A decision support system to facilitate resources allocation: an OLAP‐based neural network approach

H.C.W. Lau (Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong)
A. Ning (Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong)
W.H. Ip (Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong)
K.L. Choy (Hong Kong Polytechnic University, Hunghom, Kowloon, Hong Kong)

Journal of Manufacturing Technology Management

ISSN: 1741-038X

Publication date: 1 December 2004

Abstract

The emergence of advanced information technologies strengthens the capability to the entrepreneur to manage and manipulate data. However, the quality of information, the capability of providing the right information to the right person, and the utilization of information are still in doubt. Therefore, increasing numbers of firms have realized and started to develop as well as improve their existing information systems to fit the ever‐changing business needs of the organization to support decision‐making for the volatile business environment. Indeed, previous research studies have found that logistics management is the great frontier of cost reduction. Therefore, in this paper, an infrastructure of a decision support system is proposed to capture and maintain the business and resources allocation information with the adoption of the neural network for its artificial intelligent characteristic that mimic the operation of human brain to generate solutions systematically. The proposed system is adopted by a shipping company to assist allocation of containers.

Keywords

Citation

Lau, H., Ning, A., Ip, W. and Choy, K. (2004), "A decision support system to facilitate resources allocation: an OLAP‐based neural network approach", Journal of Manufacturing Technology Management, Vol. 15 No. 8, pp. 771-778. https://doi.org/10.1108/17410380410565357

Download as .RIS

Publisher

:

Emerald Group Publishing Limited

Copyright © 2004, Emerald Group Publishing Limited

Please note you might not have access to this content

You may be able to access this content by login via Shibboleth, Open Athens or with your Emerald account.
If you would like to contact us about accessing this content, click the button and fill out the form.
To rent this content from Deepdyve, please click the button.