Agric. Econ. - Czech, 2005, 51(2):69-79 | DOI: 10.17221/5079-AGRICECON

Fuzzy expert marketing-mix model

S. Aly, I. Vrana
Czech University of Agriculture, Prague, Czech Republic

A marketing-mix setting model is presented. The all four P's are integrated and identified. The model makes strong utilization of relevant experts' knowledge and expertise through a fuzzy-decision-making system tailored to dynamically set the marketing-mix periodically in response to changing business environment. Quantitative, qualitative, uncertain, and vague variables are handled necessary to provide a realistic solution. The proposed model is particularly applicable to changing business environments that are full of such qualitative, stochastic, uncertain, and vague variables as in the case of agricultural business (e.g., agro-food companies, fertilizers and agro-chemical producers), and generally applicable to any other business sector. The method can be considered as the fuzzy-expert system for determining values of the marketing decision variables.

Keywords: marketing-mix, fuzzy-decision-making system, stochastic variables, subjective variables, fuzzy expert system

Published: February 28, 2005  Show citation

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Aly S, Vrana I. Fuzzy expert marketing-mix model. Agric. Econ. - Czech. 2005;51(2):69-79. doi: 10.17221/5079-AGRICECON.
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    References

    1. Arinze B. (1990): Marketing Planning with Computer Models: A Case Study in the Sofware Industry. 19 (2): 117-129. Go to original source...
    2. Arinze B., Burton J. (1992): A Simulation Model for Industrial Marketing. Omega, 20 (3): 323-335. Go to original source...
    3. Cox E. (1995): Fuzzy Logic for Business and Industry. Charles River Media, Inc.
    4. Dweiri F., Meier F. A. (1996): Application of Fuzzy Decision Making in Facilities Layout Planning. International Journal of Production Research, 34 (11): 3207-3225. Go to original source...
    5. Fazolollani B., Vahidov R. (2001): Extending the Effectiveness of Simulation-based DSS through Genetic Algorithms. Journal of Information and Management, 39 (1): 53-65. Go to original source...
    6. Kotler P. (1972): Marketing Management: Analysis, Planning, and Control. Prentice-Hall, Inc., Englewood Cliffs, New Jersey.
    7. Lyrich J.E., Hooky G.J. (1989): Industrial Advertising Budget Approaches in the UK. Industrial Marketing Management Journal, 18 (4): 265-270, November. Go to original source...
    8. Magruth A.J., Hardy K.G. (1987): Selecting Sales and Distribution Channels. Industrial Marketing Management Journal, 16 (4): 273-278, May. Go to original source...
    9. Ross T.J. (1995): Fuzzy Logic with Engineering Applications. McGraw-Hill, Inc., Singapore.
    10. Zadeh L.E. (1965): Fuzzy Sets. Information and Control, 8: 338-353. Go to original source...

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