TY - GEN
T1 - An intelligent fuzzy decision support system for flexible adjustment of dye pricing to manage customer-supplier relationship
AU - Lee, Jason C.H.
AU - Choy, K. L.
AU - Leung, K. H.
N1 - Funding Information:
The authors would like to thank the Research Office of the Hong Kong Polytechnic University for supporting the project.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/5/3
Y1 - 2018/5/3
N2 - Proper dye pricing strategy is one of the essential factors that determine the success of a dye business. However, dye pricing decision is a difficult one to make due to the complicated business scenarios that dye practitioners often face. In case of the customers failed to commit the full amount of their pre-booked dye service, in the perspective of a dye house, the dye practitioner would end up losing the profit margin due to the excess dye machinery capacity remained unallocated to a customer for production. To avoid such problematic issues to happen repeatedly and periodically, dye practitioners often 'penalize' the customers who failed to fully commit the booking by, either reducing the discount that was originally provided for the customer at first, or delaying the delivery of the end products. Although the top management who make the final pricing decision are experienced in deciding and applying a proper 'penalty', the problem of inconsistency during the decision-making process with the existence of diverse factors affecting the dye service pricing has resulted in the top management spending large efforts as well as amount of time to make the final decision. In view of improving the quality of such decision and alleviate the pressure of the top management, this paper presents an intelligent system, integrating database management and fuzzy logic technique, for providing decision support in adjusting the dyeing price. Results upon conducting a case study in a dye house located in the Pearlriver delta region of the mainland China indicate that the proposed solution outperforms the manual decision-making process.
AB - Proper dye pricing strategy is one of the essential factors that determine the success of a dye business. However, dye pricing decision is a difficult one to make due to the complicated business scenarios that dye practitioners often face. In case of the customers failed to commit the full amount of their pre-booked dye service, in the perspective of a dye house, the dye practitioner would end up losing the profit margin due to the excess dye machinery capacity remained unallocated to a customer for production. To avoid such problematic issues to happen repeatedly and periodically, dye practitioners often 'penalize' the customers who failed to fully commit the booking by, either reducing the discount that was originally provided for the customer at first, or delaying the delivery of the end products. Although the top management who make the final pricing decision are experienced in deciding and applying a proper 'penalty', the problem of inconsistency during the decision-making process with the existence of diverse factors affecting the dye service pricing has resulted in the top management spending large efforts as well as amount of time to make the final decision. In view of improving the quality of such decision and alleviate the pressure of the top management, this paper presents an intelligent system, integrating database management and fuzzy logic technique, for providing decision support in adjusting the dyeing price. Results upon conducting a case study in a dye house located in the Pearlriver delta region of the mainland China indicate that the proposed solution outperforms the manual decision-making process.
KW - customer relationship management
KW - Dye pricing decision support
KW - pricing strategy
KW - smart manufacturing
UR - http://www.scopus.com/inward/record.url?scp=85048856136&partnerID=8YFLogxK
U2 - 10.1109/SMILE.2018.8353972
DO - 10.1109/SMILE.2018.8353972
M3 - Conference article published in proceeding or book
AN - SCOPUS:85048856136
T3 - Proceedings - 2018 IEEE International Conference on Smart Manufacturing, Industrial and Logistics Engineering, SMILE 2018
SP - 7
EP - 11
BT - Proceedings - 2018 IEEE International Conference on Smart Manufacturing, Industrial and Logistics Engineering, SMILE 2018
A2 - Chien, Chen-Fu
A2 - Dou, Runliang
A2 - Wu, Jie-Zheng
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Smart Manufacturing, Industrial and Logistics Engineering, SMILE 2018
Y2 - 8 February 2018 through 9 February 2018
ER -