MARC 主機 00000nam  2200349   4500 
001    AAI3089526 
005    20050105140157.5 
008    050105s2003                        eng d 
035    (UnM)AAI3089526 
040    UnM|cUnM 
100 1  Yu, Wen-Bin 
245 10 Agent-based demand forecasting for supply chain management
300    149 p 
500    Source: Dissertation Abstracts International, Volume: 64-
       05, Section: B, page: 2275 
500    Adviser: James H. Graham 
502    Thesis (Ph.D.)--University of Louisville, 2003 
520    This dissertation introduces a new agent-based demand 
       forecasting system, which incorporates causal information 
       in addition to historical demand data, to improve the 
       forecasting accuracy, especially for e-commerce and supply
       -chain oriented businesses. Due to the lack of historical 
       data resulting from shortened product life cycles, 
       proliferation of products, and shifting of consumer 
       behaviors, the ability to make accurate and consistent 
       demand forecasts using traditional forecasting methods has
       been challenged. The objectives of this study are (1) to 
       develop a forecast system to overcome limitations of 
       traditional demand forecasting methods, (2) to utilize 
       existing forecasting methods in a more effective way, and 
       (3) to improve forecast accuracy through the inclusion of 
       influential information available from both internal and 
       external data sources 
520    The agent-based forecasting system is implemented through 
       the use of four types of agents: the coordination agent, 
       the task agent, the data collection agent, and the 
       interface agent. The coordination agent provides weighted 
       overall prediction from the forecasts reported by various 
       task agents. The task agents perform forecasting by using 
       different forecasting methods or incorporating special 
       event patterns and possible causal effects for demand 
       variation. The data collection agents are controlled by 
       the coordination agent for demand data gathering. Finally,
       the interface agent communicates between the coordination 
       agent and the human user. Through the periodical re-
       evaluation of weights, the agent-based forecasting system 
       is able to provide improved forecasts and make adjustments
       when the characteristics of the demand pattern change over
       time 
520    Also, a novel combination of a pattern-matching algorithm 
       and the weighting approach is developed as an alternative 
       to traditional time series forecasting for limited demand 
       data situations. The objective of the pattern-matching 
       algorithm is to consider if certain event patterns would 
       actually represent the immediate development of the on-
       going time series and to use the candidate patterns as 
       indicators of future trends. This was shown to work well 
       in situations with small amount of historical data, such 
       as in the early introductory stages of a new product 
520    The superiority of using the multiple agent based forecast
       system over traditional forecasting methods is 
       demonstrated through four case studies. In all four cases,
       the agent-based forecasting system was able to provide 
       more accurate forecast than traditional time series and 
       causal forecasting methods. The results showed 
       statistically significant improvement over existing 
       methods 
590    School code: 0110 
590    DDC 
650  4 Computer Science 
650  4 Engineering, System Science 
690    0984 
690    0790 
710 20 University of Louisville 
773 0  |tDissertation Abstracts International|g64-05B 
856 40 |uhttp://pqdd.sinica.edu.tw/twdaoapp/servlet/
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