TY - JOUR
T1 - Designing an intelligent decision support system for effective negotiation pricing
T2 - A systematic and learning approach
AU - Fu, Xin
AU - Zeng, Xiao Jun
AU - Luo, Xin(Robert)
AU - Wang, Di
AU - Xu, Di
AU - Fan, Qing Liang
N1 - Funding Information:
This work is supported by the National Natural Science Foundation of China (No. 71301133, 71572166, 71671153, 71671149, and 71202059) and the Fundamental Research Funds for theCentral Universities (Project No. 20720161044). The authors are grateful to the reviewers and editor for their invaluable and insightful comments that have helped to improve this work. Thanks also go to all participants who have helped completed the MP3 dataset questionnaire.
Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017/4/1
Y1 - 2017/4/1
N2 - Automatic negotiation pricing and differential pricing aim to provide different customers with products/services that adequately meet their requirements at the “right” price. This often takes place with the purchase of expensive products/services and in the business-to-business context. Effective negotiation pricing can help enhance a company's profitability, balance supply and demand, and improve the customer satisfaction. However, determining the “right” price is a rather complex decision-making problem that puzzles pricing managers, as it needs to consider information from many constituents of the purchase channel. To further advance this line of research, this study proposes a systematic and learning approach that consists of three different types of fuzzy systems (FSs) to provide intelligent decision support for negotiation pricing. More specifically, the three FSs include: 1) a standard FS, which is a typical multiple inputs and single output FS that forms a mathematical mapping from the input space to the output space; 2) an SFS-SISOM, which is a linear fuzzy inference model with a single input and a single output module; and 3) a hierarchical FS, which consists of several FSs in a hierarchical manner to perform fuzzy inference. To address the existing problem of a standard FS suffering from the high-dimensional problem with a large number of influential factors, a generalized type of FS (named hierarchical FS), including its mathematical models and suitability for tackling the negotiation pricing problem, is introduced. In particular, a proof-of-concept prototype system that integrates these three FSs is also developed and presented. From a system design perspective, this artifact provides immense potential and flexibility for end users to choose the most suitable model for the given problem. The utility and effectiveness of this proposed system is illustrated and examined by three experimental datasets that vary from dimensionality and data coverage. Moreover, the performances of three different approaches are compared and discussed with respect to some important properties of decision support systems (DSSs).
AB - Automatic negotiation pricing and differential pricing aim to provide different customers with products/services that adequately meet their requirements at the “right” price. This often takes place with the purchase of expensive products/services and in the business-to-business context. Effective negotiation pricing can help enhance a company's profitability, balance supply and demand, and improve the customer satisfaction. However, determining the “right” price is a rather complex decision-making problem that puzzles pricing managers, as it needs to consider information from many constituents of the purchase channel. To further advance this line of research, this study proposes a systematic and learning approach that consists of three different types of fuzzy systems (FSs) to provide intelligent decision support for negotiation pricing. More specifically, the three FSs include: 1) a standard FS, which is a typical multiple inputs and single output FS that forms a mathematical mapping from the input space to the output space; 2) an SFS-SISOM, which is a linear fuzzy inference model with a single input and a single output module; and 3) a hierarchical FS, which consists of several FSs in a hierarchical manner to perform fuzzy inference. To address the existing problem of a standard FS suffering from the high-dimensional problem with a large number of influential factors, a generalized type of FS (named hierarchical FS), including its mathematical models and suitability for tackling the negotiation pricing problem, is introduced. In particular, a proof-of-concept prototype system that integrates these three FSs is also developed and presented. From a system design perspective, this artifact provides immense potential and flexibility for end users to choose the most suitable model for the given problem. The utility and effectiveness of this proposed system is illustrated and examined by three experimental datasets that vary from dimensionality and data coverage. Moreover, the performances of three different approaches are compared and discussed with respect to some important properties of decision support systems (DSSs).
KW - Decision support systems
KW - Hierarchical fuzzy systems
KW - Negotiation pricing
UR - http://www.scopus.com/inward/record.url?scp=85013223041&partnerID=8YFLogxK
U2 - 10.1016/j.dss.2017.02.003
DO - 10.1016/j.dss.2017.02.003
M3 - Article
AN - SCOPUS:85013223041
SN - 0167-9236
VL - 96
SP - 49
EP - 66
JO - Decision Support Systems
JF - Decision Support Systems
ER -