A Bayesian Model for Sales Forecasting at Sun Microsystems

Published Online:https://doi.org/10.1287/inte.1090.0477

References

  • Baldwin C. Y., Clark K. B., Yoffie D. B. Sun wars: Competition within a modular cluster, 1985–1990. Competing in the Age of Digital Convergence (1997) (Harvard Business Press, Boston) 123–158Google Scholar
  • Bass F. M. A new product growth model for consumer durables. Management Sci. (1969) 15(5):215–227LinkGoogle Scholar
  • Bernardo J. M., Smith A. F. M.Bayesian Theory (1994) (John Wiley & Sons, Hoboken, NJ) CrossrefGoogle Scholar
  • Box G. E. P., Jenkins G. M., Reinsel G. C.Time Series Analysis (1994) 3rd ed.(Prentice Hall, Upper Saddle River, NJ) Google Scholar
  • Bunn D., Wright G. Interaction of judgemental and statistical forecasting methods: Issues and analysis. Management Sci. (1991) 37(5):501–518LinkGoogle Scholar
  • Carter C. K., Kohn R. On Gibbs sampling for state space models. Biometrika (1994) 81(3):541–543CrossrefGoogle Scholar
  • Davis J. Dell takes wraps off channel partner program. Channel Insider (2007) December 5). Retrieved June 10, 2008, http://www.channelinsider.com/article/Dell+Takes+Wraps+Off+Channel+Partner+Program/220950_1.aspxGoogle Scholar
  • Dean J. The forbidden city of Terry Gou. Wall Street Journal (2007) August 11). Retrieved June 10, 2008, http://online.wsj.com/public/article/SB118677584137994489.htmlGoogle Scholar
  • Dolgui A., Pashkevich M. Demand forecasting for multiple slow-moving items with short requests history and unequal demand variance. Internat. J. Production Econom. (2008a) 112(2):885–894CrossrefGoogle Scholar
  • Dolgui A., Pashkevich M. On the performance of binomial and beta-binomial models of demand forecasting for multiple slow-moving inventory items. Comput. Oper. Res. (2008b) 35(3):893–905CrossrefGoogle Scholar
  • Durbin J., Koopman S. J.Time Series Analysis by State Space Methods (2001) (Oxford University Press, Oxford, UK) Google 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
  • Frühwirth-Schnatter S. Data augmentation and dynamic linear models. J. Time Ser. Anal. (1994) 15(2):183–102CrossrefGoogle Scholar
  • Gelman A., Hill J.Data Analysis Using Regression and Multilevel/Hierarchical Models (2006) (Cambridge University Press, New York) CrossrefGoogle Scholar
  • Gelman A., Carlin J., Stern H., Rubin D.Bayesian Data Analysis (2003) 2nd ed.(Chapman & Hall/CRC Press, Boca Raton, FL) Google Scholar
  • Geweke J., Bernardo J. M., Berger J. O., Dawid A. P., Smith A. F. M. Evaluating the accuracy of sampling-based approaches to calculating posterior moments. Bayesian Statistics (1992) 4(Clarendon Press, Wotton-under-Edge, Gloucestershire, UK) 169–193Google Scholar
  • Geweke J., Tanizaki H. Note on the sampling distribution for the Metropolis-Hastings algorithm. Comm. Statist. Theory Methods (2003) 32(4):775–789CrossrefGoogle Scholar
  • Ghysels E., Osborn D. R., Rodrigues P. M. M., Elliott G., Granger C., Timmermann A. Forecasting seasonal time series. Handbook of Economic Forecasting (2006) 1(North-Holland, Amsterdam) 660–706CrossrefGoogle Scholar
  • Gilks W. R., Richardson S., Spiegelhalter D. J.Markov Chain Monte Carlo in Practice (1996) (Chapman & Hall, Boca Raton, FL) CrossrefGoogle Scholar
  • Harvey A. C.Forecasting, Structural Time Series Models and the Kalman Filter (1989) (Cambridge University Press, Cambridge, UK) Google Scholar
  • Hill R. M. Applying Bayesian methodology with a uniform prior to the single period inventory model. Eur. J. Oper. Res. (1997) 98(3):555–562CrossrefGoogle Scholar
  • Huang C.-Y., Tzeng G.-H. Multiple generation product life cycle predictions using a novel two-stage fuzzy piecewise regression analysis method. Tech. Forecasting Soc. Change (2008) 75(1):12–31CrossrefGoogle Scholar
  • Kahn K. B. How to measure the impact of a forecast error on an enterprise? J. Bus. Forecasting (2003) 22(1):21–25Google Scholar
  • Lamport L.LATEX: A Document Preparation System (1994) 2nd ed.(Addison-Wesley Professional, Reading, MA) Google Scholar
  • Lawrence M. J., O'Connor M., Edmundson R. H. A field study of sales forecasting accuracy and processes. Eur. J. Oper. Res. (2000) 122(1):151–160CrossrefGoogle Scholar
  • Lee C.-Y., Lee J.-D., Kim Y. Demand forecasting for new technology with a short history in a competitive market: The case of the home networking market in South Korea. Tech. Forecasting Soc. Change (2008) 75(1):91–106CrossrefGoogle Scholar
  • Lee W. Y., Goodwin P., Fildes R., Nikolopoulos K., Lawrence M. Providing support for the use of analogies in demand forecasting tasks. Internat. J. Forecasting (2007) 23(3):377–390CrossrefGoogle Scholar
  • Lenk P. J., Rao A. G. New models from old: Forecasting product adoption by hierarchical Bayes procedures. Marketing Sci. (1990) 9(1):42–57LinkGoogle Scholar
  • Mahajan V., Muller E., Wind Y.New-Product Diffusion Models (2000) (Kluwer, Norwell, MA) Google Scholar
  • Mentzer J. T. The impact of forecasting improvement on return on shareholder value. J. Bus. Forecasting (1999) 18(Fall):8–12Google Scholar
  • Moe W. W., Fader P. S. Using advance purchase orders to forecast new product sales. Marketing Sci. (2002) 21(3):347–364LinkGoogle Scholar
  • Montgomery A. L. Creating micro-marketing pricing strategies using supermarket scanner data. Marketing Sci. (1997) 16(4):315–337LinkGoogle Scholar
  • Moon M. A., Mentzer J. T., Smith C. D. Conducting a sales forecasting audit. Internat. J. Forecasting (2003) 19(1):5–25CrossrefGoogle Scholar
  • Neelamegham R., Chintagunta P. A Bayesian model to forecast new product performance in domestic and international markets. Marketing Sci. (1999) 18(2):115–136LinkGoogle Scholar
  • Neelamegham R., Chintagunta P. K. Modeling and forecasting the sales of technology products. Quant. Marketing Econom. (2004) 2(3):195–232CrossrefGoogle Scholar
  • Niu S.-C. A piecewise-diffusion model of new-product demands. Oper. Res. (2006) 54(4):678–695LinkGoogle Scholar
  • Pole A., West M., Harrison J.Applied Bayesian Forecasting and Time Series Analysis (1994) (Chapman & Hall, Boca Raton, FL) Google Scholar
  • Qi Y., Minka T. P. Hessian-based Markov chain Monte Carlo algorithms. (2002) . Retrieved June 10, 2008, http://www.cs.purdue.edu/homes/alanqi/papers/qi-minka-HMH-AMIT-02.psGoogle Scholar
  • Rogers E. M.Diffusion of Innovations (1963) 1st ed.(Free Press, New York) Google Scholar
  • Sanders N. R. Comments on “Effective forecasting and judgmental adjustments: An empirical evaluation and strategies for improvement in supply-chain planning. Internat. J. Forecasting (2009) 25(1):24–26CrossrefGoogle Scholar
  • Sanders N. R., Ritzman L. P. On knowing when to switch from quantitative to judgmental forecasts. Internat. J. Oper. Production Management (1991) 11(6):27–37CrossrefGoogle Scholar
  • Schafer S. M.Web Standards Programmer's Reference: HTML, CSS, JavaScript, Perl, Python, and PHP (2005) (Wrox, Chicago) Google Scholar
  • Silver E. A. Bayesian determination of the reorder point of a slow moving item. Oper. Res. (1965) 13(6):989–997LinkGoogle Scholar
  • Sood A., James G. M., Tellis G. J. Functional regression: A new model for predicting market penetration of new products. Marketing Sci. (2009) 28(1):36–51LinkGoogle Scholar
  • Theil H.Applied Economic Forecasting (1971) (North-Holland, Amsterdam) Google Scholar
  • van Heerde H. J., Mela C. F., Manchanda P. The dynamic effect of innovation on market structure. J. Marketing Res. (2004) 41(2):166–183CrossrefGoogle Scholar
  • Venables W. N., Smith D. M.An Introduction to R (2002) (Network Theory, Bristol, UK) Google Scholar
  • West M., Harrison P. J.Bayesian Forecasting and Dynamic Models (1997) 2nd ed.(Springer-Verlag, New York) Google Scholar
  • Wiedermann J.Web Design Flash Sites (2006) (Taschen, Cologne, Germany) Google Scholar
  • Williams H. E., Lane D.Web Database Applications with PHP and MySQL (2004) 2nd ed.(O'Reilly Media, Sebastopol, CA) Google Scholar
  • Wisner J., Stanley L. Forecasting practices of JIT and non-JIT purchasers. Eur. J. Purchasing Supply Management (1994) 1(4):219–225CrossrefGoogle Scholar
  • Wright G., Ayton P. The psychology of forecasting. Futures (1986) 18(3):420–439CrossrefGoogle Scholar
  • Yelland P. M. A model of the product lifecycle for sales forecasting. (2004) . Technical Report TR-2004-127, Sun Microsystems Laboratories, Menlo Park, CAGoogle Scholar
  • Yelland P. M., Lee E. Forecasting product sales with dynamic linear mixture models. (2003) . Technical Report TR-2003-122, Sun Microsystems Laboratories, Menlo Park, CAGoogle Scholar
  • Zipkin P. H.Foundations of Inventory Management (2000) (McGraw-Hill/Irwin, New York) Google Scholar
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