediction Of Demandsupporting

confidence: 78%

“…In contrast to quantile regression methods, which are nonparametric, distributional regression methods are parametric. Sachs & Minner (2014) compared parametric and nonparametric modelling approaches for estimating different censoring levels using data from a large European retail chain.…”

Section: Introductionmentioning

confidence: 99%

Distributional regression for demand forecasting in e-grocery

Ulrich

Jahnke

Langrock

et al. 2021

European Journal of Operational Research

View full text Add to dashboard Cite

show abstract

“…The data-driven approach was compared with other benchmark methods and when the data sample was small. Sachs and Minner [23] extended this approach by studying the censored demand and price-dependent scenarios.…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

Data-Driven Solutions for the Newsvendor Problem: A Systematic Literature Review

Moraes

Yuan

2021

Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems

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The newsvendor problem captures the trade-off between ordering decisions, stocking costs and customer service level when the demand distribution is known. Nonetheless, in real case scenarios, it is unlikely that the decision maker knows the true demand distribution and its parameters, encouraging the use of datasets for empirical solutions that will achieve more precise results and reduce misleading decisions. Motivated by the availability of large amount of quality datasets, advances in machine learning algorithms and enhancement of computational power, the development of data-driven approaches has been emerging over the recent years. However, it is still unclear in which settings these data-driven solutions outperform the traditional model-based methods. In this paper, a systematic literature review is conducted for the descriptive analysis and classification of the most relevant studies that addressed the newsvendor problem and its variations under the data-driven approaches. The methods developed to solve the problems with unknown demand distribution are categorized and assessed. For each category, our paper discusses the relevant publications in detail and how they evidence the data-driven performance better. By identifying the gaps in the available literature, the future research directions are suggested.

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“…The data are available in the form of . Beutel and Minner (2012) and Sachs (2014) use linear programming models to deal with the case where the demand is a linear combination of some exogenous variables and a random shock. The authors adopt an objective of in‐sample cost in accordance with the framework of empirical risk minimization (ERM).…”

Section: Introductionmentioning

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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The data-driven newsvendor with censored demand observations

Cited by 35 publications

References 23 publications

Distributional regression for demand forecasting in e-grocery

Distributional regression for demand forecasting in e-grocery

Data-Driven Solutions for the Newsvendor Problem: A Systematic Literature Review

Data‐Driven Newsvendor Problems Regularized by a Profit Risk Constraint

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