2007 Fiscal Year Final Research Report Summary
A Study of Knowledge Model Development using Fuzzy Model
| Project/Area Number |
17500135
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Sensitivity informatics/Soft computing
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| Research Institution | University of Tsukuba |
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Principal Investigator |
RYOKE Mina University of Tsukuba, Graduate School of Business Seiences,, Associate Professor (10303348)
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| Co-Investigator(Kenkyū-buntansha) |
NAKAMORI Yoshiteru School of Knowledge Saence, Japan Advanced lnstitute of Science and Technology, Professor (30148598)
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| Project Period (FY) |
2005 – 2007
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| Keywords | Fuzzy Model / Knowledge Model / Systems Methodology / Risk Management / Consumer Behavior / Ddemand Prediction |
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| Research Abstract |
The purpose of this study is to propose a knowledge model development in order to predict demand and to manage a section. The typical case of the situation is the fresh food section in a supermarket which is under complex and dynamically changing circumstance. The section is focused. The knowledge model is developed by combining ensemble model to treat the fluctuation on evaluation and context model to treat the context of complex situation. A systems methodology "I-System" for knowledge integration and creation is applied to develop the knowledge model in order to manage the demand prediction in the fresh food section. The concrete approach is to divide the problem into three domains consisting of the manager's knowledge, the consumer behavior and the demand prediction and to clarify them. In the domain of the manager's knowledge, two interview surveys are carried out in order to collect the manager's knowledge on the section management and decision making items and so forth. The obtained knowledge are illustrated using KJ method, text mining techniques and Bayesian Theorem etc. In the domain of the customer behavior, a questionnaire survey is carried out via Internet in order to collect their thinking on the fresh food section. The obtained data is analyzed to find the important factors when the customers select flesh foods. Then, comparison analysis between demand side and supply side thinking is done. The results of two domains are integrated to build the demand prediction model which is different from the conventional statistical model. At the same time, the system is also developed. The system supports the manager's decision making to consider the chance loss and disposal loss.
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