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Title: サプライチェーンマネジメントのためのOLAP需要予測
Other Titles: A Demand Forecasting Approach Using OLAP for Supply Chain Management
Authors: 杉原, 敏夫
Authors (alternative): Sugihara, Toshio
Issue Date: 24-Mar-2000
Publisher: 長崎大学経済学部 / Faculty of Economis, Nagasaki University
Citation: 経済学部研究年報, 16, pp.37-50; 2000
Abstract: In present management activities,SCM(Supply Chain Management)is known as a powerful and effective management innovation technique.SCP(Supply Chain Planning)is the planning tool of SCM,and depends on the transverse,seamless flow of information from sales to stock distribution and production.Therefore, the essential information for SCP is demand forecasting data. The requlrements of the demand forecasting approach givlng optimal logistics plans and schedules are considered to be"OLAP(Online Analytical Processing)", "simultaneous forecasting of the variables related to the target variable","highly accurate,Short-range forecasting"and"quick acquisition of structural changes in the market".  To fulfill these requlrements,an adaptive demand forecasting approach uslng the Kalman Filtering technique is proposed,and its effectiveness is verified about some actual business forecasts. In particular to explain the last point,the extended Kalman Filter including selforganizing mechanism,taking account of past structural anges in a given period of time is proposed to elucidate the structural changes by market actors.The selforganizing technique includes modified GMDH-1ike method(General Method of Data Handling)and structured type of neural-networking.  Application for this approach are demand forecasting of new passenger car sales.The economic indices(Private Final Consumption Expenditure,Consumer Price Indices,etc)and business indices are selected as explanatory variables relating to the target variable.And some comparisons of forecasting accuracy(interpolation and extrapolation test)with this approach and AR,nonself-organizing model are used to evaluate the effectiveness of the proposed approach.
URI: http://hdl.handle.net/10069/26201
ISSN: 09108602
Type: Departmental Bulletin Paper
Text Version: publisher
Appears in Collections:Volume 16

Citable URI : http://hdl.handle.net/10069/26201

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