Solving the Price-Setting Newsvendor Problem with Parametric Operational Data Analytics (ODA)

References

• Akcay A, Biller B, Tayur S (2011) Improved inventory targets in the presence of limited historical demand data. Manufacturing Service Oper. Management 13(3):297–309.Link, Google Scholar
• Amrani H, Khmelnitsky E (2017) Estimation of quantiles of non-stationary demand distributions. IISE Trans. 49(4):381–394.Crossref, Google Scholar
• Aviv Y (2001) The effect of collaborative forecasting on supply chain performance. Management Sci. 47(10):1326–1343.Link, Google Scholar
• Aviv Y (2007) On the benefits of collaborative forecasting partnerships between retailers and manufacturers. Management Sci. 53(5):777–794.Link, Google Scholar
• Ban GY, Keskin NB (2021) Personalized dynamic pricing with machine learning: High-dimensional features and heterogeneous elasticity. Management Sci. 67(9):5549–5568.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
• Baron O, Hajizadeh I, Milner J (2011) Now playing: DVD purchasing for a multilocation rental firm. Manufacturing Service Oper. Management 13(2):209–226.Link, Google Scholar
• Bastani H, Bayati M (2020) Online decision making with high-dimensional covariates. Oper. Res. 68(1):276–294.Link, Google Scholar
• Bertsimas D, Perakis G (2006) Dynamic pricing: A learning approach. Lawphongpanich S, Hearn DW, Smith MJ, eds. Mathematical and Computational Models for Congestion Charging (Springer, New York), 45–79.Crossref, Google Scholar
• Bertsimas D, Vayanos P (2017) Data-driven learning in dynamic pricing using adaptive optimization. Working paper, Massachusetts Institute of Technology, Cambridge.Google Scholar
• Besbes O, Mouchtaki O (2023) How big should your data really be? Data-driven newsvendor and the transient of learning. Management Sci. 69(10):5848–5865.Link, Google Scholar
• Besbes O, Zeevi A (2011) On the minimax complexity of pricing in a changing environment. Oper. Res. 59(1):66–79.Link, Google Scholar
• Besbes O, Zeevi A (2015) On the (surprising) sufficiency of linear models for dynamic pricing with demand learning. Management Sci. 61(4):723–739.Link, Google Scholar
• Box GE, Jenkins GM (1962) Some statistical aspects of adaptive optimization and control. J. Roy. Statist. Soc. B 24(2):297–331.Crossref, Google Scholar
• Box GE, Jenkins GM (1968) Some recent advances in forecasting and control. J. Roy. Statist. Soc. Ser. C Appl. Statist. 17(2):91–109.Google Scholar
• Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time Series Analysis: Forecasting and Control (John Wiley & Sons, Hoboken, NJ).Google Scholar
• Brown RG (1962) Smoothing, Forecasting and Prediction of Discrete Time Series (Prentice-Hall, Englewood Cliffs, NJ).Google Scholar
• Bu J, Simchi-Levi D, Xu Y (2020) Online pricing with offline data: Phase transition and inverse square law. Daumé H III, Singh A, eds. Proc. 37th Internat. Conf. Machine Learn. (PMLR, New York), 1202–1210.Google Scholar
• Cao Y, Shen ZJM (2019) Quantile forecasting and data-driven inventory management under nonstationary demand. Oper. Res. Lett. 47(6):465–472.Crossref, Google Scholar
• Chan LMA, Shen ZJM, Simchi-Levi D, Swann J (2005) Coordination of pricing and inventory decisions: A survey and classification. Simchi-Levi D, David Wu S, Max Shen Z, eds. Handbook of Quantitative Supply Chain Analysis: Modeling in the E-Business Era (Springer, Boston), 335–392.Google Scholar
• Chen B, Chao X, Ahn HS (2019) Coordinating pricing and inventory replenishment with nonparametric demand learning. Oper. Res. 67(4):1035–1052.Abstract, Google Scholar
• Chen B, Chao X, Wang Y (2020) Data-based dynamic pricing and inventory control with censored demand and limited price changes. Oper. Res. 68(5):1445–1456.Link, Google Scholar
• Chen L, Luo W, Shang K (2017) Measuring the bullwhip effect: Discrepancy and alignment between information and material flows. Manufacturing Service Oper. Management 19(1):36–51.Link, Google Scholar
• Chen F, Drezner Z, Ryan JK, Simchi-Levi D (2000) Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information. Management Sci. 46(3):436–443.Link, Google Scholar
• Cheung WC, Simchi-Levi D, Wang H (2017) Dynamic pricing and demand learning with limited price experimentation. Oper. Res. 65(6):1722–1731.Link, 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
• Clemen RT (1989) Combining forecasts: A review and annotated bibliography. Internat. J. Forecasting 5(4):559–583.Crossref, 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
• den Boer AV (2015) Tracking the market: Dynamic pricing and learning in a changing environment. Eur. J. Oper. Res. 247(3):914–927.Crossref, Google Scholar
• den Boer AV, Zwart B (2015) Dynamic pricing and learning with finite inventories. Oper. Res. 63(4):965–978.Link, Google Scholar
• den Boer AV, Perry O, Zwart B (2018) Dynamic pricing policies for an inventory model with random windows of opportunities. Naval Res. Logist. 65(8):660–675.Crossref, Google Scholar
• Feng Q, Shanthikumar JG (2018a) How research in production and operations management may evolve in the era of big data. Production Oper. Management 27(9):1670–1684.Crossref, Google Scholar
• Feng Q, Shanthikumar JG (2018b) Supply and demand functions in inventory models. Oper. Res. 66(1):77–91.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 non-parametric operational data analytics (ODA). Production Oper. Management 32(9):2685–2703.Crossref, Google Scholar
• Feng Q, Luo S, Shanthikumar JG (2020) Integrating dynamic pricing with inventory decisions under lost sales. Management Sci. 66(5):2232–2247.Link, Google Scholar
• Feng Q, Luo S, Zhang D (2014) Dynamic inventory-pricing control under backorder: Demand estimation and policy optimization. Manufacturing Service Oper. Management 16(1):149–160.Link, Google Scholar
• Feng Q, Shanthikumar JG, Xue M (2022) Consumer choice models and estimation: A review and extension. Production Oper. Management 31(2):847–867.Crossref, Google Scholar
• Gallien J, Mersereau AJ, Garro A, Mora AD, Vidal MN (2015) Initial shipment decisions for new products at Zara. Oper. Res. 63(2):269–286.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
• Harrison P, Stevens C (1971) A Bayesian approach to short-term forecasting. J. Oper. Res. Soc. 22(4):341–362.Crossref, Google Scholar
• Harsha P, Natarajan R, Subramanian D (2021) A prescriptive machine-learning framework to the price-setting newsvendor problem. INFORMS J. Optim. 3(3):227–253.Link, Google Scholar
• Hayes RH (1969) Statistical estimation problems in inventory control. Management Sci. 15(11):686–701.Link, Google Scholar
• He R, Lu Y (2021) A robust price-setting newsvendor problem. Production Oper. Management 30(1):276–292.Crossref, Google Scholar
• Holt CC (1957) Forecasting Seasonals and Trends by Exponentially Weighted Moving Averages (Defense Technical Information Center, Fort Belvoir, VA).Google Scholar
• Homem-de Mello T, Bayraksan G (2014) Monte Carlo sampling-based methods for stochastic optimization. Surveys Oper. Res. Management Sci. 19(1):56–85.Crossref, Google Scholar
• Hu K, Acimovic J, Erize F, Thomas DJ, Van Mieghem JA (2019) Forecasting new product life cycle curves: Practical approach and empirical analysis. Manufacturing Service Oper. Management 21(1):66–85.Link, Google Scholar
• Huang T, Van Mieghem JA (2014) Clickstream data and inventory management: Model and empirical analysis. Production Oper. Management 23(3):333–347.Crossref, Google Scholar
• Huh W, Janakiraman G (2008) (s, S) optimality in joint inventory-pricing control: An alternate approach. Oper. Res. 56(3):783–790.Link, Google Scholar
• Janssen E, Strijbosch L, Brekelmans R (2009) Assessing the effects of using demand parameters estimates in inventory control and improving the performance using a correction function. Internat. J. Production Econom. 118(1):34–42.Crossref, Google Scholar
• Kahn JA (1987) Inventories and the volatility of production. Amer. Econom. Rev. 77(4):667–679.Google Scholar
• Kahn KB (2006) New Product Forecasting: An Applied Approach (Routledge, New York).Google Scholar
• Keskin NB, Zeevi A (2017) Chasing demand: Learning and earning in a changing environment. Math. Oper. Res. 42(2):277–307.Link, Google Scholar
• Keskin NB, Min X, Song JSJ (2021) The nonstationary newsvendor: Data-driven nonparametric learning. Working paper, Fuqua School of Business, Duke University, Durham, NC.Google Scholar
• Kocabıyıkoğlu A, Popescu I (2011) An elasticity approach to the newsvendor with price-sensitive demand. Oper. Res. 59(2):301–312.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
• Lim AEB, Shanthikumar JG (2007) Relative entropy, exponential utility, and robust dynamic pricing. Oper. Res. 55(2):198–214.Link, Google Scholar
• Lim AEB, Shanthikumar JG, Shen ZJM (2006) Model uncertainty, robust optimization and learning. INFORMS TutORials in Operations Research (INFORMS, Catonsville, MD), 66–94.Google Scholar
• Lin S, Chen Y, Li Y, Shen ZJM (2022) Data-driven newsvendor problems regularized by a profit risk constraint. Production Oper. Management 31(4):1630–1644.Crossref, Google Scholar
• Liyanage L, Shanthikumar JG (2005) A practical inventory control policy using operational statistics. Oper. Res. Lett. 33(4):341–348.Crossref, Google Scholar
• Lu Y, Simchi-Levi D (2013) On the unimodality of the profit function of the pricing newsvendor. Production Oper. Management 22(3):615–625.Crossref, Google Scholar
• Lu M, Shanthikumar JG, Shen ZJM (2015) Technical note—Operational statistics: Properties and the risk-averse case. Naval Res. Logist. 62(3):206–214.Crossref, Google Scholar
• Petruzzi N, Dada CM (1999) Pricing and the newsvendor problem: A review with extensions. Oper. Res. 47(2):183–194.Link, Google Scholar
• Qin H, Simchi-Levi D, Wang L (2022) Data-driven approximation schemes for joint pricing and inventory control models. Management Sci. 68(9):6591–6609.Link, Google Scholar
• Ramamurthy V, Shanthikumar JG, Shen ZJM (2012) Inventory policy with parametric demand: Operational statistics, linear correction, and regression. Production Oper. Management 21(2):291–308.Crossref, Google Scholar
• Siegel A, Wagner M (2021) Profit estimation error in the newsvendor model under a parametric demand distribution. Management Sci. 67(8):4863–4879.Link, Google Scholar
• Simchi-Levi D, Sun R, Wu MX, Zhu R (2023) Calibrating sales forecast in a pandemic using online non-parametric regression model. Management Sci. 70(10):6502–6518.Google Scholar
• Whitin TM (1955) Inventory control and price theory. Management Sci. 2(1):61–80.Link, Google Scholar
• Yano CA, Gilbert SM (2003) Coordinated pricing and production/procurement decisions: A review. Chakravarty A, Eliashberg J, eds. Managing Business Interfaces: Marketing, Engineering and Manufacturing Perspectives (Kluwer Academic Publishers, Boston), 65–103.Google Scholar
• Young L (1978) Price, inventory and the structure of uncertain demand. New Zealand Oper. Res. 6:157–177.Google Scholar
• Zinn W, Marmorstein H (1990) Comparing two alternative methods of determining safety stock levels: The demand and the forecast systems. J. Bus. Logist. 11(1):95–110.Google Scholar