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Title: An intelligent fuzzy decision support system for flexible adjustment of dye pricing to manage customer-supplier relationship
Authors: Lee, JCH 
Choy, KL 
Leung, KH 
Keywords: Customer relationship management
Dye pricing decision support
Pricing strategy
Smart manufacturing
Issue Date: 2018
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: Proceedings - 2018 IEEE International Conference on Smart Manufacturing, Industrial and Logistics Engineering, SMILE 2018, 2018, v. 2018-January, p. 7-11 How to cite?
Abstract: 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.
Description: 2018 IEEE International Conference on Smart Manufacturing, Industrial and Logistics Engineering, SMILE 2018, Hsinchu, Taiwan, 8-9 February 2018
ISBN: 9781538631836
DOI: 10.1109/SMILE.2018.8353972
Appears in Collections:Conference Paper

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