Why Empirical Research Is Good for Operations Management, and What Is Good Empirical Operations Management?

Published Online:https://doi.org/10.1287/msom.2019.0812

References

  • Akşin Z, Ata B, Emadi SM, Su CL (2013) Structural estimation of callers delay sensitivity in call centers. Management Sci. 59(12):2727–2746.LinkGoogle Scholar
  • Akşin Z, Ata B, Emadi SM, Su CL (2016) Impact of delay announcements in call centers: An empirical approach. Oper. Res. 65(1):242–265.LinkGoogle Scholar
  • Allon G, Federgruen A, Pierson M (2011) How much is a reduction of your customers’ wait worth? An empirical study of the fast-food drive-thru industry based on structural estimation methods. Manufacturing Service Oper. Management 13(4):489–507.LinkGoogle Scholar
  • Anupindi R, Dada M, Gupta S (1998) Estimation of consumer demand with stock-out based substitution: An application to vending machine products. Marketing Sci. 17(4):406–423.LinkGoogle Scholar
  • Baron O, Callen JL, Segal D (2018) Does the bullwhip matter economically? A cross-sectional firm-level analysis. Working paper, University of Toronto, Toronto.Google Scholar
  • Ben-Akiva ME, Lerman SR (1985) Discrete Choice Analysis: Theory and Application to Travel Demand (MIT Press, Cambridge, MA).Google Scholar
  • Bernstein F, Federgruen A (2005) Decentralized supply chains with competing retailers under demand uncertainty. Management Sci. 51(1):18–29.LinkGoogle Scholar
  • Bitran G, Caldentey R (2003) An overview of pricing models for revenue management. Manufacturing Service Oper. Management 5(3):203–229.LinkGoogle Scholar
  • Bowman EH (1963) Consistency and optimality in managerial decision making. Management Sci. 9(2):310–321.LinkGoogle Scholar
  • Bray RL, Mendelson H (2015) Production smoothing and the bullwhip effect. Manufacturing Service Oper. Management 17(2):208–220.LinkGoogle Scholar
  • Bray RL, Yao YO, Duan Y, Huo J (2019) Ration gaming and the bullwhip effect. Oper. Res. 67(2):453–467.Google Scholar
  • Brown L, Gans N, Mandelbaum A, Sakov A, Shen H, Zeltyn S, Zhao L (2005) Statistical analysis of a telephone call center: A queueing-science perspective. J. Amer. Statist. Assoc. 100(469):36–50.CrossrefGoogle Scholar
  • Cachon GP (2003) Supply chain coordination with contracts. Handbooks Oper. Res. Management Sci. 11:227–339.CrossrefGoogle Scholar
  • Cachon GP, Gallino S, Olivares M (2019) Does adding inventory increase sales? Evidence of a scarcity effect in U.S. automobile dealerships. Management Sci. 65(4):1469–1485.LinkGoogle Scholar
  • Cachon GP, Randall T, Schmidt G (2007) In search of the bullwhip effect. Manufacturing Service Oper. Management 9(4):457–479.LinkGoogle Scholar
  • Caro F, Gallien J, Díaz M, García J, Corredoira JM, Montes M, Ramos JA, Correa J (2010) Zara uses operations research to reengineer its global distribution process. Interfaces 40(1):71–84.LinkGoogle Scholar
  • Davis J, Gallego G, Topaloglu H (2014) Assortment optimization under variants of the nested logit model. Oper. Res. 62(2):250–273.LinkGoogle Scholar
  • DeHoratius N, Raman A (2008) Inventory record inaccuracy: An empirical analysis. Management Sci. 54(4):627–641.LinkGoogle Scholar
  • DiNardo JE, Pischke JS (1997) The returns to computer use revisited: Have pencils changed the wage structure too? Quart. J. Econom. 112(1):291–303.CrossrefGoogle Scholar
  • Fisher M (2007) Strengthening the empirical base of operations management. Manufacturing Service Oper. Management 9(4):368–382.LinkGoogle Scholar
  • Fisher M, Raman A (1996) Reducing the cost of demand uncertainty through accurate response to early sales. Oper. Res. 44(1):87–99.LinkGoogle Scholar
  • Fisher M, Vaidyanathan R (2014) A demand estimation procedure for retail assortment optimization with results from implementations. Management Sci. 60(10):2401–2415.LinkGoogle Scholar
  • Fisher M, Gallino S, Li J (2017a) Competition-based dynamic pricing in online retailing: A methodology validated with field experiments. Management Sci. 64(6):2496–2514.LinkGoogle Scholar
  • Fisher M, Gallino S, Netessine S (2017b) Setting retail staffing levels: A methodology validated with implementation. Wharton Working Paper, University of Pennsylvania, Philadelphia.Google Scholar
  • Fisher ML, Ittner CD (1999) The impact of product variety on automobile assembly operations: Empirical evidence and simulation analysis. Management Sci. 45(6):771–786.LinkGoogle Scholar
  • Fisher ML, Jaikumar R (1981) A generalized assignment heuristic for vehicle routing. Networks 11(2):109–124.CrossrefGoogle Scholar
  • Fisher ML, Krishnan J, Netessine S (2009) Are your staffing levels correct? Internat. Commerce Rev. 8(2):110–115.CrossrefGoogle Scholar
  • Gallino S, Moreno A (2014) Integration of online and offline channels in retail: The impact of sharing reliable inventory availability information. Management Sci. 60(6):1434–1451.LinkGoogle Scholar
  • Gans N, Koole G, Mandelbaum A (2003) Telephone call centers: Tutorial, review, and research prospects. Manufacturing Service Oper. Management 5(2):79–141.LinkGoogle Scholar
  • Gaur V, Fisher ML, Raman A (2005) An econometric analysis of inventory turnover performance in retail services. Management Sci. 51(2):181–194.LinkGoogle Scholar
  • Gerchak Y, Mossman D (1992) On the effect of demand randomness on inventories and costs. Oper. Res. 40(4):804–807.LinkGoogle Scholar
  • Glaeser CK, Fisher M, Su X (2019) Optimal retail location: Empirical methodology and application to practice. Manufacturing & Service Oper. Management 21(1):86–102.LinkGoogle Scholar
  • Guajardo JA, Cohen MA, Kim SH, Netessine S (2012) Impact of performance-based contracting on product reliability: An empirical analysis. Management Sci. 58(5):961–979.LinkGoogle Scholar
  • Harsha P, Subramanian S, Uichanco J (2019) Dynamic pricing of omnichannel inventories. Manufacturing Service Oper. Management 21(1):47–65.LinkGoogle Scholar
  • Ho TH, Lim N, Reza S, Xia X (2017) OM forum—Causal inference models in operations management. Manufacturing Service Oper. Management 19(4):509–525.LinkGoogle Scholar
  • Huckman RS, Staats BR (2011) Fluid tasks and fluid teams: The impact of diversity in experience and team familiarity on team performance. Manufacturing Service Oper. Management 13(3):310–328.LinkGoogle Scholar
  • Huckman RS, Staats BR, Upton DM (2009) Team familiarity, role experience, and performance: Evidence from Indian software services. Management Sci. 55(1):85–100.LinkGoogle Scholar
  • Kc DS, Staats BR (2012) Accumulating a portfolio of experience: The effect of focal and related experience on surgeon performance. Manufacturing Service Oper. Management 14(4):618–633.LinkGoogle Scholar
  • Kc DS, Terwiesch C (2009) Impact of workload on service time and patient safety: An econometric analysis of hospital operations. Management Sci. 55(9):1486–1498.LinkGoogle Scholar
  • Kesavan S, Gaur V, Raman A (2010) Do inventory and gross margin data improve sales forecasts for U.S. public retailers? Management Sci. 56(9):1519–1533.LinkGoogle Scholar
  • Kesavan S, Staats BR, Gilland WG (2014) Labor-mix and volume flexibility: Evidence from a retailer. Management Sci. 60(8):1884–1906.LinkGoogle Scholar
  • Kök A, Fisher M (2007) Demand estimation and assortment optimization under substitution: Methodology and application. Oper. Res. 55(6):1001–1021.LinkGoogle Scholar
  • Krueger AB (1993) How computers have changed the wage structure: Evidence from microdata, 1984–1989. Quart. J. Econom. 108(1):33–60.CrossrefGoogle Scholar
  • Lee HL, Padmanabhan V, Whang S (1997) Information distortion in a supply chain: The bullwhip effect. Management Sci. 43(4):546–558.LinkGoogle Scholar
  • Li J, Moreno A, Zhang DJ (2015) Agent behavior in the sharing economy: Evidence from Airbnb. Working Paper Series 1298, Ross School of Business, University of Michigan, Ann Arbor.Google Scholar
  • Lippman SA, McCardle KF (1997) The competitive newsboy. Oper. Res. 45(1):54–65.LinkGoogle Scholar
  • Lu Y, Musalem A, Olivares M, Schilkrut A (2013) Measuring the effect of queues on customer purchases. Management Sci. 59(8):1743–1763.LinkGoogle Scholar
  • MacDuffie JP (1997) The road to “root cause”: Shop-floor problem-solving at three auto assembly plants. Management Sci. 43(4):479–502.LinkGoogle Scholar
  • Mackelprang AW, Malhotra MK (2015) The impact of bullwhip on supply chains: Performance pathways, control mechanisms, and managerial levers. J. Oper. Management 36:15–32.CrossrefGoogle Scholar
  • Moreno A, Terwiesch C (2015) Pricing and production flexibility: An empirical analysis of the U.S. automotive industry. Manufacturing Service Oper. Management 17(4):428–444.LinkGoogle Scholar
  • Musalem A, Olivares M, Bradlow E, Terwiesch C, Corsten D (2010) Structural estimation of the effect of out-of-stocks. Management Sci. 56(7):1180–1197.LinkGoogle Scholar
  • Narayanan S, Balasubramanian S, Swaminathan JM (2009) A matter of balance: Specialization, task variety, and individual learning in a software maintenance environment. Management Sci. 55(11):1861–1876.LinkGoogle Scholar
  • Narayanan S, Balasubramanian S, Swaminathan JM (2011) Managing outsourced software projects: An analysis of project performance and customer satisfaction. Production Oper. Management 20(4):508–521.CrossrefGoogle Scholar
  • Olivares M, Cachon G (2009) Competing retailers and inventory: An empirical investigation of General Motors' dealerships in isolated markets. Management Sci. 55(9):1586–1604.LinkGoogle Scholar
  • Olivares M, Terwiesch C, Cassorla L (2008) Structural estimation of the newsvendor model: An application to reserving operating room time. Management Sci. 54(1):41–55.LinkGoogle Scholar
  • Parker C, Ramdas K, Savva N (2016) Is IT enough? Evidence from a natural experiment in India’s agriculture markets. Management Sci. 62(9):2481–2503.LinkGoogle Scholar
  • Perdikaki O, Kesavan S, Swaminathan JM (2012) Effect of retail store traffic on conversion rate and sales. Manufacturing Service Oper. Management 14(1):145–162.LinkGoogle Scholar
  • Raman A, Gaur V, Kesavan S (2006) David Berman. Harvard Business School Case 605‐081, Harvard University, Boston.Google Scholar
  • Rusmevichientong P, Shmoys D (2014) Assortment optimization under the multinomial logit model with random choice parameters. Production Oper. Management 23(11):2023–2039.Google Scholar
  • Shah R, Ward PT (2003) Lean manufacturing: Context, practice bundles, and performance. J. Oper. Management 21(2):129–149.CrossrefGoogle Scholar
  • Siemsen E, Roth AV, Balasubramanian S, Anand G (2009) The influence of psychological safety and confidence in knowledge on employee knowledge sharing. Manufacturing Service Oper. Management 11(3):429–447.LinkGoogle Scholar
  • Silver EA, Pyke DF, Peterson R (1998) Inventory Management and Production Planning and Scheduling (Wiley, New York).Google Scholar
  • Simonsohn U, Nelson LD, Simmons JP (2014) P-curve: A key to the file-drawer. J. Experiment. Psych. General 143(2):534–547.CrossrefGoogle Scholar
  • Smiddy H, Naum L (1954) Evolution of a “science of managing” in America. Management Sci. 1(1):1–31.LinkGoogle Scholar
  • Song H, Tucker AL, Murrell KL (2015) The diseconomies of queue pooling: An empirical investigation of emergency department length of stay. Management Sci. 61(12):3032–3053.LinkGoogle Scholar
  • Staats BR, Brunner DJ, Upton DM (2011) Lean principles, learning, and knowledge work: Evidence from a software services provider. J. Oper. Management 29(5):376–390.CrossrefGoogle Scholar
  • Staats BR, KC D, Gino F (2018) Maintaining beliefs in the face of negative news: The moderating role of experience. Management Sci. 64(2):804–824.LinkGoogle Scholar
  • Terwiesch C (2019) Empirical research in operations management: From field studies to analyzing digital exhaust. Manufacturing Service Oper. Management, ePub ahead of print May 15, https://doi.org/10.1287/msom.2018.0723.LinkGoogle Scholar
  • Terwiesch C, Olivares M, Staats BR, Gaur V (2019) A review of empirical operations management over the last two decades. Manufacturing Service Oper. Management, ePub ahead of print June 19, https://doi.org/10.1287/msom.2018.0755.LinkGoogle Scholar
  • Train KE (2009) Discrete Choice Methods with Simulation (Cambridge University Press, Cambridge, UK).CrossrefGoogle Scholar
  • Tucker AL (2004) The impact of operational failures on hospital nurses and their patients. J. Oper. Management 22(2):151–169.CrossrefGoogle Scholar
  • Tucker AL (2007) An empirical study of system improvement by frontline employees in hospital units. Manufacturing Service Oper. Management 9(4):492–505.LinkGoogle Scholar
  • Tucker AL (2015) The impact of workaround difficulty on frontline employees’ response to operational failures: A laboratory experiment on medication administration. Management Sci. 62(4):1124–1144.LinkGoogle Scholar
  • Tucker AL, Spear SJ (2006) Operational failures and interruptions in hospital nursing. Health Services Res. 41(3, part 1):643–662.Google Scholar
  • Vulcano G, Van Ryzin G, Ratliff R (2012) Estimating primary demand for substitutable products from sales transaction data. Oper. Res. 60(2):313–334.LinkGoogle Scholar
  • Zhang DJ, Allon G, Van Mieghem JA (2017) Does social interaction improve learning outcomes? Evidence from field experiments on massive open online courses. Manufacturing Service Oper. Management 19(3):347–367Google Scholar
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