Data Analytics in Operations Management: A Review
Published Online:19 Sep 2019https://doi.org/10.1287/msom.2019.0805
References
- (2014) Making better fulfillment decisions on the fly in an online retail environment. Manufacturing Service Oper. Management 17(1):34–51.Link, Google Scholar
- (2001) Inverse optimization. Oper. Res. 49(5):771–783.Link, Google Scholar
- (2019) Pricing for heterogeneous products: Analytics for ticket reselling. Working paper, Massachusetts Institute of Technology, Cambridge. Google Scholar
- (2015) Accurate emergency department wait time prediction. Manufacturing Service Oper. Management 18(1):141–156.Link, Google Scholar
- (2008) Mostly Harmless Econometrics: An Empiricist’s Companion (Princeton University Press, Princeton, NJ).Crossref, Google Scholar
- (2015) Assortment optimization under consider-then-choose choice models. Working paper, London Business School, London. Google Scholar
- (2018) The approximability of assortment optimization under ranking preferences. Oper. Res. 66(6):1661–1669.Link, Google Scholar
- (2019) Data-driven incentive design in the Medicare shared savings program. Oper. Res. 67(4):1002–1026.Google Scholar
- (2014) Matching supply and demand: Delayed two-phase distribution at Yedioth Group—models, algorithms, and information technology. Interfaces 44(5):445–460.Link, Google Scholar
- (2018a) Leveraging comparables for new product sales forecasting. Working paper, Massachusetts Institute of Technology, Cambridge.Google Scholar
- (2018b) Detecting customer trends for optimal promotion targeting. Working paper, Massachusetts Institute of Technology, Cambridge.Google Scholar
- (2018c) Scheduling promotion vehicles to boost profits. Management Sci. 65(1):50–70.Link, Google Scholar
- (2017) Personalized dynamic pricing with machine learning. Working paper, London Business School, London.Google Scholar
- (2019) The big data newsvendor: Practical insights from machine learning. Oper. Res. 67(1):90–108.Link, Google Scholar
- (2018) Dynamic procurement of new products with covariate information: The residual tree method. Manufacturing Service Oper. Management, ePub ahead of print December 10, https://doi.org/10.1287/msom.2018.0725.Link, Google Scholar
- (2018) Opportunistic returns and dynamic pricing: Empirical evidence from online retailing in emerging markets. Harvard Business School Working Paper 19-030, Harvard University, Boston.Google Scholar
- (2015) Online decision-making with high-dimensional covariates. Working paper, University of Pennsylvania, Philadelphia.Google Scholar
- (2019) A dynamic clustering approach to data-driven assortment personalization. Management Sci. 65(5):2095–2115.Abstract, Google Scholar
- (2016) The power and limits of predictive approaches to observational-data-driven optimization. Working paper, Massachusetts Institute of Technology, Cambridge.Google Scholar
- (2019) From predictive to prescriptive analytics. Management Sci., ePub ahead of print August 23, https://doi.org/10.1287/mnsc.2018.3253. Google Scholar
- (2019) Exact first-choice product line optimization. Oper. Res. 67(3):651–670.Google Scholar
- (2011) Theory and applications of robust optimization. SIAM Rev. 53(3):464–501.Crossref, Google Scholar
- (2013) Fairness, efficiency, and flexibility in organ allocation for kidney transplantation. Oper. Res. 61(1):73–87.Link, Google Scholar
- (2016) An analytics approach to designing combination chemotherapy regimens for cancer. Management Sci. 62(5):1511–1531.Link, Google Scholar
- (2017) Personalized diabetes management using electronic medical records. Diabetes Care 40(2):210–217.Crossref, Google Scholar
- (2016) A Markov chain approximation to choice modeling. Oper. Res. 64(4):886–905.Link, Google Scholar
- (2018) Mining optimal policies: A pattern recognition approach to model analysis. Working paper, University of California, Los Angeles, Los Angeles. Google Scholar
- (2019) Optimization-driven framework to understand healthcare network costs and resource allocation. Working paper, University of California, Los Angeles, Los Angeles.Google Scholar
- (1996) Bagging predictors. Machine Learn. 24(2):123–140.Crossref, Google Scholar
- (2001) Random forests. Machine Learn. 45(1):5–32.Crossref, Google Scholar
- (1984) Classification and Regression Trees (CRC Press, Boca Raton, FL).Google Scholar
- (2019) First delivery gaps: A supply chain level to reduce product returns in online retail. Working paper, Massachusetts Institute of Technology, Cambridge.Google Scholar
- (2015) A statistical learning approach to personalization in revenue management. Working paper, Massachusetts Institute of Technology, Cambridge.Google Scholar
- (2018) Interpretable optimal stopping. Working paper, INSEAD, Fontainebleau, France. Google Scholar
- (2016) Feature-based dynamic pricing. Working paper, New York University, New York.Google Scholar
- (2017a) Optimizing promotions for multiple items in supermarkets. Working paper, Massachusetts Institute of Technology, Cambridge.Google Scholar
- (2017b) The impact of linear optimization on promotion planning. Oper. Res. 65(2):446–468.Link, Google Scholar
- (2019a) High-low promotion policies for peak-end demand models. Working paper, Massachusetts Institute of Technology, Cambridge.Google Scholar
- (2019b) Bounded memory peak end models can be surprisingly good. Working paper, Massachusetts Institute of Technology, Cambridge.Google Scholar
- (2010) Distributionally robust optimization under moment uncertainty with application to data-driven problems. Oper. Res. 58(3):595–612.Link, Google Scholar
- (2017) Toward a rigorous science of interpretable machine learning. Working paper, Harvard University, Cambridge.Google Scholar
- (2017) Smart “predict, then optimize.” Working paper, Columbia University, New York.Google Scholar
- (2019) A data-driven approach to personalized bundle pricing and recommendation. Manufacturing Service Oper. Management, ePub ahead of print July 19, https://doi.org/10.1287/msom.2018.0756.Google Scholar
- (2019) Learning preferences with side information. Management Sci. 65(7):3131–3149.Link, Google Scholar
- (2013) A nonparametric approach to modeling choice with limited data. Management Sci. 59(2):305–322.Link, Google Scholar
- (2019) Assortment optimization with small consideration sets. Oper. Res. Forthcoming.Google Scholar
- (2015) Analytics for an online retailer: Demand forecasting and price optimization. Manufacturing Service Oper. Management 18(1):69–88.Link, Google Scholar
- (2016) The value of rapid delivery in online retailing. Working paper, University of Pennsylvania, Philadelphia.Google Scholar
- (2014) Comprehensible classification models: A position paper. ACM SIGKDD Explorations Newslett. 15(1):1–10.Crossref, Google Scholar
- (2018) Optimal retail location: Empirical methodology and application to practice. Manufacturing Service Oper. Management 21(1):86–102.Google Scholar
- (2014) Real-time optimization of personalized assortments. Management Sci. 60(6):1532–1551.Link, Google Scholar
- (2017) Small-data, large-scale linear optimization with uncertain objectives. Working paper, University of Southern California, Los Angeles.Google Scholar
- (2017) Maximizing intervention effectiveness. Working paper, University of Southern California, Los Angeles.Google Scholar
- (2019) Dynamic pricing of omnichannel inventories. Manufacturing Service Oper. Management 21(1):47–65.Link, Google Scholar
- (2003) A Lagrangian decomposition approach to weakly coupled dynamic optimization problems and its applications. Unpublished PhD thesis, Massachusetts Institute of Technology, Cambridge.Google Scholar
- (2017) Service region design for urban electric vehicle sharing systems. Manufacturing Service Oper. Management 19(2):309–327.Link, Google Scholar
- (2017) OM Forum: Causal inference models in operations management. Manufacturing Service Oper. Management 19(4):509–525.Link, Google Scholar
- (2019) Dynamic pricing in high-dimensions. J. Machine Learn. Res. 20(9):1–49.Google Scholar
- . (2017) Multi-nanoparticle protein corona characterization of human plasma and machine learning enable accurate identification and discrimination of cancers at early stages. Working paper, Harvard Medical School, Cambridge, MA.Google Scholar
- (2018) Staff planning for hospitals with cost estimation and optimization. Working paper, University of North Carolina at Chapel Hill, Chapel Hill.Google Scholar
- (2017) Integrated anesthesiologist and room scheduling for surgeries: Methodology and application. Oper. Res. 65(6):1460–1478.Link, Google Scholar
- (1996) Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc. B. 58(1):267–288.Crossref, Google Scholar
- (2015) Mixed integer linear programming formulation techniques. SIAM Rev. 57(1):3–57.Crossref, Google Scholar
- (2016) Block allocation at a large academic medical center. Ann. Surgery 264(6):973–981.Crossref, Google Scholar

