IRMA-International.org: Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Data Mining for Combining Forecasts in Inventory Management

Data Mining for Combining Forecasts in Inventory Management
View Sample PDF
Author(s): Chi Kin Chan (The Hong Kong Polytechnic University, Hong Kong)
Copyright: 2005
Pages: 5
Source title: Encyclopedia of Information Science and Technology, First Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-59140-553-5.ch123

Purchase

View Data Mining for Combining Forecasts in Inventory Management on the publisher's website for pricing and purchasing information.

Abstract

The traditional approach to forecasting involves choosing the forecasting method judged most appropriate of the available methods and applying it to some specific situations. The choice of a method depends upon the characteristics of the series and the type of application. The rationale behind such an approach is the notion that a “best” method exists and can be identified. Further that the “best” method for the past will continue to be the best for the future. An alternative to the traditional approach is to aggregate information from different forecasting methods by aggregating forecasts. This eliminates the problem of having to select a single method and rely exclusively on its forecasts.

Related Content

Adeyinka Tella, Oluwakemi Titilola Olaniyi, Aderinola Ololade Dunmade. © 2021. 24 pages.
Md. Maidul Islam. © 2021. 17 pages.
Peterson Dewah. © 2021. 23 pages.
Lungile Precious Luthuli, Thobekile K. Buthelezi. © 2021. 14 pages.
Delight Promise Udochukwu, Chidimma Oraekwe. © 2021. 13 pages.
Julie Moloi. © 2021. 18 pages.
Mandisa Msomi, Lungile Preciouse Luthuli, Trywell Kalusopa. © 2021. 17 pages.
Body Bottom