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JACIII Vol.15 No.2 pp. 134-144
doi: 10.20965/jaciii.2011.p0134
(2011)

Paper:

Mean Local Trend Error and Fuzzy-Inference-Based Multicriteria Evaluation for Supply Chain Demand Forecasting

Jingpei Dan*,**, Fuding Xie***, Fangyan Dong*, and Kaoru Hirota*

*Department of Computational Intelligence and Systems Science, Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan

**Department of Computer Science, Chongqing University, Chongqing 400044, P. R. China

***Department of Computer Science, Liaoning Normal University, Liaoning Dalian 116029, P. R. China

Received:
October 6, 2010
Accepted:
January 17, 2011
Published:
March 20, 2011
Keywords:
supply chain, time series data, fuzzy inference, multicriteria evaluation, forecasting
Abstract

To overcome the inefficiency arising from the separate use of conventional forecast accuracy measures that suffer from the bullwhip effect, especially in uncertain and vague supply chain environments, a forecast accuracy measure, Mean Local Trend Error (MLTE) and a fuzzy-inference-based multicriteria evaluation method are proposed. In contrast to conventional measures, MLTE survives the bullwhip effect by evaluating forecasts based on local trend error. The proposed evaluation method applies fuzzy inference to deal with the uncertainty and vagueness in supply chains and makes a comprehensive evaluation by using an aggregated forecast accuracy index (ACCURACY), which is developed based on fuzzy inference by integrating the proposed MLTE and a conventional measure MAPE, thereby enhancing its efficiency for evaluating supply chain demand forecasts. The proposed MLTE and evaluation method are confirmed by comparative experiments with MAPE based on evaluating four typical forecasting methods – a simple moving average, single exponential smoothing, autoregressive, and autoregressive moving average – on an actual manufacturing-order dataset. The results show that MLTE yields a triple and ACCURACY a quadruple improvement in terms of average distinguishability compared to MAPE. The proposal has potential applications in stock market forecast evaluations.

Cite this article as:
Jingpei Dan, Fuding Xie, Fangyan Dong, and Kaoru Hirota, “Mean Local Trend Error and Fuzzy-Inference-Based Multicriteria Evaluation for Supply Chain Demand Forecasting,” J. Adv. Comput. Intell. Intell. Inform., Vol.15, No.2, pp. 134-144, 2011.
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References
  1. [1] C. Carlsson and R. Fullér, “Fuzzy approach to the bullwhip effect,” Proc. of the 15th European Meeting on Cybernetics and Systems Research, Vienna, April 25-18, pp. 228-233, 2000.
  2. [2] B. Jeong et al., “A computerized causal forecasting system using genetic algorithms in supply chain management,” J. of Systems and software, Vol.60, No.3, pp. 223-237, 2002.
  3. [3] W. Y. Liang and C. C. Huang, “Agent-based demand forecast in multi-echelon supply chain,” Decision Support Systems, Vol.42, No.1, pp. 390-407, 2006.
  4. [4] T. Hosoda and S. M. Disney, “On variance amplification in a threeechelon supply chain with minimum mean square error forecasting,” Omega, Vol.34, No.4, pp. 344-358, 2006.
  5. [5] L. Aburto and R. Weber, “Improved supply chain management based on hybrid demand forecasts,” Applied Soft Computing, Vol.7, No.1, pp. 136-144, 2007.
  6. [6] R. Carbonneau et al., “Application of machine learning techniques for supply Chain demand forecasting,” European J. of Operational Research, Vol.184, No.3, pp. 1140-1154, 2008.
  7. [7] L. Ferbar et al., “Demand forecasting methods in a supply chain: Smoothing and denoising,” Int. J. of Production Economics, Vol.118, No.1, pp. 49-54, 2009.
  8. [8] J. P. Dan et al., “Multistep- ahead supply chain demand forecast based on echo state networks,” Proc. of the Eighth Int. Conf. on Information Management and Sciences, pp. 669-672, 2009.
  9. [9] H. E. Sayed et al., “A hybrid statistical genetic-based demand forecasting expert system,” Expert Systems with Applications, Vol.36, No.9, pp. 11662-11670, 2009.
  10. [10] Q. Wu, “Product demand forecasts using wavelet kernel support vector machine and particle swarm optimization in manufacture system,” J. of Computational and Applied Mathematics, Vol.233, No.10, pp. 2481-2491, 2010.
  11. [11] H. L. Lee et al., “Information distortion in a supply chain: the bullwhip effect,” Management Science, Vol.43, No.4, pp. 546-558, 1997.
  12. [12] R. J. Hyndman and A. B. Koehler, “Another look at measures of forecast accuracy,” Int. J. of Forecasting, Vol.22, No.4, pp. 679-688, 2006.
  13. [13] A. Kerkkänen et al., “Demand forecasting errors in industrial context: Measurement and impacts,” Int. J. of Production Economics, Vol.118, No.1, pp. 43-48, 2009.
  14. [14] R. Grimberg et al., “Fuzzy inference system used for a quantitative evaluation of the material discontinuities detected by eddy current sensors,” Sensors and Actuators A: Physical, Vol.81, No.1-3, pp. 248-250, 2000.
  15. [15] H. C. Chou et al., “Evaluating new drugs by fuzzy inference system,” Int. Computer Symposium, Taipei, Taiwan, 2004.
  16. [16] W. Ocampo-Duque et al., “Assessing water quality in rivers with fuzzy inference systems: A case study,” Environment International, Vol.32, No.6, pp. 733-742, 2006.
  17. [17] S. M. Mazloumzadeh et al., “Evaluation of general-purpose lifters for the date harvest industry based on a fuzzy inference system,” Computers and Electronics in Agriculture, Vol.60, No.1, pp. 60-66, 2008.
  18. [18] F. Ahmed et al., “Fuzzy inference system for software product family process evaluation,” Information Sciences, Vol.178, No.13, pp. 2780-2793, 2008.
  19. [19] J. Chen et al., “Study a Fuzzy Inference System on the Supplier Evaluation in E-Manufacturing,” Applied Mechanics and Materials, Vol.16, No.19, pp. 189-192, 2009.
  20. [20] T. J. Ross, “Fuzzy logic with engineering applications,” John Wiley & Sons, Second edition, 2004.
  21. [21] V. Kreinovich et al., “Gaussian membership functions are most adequate in representing uncertainty in measurements,” NASA, Johnson Space Center, North American Fuzzy Logic Information Processing Society (NAFIPS 1992), Vol.2, pp. 618-624, 1992.
  22. [22] StatsCan, Statistics Canada Table 304-0014, 2004.
  23. [23] Z. Chen and Y. Yang, “Assessing Forecast Accuracy Measures,” 2004.
    http://www.stat.iastate.edu/preprint/articles/2004-10.pdf

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