Demand Forecasting Behavior: System Neglect and Change Detection

Published Online:https://doi.org/10.1287/mnsc.1110.1382

References

  • Adams J. A. Response feedback and learning. Psych. Bull. (1968) 70(6):486–504CrossrefGoogle Scholar
  • Andrawis R. R., Atiya A. F. A new Bayesian formulation for Holt's exponential smoothing. J. Forecasting (2009) 28(3):218–234CrossrefGoogle Scholar
  • Andreassen P. B., Kraus S. J. Judgmental extrapolation and the salience of change. J. Forecasting (1990) 9(4):347–372CrossrefGoogle Scholar
  • Asparouhova E., Hertzel M., Lemmon M. Inference from streaks in random outcomes: Experimental evidence on beliefs in regime shifting and the law of small numbers. Management Sci. (2009) 55(11):1766–1782LinkGoogle Scholar
  • Barberis N., Shleifer A., Vishny R. A model of investor sentiment. J. Financial Econom. (1998) 49(3):307–343CrossrefGoogle Scholar
  • Barry D. M., Pitz G. F. Detection of change in nonstationary, random sequences. Organ. Behav. Human Performance (1979) 24(1):111–125CrossrefGoogle Scholar
  • Bates D. M., Pinheiro J. C. Computational methods for multilevel modeling. (1998) . Technical Memorandum BL0112140-980226-01TM. Bell Labs, Lucent Technologies, Murray Hill, NJGoogle Scholar
  • Baucells M., Weber M., Welfens F. Reference-point formation and updating. Management Sci. (2011) 57(3):506–519LinkGoogle Scholar
  • Bendoly E., Donohue K., Schultz K. L. Behavior in operations management: Assessing recent findings and revisiting old assumptions. J. Oper. Management (2006) 24(6):737–752CrossrefGoogle Scholar
  • Bloomfield R., Hales J. Predicting the next step of a random walk: Experimental evidence of regime-shifting beliefs. J. Financial Econom. (2002) 65(3):397–414CrossrefGoogle Scholar
  • Bolger F., Harvey N. Context-sensitive heuristics in statistical reasoning. Quart. J. Experiment. Psych. (1993) 46A(4):779–811CrossrefGoogle Scholar
  • Bolton G., Katok E. Learning-by-doing in the newsvendor problem: A laboratory investigation of the role of experience and feedback. Manufacturing Service Oper. Management (2008) 10(3):519–538LinkGoogle Scholar
  • Brav A., Heaton J. B. Competing theories of financial anomalies. Rev. Financial Stud. (2002) 15(2):575–606CrossrefGoogle Scholar
  • Carbone R., Gorr W. Accuracy of judgmental forecasting of time series. Decision Sci. (1985) 16(2):153–160CrossrefGoogle Scholar
  • Chapman G. B., Johnson E. J., Gilovich T., Griffin D., Kahneman D. Incorporating the irrelevant: Anchors in judgments of belief and value. Heuristics and Biases (2002) (Cambridge University Press, Cambridge UK) 120–138CrossrefGoogle Scholar
  • Chopra S., Meindl P.Supply Chain Management (2009) (Prentice Hall, Upper Saddle River, NJ) Google Scholar
  • Croson R., Donohue K. Impact of POS data sharing on supply chain management: An experimental study. Production Oper. Management (2003) 12(1):1–11CrossrefGoogle Scholar
  • Croson R., Donohue K., Katok E., Sterman J. Order stability in supply chains: Coordination risk and the role of coordination stock. (2005) . Working paper, University of Texas at Dallas, RichardsonGoogle Scholar
  • DeBondt W. F. M. Betting on trends: Intuitive forecasts of financial risk and return. Internat. J. Forecasting (1993) 9(3):355–371CrossrefGoogle Scholar
  • Edwards W., Kleinmuntz B. Conservatism in human information processing. Formal Representation of Human Judgment (1968) (Wiley, New York) 17–52Google Scholar
  • Epley N., Gilovich T. Putting adjustment back in the anchoring and adjustment heuristic. Psych. Sci. (2001) 12(5):391–396CrossrefGoogle Scholar
  • Fildes R., Goodwin P., Lawrence M., Nikolopoulos K. Effective forecasting and judgmental adjustments: An empirical evaluation and strategies for improvement in supply-chain planning. Internat. J. Forecasting (2009) 25(1):3–23CrossrefGoogle Scholar
  • Fischbacher U. z-Tree: Zurich toolbox for ready-made economic experiments. Experiment. Econom. (2007) 10(2):171–178CrossrefGoogle Scholar
  • Gardner E. S. Exponential smoothing: The state of the art. J. Forecasting (1985) 4(1):1–28CrossrefGoogle Scholar
  • Gardner E. S. Exponential smoothing: The state of the art—Part II. Internat. J. Forecasting (2006) 22(4):637–666CrossrefGoogle Scholar
  • Gehring W. J., Goss B., Coles M. G. H., Meyer D. E., Donchin E. A neural system for error detection and compensation. Psych. Sci. (1993) 4(6):385–390CrossrefGoogle Scholar
  • Graves S. C. A single-item inventory model for a nonstationary demand process. Manufacturing Service Oper. Management (1999) 1(1):50–61LinkGoogle Scholar
  • Griffin D., Tversky A. The weighing of evidence and the determinants of confidence. Cognitive Psych. (1992) 24(3):411–435CrossrefGoogle Scholar
  • Harrison P. J. Exponential smoothing and short-term sales forecasting. Management Sci. (1967) 13(11):821–842LinkGoogle Scholar
  • Harvey N. Use of heuristics: Insights from forecasting research. Thinking Reasoning (2007) 13(1):5–24CrossrefGoogle Scholar
  • Hyndman R. J., Koehler A. B., Snyder R. D., Grose S. A state space framework for automatic forecasting using exponential smoothing methods. Internat. J. Forecasting (2002) 18(3):439–454CrossrefGoogle Scholar
  • Kahneman D., Tversky A. Subjective probability: A judgment of representativeness. Cognitive Psych. (1972) 3(3):430–454CrossrefGoogle Scholar
  • Kremer M., Minner S., Van Wassenhove L. N. Do random errors explain newsvendor behavior? Manufacturing Service Oper. Management (2010) 12(4):673–681LinkGoogle Scholar
  • Larrick R. P., Soll J. B. Intuitions about combining opinions: Misappreciation of the averaging principle. Management Sci. (2006) 52(1):111–127LinkGoogle Scholar
  • Lawrence M., O'Connor M. Exploring judgmental forecasting. Internat. J. Forecasting (1992) 8(1):15–26CrossrefGoogle Scholar
  • Lawrence M., O'Connor M. The anchor and adjustment heuristic in time-series forecasting. J. Forecasting (1995) 14(5):443–451CrossrefGoogle Scholar
  • Lawrence M. J., Edmundson R. H., O'Connor M. J. An examination of the accuracy of judgmental extrapolation of time series. Internat. J. Forecasting (1985) 1(1):25–35CrossrefGoogle Scholar
  • Lawrence M., Goodwin P., O'Connor M., Önkal D. Judgemental forecasting: A review of progress over the last 25 years. Internat. J. Forecasting (2006) 22(3):493–518CrossrefGoogle Scholar
  • Makridakis S., Hibon M. The M3-competition: Results, conclusions and implications. Internat. J. Forecasting (2000) 16(4):451–476CrossrefGoogle Scholar
  • Makridakis S., Wheelwright S., Hyndman R.Forecasting: Methods and Applications (1998) (Wiley, New York) Google Scholar
  • Massey C., Wu G. Detecting regime shifts: The causes of under- and overreaction. Management Sci. (2005) 51(6):932–947LinkGoogle Scholar
  • McNamara J. M., Houston A. I. Memory and the efficient use of information. J. Theoretical Biology (1987) 125(4):385–395CrossrefGoogle Scholar
  • Muth J. F. Optimal properties of exponentially weighted forecasts. J. Amer. Statist. Assoc. (1960) 55(290):299–306CrossrefGoogle Scholar
  • Nahmias S.Production and Operations Analysis (2008) (Irwin, Chicago) Google Scholar
  • Oliva R., Watson N. Managing functional biases in organizational forecasts: A case study of consensus forecasting in supply chain planning. Production Oper. Management (2009) 18(2):138–151CrossrefGoogle Scholar
  • Poteshman A. M. Underreaction, overreaction, and increasing misreaction to information in the options market. J. Finance (2001) 56(3):851–876CrossrefGoogle Scholar
  • Rabin M. Inference by believers in the law of small numbers. Quarterly J. Econom. (2002) 117(3):775–816CrossrefGoogle Scholar
  • Rabin M., Vayanos D. The gambler's and hot-hand fallacies: Theory and applications. Rev. Econom. Stud. (2010) 77(2):730–778CrossrefGoogle Scholar
  • Rustichini A., Glimcher P. W., Camerer C., Poldrack R., Fehr E. Neuroeconomics: Formal models of decision-making and cognitive neuroscience. Neuroeconomics (2008) (Elsevier, London) 33–46Google Scholar
  • Sanders N. Accuracy of judgmental forecasts: A comparison. Omega (1992) 20(3):353–364CrossrefGoogle Scholar
  • Sanders N. The impact of task properties feedback on time series judgmental forecasting tasks. Omega (1997) 25(2):135–144CrossrefGoogle Scholar
  • Sanders N., Manrodt K. B. Forecasting software in practice: Use, satisfaction, and performance. Interfaces (2003a) 33(5):90–93LinkGoogle Scholar
  • Sanders N., Manrodt K. B. The efficacy of using judgmental versus quantitative forecasting methods in practice. Omega (2003b) 31(6):511–522CrossrefGoogle Scholar
  • Schweitzer M. E., Cachon G. Decision bias in the newsvendor problem with a known demand distribution: Experimental evidence. Management Sci. (2000) 46(3):404–420LinkGoogle Scholar
  • Stone E. R., Opel R. B. Training to improve calibration and discrimination: The effects of performance and environmental feedback. Organ. Behav. Human Decision Processes (2000) 83(2):282–309CrossrefGoogle Scholar
  • Su X. Bounded rationality in newsvendor models. Manufacturing and Service Oper. Management (2008) 10(4):566–589LinkGoogle Scholar
  • Verbeek M.A Guide to Modern Econometrics (2000) (Wiley, New York) Google Scholar
  • Wiener N.Cybernetics or Control and Communication in the Animal and the Machine (1948) (Wiley, New York) Google Scholar
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