Journal of the Operational Research Society

, Volume 59, Issue 9, pp 1150–1172 | Cite as

Forecasting and operational research: a review

Review Paper


From its foundation, operational research (OR) has made many substantial contributions to practical forecasting in organizations. Equally, researchers in other disciplines have influenced forecasting practice. Since the last survey articles in JORS, forecasting has developed as a discipline with its own journals. While the effect of this increased specialization has been a narrowing of the scope of OR's interest in forecasting, research from an OR perspective remains vigorous. OR has been more receptive than other disciplines to the specialist research published in the forecasting journals, capitalizing on some of their key findings. In this paper, we identify the particular topics of OR interest over the past 25 years. After a brief summary of the current research in forecasting methods, we examine those topic areas that have grabbed the attention of OR researchers: computationally intensive methods and applications in operations and marketing. Applications in operations have proved particularly important, including the management of inventories and the effects of sharing forecast information across the supply chain. The second area of application is marketing, including customer relationship management using data mining and computer-intensive methods. The paper concludes by arguing that the unique contribution that OR can continue to make to forecasting is through developing models that link the effectiveness of new forecasting methods to the organizational context in which the models will be applied. The benefits of examining the system rather than its separate components are likely to be substantial.


forecasting supply chain market models data mining operations 


  1. Allen PG and Fildes R (2001). Econometric forecasting. In: Armstrong J.S. (ed). Principles of Forecasting. Kluwer: Norwell, MA, pp. 303–362.CrossRefGoogle Scholar
  2. Allen PG and Fildes R (2005). Levels, differences and ECMs—Principles for improved econometric forecasting. Oxford Bull Econ Statist 67: 881–904.CrossRefGoogle Scholar
  3. Armstrong JS (2001a). Judgmental bootstrapping: Inferring experts' rules for forecasting. In: Armstrong J.S. (ed). Principles of Forecasting: A Handbook for Researchers and Practitioners. Kluwer: Norwell, MA.CrossRefGoogle Scholar
  4. Armstrong JS (2001b). Standards and practices for forecasting. In: Armstrong J.S. (ed). Principles of Forecasting: A Handbook for Researchers and Practitioners. Kluwer Academic: Boston, London, pp. 679–732.CrossRefGoogle Scholar
  5. Armstrong JS and Collopy F (1992). Error measures for generalizing about forecasting methods—Empirical comparisons. Int J Forecasting 8: 69–80.CrossRefGoogle Scholar
  6. Armstrong JS and Collopy F (1993). Causal forces—Structuring knowledge for time-series extrapolation. J Forecasting 12(2): 103–115.CrossRefGoogle Scholar
  7. Armstrong JS, Brodie RJ and McIntyre SH (1987). Forecasting methods for marketing—Review of empirical research. Int J Forecasting 3: 355–376.CrossRefGoogle Scholar
  8. Aviv Y (2001). The effect of collaborative forecasting on supply chain performance. Mngt Sci 47: 1326–1343.CrossRefGoogle Scholar
  9. Aviv Y (2002). Gaining benefits from joint forecasting and replenishment processes: The case of auto-correlated demand. Manufa Service Opns Mngt 4: 55–74.CrossRefGoogle Scholar
  10. Baesens B, Van Gestel T, Viaene S, Stepanova M, Suykens J and Vanthienen J (2003). Benchmarking state-of-the-art classification algorithms for credit scoring. J Opl Res Soc 54: 627–635.CrossRefGoogle Scholar
  11. Baesens B, Verstraeten G, Van den Poel D, Egmont-Petersen M, Van Kenhove P and Vanthienen J (2004). Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers. Eur J Opl Res 156: 508–523.CrossRefGoogle Scholar
  12. Bass FM (1969). A new product growth model for consumer durables. Mngt Sci 15: 215–227.CrossRefGoogle Scholar
  13. Bates JM and Granger CWJ (1969). Combination of forecasts. Opl Res Quart 20(4): 451–468.CrossRefGoogle Scholar
  14. Beer S (1975). Platform for Change. Wiley: Chichester, UK.Google Scholar
  15. Bemmaor AC and Franses PH (2005). The diffusion of marketing science in the practitioners' community: Opening the black box. Appl Stochastic Models Buss Indust 21(4–5): 289–301.CrossRefGoogle Scholar
  16. Berry MJR and Linoff GS (2004). Data Mining Techniques for Marketing, Sales and Customer Support. Wiley: New York.Google Scholar
  17. Blattberg RC and Hoch SJ (1990). Database models and managerial intuition—50 percent model + 50 percent manager. Mngt Sci 36: 887–899.CrossRefGoogle Scholar
  18. Bordley RF (1982). The combination of forecasts—A Bayesian approach. J Opl Res Soc 33: 171–174.CrossRefGoogle Scholar
  19. Bose I and Mahapatra RK (2001). Business data mining—A machine learning perspective. Inform Manage-Amster 39: 211–225.CrossRefGoogle Scholar
  20. Boser BE, Guyon IM and Vapnik V (1992). A training algorithm for optimal margin classifiers.Paper presented at the 5th Annual ACM Workshop on COLT, Pittsburgh, PA.Google Scholar
  21. Box GEP, Jenkins GM and Reinsel GC (1994). Time Series Analysis: Forecasting & Control, 3rd edn.. Prentice-Hall: Upper Saddle River, NJ.Google Scholar
  22. Boylan JE and Syntetos AA (2003). Intermittent demand forecasting: Size-interval methods based on average and smoothing. Proceedings of the International Conference on Quantitative Methods in Industry and Commerce, Athens, Greece.Google Scholar
  23. Boylan JE, Syntetos AA and Karakostas GC (2006). Classification for forecasting and stock-control: A case-study. J Opl Res Soc, advance online publication, 18 October 2006, doi: 10.1057/palgrave.jors.2602312.Google Scholar
  24. Bramson MJ, Helps IG and Watson-Gandy JACC (eds). (1972). Forecasting in action. Operational Research Society and Society for Long Range Planning.Google Scholar
  25. Breiman L (1984). Classification and Regression Trees. Wadsworth International Group: Belmont, CA.Google Scholar
  26. Breiman L (1996). Bagging predictors. Mach Learn 24: 123–140.Google Scholar
  27. Breiman L (2001a). Statistical modeling: The two cultures. Statist Sci 16: 199–215.CrossRefGoogle Scholar
  28. Breiman L (2001b). Random forests. Mach Learn 45: 5–32.CrossRefGoogle Scholar
  29. Brodie R, Danaher PJ, Kumar V and Leeflang PSH (2001). Econometric models for forecasting market share. In: Armstrong J.S. (ed). Principles of Forecasting: A Handbook for Researchers and Practitioners. Kluwer: Norwell, MA.Google Scholar
  30. Brown RG (1963). Smoothing Forecasting and Prediction of Discrete Time Series. Prentice-Hall: Inc, Englewood Cliffs, NJ.Google Scholar
  31. Bryson N and Joseph A (2001). Optimal techniques for class-dependent attribute discretization. J Opl Res Soc 52: 1130–1143.CrossRefGoogle Scholar
  32. Buckinx W and van den Poel D (2005). Customer base analysis: Partial defection of behaviourally loyal clients in a non-contractual FMCG retail setting. Eur J Opl Res 164: 252–268.CrossRefGoogle Scholar
  33. Bucklin RE and Gupta S (1999). Commercial use of UPC scanner data: Industry and academic perspectives. Market Sci 18(3): 247–273.CrossRefGoogle Scholar
  34. Bult JR and Wansbeek T (1995). Optimal selection for direct mail. Market Sci 14: 378–394.CrossRefGoogle Scholar
  35. Bunn DW and Seigal JP (1983). Forecasting the effects of television programming upon electricity loads. J Opl Res Soc 34(1): 17–25.CrossRefGoogle Scholar
  36. Bunn DW and Vassilopoulos AI (1999). Comparison of seasonal estimation methods in multi-item short-term forecasting. Int J Forecasting 15: 431–443.CrossRefGoogle Scholar
  37. Bunn DW and Wright G (1991). Interaction of judgmental and statistical forecasting methods—issues and analysis. Mngt Sci 37(5): 501–518.CrossRefGoogle Scholar
  38. Cachon GP and Lariviere MA (2001). Contracting to assure supply: How to share demand forecasts in a supply chain. Mngt Sci 47(5): 629–646.CrossRefGoogle Scholar
  39. Campos J, Ericsson NR and Hendry DF . (2005). General-to-specific modeling: An overview and selected bibliography (No. 838). Board of Governors of the Federal Reserve System.Google Scholar
  40. Chatfield C (1995). Model uncertainty, data mining and statistical inference. J R Stat Soc Ser A—Stat Soc 158: 419–466.CrossRefGoogle Scholar
  41. Chatfield C (2001). Prediction intervals for time-series forecasting. In: Armstrong J.S. (ed). Principles of Forecasting. Kluwer: Norwell, MA.Google Scholar
  42. Chawla NV, Bowyer KW, Hall LO and Kegelmeyer WP (2002). SMOTE: Synthetic minority over-sampling technique. J Artif Intell Res 16: 321–357.Google Scholar
  43. Chen F, Drezner Z, Ryan JK and Simchi-Levi D (2000). Quantifying the bullwhip effect in a simple supply chain: The impact of forecasting, lead times, and information. Mngt Sci 46: 436–443.CrossRefGoogle Scholar
  44. Chen H and Boylan JE (2007). Use of individual and group seasonal indices in subaggregate demand forecasting. J Opl Res Soc 58(12): 1660–1671.CrossRefGoogle Scholar
  45. Chen MS, Han JW and Yu PS (1996). Data mining: An overview from a database perspective. IEEE Trans Knowl Data Eng 8: 866–883.CrossRefGoogle Scholar
  46. Clements MP and Hendry DF (1998). Forecasting Economic Time Series. Cambridge University Press: Cambridge, UK.CrossRefGoogle Scholar
  47. Clements MP and Taylor N (2001). Bootstrapping prediction intervals for autoregressive models. Int J Forecasting 17: 247–267.CrossRefGoogle Scholar
  48. Cohn D, Atlas L and Ladner R (1994). Improving generalization with active learning. Mach Learn 15(2): 201–221.Google Scholar
  49. Cohn D, Ghahramani Z and Jordan MI (1996). Active learning with statistical models. J Artif Intell Res 4: 129–145.Google Scholar
  50. Collopy F and Armstrong JS (1992). Rule-based forecasting—Development and validation of an expert systems—approach to combining time-series extrapolations. Mngt Sci 38(10): 1394–1414.CrossRefGoogle Scholar
  51. Cooper LG, Baron P, Levy W, Swisher M and Gogos P (1999). PromoCast (TM): A new forecasting method for promotion planning. Market Sci 18: 301–316.CrossRefGoogle Scholar
  52. Crone SF, Lessmann S and Stahlbock R (2006). The impact of preprocessing on data mining: An evaluation of classifier sensitivity in direct marketing. Eur J Opl Res 173: 781–800.CrossRefGoogle Scholar
  53. Crook JN, Edelman DB and Thomas LC (2001). Special issue: Credit scoring and data mining—Editorial overview. J Opl Res Soc 52: 972–973.CrossRefGoogle Scholar
  54. Croston JD (1972). Forecasting and stock control for intermittent demand. Opl Res Quart 23: 289–303.CrossRefGoogle Scholar
  55. De Bodt MA and Van Wassenhove L (1983). Cost increases due to demand uncertainty in MRP lot sizing. Decis Sci 14: 345–361.CrossRefGoogle Scholar
  56. Debuse JCW and Rayward-Smith VJ (1999). Discretisation of continuous commercial database features for a simulated annealing data mining algorithm. Appl Intell 11: 285–295.CrossRefGoogle Scholar
  57. De Gooijer JG and Hyndman RJ (2006). 25 years of time series forecasting. Int J Forecasting 22: 443–473.CrossRefGoogle Scholar
  58. Dekker M, van Donselaar K and Ouwehand P (2004). How to use aggregation and combined forecasting to improve seasonal demand forecasts. Int J Product Econ 90: 151–167.CrossRefGoogle Scholar
  59. Diebold FX (2006). Elements of Forecasting, 4th edn.. South-Western College Publishing: Cincinnati, OH.Google Scholar
  60. Divakar S, Ratchford BT and Shankar V (2005). CHAN4CAST: A multichannel, multiregion sales forecasting model and decision support system for consumer packaged goods. Market Sci 24: 334–350.CrossRefGoogle Scholar
  61. Eaves AHC and Kingsman BG (2004). Forecasting for the ordering and stock-holding of spare parts. J Opl Res Soc 55: 431–437.CrossRefGoogle Scholar
  62. Engle RF (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United-Kingdom inflation. Econometrica 50: 987–1007.CrossRefGoogle Scholar
  63. Engle RF and Granger CWJ (1987). Cointegration and error correction—Representation, estimation and testing. Econometrica 55: 251–276.CrossRefGoogle Scholar
  64. Fader PS and Hardie BGS (2001). Forecasting trial sales of new consumer packaged goods. In: Armstrong J.S. (ed). Principles of Forecasting: A Handbook for Researchers and Practitioners. Kluwer: Norwell, MA.Google Scholar
  65. Fayyad U, Piatetsky-Shapiro G and Smyth P (1996). From data mining to knowledge discovery in databases. AI Mag 17: 37–54.Google Scholar
  66. Fildes R (1979). Quantitative forecasting—The state of the art: Extrapolative models. J Opl Res Soc 30: 691–710.Google Scholar
  67. Fildes R (1985). Quantitative forecasting—The state of the art: Econometric models. J Opl Res Soc 36: 549–580.Google Scholar
  68. Fildes R (1989). Evaluation of aggregate and individual forecast method selection-rules. Mngt Sci 35: 1056–1065.CrossRefGoogle Scholar
  69. Fildes R (1992). The evaluation of extrapolative forecasting methods. Int J Forecasting 8: 81–98.CrossRefGoogle Scholar
  70. Fildes R (2001). Beyond forecasting competitions. Int J Forecasting 17(4): 556–560.Google Scholar
  71. Fildes R (2002). Telecommunications demand forecasting—A review. Int J Forecasting 18: 489–522.CrossRefGoogle Scholar
  72. Fildes R (2006). The forecasting journals and their contribution to forecasting research: Citation analysis and expert opinion. Int J Forecasting 22: 415–432.CrossRefGoogle Scholar
  73. Fildes R and Goodwin P (2007). Against your better judgment? How organizations can improve their use of management judgment in forecasting. Interfaces 37: 570–576.CrossRefGoogle Scholar
  74. Fildes R and Makridakis S (1995). The impact of empirical accuracy studies on time-series analysis and forecasting. Int Statist Rev 63: 289–308.CrossRefGoogle Scholar
  75. Fildes R and Nikolopoulos K (2006). Spyros Makridakis: An interview with the International Journal of Forecasting. Int J Forecasting 22: 625–636.CrossRefGoogle Scholar
  76. Fildes R and Ord JK (2002). Forecasting competitions: Their role in improving forecasting practice and research. In: Clements M.P. and Hendry D.F. (eds). A Companion to Economic Forecasting. Blackwell: Oxford.Google Scholar
  77. Fildes R, Randall A and Stubbs P (1997). One day ahead demand forecasting in the utility industries: Two case studies. J Opl Res Soc 48: 15–24.CrossRefGoogle Scholar
  78. Fildes R, Hibon M, Makridakis S and Meade N (1998). Generalising about univariate forecasting methods: Further empirical evidence. Int J Forecasting 14: 339–358.CrossRefGoogle Scholar
  79. Fildes R, Goodwin P and Lawrence M (2006). The design features of forecasting support systems and their effectiveness. Decis Support Syst 42: 351–361.CrossRefGoogle Scholar
  80. Fildes R, Goodwin P, Lawrence M and Nikolopoulos K (2008). Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning. Int J Forecasting 24, forthcoming.Google Scholar
  81. Finlay S (2008). Towards profitability: A utility approach to the credit scoring problem. J Opel Res Soc 59(7): 921–931.CrossRefGoogle Scholar
  82. Forrester J (1961). Industrial Dynamics. MIT Press: Cambridge, MA and Wiley: NY.Google Scholar
  83. Freund Y and Schapire RE (1997). A decision-theoretic generalization of on-line learning and an application to boosting. J Comput System Sci 55: 115–139.CrossRefGoogle Scholar
  84. Gardner ES (1990). Evaluating forecast performance in an inventory control system. Mngt Sci 36: 490–499.CrossRefGoogle Scholar
  85. Gardner ES (2006). Exponential smoothing: The state of the art—Part II. Int J Forecasting 22: 637–666.CrossRefGoogle Scholar
  86. Gardner ES and Diaz-Saiz J (2008). Exponential smoothing in the telecommunications data. Int J Forecasting 24(1): 170–174.CrossRefGoogle Scholar
  87. Gardner ES and Koehler AB (2005). Comments on a patented bootstrapping method for forecasting intermittent demand. Int J Forecasting 21: 617–618.CrossRefGoogle Scholar
  88. Gardner ES and McKenzie E (1985). Forecasting trends in time-series. Mngt Sci 31: 1237–1246.CrossRefGoogle Scholar
  89. Ghobbar AA and Friend CH (2002). Sources of intermittent demand for aircraft spare parts within airline operations. J Air Transport Mngt 8: 221–231.CrossRefGoogle Scholar
  90. Ghobbar AA and Friend CH (2003). Evaluation of forecasting methods for intermittent parts demand in the field of aviation: A predictive model. Comput Opns Res 30: 2097–2014.CrossRefGoogle Scholar
  91. Gilbert CL (1986). Professor Hendry's econometric methodology. Oxford Bull Econom Statist 48(3): 283–307.CrossRefGoogle Scholar
  92. Green PE, Krieger AM and Wind Y (2001). Thirty years of conjoint analysis: Reflections and prospects. Interfaces 31(3): S56–S73.CrossRefGoogle Scholar
  93. Hand DJ (1998). Data mining: Statistics and more? Am Statist 52: 112–118.Google Scholar
  94. Hanssens DM, Parsons LJ and Schultz RL (2001). Market Response Models: Econometric and Time Series Analysis, 2nd edn.. Kluwer: Norwell, MA.Google Scholar
  95. Harrison PJ and Stevens CF (1971). A Bayesian approach to short-term forecastiadf888ng. Opl Res Quart 22: 341–362.CrossRefGoogle Scholar
  96. Harvey AC (1984). A unified view of statistical forecasting procedures. J Forecasting 3: 245–275.CrossRefGoogle Scholar
  97. Harvey N (2001). Improving judgment in forecasting. In: Armstrong J.S. (ed). Principles of Forecasting. Kluwer: Norwell, MA, pp. 59–80.CrossRefGoogle Scholar
  98. Hendry DF and Mizon GE (1978). Serial-correlation as a convenient simplification, not a nuisance—Comment on a study of demand for money by Bank of England. Econom J 88(351): 549–563.Google Scholar
  99. Hippert HS, Pedreira CE and Souza RC (2001). Neural networks for short-term load forecasting: A review and evaluation. IEEE Trans Power Syst 16(1): 44–55.CrossRefGoogle Scholar
  100. Hyndman RJ and Koehler AB (2006). Another look at measures of forecast accuracy. Int J Forecasting 22: 679–688.CrossRefGoogle Scholar
  101. Hyndman RJ, Koehler AB, Snyder RD and Grose S (2002). A state space framework for automatic forecasting using exponential smoothing methods. Int J Forecasting 18(3): 439–454.CrossRefGoogle Scholar
  102. Hyndman RJ, Koehler AB, Ord JK and Snyder RD (2008). Forecasting with Exponential Smoothing: The State Space Approach. Springer: Berlin.CrossRefGoogle Scholar
  103. Jain AK, Duin RPW and Mao JC (2000). Statistical pattern recognition: A review. IEEE Trans Pattern Anal 22: 4–37.CrossRefGoogle Scholar
  104. Janssens D, Brijs T, Vanhoof K and Wets G (2006). Evaluating the performance of cost-based discretization versus entropy- and error-based discretization. Comput Opns Res 33(11): 3107–3123.CrossRefGoogle Scholar
  105. Johnston FR and Boylan JE (1996). Forecasting for items with intermittent demand. J Opl Res Soc 47: 113–121.CrossRefGoogle Scholar
  106. Kaefer F, Heilman CM and Ramenofsky SD (2005). A neural network application to consumer classification to improve the timing of direct marketing activities. Comput Opns Res 32: 2595–2615.CrossRefGoogle Scholar
  107. Keogh EJ and Kasetty S (2003). On the need for time series data mining benchmarks: A survey and empirical demonstration. Data Min Knowl Discov 7(4): 349–371.CrossRefGoogle Scholar
  108. Kim KJ, Moskowitz H and Koksalan M (1996). Fuzzy versus statistical linear regression. Eur J Opl Res 92(2): 417–434.CrossRefGoogle Scholar
  109. Kim Y, Street WN, Russell GJ and Menczer F (2005). Customer targeting: A neural network approach guided by genetic algorithms. Mngt Sci 51: 264–276.CrossRefGoogle Scholar
  110. Koehler AB, Snyder RD and Ord JK (2001). Forecasting models and prediction intervals for the multiplicative Holt-Winters method. Int J Forecasting 17: 269–286.CrossRefGoogle Scholar
  111. Küsters U, McCullough B and Bell M (2006). Forecasting software: Past, present and future. Int J Forecasting 22: 599–615.CrossRefGoogle Scholar
  112. Lawrence MJ, Edmundson RH and Oconnor MJ (1986). The accuracy of combining judgmental and statistical forecasts. Mngt Sci 32(12): 1521–1532.CrossRefGoogle Scholar
  113. Lawrence M, Goodwin P, O'Connor M and Onkal D (2006). Judgmental forecasting: A review of progress over the last 25 years. Int J Forecasting 22: 493–518.CrossRefGoogle Scholar
  114. Lee H, Padmanabhan V and Whang S (1997a). Information distortion in supply chain: The Bullwhip effect. Mngt Sci 43: 546–559.CrossRefGoogle Scholar
  115. Lee H, Padmanabhan V and Whang S (1997b). The bullwhip effect in supply chains. Sloan Mngt Rev 38(3): 93–102.Google Scholar
  116. Lee H, So KC and Tang CS (2000). The value of information sharing in a two-level supply chain. Mngt Sci 46: 626–643.CrossRefGoogle Scholar
  117. Lee WY, Goodwin P, Fildes R, Nikolopoulos K and Lawrence M (2007). Providing support for the use of analogies in demand forecasting tasks. Int J Forecasting 23(3): 377–390.CrossRefGoogle Scholar
  118. Leitch G and Tanner JE (1991). Economic-forecast evaluation—Profits versus the conventional error measures. Amer Econom Rev 81(3): 580–590.Google Scholar
  119. Liao KP and Fildes R (2005). The accuracy of a procedural approach to specifying feedforward neural networks for forecasting. Comput Opns Res 32: 2151–2169.CrossRefGoogle Scholar
  120. Lilien GL and Rangaswamy A (2004). Marketing Engineering, 2nd edn.. Addison-Wesley: Reading, MA.Google Scholar
  121. Lovie AD and Lovie P (1986). The flat maximum effect and linear scoring models for prediction. J Forecasting 5: 159–168.CrossRefGoogle Scholar
  122. MacGregor D (2001). Decomposition for judgemental forecasting and estimation. In: Armstrong J.S. (ed). Principles of Forecasting. Kluwer: Norwell, MA, pp. 107–123.CrossRefGoogle Scholar
  123. Makridakis S and Hibon M (1979). Accuracy of forecasting—Empirical investigation. J R Statist Soc (A) 142: 97–145.Google Scholar
  124. Makridakis S and Hibon M (2000). The M3-competition: Results, conclusions and implications. Int J Forecasting 16: 451–476.CrossRefGoogle Scholar
  125. Makridakis S, Andersen A, Carbone R, Fildes R, Hibon M and Lewandowski R et al (1982). The accuracy of extrapolation (time-series) methods—Results of a forecasting competition. J Forecasting 1: 111–153.CrossRefGoogle Scholar
  126. Mangasarian OL (1965). Linear and nonlinear separation of patterns by linear programming. Opns Res 13: 444–452.CrossRefGoogle Scholar
  127. Meade N (1985). Forecasting using growth curves—An adaptive approach. J Opl Res Soc 36: 1103–1115.Google Scholar
  128. Meade N (2000). Evidence for the selection of forecasting methods. J Forecasting 19(6): 515–535.CrossRefGoogle Scholar
  129. Meade N and Islam T (1998). Technological forecasting—Model selection, model stability, and combining models. Mngt Sci 44: 1115–1130.CrossRefGoogle Scholar
  130. Meade N and Islam T (2006). Modelling and forecasting the diffusion of innovation—A 25-year review. Int J Forecasting 22: 519–545.CrossRefGoogle Scholar
  131. Mehra RK (1979). Kalman filters and their applications to forecasting. In: Makridakis S. and Wheelwright S.C. (eds). Forecasting. North-Holland: Amsterdam.Google Scholar
  132. Meiri R and Zahavi J (2006). Using simulated annealing to optimize the feature selection problem in marketing applications. Eur J Opl Res 171: 842–858.CrossRefGoogle Scholar
  133. Miller DM and Williams D (2004). Damping seasonal factors: Shrinkage estimators for the X-12-ARIMA program. Int J Forecasting 20: 529–549.CrossRefGoogle Scholar
  134. Montgomery AL (2005). The implementation challenge of pricing decision support systems for retail managers. Applied Stochastic Models Bus Indust 21(4–5): 367–378.CrossRefGoogle Scholar
  135. Morwitz VG (2001). Methods for forecasting with intentions data. In: Armstrong J.S. (ed). Principles of Forecasting. Kluwer: Norwell, MA, pp. 33–56.CrossRefGoogle Scholar
  136. Morwitz VG, Steckel JH and Gupta A (2007). When do purchase intentions predict sales? Int J Forecasting 23(3): 347–364.CrossRefGoogle Scholar
  137. Murthy SK (1998). Automatic construction of decision trees from data: A multi-disciplinary survey. Data Min Knowl Disc 2: 345–389.CrossRefGoogle Scholar
  138. Neelamegham R and Chintagunta P (1999). A Bayesian model to forecast new product performance in domestic and international markets. Market Sci 18: 115–136.CrossRefGoogle Scholar
  139. Newbold P and Granger CWJ (1974). Experience with forecasting univariate time series and the combination of forecasts. J R Statist Soc (A) 137: 131–164.Google Scholar
  140. Olafsson S (2006). Introduction to operations research and data mining. Comput Opns Res 33: 3067–3069.CrossRefGoogle Scholar
  141. Olafsson S, Li X and Wu S (2008). Operations research and data mining. Eur J Opl Res 187(3): 1429–1448.CrossRefGoogle Scholar
  142. Onn KP and Mercer A (1998). The direct marketing of insurance. Eur J Opl Res 109: 541–549.CrossRefGoogle Scholar
  143. Ord JK, Koehler AB and Snyder RD (1997). Estimation and prediction for a class of dynamic nonlinear statistical models. J Amer Statist Assoc 92(440): 1621–1629.CrossRefGoogle Scholar
  144. Padmanabhan B and Tuzhilin A (2003). On the use of optimization for data mining: Theoretical interactions and eCRM opportunities. Mngt Sci 49: 1327–1343.CrossRefGoogle Scholar
  145. Pendharkar P and Nanda S (2006). A misclassification cost-minimizing evolutionary-neural classification approach. Nav Res Log 53(5): 432–447.CrossRefGoogle Scholar
  146. Pidd M (2003). Tools for Thinking. Wiley: Chichester, UK.Google Scholar
  147. Poon SH and Granger CWJ (2003). Forecasting volatility in financial markets: A review. J Econom Literature 41: 478–539.CrossRefGoogle Scholar
  148. Provost F and Fawcett T (2001). Robust classification for imprecise environments. Mach Learn 42(3): 203–231.CrossRefGoogle Scholar
  149. Quinlan JR (1979). Discovering Rules by induction from large collection of examples. In: Michie D. (ed). Expert Systems in the Micro-electronic Age. Edinburgh University Press: Edinburgh.Google Scholar
  150. Quinlan JR (1993). C45: Programs for Machine Learning. Morgan Kaufmann Publishers: San Mateo, CA.Google Scholar
  151. Reid DJ (1972). A comparison of forecasting techniques on economic time series. In: Bramson M.J., Helps I.G. and Watson-Gandy J.A.C.C. (eds). Forecasting in Action. Operational Research Society and the Society for Long Range Planning.Google Scholar
  152. Rosset S, Neumann E, Eick U and Vatnik N (2003). Customer lifetime value models for decision support. Data Min Knowl Disc 7(3): 321–339.CrossRefGoogle Scholar
  153. Rossi PE and Allenby GM (2003). Bayesian statistics and marketing. Market Sci 22: 304–328.CrossRefGoogle Scholar
  154. Rowe G and Wright G (2001). Expert opinion in forecasting: The role of the Delphi technique. In: Armstrong J.S. (ed). Principles of Forecasting. Kluwer: Norwell, MA.Google Scholar
  155. Rumelhart DE and McClelland JL (1986). University of California San Diego. PDP Research Group. Parallel Distributed Processing: Explorations in the Microstructure of Cognition. MIT Press: Cambridge, MA.Google Scholar
  156. Salchenberger LM, Cinar EM and Lash NA (1992). Neural networks—A new tool for predicting thrift failures. Decis Sci 23(4): 899–916.CrossRefGoogle Scholar
  157. Sanders NR and Manrodt KB (1994). Forecasting practices in United-States corporations—Survey results. Interfaces 24(2): 92–100.CrossRefGoogle Scholar
  158. Sani B and Kingsman BG (1997). Selecting the best periodic inventory control and demand forecasting methods for low demand items. J Opl Res Soc 48: 700–713.CrossRefGoogle Scholar
  159. Sawhney MS and Eliashberg J (1996). A parsimonious model for forecasting gross box-office revenues of motion pictures. Market Sci 15: 113–131.CrossRefGoogle Scholar
  160. Scholkopf B, Sung KK, Burges CJC, Girosi F, Niyogi P, Poggio T and Vapnik V (1997). Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Trans Signal Process 45: 2758–2765.CrossRefGoogle Scholar
  161. Shale EA, Boylan JE and Johnston FR (2006). Forecasting for intermittent demand: The estimation of an unbiased average. J Opl Res Soc 57: 588–592.CrossRefGoogle Scholar
  162. Sharda R (1994). Neural networks for the MS/OR analyst—An application bibliography. Interfaces 24: 116–130.CrossRefGoogle Scholar
  163. Shaw MJ, Subramaniam C, Tan GW and Welge ME (2001). Knowledge management and data mining for marketing. Decis Support Syst 31: 127–137.CrossRefGoogle Scholar
  164. Shenstone L and Hyndman RJ (2005). Stochastic models underlying Croston's method for intermittent demand forecasting. J Forecasting 24: 389–402.CrossRefGoogle Scholar
  165. Shore H and Benson-Karhi D (2007). Forecasting S-shaped diffusion processes via response modelling methodology. J Opl Res Soc 58(6): 720–728.CrossRefGoogle Scholar
  166. Smaros J (2007). Forecasting collaboration in the European grocery sector: Observations from a case study. J Opns Mngt 25: 702–716.Google Scholar
  167. Smith KA and Gupta JND (2000). Neural networks in business: Techniques and applications for the operations researcher. Comput Opns Res 27: 1023–1044.CrossRefGoogle Scholar
  168. Smith KA, Willis RJ and Brooks M (2000). An analysis of customer retention and insurance claim patterns using data mining: A case study. J Opns Res Soc 51: 532–541.CrossRefGoogle Scholar
  169. Smith-Miles KA (2008). Cross-disciplinary perspectives on meta-learning for algorithm selection. ACM Comput Surveys 40, forthcoming.Google Scholar
  170. Smola AJ and Schölkopf B (2004). A tutorial on support vector regression. Statist Comput 14: 199–222.CrossRefGoogle Scholar
  171. Snyder RD, Koehler AB and Ord JK (2002). Forecasting for inventory control with exponential smoothing. Int J Forecasting 18: 5–18.CrossRefGoogle Scholar
  172. Sultan F, Farley JU and Lehmann DR (1990). A meta-analysis of applications of diffusion-models. J Market Res 27(1): 70–77.CrossRefGoogle Scholar
  173. Syntetos AA and Boylan JE (2001). On the bias on intermittent demand estimates. Int J Product Econ 71: 457–466.CrossRefGoogle Scholar
  174. Syntetos AA and Boylan JE (2005). The accuracy of intermittent demand estimates. Int J Forecasting 21: 303–314.CrossRefGoogle Scholar
  175. Syntetos AA and Boylan JE (2006). On the stock-control performance of intermittent demand estimators. Int J Product Econ 103: 36–47.CrossRefGoogle Scholar
  176. Syntetos AA, Boylan JE and Croston JD (2005). On the categorisation of demand patterns. J Opl Res Soc 56: 495–503.CrossRefGoogle Scholar
  177. Talukdar D, Sudhir K and Ainslie A (2002). Investigating new product diffusion across products and countries. Market Sci 21: 97–114.CrossRefGoogle Scholar
  178. Tam KY and Kiang MY (1992). Managerial applications of neural networks—The case of bank failure predictions. Mngt Sci 38: 926–947.CrossRefGoogle Scholar
  179. Tan PN, Steinbach M and Kumar V (2005). Introduction to Data Mining, 1st edn.. Pearson Addison Wesley: Boston.Google Scholar
  180. Tashman LJ and Hoover J (2001). Diffusion of forecasting principles through software. In: Armstrong JS (ed) Principles of Forecasting: A handbook for researchers and practitioners. Kluwer: Norwell, MA, pp. 651–676.Google Scholar
  181. Tay AS and Wallis KF (2000). Density forecasting: A survey. J Forecasting 19(4): 235–254.CrossRefGoogle Scholar
  182. Taylor JW (2003). Exponential smoothing with a damped multiplicative trend. Int J Forecasting 19: 715–725.CrossRefGoogle Scholar
  183. Taylor JW (2007). Forecasting daily supermarket sales using exponentially weighted quantile regression. Eur J Opl Res 178(1): 154–167.CrossRefGoogle Scholar
  184. Taylor JW and Buizza R (2006). Density forecasting for weather derivative pricing. Int J Forecasting 22(1): 29–42.CrossRefGoogle Scholar
  185. Terasvirta T, van Dijk D and Medeiros MC (2005). Linear models, smooth transition autoregressions, and neural networks for forecasting macroeconomic time series: A re-examination. Int J Forecasting 21: 755–774.CrossRefGoogle Scholar
  186. Thomas LC (2000). A survey of credit and behavioural scoring: Forecasting financial risk of lending to consumers. Int J Forecasting 16: 149–172.CrossRefGoogle Scholar
  187. Thomas JW (2006). New Product sales forecasting., accessed 12 April 2007.
  188. Thomas LC, Crook JN and Edelman DB (2002). Credit Scoring and its Applications. Philadelphia: SIAM.CrossRefGoogle Scholar
  189. Trigg DW and Leach AG (1967). Exponential smoothing with an adaptive response rate. Opl Res Quart 18: 53–59.CrossRefGoogle Scholar
  190. Urban GL, Hauser JR and Roberts JH (1990). Prelaunch forecasting of new automobiles. Man Sci 36: 401–421.CrossRefGoogle Scholar
  191. Van den Bulte C and Lilien GL (1997). Bias and systematic change in the parameter estimates of macro-level diffusion models. Market Sci 16: 338–353.CrossRefGoogle Scholar
  192. Van den Poel D and Lariviere B (2004). Customer attrition analysis for financial services using proportional hazard models. Eur J Opl Res 157: 196–217.CrossRefGoogle Scholar
  193. Vapnik VN (2000). The Nature of Statistical Learning Theory, 2nd edn. Springer: New York.CrossRefGoogle Scholar
  194. Vapnik VN and Chervonenkis AIA (1979). Theorie der Zeichenerkennung. Akademie-Verlag: Berlin.Google Scholar
  195. Viaene S and Dedene G (2005). Cost-sensitive learning and decision making revisited. Eur J Opl Res 166(1): 212–220.CrossRefGoogle Scholar
  196. Wierenga B, Van Bruggen GH and Staelin R (1999). The success of marketing management support systems. Market Sci 18: 196–207.CrossRefGoogle Scholar
  197. Willemain TR, Smart CN, Shockor JH and Desautels PA (1994). Forecasting intermittent demand in manufacturing—A comparative evaluation of Croston's method. Int J Forecasting 10: 529–538.CrossRefGoogle Scholar
  198. Willemain TR, Smart CN and Schwarz HF (2004). A new approach to forecasting intermittent demand for service parts inventories. Int J Forecasting 20: 375–387.CrossRefGoogle Scholar
  199. Wilson RL and Sharda R (1994). Bankruptcy prediction using neural networks. Decis Support Syst 11(5): 545–557.CrossRefGoogle Scholar
  200. Wittink DR and Bergestuen T (2001). Forecasting with conjoint analysis. In: Armstrong J.S. (ed). Principles of Forecasting. Kluwer: Norwell, MA, pp. 147–170.CrossRefGoogle Scholar
  201. Yajima Y (2005). Linear programming approaches for multicategory support vector machines. Eur J Opl Res 162: 514–531.CrossRefGoogle Scholar
  202. Yang JY and Olafsson S (2006). Optimization-based feature selection with adaptive instance sampling. Comput Opns Res 33: 3088–3106.CrossRefGoogle Scholar
  203. Yoon YO, Swales G and Margavio TM (1993). A comparison of discriminant analysis versus artificial neural networks. J Opl Res Soc 44: 51–60.CrossRefGoogle Scholar
  204. Yu Z, Yan H and Cheng T (2002). Modelling the benefits of information sharing-based partnerships in a two-level supply chain. J Opl Res Soc 53: 436–446.CrossRefGoogle Scholar
  205. Zhang GQ (2000). Neural networks for classification: A survey. IEEE Trans Systems Man Cybernet Part C—Appl Rev 30: 451–462.CrossRefGoogle Scholar
  206. Zhang G, Patuwo BE and Hu MY (1998). Forecasting with artificial neural networks: The state of the art. Int J Forecasting 14: 35–62.CrossRefGoogle Scholar
  207. Zhang GQ, Hu MY, Patuwo BE and Indro DC (1999). Artificial neural networks in bankruptcy prediction: General framework and cross-validation analysis. Eur J Opl Res 116: 16–32.CrossRefGoogle Scholar
  208. Zhang GQP (2000). Neural networks for classification: A survey. IEEE Trans Systems Man Cybernet Part C—Appl Rev 30(4): 451–462.CrossRefGoogle Scholar
  209. Zhao X, Xie J and Leung J (2002). The impact of forecasting model selection on the value of information sharing in a supply chain. Eur J Opl Res 142: 321–344.CrossRefGoogle Scholar
  210. Zheng ZG and Padmanabhan B (2006). Selectively acquiring customer information: A new data acquisition problem and an active learning-based solution. Mngt Sci 52(5): 697–712.CrossRefGoogle Scholar

Copyright information

© Palgrave Macmillan Ltd 2008

Authors and Affiliations

  • R Fildes
    • 1
  • K Nikolopoulos
    • 2
  • S F Crone
    • 1
  • A A Syntetos
    • 3
  1. 1.Lancaster UniversityLancasterUK
  2. 2.University of ManchesterManchesterUK
  3. 3.University of SalfordSalfordUK

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