Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 31100
An Autonomous Collaborative Forecasting System Implementation – The First Step towards Successful CPFR System

Authors: Chi-Fang Huang, Yun-Shiow Chen, Yun-Kung Chung


In the past decade, artificial neural networks (ANNs) have been regarded as an instrument for problem-solving and decision-making; indeed, they have already done with a substantial efficiency and effectiveness improvement in industries and businesses. In this paper, the Back-Propagation neural Networks (BPNs) will be modulated to demonstrate the performance of the collaborative forecasting (CF) function of a Collaborative Planning, Forecasting and Replenishment (CPFR®) system. CPFR functions the balance between the sufficient product supply and the necessary customer demand in a Supply and Demand Chain (SDC). Several classical standard BPN will be grouped, collaborated and exploited for the easy implementation of the proposed modular ANN framework based on the topology of a SDC. Each individual BPN is applied as a modular tool to perform the task of forecasting SKUs (Stock-Keeping Units) levels that are managed and supervised at a POS (point of sale), a wholesaler, and a manufacturer in an SDC. The proposed modular BPN-based CF system will be exemplified and experimentally verified using lots of datasets of the simulated SDC. The experimental results showed that a complex CF problem can be divided into a group of simpler sub-problems based on the single independent trading partners distributed over SDC, and its SKU forecasting accuracy was satisfied when the system forecasted values compared to the original simulated SDC data. The primary task of implementing an autonomous CF involves the study of supervised ANN learning methodology which aims at making “knowledgeable" decision for the best SKU sales plan and stocks management.

Keywords: Artificial Neural Networks, Global Logistics, CPFR, supply and demand chain

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1627


[1] VICS Association, Collaborative Planning Forecasting and Replenishment Voluntary Guidelines, 2002, Available:
[2] Croson, R. and K. Donohue, "Behavioral Causes of the Bullwhip Effect and the Observed Value of Inventory Information," Management Science, vol.52, no.3, pp.323-336, 2006.
[3] Caro, F. and J. Gallien, "Dynamic Assortment with Demand Learning for Seasonal Consumer Goods," Management Science, vol.53, no.2, pp.276-292, 2007.
[4] Seifert, D., Collaborative Planning, Forecasting and Replenishment: How to Create a Supply Chain Advantage, AMACOM, American Management Association, 2003.
[5] Caridi, M., R. Cigolini and D. De Marco, "Linking autonomous agents to CPFR to improve SCM," Journal of Enterprise Information Management, vol.19, no.5, pp.465-482, 2006.
[6] Cheng, Yun-Hui, Hai-Wei Liao and Yun-Shiow Chen, "Implementation of a Back-Propagation Neural Network for Demand Forecasting in a Supply Chain - A Practical Case Study," in 2006 IEEE International Conference on Service Operations, Logistics and Informatics (SOLI '06), pp.1036-1041.
[7] Danese, P. "Designing CPFR collaborations: insights from seven case studies," International Journal of Operations and Production Management, vol.27, no.2, pp.81-204, 2007.
[8] Chandra, C. and J. Grabis, "Application of multi-steps forecasting for restraining the bullwhip effect and improving inventory performance under autoregressive demand," European Journal of Operational Research, vol.166, no.2, pp.337-350, 2005.
[9] Vereecke, A. and S. Muylle, "Performance improvement through supply chain collaboration in Europe," International Journal of Operations and Production Management, Vol.26, No.11, pp.1176-1198, 2006.
[10] Gurbuz, M. C., K. Moinzadeh and Y-P Zhou, "Coordinated Replenishment Strategies in Inventory/Distribution Systems," Management Science, vol.53, no.2, pp.293-307, 2007.
[11] Harrington, L. H., "9 steps to success with CPFR," Transportation and Distribution, pp.50-52, April, 2003.
[12] Chang, Ti-H, H-P Fu, W-I Lee, Y Lin and H-C Hsueh, "A study of an augmented CPFR model for the 3C retail industry," Supply Chain Management: An International Journal, vol.12, no.3, pp.200-209, 2007.
[13] Fliedner, G, "CPFR: an emerging supply chain tool," Industrial Management and Data Systems, vol.103, no.1/2, pp.14-21, 2003.
[14] Kotsialos, A., M. Papageorgiou, A. Poulimenos, "Long-term sales forecasting using holt-winters and neural network methods," Journal of Forecasting, vol.24, no. 5, pp.353-368, 2005.
[15] Bishop, Christopher M., Neural Networks for Pattern Recognition, Oxford University Press, 2004.
[16] Haykin, S., Neural Networks: A Comprehensive Foundation, 2nd Ed., Macmillan College Publishing, New York, 2001.
[17] Jeng-Bin Li and Yun-Kung Chung, "A Novel Back-propagation Neural Network Training Algorithm Designed by an Ant Colony Optimization," Transmission and Distribution Conference and Exhibition: Asia and Pacific, 2005 IEEE/PES, pp.1-5.
[18] Tawfiq, A. S. and E. A. Ibrahim, "Artificial neural networks as applied to long-term demand forecasting," Artificial Intelligence in Engineering, vol.13, no.2, pp.189-197, 1999.
[19] Sima, J., "Neural Expert System," Neural Networks, vol.8, no.2, pp.261-271, 1995.
[20] Fu, Li-Min, Neural Networks in Computer Intelligence, McGraw-Hill, 1995.
[21] Caridi, M., R. Cigolini and D. De Marco, "Improving supply-chain collaboration by linking intelligent agents to CPFR," International Journal of Production Research, vol.43, no.20, pp.4191-4218, 2005.
[22] Gaur, V., Giloni, A. and Seshadri, S., "Information sharing in a supply chain under ARMA demand", Management Science, vol.51, no.6, pp.961-969, 2005.
[23] Pramatari, K. P. T, "The impact of collaborative store ordering on shelf availability," Supply Chain Management: An International Journal, vol.13, no.1, pp.49-61, 2008.
[24] Aviv, Y., "On the benefits of collaborative forecasting partnerships between retailers and manufacturers," Management Science, vol.53, no.5, pp.777-794, 2007.
[25] Hrycej, T. Modular learning in Neural Networks: A Modularized Approach to Classification, Wiley, New York, 1992.
[26] Kehagias, A. and V. Petridis, "Predictive Modular Neural Networks for Time Series Classification," Neural Networks, vol.10, no.1, 1997, pp.31-49.
[27] Saito, K. and R. Nakano, "Discovery of relevant weights by minimizing cross-validation error," Proc. PAKDD 2000, LNAI 1805, pp. 372-375.
[28] Chi-Fang Huang, "Design of an integrated system of artificial neural networks and grey theory for collaborative prediction," MS thesis, Dept. of Industrial Engineering, Yuan Ze University, Taiwan, 2006.