A data-driven newsvendor problem: From data to decision
•We identify and conceptualize three levels of data-driven inventory management.•We investigate the impact of the levels on the performance in a newsvendor problem.•We present solution approaches based on Machine Learning for the newsvendor problem.•We compare our methods to well-established approac...
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| Vydáno v: | European journal of operational research Ročník 278; číslo 3; s. 904 - 915 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.11.2019
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| ISSN: | 0377-2217, 1872-6860 |
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| Abstract | •We identify and conceptualize three levels of data-driven inventory management.•We investigate the impact of the levels on the performance in a newsvendor problem.•We present solution approaches based on Machine Learning for the newsvendor problem.•We compare our methods to well-established approaches on a real-world data set.•Data-driven methods outperform their model-based counterparts in most cases.
Retailers that offer perishable items are required to make ordering decisions for hundreds of products on a daily basis. This task is non-trivial because the risk of ordering too much or too little is associated with overstocking costs and unsatisfied customers. The well-known newsvendor model captures the essence of this trade-off. Traditionally, this newsvendor problem is solved based on a demand distribution assumption. However, in reality, the true demand distribution is hardly ever known to the decision maker. Instead, large datasets are available that enable the use of empirical distributions. In this paper, we investigate how to exploit this data for making better decisions. We identify three levels on which data can generate value, and we assess their potential. To this end, we present data-driven solution methods based on Machine Learning and Quantile Regression that do not require the assumption of a specific demand distribution. We provide an empirical evaluation of these methods with point-of-sales data for a large German bakery chain. We find that Machine Learning approaches substantially outperform traditional methods if the dataset is large enough. We also find that the benefit of improved forecasting dominates other potential benefits of data-driven solution methods. |
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| AbstractList | •We identify and conceptualize three levels of data-driven inventory management.•We investigate the impact of the levels on the performance in a newsvendor problem.•We present solution approaches based on Machine Learning for the newsvendor problem.•We compare our methods to well-established approaches on a real-world data set.•Data-driven methods outperform their model-based counterparts in most cases.
Retailers that offer perishable items are required to make ordering decisions for hundreds of products on a daily basis. This task is non-trivial because the risk of ordering too much or too little is associated with overstocking costs and unsatisfied customers. The well-known newsvendor model captures the essence of this trade-off. Traditionally, this newsvendor problem is solved based on a demand distribution assumption. However, in reality, the true demand distribution is hardly ever known to the decision maker. Instead, large datasets are available that enable the use of empirical distributions. In this paper, we investigate how to exploit this data for making better decisions. We identify three levels on which data can generate value, and we assess their potential. To this end, we present data-driven solution methods based on Machine Learning and Quantile Regression that do not require the assumption of a specific demand distribution. We provide an empirical evaluation of these methods with point-of-sales data for a large German bakery chain. We find that Machine Learning approaches substantially outperform traditional methods if the dataset is large enough. We also find that the benefit of improved forecasting dominates other potential benefits of data-driven solution methods. |
| Author | Müller, Sebastian Fleischmann, Moritz Huber, Jakob Stuckenschmidt, Heiner |
| Author_xml | – sequence: 1 givenname: Jakob surname: Huber fullname: Huber, Jakob email: jakob@informatik.uni-mannheim.de organization: Data and Web Science Group, University of Mannheim, B6 26, Mannheim 68159, Germany – sequence: 2 givenname: Sebastian surname: Müller fullname: Müller, Sebastian email: s.mueller@bwl.uni-mannheim.de organization: Business School, University of Mannheim, Schloss, Mannheim 68131, Germany – sequence: 3 givenname: Moritz surname: Fleischmann fullname: Fleischmann, Moritz email: mfleischmann@bwl.uni-mannheim.de organization: Business School, University of Mannheim, Schloss, Mannheim 68131, Germany – sequence: 4 givenname: Heiner surname: Stuckenschmidt fullname: Stuckenschmidt, Heiner email: heiner@informatik.uni-mannheim.de organization: Data and Web Science Group, University of Mannheim, B6 26, Mannheim 68159, Germany |
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| Cites_doi | 10.1016/0893-6080(91)90009-T 10.1137/S1052623499363220 10.1214/aos/1013203451 10.1016/S0169-2070(97)00044-7 10.1023/A:1010933404324 10.1016/j.eswa.2013.12.011 10.1287/opre.2018.1757 10.1016/j.ejor.2016.07.015 10.1016/S0169-2070(01)00110-8 10.1002/1099-131X(200007)19:4<299::AID-FOR775>3.0.CO;2-V 10.1016/j.cageo.2010.07.005 10.1016/S0927-0507(03)10006-0 10.1287/moor.1070.0272 10.1016/j.dss.2005.01.008 10.1016/j.eswa.2017.01.022 10.1016/0377-2217(95)00134-4 10.1016/j.ijforecast.2006.03.001 10.1108/09600030710840822 10.1057/jors.1976.13 10.1016/j.ejor.2010.11.024 10.1057/jors.1993.141 10.1016/j.ijpe.2011.04.017 10.1287/opre.1050.0238 10.1287/mnsc.47.8.1101.10231 10.1016/j.ejor.2016.06.035 10.1016/j.ejor.2006.02.006 10.1016/j.ejor.2006.12.004 10.1016/j.ijforecast.2011.04.001 10.1287/opre.2015.1422 10.1287/opre.1070.0486 10.1016/j.ijpe.2013.04.039 |
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| References | Ke, Meng, Finley, Wang, Chen, Ma, Ye, Liu (bib0024) 2017 Oroojlooyjadid, Snyder, Takác (bib0032) 2016 Cannon (bib0010) 2011; 37 Hyndman, Koehler, Snyder, Grose (bib0023) 2002; 18 Conrad (bib0012) 1976; 27 Kleywegt, Shapiro, Homem-de Mello (bib0026) 2002; 12 Sachs, Minner (bib0038) 2014; 149 Trapero, Cardós, Kourentzes (bib0046) 2018; In Press Crone, Kourentzes (bib0014) 2009 Hyndman, Koehler, Ord, Snyder (bib0022) 2008 Perakis, Roels (bib0033) 2008; 56 Koenker (bib0027) 2005 Levi, Roundy, Shmoys (bib0031) 2007; 32 Qin, Wang, Vakharia, Chen, Seref (bib0036) 2011; 213 Zhang, Patuwo, Hu (bib0048) 1998; 14 Bertsimas, Thiele (bib0007) 2006; 54 Gallego, Moon (bib0016) 1993; 44 R Core Team (bib0037) 2017 Bertsimas, Kallus (bib0006) 2018; forthcoming Thomassey, Fiordaliso (bib0045) 2006; 42 Barrow, Crone, Kourentzes (bib0002) 2010 Ben-Tal, Ghaoui, Nemirovski (bib0004) 2009 Hyndman, Khandakar (bib0020) 2008; 26 Zhang, Gao (bib0049) 2017; 10634 Shapiro (bib0040) 2003; 10 Kingma, Ba (bib0025) 2015 Takeuchi, Le, Sears, Smola (bib0042) 2006; 7 Prak, Teunter (bib0034) 2018; In Press Taylor (bib0044) 2007; 178 Breiman (bib0009) 2001; 45 Silver, Pyke, Thomas (bib0041) 2017 Beutel, Minner (bib0008) 2012; 140 Prak, Teunter, Syntetos (bib0035) 2017; 256 Van Woensel, Van Donselaar, Broekmeulen, Fransoo (bib0047) 2007; 37 Carbonneau, Laframboise, Vahidov (bib0011) 2008; 184 Godfrey, Powell (bib0017) 2001; 47 Crone, Hibon, Nikolopoulos (bib0013) 2011; 27 Taylor (bib0043) 2000; 19 Kourentzes, Barrow, Crone (bib0028) 2014; 41 Scarf (bib0039) 1958 Huber, Gossmann, Stuckenschmidt (bib0019) 2017; 76 Bergstra, Bengio (bib0005) 2012; 13 Hyndman, Koehler (bib0021) 2006; 22 Ban, Rudin (bib0001) 2018; 67 Barrow, Kourentzes (bib0003) 2018; 264 Hornik (bib0018) 1991; 4 Levi, Perakis, Uichanco (bib0030) 2015; 63 Friedman (bib0015) 2001; 29 Lau, Lau (bib0029) 1996; 92 Bergstra (10.1016/j.ejor.2019.04.043_bib0005) 2012; 13 Barrow (10.1016/j.ejor.2019.04.043_bib0003) 2018; 264 Kingma (10.1016/j.ejor.2019.04.043_sbref0025) 2015 Kleywegt (10.1016/j.ejor.2019.04.043_bib0026) 2002; 12 Taylor (10.1016/j.ejor.2019.04.043_bib0043) 2000; 19 Barrow (10.1016/j.ejor.2019.04.043_sbref0002) 2010 Shapiro (10.1016/j.ejor.2019.04.043_bib0040) 2003; 10 Trapero (10.1016/j.ejor.2019.04.043_bib0046) 2018; In Press Bertsimas (10.1016/j.ejor.2019.04.043_bib0007) 2006; 54 Beutel (10.1016/j.ejor.2019.04.043_bib0008) 2012; 140 Hyndman (10.1016/j.ejor.2019.04.043_bib0022) 2008 Takeuchi (10.1016/j.ejor.2019.04.043_bib0042) 2006; 7 Koenker (10.1016/j.ejor.2019.04.043_bib0027) 2005 Kourentzes (10.1016/j.ejor.2019.04.043_bib0028) 2014; 41 Thomassey (10.1016/j.ejor.2019.04.043_bib0045) 2006; 42 Hornik (10.1016/j.ejor.2019.04.043_bib0018) 1991; 4 Hyndman (10.1016/j.ejor.2019.04.043_bib0020) 2008; 26 Crone (10.1016/j.ejor.2019.04.043_bib0013) 2011; 27 Oroojlooyjadid (10.1016/j.ejor.2019.04.043_sbref0032) 2016 Conrad (10.1016/j.ejor.2019.04.043_bib0012) 1976; 27 Crone (10.1016/j.ejor.2019.04.043_sbref0014) 2009 Prak (10.1016/j.ejor.2019.04.043_bib0035) 2017; 256 Perakis (10.1016/j.ejor.2019.04.043_bib0033) 2008; 56 Levi (10.1016/j.ejor.2019.04.043_bib0031) 2007; 32 Zhang (10.1016/j.ejor.2019.04.043_bib0049) 2017; 10634 Sachs (10.1016/j.ejor.2019.04.043_bib0038) 2014; 149 Zhang (10.1016/j.ejor.2019.04.043_bib0048) 1998; 14 Hyndman (10.1016/j.ejor.2019.04.043_bib0021) 2006; 22 Levi (10.1016/j.ejor.2019.04.043_bib0030) 2015; 63 Van Woensel (10.1016/j.ejor.2019.04.043_bib0047) 2007; 37 Huber (10.1016/j.ejor.2019.04.043_bib0019) 2017; 76 Gallego (10.1016/j.ejor.2019.04.043_bib0016) 1993; 44 Carbonneau (10.1016/j.ejor.2019.04.043_bib0011) 2008; 184 Lau (10.1016/j.ejor.2019.04.043_bib0029) 1996; 92 Silver (10.1016/j.ejor.2019.04.043_bib0041) 2017 Bertsimas (10.1016/j.ejor.2019.04.043_bib0006) 2018; forthcoming Hyndman (10.1016/j.ejor.2019.04.043_bib0023) 2002; 18 Scarf (10.1016/j.ejor.2019.04.043_bib0039) 1958 Prak (10.1016/j.ejor.2019.04.043_bib0034) 2018; In Press Cannon (10.1016/j.ejor.2019.04.043_bib0010) 2011; 37 Qin (10.1016/j.ejor.2019.04.043_bib0036) 2011; 213 Friedman (10.1016/j.ejor.2019.04.043_bib0015) 2001; 29 Taylor (10.1016/j.ejor.2019.04.043_bib0044) 2007; 178 Ke (10.1016/j.ejor.2019.04.043_bib0024) 2017 Ban (10.1016/j.ejor.2019.04.043_bib0001) 2018; 67 Godfrey (10.1016/j.ejor.2019.04.043_bib0017) 2001; 47 R Core Team (10.1016/j.ejor.2019.04.043_bib0037) 2017 Ben-Tal (10.1016/j.ejor.2019.04.043_bib0004) 2009 Breiman (10.1016/j.ejor.2019.04.043_bib0009) 2001; 45 |
| References_xml | – volume: 54 start-page: 150 year: 2006 end-page: 168 ident: bib0007 article-title: A robust optimization approach to inventory theory publication-title: Operations Research – volume: 213 start-page: 361 year: 2011 end-page: 374 ident: bib0036 article-title: The newsvendor problem: Review and directions for future research publication-title: European Journal of Operational Research – volume: 63 start-page: 1294 year: 2015 end-page: 1306 ident: bib0030 article-title: The data-driven newsvendor problem: new bounds and insights publication-title: Operations Research – volume: 10634 start-page: 912 year: 2017 end-page: 921 ident: bib0049 article-title: Assessing the performance of deep learning algorithms for newsvendor problem publication-title: Proceedings of the ICONIP 2017 – volume: 13 start-page: 281 year: 2012 end-page: 305 ident: bib0005 article-title: Random search for hyper-parameter optimization publication-title: Journal of Machine Learning Research – year: 2015 ident: bib0025 article-title: Adam: A method for stochastic optimization publication-title: Proceedings of the international conference on learning representations 2015 – year: 2017 ident: bib0041 article-title: Inventory and production management in supply chains – volume: 264 start-page: 967 year: 2018 end-page: 977 ident: bib0003 article-title: The impact of special days in call arrivals forecasting: a neural network approach to modelling special days publication-title: European Journal of Operational Research – volume: 45 start-page: 5 year: 2001 end-page: 32 ident: bib0009 article-title: Random forests publication-title: Machine Learning – volume: 56 start-page: 188 year: 2008 end-page: 203 ident: bib0033 article-title: Regret in the newsvendor model with partial information publication-title: Operations Research – volume: 7 start-page: 1231 year: 2006 end-page: 1264 ident: bib0042 article-title: Nonparametric quantile estimation publication-title: Journal of Machine Learning Research – volume: 42 start-page: 408 year: 2006 end-page: 421 ident: bib0045 article-title: A hybrid sales forecasting system based on clustering and decision trees publication-title: Decision Support Systems – volume: In Press start-page: 1 year: 2018 end-page: 13 ident: bib0046 article-title: Empirical safety stock estimation based on kernel and GARCH models publication-title: Omega – year: 2010 ident: bib0002 article-title: An evaluation of neural network ensembles and model selection for time series prediction publication-title: Proceedings of the 2010 international joint conference on neural networks (IJCNN) – volume: 76 start-page: 140 year: 2017 end-page: 151 ident: bib0019 article-title: Cluster-based hierarchical demand forecasting for perishable goods publication-title: Expert Systems with Applications – start-page: 232 year: 2009 end-page: 238 ident: bib0014 article-title: Forecasting seasonal time series with multilayer perceptrons-an empirical evaluation of input vector specifications for deterministic seasonality publication-title: Proceedings of the 2009 international conference on data mining – volume: 14 start-page: 35 year: 1998 end-page: 62 ident: bib0048 article-title: Forecasting with artificial neural networks: the state of the art publication-title: International Journal of Forecasting – volume: 18 start-page: 439 year: 2002 end-page: 454 ident: bib0023 article-title: A state space framework for automatic forecasting using exponential smoothing methods publication-title: International Journal of Forecasting – volume: 10 start-page: 353 year: 2003 end-page: 425 ident: bib0040 article-title: Monte carlo sampling methods publication-title: Handbooks in operations research and management science – volume: forthcoming year: 2018 ident: bib0006 article-title: From predictive to prescriptive analytics publication-title: Management Science – year: 2008 ident: bib0022 article-title: Forecasting with exponential smoothing: The state space approach – year: 2009 ident: bib0004 article-title: Robust optimization – volume: 27 start-page: 635 year: 2011 end-page: 660 ident: bib0013 article-title: Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction publication-title: International Journal of Forecasting – volume: 67 start-page: 90 year: 2018 end-page: 108 ident: bib0001 article-title: The big data newsvendor: practical insights from machine learning publication-title: Operations Research – volume: 44 start-page: 825 year: 1993 end-page: 834 ident: bib0016 article-title: The distribution free newsboy problem: review and extensions publication-title: The Journal of the Operational Research Society – start-page: 201 year: 1958 end-page: 209 ident: bib0039 article-title: A min-max solution of an inventory problem publication-title: Studies in the mathematical theory of inventory and production – volume: 19 start-page: 299 year: 2000 end-page: 311 ident: bib0043 article-title: A quantile regression approach to estimating the distribution of multiperiod returns publication-title: The Journal of Forecasting – volume: 29 start-page: 1189 year: 2001 end-page: 1232 ident: bib0015 article-title: Greedy function approximation: a gradient boosting machine publication-title: The Annals of Statistics – volume: 4 start-page: 251 year: 1991 end-page: 257 ident: bib0018 article-title: Approximation capabilities of multilayer feedforward networks publication-title: Neural Networks – volume: 47 start-page: 1101 year: 2001 end-page: 1112 ident: bib0017 article-title: An adaptive, distribution-free algorithm for the newsvendor problem with censored demands, with applications to inventory and distribution publication-title: Management Science – year: 2016 ident: bib0032 article-title: Applying deep learning to the newsvendor problem publication-title: CoRR – volume: 149 start-page: 28 year: 2014 end-page: 36 ident: bib0038 article-title: The data-driven newsvendor with censored demand observations publication-title: International Journal of Production Economics – volume: 37 start-page: 704 year: 2007 end-page: 718 ident: bib0047 article-title: Consumer responses to shelf out-of-stocks of perishable products publication-title: International Journal of Physical Distribution & Logistics Management – volume: 26 start-page: 1 year: 2008 end-page: 22 ident: bib0020 article-title: Automatic time series forecasting: the forecast package for R publication-title: Journal of Statistical Software – start-page: 3146 year: 2017 end-page: 3154 ident: bib0024 article-title: LightGBM: A highly efficient gradient boosting decision tree publication-title: Advances in Neural Information Processing Systems 30 – volume: 256 start-page: 454 year: 2017 end-page: 461 ident: bib0035 article-title: On the calculation of safety stocks when demand is forecasted publication-title: European Journal of Operational Research – year: 2017 ident: bib0037 article-title: R: A language and environment for statistical computing – volume: 12 start-page: 479 year: 2002 end-page: 502 ident: bib0026 article-title: The sample average approximation method for stochastic discrete optimization publication-title: SIAM Journal on Optimization – volume: 41 start-page: 4235 year: 2014 end-page: 4244 ident: bib0028 article-title: Neural network ensemble operators for time series forecasting publication-title: Expert Systems with Applications – year: 2005 ident: bib0027 article-title: Quantile regression – volume: 178 start-page: 154 year: 2007 end-page: 167 ident: bib0044 article-title: Forecasting daily supermarket sales using exponentially weighted quantile regression publication-title: European Journal of Operational Research – volume: 37 start-page: 1277 year: 2011 end-page: 1284 ident: bib0010 article-title: Quantile regression neural networks: Implementation in R and application to precipitation downscaling publication-title: Computers and Geosciences – volume: 27 start-page: 123 year: 1976 end-page: 127 ident: bib0012 article-title: Sales data and the estimation of demand publication-title: Operational Research Quarterly – volume: 22 start-page: 679 year: 2006 end-page: 688 ident: bib0021 article-title: Another look at measures of forecast accuracy publication-title: International journal of forecasting – volume: In Press year: 2018 ident: bib0034 article-title: A general method for addressing forecasting uncertainty in inventory models publication-title: International Journal of Forecasting – volume: 140 start-page: 637 year: 2012 end-page: 645 ident: bib0008 article-title: Safety stock planning under causal demand forecasting publication-title: International Journal of Production Economics – volume: 184 start-page: 1140 year: 2008 end-page: 1154 ident: bib0011 article-title: Application of machine learning techniques for supply chain demand forecasting publication-title: European Journal of Operational Research – volume: 92 start-page: 254 year: 1996 end-page: 265 ident: bib0029 article-title: Estimating the demand distributions of single-period items having frequent stockouts publication-title: European Journal of Operational Research – volume: 32 start-page: 821 year: 2007 end-page: 839 ident: bib0031 article-title: Provably near-optimal sampling-based policies for stochastic inventory control models publication-title: Mathematics of Operations Research – volume: 4 start-page: 251 issue: 2 year: 1991 ident: 10.1016/j.ejor.2019.04.043_bib0018 article-title: Approximation capabilities of multilayer feedforward networks publication-title: Neural Networks doi: 10.1016/0893-6080(91)90009-T – volume: In Press start-page: 1 year: 2018 ident: 10.1016/j.ejor.2019.04.043_bib0046 article-title: Empirical safety stock estimation based on kernel and GARCH models publication-title: Omega – year: 2017 ident: 10.1016/j.ejor.2019.04.043_bib0041 – volume: 12 start-page: 479 issue: 2 year: 2002 ident: 10.1016/j.ejor.2019.04.043_bib0026 article-title: The sample average approximation method for stochastic discrete optimization publication-title: SIAM Journal on Optimization doi: 10.1137/S1052623499363220 – volume: 29 start-page: 1189 issue: 5 year: 2001 ident: 10.1016/j.ejor.2019.04.043_bib0015 article-title: Greedy function approximation: a gradient boosting machine publication-title: The Annals of Statistics doi: 10.1214/aos/1013203451 – volume: 14 start-page: 35 issue: 1 year: 1998 ident: 10.1016/j.ejor.2019.04.043_bib0048 article-title: Forecasting with artificial neural networks: the state of the art publication-title: International Journal of Forecasting doi: 10.1016/S0169-2070(97)00044-7 – volume: 45 start-page: 5 issue: 1 year: 2001 ident: 10.1016/j.ejor.2019.04.043_bib0009 article-title: Random forests publication-title: Machine Learning doi: 10.1023/A:1010933404324 – volume: 41 start-page: 4235 issue: 9 year: 2014 ident: 10.1016/j.ejor.2019.04.043_bib0028 article-title: Neural network ensemble operators for time series forecasting publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2013.12.011 – volume: 67 start-page: 90 issue: 1 year: 2018 ident: 10.1016/j.ejor.2019.04.043_bib0001 article-title: The big data newsvendor: practical insights from machine learning publication-title: Operations Research doi: 10.1287/opre.2018.1757 – volume: 264 start-page: 967 issue: 3 year: 2018 ident: 10.1016/j.ejor.2019.04.043_bib0003 article-title: The impact of special days in call arrivals forecasting: a neural network approach to modelling special days publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2016.07.015 – volume: 18 start-page: 439 issue: 3 year: 2002 ident: 10.1016/j.ejor.2019.04.043_bib0023 article-title: A state space framework for automatic forecasting using exponential smoothing methods publication-title: International Journal of Forecasting doi: 10.1016/S0169-2070(01)00110-8 – year: 2009 ident: 10.1016/j.ejor.2019.04.043_bib0004 – volume: 19 start-page: 299 year: 2000 ident: 10.1016/j.ejor.2019.04.043_bib0043 article-title: A quantile regression approach to estimating the distribution of multiperiod returns publication-title: The Journal of Forecasting doi: 10.1002/1099-131X(200007)19:4<299::AID-FOR775>3.0.CO;2-V – volume: 37 start-page: 1277 issue: 9 year: 2011 ident: 10.1016/j.ejor.2019.04.043_bib0010 article-title: Quantile regression neural networks: Implementation in R and application to precipitation downscaling publication-title: Computers and Geosciences doi: 10.1016/j.cageo.2010.07.005 – volume: 10 start-page: 353 year: 2003 ident: 10.1016/j.ejor.2019.04.043_bib0040 article-title: Monte carlo sampling methods doi: 10.1016/S0927-0507(03)10006-0 – volume: 32 start-page: 821 issue: 4 year: 2007 ident: 10.1016/j.ejor.2019.04.043_bib0031 article-title: Provably near-optimal sampling-based policies for stochastic inventory control models publication-title: Mathematics of Operations Research doi: 10.1287/moor.1070.0272 – year: 2016 ident: 10.1016/j.ejor.2019.04.043_sbref0032 article-title: Applying deep learning to the newsvendor problem publication-title: CoRR – volume: 26 start-page: 1 issue: 3 year: 2008 ident: 10.1016/j.ejor.2019.04.043_bib0020 article-title: Automatic time series forecasting: the forecast package for R publication-title: Journal of Statistical Software – volume: 42 start-page: 408 issue: 1 year: 2006 ident: 10.1016/j.ejor.2019.04.043_bib0045 article-title: A hybrid sales forecasting system based on clustering and decision trees publication-title: Decision Support Systems doi: 10.1016/j.dss.2005.01.008 – year: 2005 ident: 10.1016/j.ejor.2019.04.043_bib0027 – volume: 76 start-page: 140 year: 2017 ident: 10.1016/j.ejor.2019.04.043_bib0019 article-title: Cluster-based hierarchical demand forecasting for perishable goods publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2017.01.022 – volume: 92 start-page: 254 issue: 2 year: 1996 ident: 10.1016/j.ejor.2019.04.043_bib0029 article-title: Estimating the demand distributions of single-period items having frequent stockouts publication-title: European Journal of Operational Research doi: 10.1016/0377-2217(95)00134-4 – volume: 7 start-page: 1231 year: 2006 ident: 10.1016/j.ejor.2019.04.043_bib0042 article-title: Nonparametric quantile estimation publication-title: Journal of Machine Learning Research – volume: 22 start-page: 679 issue: 4 year: 2006 ident: 10.1016/j.ejor.2019.04.043_bib0021 article-title: Another look at measures of forecast accuracy publication-title: International journal of forecasting doi: 10.1016/j.ijforecast.2006.03.001 – year: 2017 ident: 10.1016/j.ejor.2019.04.043_bib0037 – volume: 37 start-page: 704 issue: 9 year: 2007 ident: 10.1016/j.ejor.2019.04.043_bib0047 article-title: Consumer responses to shelf out-of-stocks of perishable products publication-title: International Journal of Physical Distribution & Logistics Management doi: 10.1108/09600030710840822 – volume: 27 start-page: 123 issue: 1 year: 1976 ident: 10.1016/j.ejor.2019.04.043_bib0012 article-title: Sales data and the estimation of demand publication-title: Operational Research Quarterly doi: 10.1057/jors.1976.13 – volume: 213 start-page: 361 issue: 2 year: 2011 ident: 10.1016/j.ejor.2019.04.043_bib0036 article-title: The newsvendor problem: Review and directions for future research publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2010.11.024 – volume: 44 start-page: 825 issue: 8 year: 1993 ident: 10.1016/j.ejor.2019.04.043_bib0016 article-title: The distribution free newsboy problem: review and extensions publication-title: The Journal of the Operational Research Society doi: 10.1057/jors.1993.141 – start-page: 3146 year: 2017 ident: 10.1016/j.ejor.2019.04.043_bib0024 article-title: LightGBM: A highly efficient gradient boosting decision tree – volume: 140 start-page: 637 issue: 2 year: 2012 ident: 10.1016/j.ejor.2019.04.043_bib0008 article-title: Safety stock planning under causal demand forecasting publication-title: International Journal of Production Economics doi: 10.1016/j.ijpe.2011.04.017 – volume: 54 start-page: 150 issue: 1 year: 2006 ident: 10.1016/j.ejor.2019.04.043_bib0007 article-title: A robust optimization approach to inventory theory publication-title: Operations Research doi: 10.1287/opre.1050.0238 – volume: 47 start-page: 1101 issue: 8 year: 2001 ident: 10.1016/j.ejor.2019.04.043_bib0017 article-title: An adaptive, distribution-free algorithm for the newsvendor problem with censored demands, with applications to inventory and distribution publication-title: Management Science doi: 10.1287/mnsc.47.8.1101.10231 – year: 2015 ident: 10.1016/j.ejor.2019.04.043_sbref0025 article-title: Adam: A method for stochastic optimization – volume: 256 start-page: 454 issue: 2 year: 2017 ident: 10.1016/j.ejor.2019.04.043_bib0035 article-title: On the calculation of safety stocks when demand is forecasted publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2016.06.035 – volume: 178 start-page: 154 issue: 1 year: 2007 ident: 10.1016/j.ejor.2019.04.043_bib0044 article-title: Forecasting daily supermarket sales using exponentially weighted quantile regression publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2006.02.006 – volume: 184 start-page: 1140 issue: 3 year: 2008 ident: 10.1016/j.ejor.2019.04.043_bib0011 article-title: Application of machine learning techniques for supply chain demand forecasting publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2006.12.004 – volume: 27 start-page: 635 issue: 3 year: 2011 ident: 10.1016/j.ejor.2019.04.043_bib0013 article-title: Advances in forecasting with neural networks? Empirical evidence from the NN3 competition on time series prediction publication-title: International Journal of Forecasting doi: 10.1016/j.ijforecast.2011.04.001 – volume: 13 start-page: 281 year: 2012 ident: 10.1016/j.ejor.2019.04.043_bib0005 article-title: Random search for hyper-parameter optimization publication-title: Journal of Machine Learning Research – volume: In Press year: 2018 ident: 10.1016/j.ejor.2019.04.043_bib0034 article-title: A general method for addressing forecasting uncertainty in inventory models publication-title: International Journal of Forecasting – year: 2008 ident: 10.1016/j.ejor.2019.04.043_bib0022 – volume: 10634 start-page: 912 year: 2017 ident: 10.1016/j.ejor.2019.04.043_bib0049 article-title: Assessing the performance of deep learning algorithms for newsvendor problem – year: 2010 ident: 10.1016/j.ejor.2019.04.043_sbref0002 article-title: An evaluation of neural network ensembles and model selection for time series prediction – start-page: 232 year: 2009 ident: 10.1016/j.ejor.2019.04.043_sbref0014 article-title: Forecasting seasonal time series with multilayer perceptrons-an empirical evaluation of input vector specifications for deterministic seasonality – volume: forthcoming year: 2018 ident: 10.1016/j.ejor.2019.04.043_bib0006 article-title: From predictive to prescriptive analytics publication-title: Management Science – volume: 63 start-page: 1294 issue: 6 year: 2015 ident: 10.1016/j.ejor.2019.04.043_bib0030 article-title: The data-driven newsvendor problem: new bounds and insights publication-title: Operations Research doi: 10.1287/opre.2015.1422 – volume: 56 start-page: 188 issue: 1 year: 2008 ident: 10.1016/j.ejor.2019.04.043_bib0033 article-title: Regret in the newsvendor model with partial information publication-title: Operations Research doi: 10.1287/opre.1070.0486 – volume: 149 start-page: 28 year: 2014 ident: 10.1016/j.ejor.2019.04.043_bib0038 article-title: The data-driven newsvendor with censored demand observations publication-title: International Journal of Production Economics doi: 10.1016/j.ijpe.2013.04.039 – start-page: 201 year: 1958 ident: 10.1016/j.ejor.2019.04.043_bib0039 article-title: A min-max solution of an inventory problem |
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| Title | A data-driven newsvendor problem: From data to decision |
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