An Autonomous Collaborative Forecasting System Implementation – The First Step towards Successful CPFR System
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.
[1] VICS Association, Collaborative Planning Forecasting and
Replenishment Voluntary Guidelines, 2002, Available: www.vics.org.
[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.
[1] VICS Association, Collaborative Planning Forecasting and
Replenishment Voluntary Guidelines, 2002, Available: www.vics.org.
[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.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:57667", author = "Chi-Fang Huang and Yun-Shiow Chen and Yun-Kung Chung", title = "An Autonomous Collaborative Forecasting System Implementation – The First Step towards Successful CPFR System", abstract = "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 = "CPFR, artificial neural networks, global logistics,
supply and demand chain.", volume = "2", number = "11", pages = "1225-10", }
