Fuzzy expert systems and challenge of new product pricing

被引:34
|
作者
Haji, Alireza [2 ]
Assadi, Morteza [1 ]
机构
[1] Wichita State Univ, Dept Ind Engn, Wichita, KS 67260 USA
[2] Sharif Univ Technol, Dept Ind Engn, Tehran, Iran
关键词
New product; Pricing factors; Fuzzy logic; Fuzzy expert system; LOGIC; MODELS;
D O I
10.1016/j.cie.2007.03.005
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
This paper is focused on the representation and treatment of knowledge and data uncertainty within the context of an important industrial challenge, i.e., new product pricing. The most well known participating factor in pricing process is cost meanwhile the other factors like customer value and firm's strategy should be considered in the pricing process, as well. Besides, there are other important factors like the risks that consumer bear in purchasing new product which must be carefully analyzed and considered. Nonetheless, many of these factors are blended with uncertainty. In recent decades, fuzzy logic was well developed and implemented in many applications to treat vagueness in complicated systems. Finding the pricing process a critical and complicated process which includes many vague parameters, we tried to design a fuzzy expert system to cope with this challenge. In this paper, after a brief introduction of fuzzy logic which has revealed a methodology to work with uncertainty and imitate humans reasoning, the pricing factors are introduced. Then a fuzzy expert system is designed to find the appropriate price of the new product considering the related parameters. (C) 2007 Elsevier Ltd. All rights reserved.
引用
收藏
页码:616 / 630
页数:15
相关论文
共 50 条
  • [1] The use of fuzzy logic and expert systems for rating and pricing firms A new perspective on valuation
    Malagoli, Stefano
    Magni, Carlo Alberto
    Mastroleo, Giovanni
    MANAGERIAL FINANCE, 2007, 33 (11) : 836 - 852
  • [2] Fuzzy Expert Pricing Systems and Optimization Techniques in Marketing Science
    Dairo, Adeolu
    Szucs, Krisztian
    FUZZY SYSTEMS AND DATA MINING VI, 2020, 331 : 255 - 260
  • [3] Robust New Product Pricing
    Handel, Benjamin R.
    Misra, Kanishka
    MARKETING SCIENCE, 2015, 34 (06) : 864 - 881
  • [4] Exploiting Fuzzy Expert Systems in Cardiology
    Sourla, Efrosini
    Syrimpeis, Vasileios
    Stamatopoulou, Konstantina-Maria
    Merekoulias, Georgios
    Tsakalidis, Athanasios
    Tzimas, Giannis
    ENGINEERING APPLICATIONS OF NEURAL NETWORKS, PT II, 2013, 384 : 80 - 89
  • [5] Knowledge verification for fuzzy expert systems
    Wu, Po-Han
    Hwang, Gwo-Haur
    Liu, Hsiang-Ming
    Hwang, Gwo-Jen
    Tseng, Judy C. R.
    Huang, Yueh-Min
    JOURNAL OF THE CHINESE INSTITUTE OF ENGINEERS, 2008, 31 (06) : 997 - 1009
  • [6] A new method of ozone forecasting using fuzzy expert and neural network systems
    Heo, JS
    Kim, DS
    SCIENCE OF THE TOTAL ENVIRONMENT, 2004, 325 (1-3) : 221 - 237
  • [7] An empirical evaluation of the inferential capacity of defeasible argumentation, non-monotonic fuzzy reasoning and expert systems
    Rizzo, Lucas
    Longo, Luca
    EXPERT SYSTEMS WITH APPLICATIONS, 2020, 147
  • [8] Visualization and Interpretation Tool for Expert Systems Based on Fuzzy Cognitive Maps
    Garzon Casado, Alvaro
    Cano Marchal, Pablo
    Gomez Ortega, Juan
    Gamez Garcia, Javier
    IEEE ACCESS, 2019, 7 : 6140 - 6150
  • [9] Multiple parallel fuzzy expert systems utilizing a hierarchical fuzzy model
    Aly, S.
    Vrana, I.
    AGRICULTURAL ECONOMICS-ZEMEDELSKA EKONOMIKA, 2007, 53 (02): : 89 - 93
  • [10] Fuzzy expert systems for prediction of ICU admission in patients with COVID-19
    Asl, A. A. Sadat
    Ershadi, M. M.
    Sotudian, S.
    Li, X.
    Dick, S.
    INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS, 2022, 16 (01): : 159 - 168