Fuzzy Forecast Combining for Apparel Demand Forecasting

In this chapter, we present a novel approach for apparel demand forecasting that constitutes a main ingredient for a decision support system we designed. Our contribution is twofold. First, we develop a method that generates forecasts based on the inherent seasonal demand pattern at product category level. This pattern is identified by estimating lost sales and the effects of special events and pricing on demand. The method also allows easy integration of product managers’ qualitative information on factors that may affect demand. Second, we develop a fuzzy forecast combiner. The combiner calculates the final forecast using a weighted average of forecasts generated by independent methods. Combination weights are adaptive in the sense that the weights of the better-performing methods are increased over time. Forecast combination operations employ fuzzy logic. We illustrate our approach with a simulation study that uses data from a Turkish apparel firm.

[1]  Lotfi A. Zadeh,et al.  Fuzzy Sets , 1996, Inf. Control..

[2]  J. M. Bates,et al.  The Combination of Forecasts , 1969 .

[3]  C. Granger,et al.  Experience with Forecasting Univariate Time Series and the Combination of Forecasts , 1974 .

[4]  R. L. Winkler,et al.  The Combination of Forecasts , 1983 .

[5]  C. Granger,et al.  Forecasting Economic Time Series. , 1988 .

[6]  C. Granger.  Invited review combining forecasts—twenty years later , 1989 .

[7]  L. A. Zedeh.  Knowledge representation in fuzzy logic , 1989 .

[8]  R. Clemen.  Combining forecasts: A review and annotated bibliography , 1989 .

[9]  A. Zellner,et al.  Forecasting turning points in international output growth rates using Bayesian exponentially weighted autoregression, time-varying parameter, and pooling techniques , 1991 .

[10]  Fred Collopy,et al.  Expert Opinions About Extrapolation and the Mystery of the Overlooked Discontinuities , 1992 .

[11]  Timo Teräsvirta,et al.  The combination of forecasts using changing weights , 1994 .

[12]  R. Fildes,et al.  The Impact of Empirical Accuracy Studies On Time Series Analysis and Forecasting , 1995 .

[13]  J. Mendel.  Fuzzy logic systems for engineering: a tutorial , 1995, Proc. IEEE.

[14]  James Richardson.  Vertical Integration and Rapid Response in Fashion Apparel , 1996 .

[15]  Stephen Yurkovich,et al.  Fuzzy Control , 1997 .

[16]  A. Fiordaliso.  A nonlinear forecasts combination method based on Takagi–Sugeno fuzzy systems , 1998 .

[17]  J. Stock,et al.  A Comparison of Linear and Nonlinear Univariate Models for Forecasting Macroeconomic Time Series , 1998 .

[18]  S. French,et al.  Forecasting with judgment , 1998 .

[19]  Fred Collopy,et al.  Integration of Statistical Methods and Judgment for Time Series Forecasting: Principles from Empirical Research , 2004 .

[20]  H. White,et al.  Cointegration, causality, and forecasting : a festschrift in honour of Clive W.J. Granger , 1999 .

[21]  Spyros Makridakis,et al.  The M3-Competition: results, conclusions and implications , 2000 .

[22]  J. Scott Armstrong,et al.  Principles of forecasting , 2001 .

[23]  J. Scott Armstrong,et al.  Principles of forecasting : a handbook for researchers and practitioners , 2001 .

[24]  Petri Mähönen,et al.  Fuzzy logic-based forecasting model , 2001 .

[25]  Jim Burruss,et al.  Forecasting for Short-Lived Products: Hewlett-Packard's Journey , 2003 .

[26]  Allan Timmermann,et al.  Optimal Forecast Combination Under Regime Switching , 2004 .

[27]  Heung Wong,et al.  Determining when to update the weights in combined forecasts for product demand--an application of the CUSUM technique , 2004, Eur. J. Oper. Res..

[28]  A. Timmermann.  Chapter 4 Forecast Combinations , 2006 .

[29]  Keith J. Burnham,et al.  Fuzzy decision support system for demand forecasting with a learning mechanism , 2006, Fuzzy Sets Syst..

[30]  C. Granger,et al.  Handbook of Economic Forecasting , 2006 .

[31]  Rob J Hyndman,et al.  Another look at measures of forecast accuracy , 2006 .

[32]  Robert Fildes,et al.  Against Your Better Judgment? How Organizations Can Improve Their Use of Management Judgment in Forecasting , 2007, Interfaces.

[33]  Juan R. Correa.  Optimization of a fast-response distribution network , 2007 .

[34]  Yong Yu,et al.  Fashion retail forecasting by evolutionary neural networks , 2008 .

[35]  Alper Sen,et al.  The US fashion industry: A supply chain review , 2008 .

[36]  Yong Yu,et al.  Sales forecasting using extreme learning machine with applications in fashion retailing , 2008, Decis. Support Syst..

[37]  Engin Yesil,et al.  Adaptive fuzzy model based inverse controller design using BB-BC optimization algorithm , 2011, Expert Syst. Appl..

[38]  Shyi-Ming Chen,et al.  TAIEX Forecasting Based on Fuzzy Time Series and Fuzzy Variation Groups , 2011, IEEE Transactions on Fuzzy Systems.

[39]  Yong Yu,et al.  An intelligent fast sales forecasting model for fashion products , 2011, Expert Syst. Appl..

[40]  M. Kaya,et al.  Fuzzy forecast combiner design for fast fashion demand forecasting , 2012, 2012 International Symposium on Innovations in Intelligent Systems and Applications.

[41]  Mrinalini Shah,et al.  Fuzzy based trend mapping and forecasting for time series data , 2012, Expert Syst. Appl..