Flexible Data Aggregation for Prediction and Decision Making with Contextual Information: Applications in Retailing

References

• Adam H, He P, Zheng F (2024) Machine learning for demand estimation in long tail markets. Management Sci. 70(8):5040–5065.Link, Google Scholar
• Arora S, Taylor JW, Mak HY (2023) Probabilistic forecasting of patient waiting times in an emergency department. Manufacturing Service Oper. Management 25(4):1489–1508.Link, Google Scholar
• Ban GY, Rudin C (2019) The big data newsvendor: Practical insights from machine learning. Oper. Res. 67(1):90–108.Link, Google Scholar
• Bastani H (2021) Predicting with proxies: Transfer learning in high dimension. Management Sci. 67(5):2964–2984.Link, Google Scholar
• Bastani H, Simchi-Levi D, Zhu R (2022) Meta dynamic pricing: Transfer learning across experiments. Management Sci. 68(3):1865–1881.Link, Google Scholar
• Beran R (1996) Stein estimation in high dimensions: A retrospective. Brunner E, Denker M, eds. Res. Developments Probab. Statistics (Madan Puri Festschrift, VSP, Leiden, Netherlands), 91–110.Google Scholar
• Bertsimas D, Kallus N (2020) From predictive to prescriptive analytics. Management Sci. 66(3):1025–1044.Link, Google Scholar
• Besbes O, Mouchtaki O (2023) How big should your data really be? Data-driven newsvendor: Learning one sample at a time. Management Sci. 69(10):5848–5865.Link, Google Scholar
• Brown LD (1971) Admissible estimators, recurrent diffusions, and insoluble boundary value problems. Ann. Math. Statist. 42(3):855–903.Crossref, Google Scholar
• Brown LD, Zhao LH (2012) A geometrical explanation of stein shrinkage. Statist. Sci. 27(1):24–30.Crossref, Google Scholar
• Chen Z, Zhao L (2025) Combining forecasts from multiple experts for multiple variables. Management Sci., ePub ahead of print November 13, https://doi.org/10.1287/mnsc.2024.06161.Link, Google Scholar
• Chou W, Gray C, Kallus N, Bibaut A, Ejdemyr S (2025) Evaluating decision rules across many weak experiments. Antonie L, Pei J, Yu X, Chierichetti F, Lauw HW, Sun Y, Parthasarathy S, eds. Proc. 31st ACM SIGKDD Conf. Knowledge Discovery Data Mining, vol. 2 (Association for Computing Machinery, New York), 4365–4374.Google Scholar
• Chu LY, Shanthikumar JG, Shen ZJM (2008) Solving operational statistics via a Bayesian analysis. Oper. Res. Lett. 36(1):110–116.Crossref, Google Scholar
• Chu LY, Feng Q, Shanthikumar JG, Shen ZJM, Wu J (2025) Solving the price-setting newsvendor problem with parametric operational data analytics (ODA). Management Sci. 71(8):6627–6646.Link, Google Scholar
• Chuang HHC, Chou YC, Oliva R (2021) Cross-item learning for volatile demand forecasting: An intervention with predictive analytics. J. Oper. Management 67(7):828–852.Crossref, Google Scholar
• Cohen MC, Zhang R, Jiao K (2022a) Data aggregation and demand prediction. Oper. Res. 70(5):2597–2618.Link, Google Scholar
• Cohen MC, Gras PE, Pentecoste A, Zhang R (2022b) Demand Prediction in Retail: A Practical Guide to Leverage Data and Predictive Analytics (Springer, Berlin).Crossref, Google Scholar
• Cui R, Allon G, Bassamboo A, Van Mieghem JA (2015) Information sharing in supply chains: An empirical and theoretical valuation. Management Sci. 61(11):2803–2824.Link, Google Scholar
• Cui R, Gallino S, Moreno A, Zhang DJ (2018) The operational value of social media information. Production Oper. Management 27(10):1749–1769.Crossref, Google Scholar
• Duan Y, Wang K (2023) Adaptive and robust multi-task learning. Ann. Statist. 51(5):2015–2039.Crossref, Google Scholar
• Duan L, Xu D, Tsang I (2012) Learning with augmented features for heterogeneous domain adaptation. Preprint, submitted June 20, https://arxiv.org/abs/1206.4660.Google Scholar
• ECDB (2024) Community group buying in China: A social commerce phenomenon. Retrieved July 25, https://ecommercedb.com/insights/community-group-buying-in-china-a-social-commerce-phenomenon/4663.Google Scholar
• Efron B, Morris C (1977) Stein’s paradox in statistics. Sci. Amer. 236(5):119–127.Crossref, Google Scholar
• Elmachtoub AN, Grigas P (2022) Smart “predict, then optimize”. Management Sci. 68(1):9–26.Link, Google Scholar
• Feng Q, Shanthikumar JG (2022) Developing operations management data analytics. Production Oper. Management 31(12):4544–4557.Crossref, Google Scholar
• Feng Q, Shanthikumar JG (2023) The framework of parametric and nonparametric operational data analytics. Production Oper. Management 32(9):2685–2703.Crossref, Google Scholar
• Feng Q, Li L, Shanthikumar JG (2023) Transfer learning, cross learning and co-learning across newsvendor systems with operational data analytics (ODA). Preprint, submitted November 11, https://doi.org/10.2139/ssrn. 4629760.Google Scholar
• Fildes R, Goodwin P, Önkal D (2019) Use and misuse of information in supply chain forecasting of promotion effects. Internat. J. Forecasting 35(1):144–156.Crossref, Google Scholar
• Fildes R, Ma S, Kolassa S (2022) Retail forecasting: Research and practice. Internat. J. Forecasting 38(4):1283–1318.Crossref, Google Scholar
• Fisher M, Gallino S, Li J (2018) Competition-based dynamic pricing in online retailing: A methodology validated with field experiments. Management Sci. 64(6):2496–2514.Link, Google Scholar
• Goh J, Bayati M, Zenios SA, Singh S, Moore D (2018) Data uncertainty in Markov chains: Application to cost-effectiveness analyses of medical innovations. Oper. Res. 66(3):697–715.Link, Google Scholar
• Guo X, Grushka-Cockayne Y, De Reyck B (2022) Forecasting airport transfer passenger flow using real-time data and machine learning. Manufacturing Service Oper. Management 24(6):3193–3214.Link, Google Scholar
• Guo X, Lichtendahl KC Jr, Grushka-Cockayne Y (2025) Bayesian ensembles of exponentially smoothed life-cycle forecasts. Manufacturing Service Oper. Management 27(1):230–248.Link, Google Scholar
• Gupta V, Kallus N (2022) Data pooling in stochastic optimization. Management Sci. 68(3):1595–1615.Link, Google Scholar
• Gupta V, Rusmevichientong P (2021) Small-data, large-scale linear optimization with uncertain objectives. Management Sci. 67(1):220–241.Link, Google Scholar
• Harel M, Mannor S (2011) Learning from multiple outlooks. Getoor L, Scheffer T, eds. Proc. 28th Internat. Conf. Machine Learn. (Omnipress, Madison, WI), 401–408.Google Scholar
• Hastie T, Tibshirani R, Friedman J (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer, New York).Crossref, Google Scholar
• Huang T, Fildes R, Soopramanien D (2019) Forecasting retailer product sales in the presence of structural change. Eur. J. Oper. Res. 279(2):459–470.Crossref, Google Scholar
• Huang J, Shang K, Yang Y, Zhou W, Li Y (2025) Taylor approximation of inventory policies for one-warehouse, multi-retailer systems with demand feature information. Management Sci. 71(1):879–897.Abstract, Google Scholar
• Jiang Z, Li J (2025) Instrumenting while experimenting: An empirical method for competitive pricing at scale. Oper. Res. 73(5):2477–2495.Link, Google Scholar
• Kesavan S, Gaur V, Raman A (2010) Do inventory and gross margin data improve sales forecasts for us public retailers? Management Sci. 56(9):1519–1533.Link, Google Scholar
• Krogh A, Vedelsby J (1994) Neural network ensembles, cross validation, and active learning. Tesauro G, Touretzky DS, Leen TK, eds. Advances in Neural Information Processing Systems, vol. 7 (MIT Press, Cambridge, MA), 231–238.Google Scholar
• Le TM, Clarke BS (2022) Model averaging is asymptotically better than model selection for prediction. J. Machine Learn. Res. 23(33):1–53.Google Scholar
• Lei D, Qi Y, Liu S, Geng D, Zhang J, Hu H, Shen ZJM (2025) Pooling and boosting for demand prediction in retail: A transfer learning approach. Manufacturing Service Oper. Management 27(6):1779–1794.Link, Google Scholar
• Levi R, Perakis G, Uichanco J (2015) The data-driven newsvendor problem: New bounds and insights. Oper. Res. 63(6):1294–1306.Link, Google Scholar
• Levi R, Roundy RO, Shmoys DB (2007) Provably near-optimal sampling-based policies for stochastic inventory control models. Math. Oper. Res. 32(4):821–839.Link, Google Scholar
• Li C, Peng Z, Rong Y (2023) Mostly beneficial clustering: Aggregating data for operational decision making. Preprint, submitted November 29, https://arxiv.org/abs/2311.17326.Google Scholar
• Li N, Fei Y, Yang Y, Zhang X (2025) Averaging estimators of heterogeneous treatment effects under additive models. Econom. Theory, ePub ahead of print October 22, https://doi.org/10.1017/S0266466625100200.Crossref, Google Scholar
• Liao J, Zong X, Zhang X, Zou G (2019) Model averaging based on leave-subject-out cross-validation for vector autoregressions. J. Econom. 209(1):35–60.Crossref, Google Scholar
• Liyanage LH, Shanthikumar JG (2005) A practical inventory control policy using operational statistics. Oper. Res. Lett. 33(4):341–348.Crossref, Google Scholar
• Ma S, Fildes R, Huang T (2016) Demand forecasting with high dimensional data: The case of sku retail sales forecasting with intra-and inter-category promotional information. Eur. J. Oper. Res. 249(1):245–257.Crossref, Google Scholar
• Makridakis S, Spiliotis E, Assimakopoulos V (2022) M5 accuracy competition: Results, findings, and conclusions. Internat. J. Forecasting 38(4):1346–1364.Crossref, Google Scholar
• Nabi S, Nassif H, Hong J, Mamani H, Imbens G (2022) Bayesian meta-prior learning using empirical bayes. Management Sci. 68(3):1737–1755.Link, Google Scholar
• Nam J, Kim S (2015) Heterogeneous defect prediction. Di Nitto E, Harman M, Heymans P, eds. Proc. 10th Joint Meeting Foundations Software Engrg. (Association for Computing Machinery, New York), 508–519.Google Scholar
• Oroojlooyjadid A, Snyder LV, Takáč M (2020) Applying deep learning to the newsvendor problem. IISE Trans. 52(4):444–463.Crossref, Google Scholar
• Peng J, Li Y, Yang Y (2025) On optimality of mallows model averaging. J. Amer. Statist. Assoc. 120(550):1152–1163.Crossref, Google Scholar
• Qi M, Mak HY, Shen ZJM (2020) Data-driven research in retail operations—A review. Naval Res. Logist. 67(8):595–616.Crossref, Google Scholar
• Shapiro A, Dentcheva D, Ruszczynski A (2021) Lectures on Stochastic Programming: Modeling and Theory (SIAM, Philadelphia).Crossref, Google Scholar
• Stein C (1956) Inadmissibility of the usual estimator for the mean of a multivariate normal distribution. Neyman J, ed. Proc. 3rd Berkeley Sympos. Math. Statist. Probability, vol. 1: Contributions Theory Statist., (University of California Press, Berkeley, CA), 197–207.Google Scholar
• Stigler SM (1990) The 1988 Neyman memorial lecture: A Galtonian perspective on shrinkage estimators. Statist. Sci. 5(1):147–155.Google Scholar
• Supermarket News (2019) Fresh foods are where it’s at for brick-and-mortar. Retrieved July 25, https://www.supermarketnews.com/deli/fresh-foods-are-where-it-s-brick-and-mortar.Google Scholar
• Syntetos AA, Babai Z, Boylan JE, Kolassa S, Nikolopoulos K (2016) Supply chain forecasting: Theory, practice, their gap and the future. Eur. J. Oper. Res. 252(1):1–26.Crossref, Google Scholar
• Van der Laan MJ, Polley EC, Hubbard AE (2007) Super learner. Statist. Appl. Genetics Molecular Biology 6(1).Google Scholar
• Wang C, Mahadevan S (2011) Heterogeneous domain adaptation using manifold alignment. Walsh T, ed. Proc.22nd Internat. Joint Conf. Artificial Intelligence, vol. 2 (AAAI Press, Menlo Park, CA), 1541–1546.Google Scholar
• Zhang X, Liu CA (2023) Model averaging prediction by k-fold cross-validation. J. Econom. 235(1):280–301.Crossref, Google Scholar
• Zhang X, Ye ZS, Haskell WB (2025) Error propagation in asymptotic analysis of the data-driven (s, s) inventory policy. Oper. Res. 73(1):1–21.Link, Google Scholar
• Zhao X, Zhu R, Haskell WB (2025) Learning to price supply chain contracts against a learning retailer. Management Sci., ePub ahead of print July 21, https://doi.org/10.1287/mnsc.2022.03339.Google Scholar
• Zhou JT, Tsang IW, Pan SJ, Tan M (2014) Heterogeneous domain adaptation for multiple classes. Proc. 17th Internat. Conf. Artificial Intelligence Statistics, Proceedings of Machine Learning Research, vol. 33 (PMLR, Reykjavík, Iceland), 1095–1103.Google Scholar