Design and development of a knowledge discovery system in inventory management

C. A. Mitrea; Ka Man Lee

doi:10.1109/IEEM.2009.5373063

Design and development of a knowledge discovery system in inventory management

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

1 Citation (Scopus)

Abstract

To manage the inventory efficiently, it is necessary to have accurate forecasting. To extract and deploy the knowledge associated with forecasting attracts the attention of both academic and practitioners. Knowledge is regarded as a valuable asset for enterprises and it can be manipulated through intelligence techniques like Artificial Neural Networks (ANN). ANN has the special ability to learn facts about one knowledge domain by inputting data obtained from observations. This study focuses on exploring how ANN learns and analyzes different types of ANN and ANN architectures used in the demand forecasting. The feasibility of the proposed approach to the demand forecasting issue is demonstrated with numeric data. The significance of this study is to adopt ANN as a knowledge discovery system thereby enhancing the inventory management.

Original language	English
Title of host publication	IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management
Pages	1449-1453
Number of pages	5
DOIs	https://doi.org/10.1109/IEEM.2009.5373063
Publication status	Published - 1 Dec 2009
Externally published	Yes
Event	IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2009 - Hong Kong, Hong Kong Duration: 8 Dec 2009 → 11 Dec 2009

Conference

Conference	IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2009
Country/Territory	Hong Kong
City	Hong Kong
Period	8/12/09 → 11/12/09

Keywords

Forecasting
Inventory management
Knowledge discovery process
Neural networks
Rule extraction
Weights

ASJC Scopus subject areas

Management of Technology and Innovation
Information Systems and Management
Industrial and Manufacturing Engineering
Safety, Risk, Reliability and Quality

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

More information

10.1109/IEEM.2009.5373063

Cite this

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Mitrea, CA & Lee, KM 2009, Design and development of a knowledge discovery system in inventory management. in IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management., 5373063, pp. 1449-1453, IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2009, Hong Kong, Hong Kong, 8/12/09. https://doi.org/10.1109/IEEM.2009.5373063

Design and development of a knowledge discovery system in inventory management. / Mitrea, C. A.; Lee, Ka Man.
IEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management. 2009. p. 1449-1453 5373063.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - To manage the inventory efficiently, it is necessary to have accurate forecasting. To extract and deploy the knowledge associated with forecasting attracts the attention of both academic and practitioners. Knowledge is regarded as a valuable asset for enterprises and it can be manipulated through intelligence techniques like Artificial Neural Networks (ANN). ANN has the special ability to learn facts about one knowledge domain by inputting data obtained from observations. This study focuses on exploring how ANN learns and analyzes different types of ANN and ANN architectures used in the demand forecasting. The feasibility of the proposed approach to the demand forecasting issue is demonstrated with numeric data. The significance of this study is to adopt ANN as a knowledge discovery system thereby enhancing the inventory management.

KW - Forecasting

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KW - Knowledge discovery process

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Y2 - 8 December 2009 through 11 December 2009

ER -

Design and development of a knowledge discovery system in inventory management

Abstract

Conference

Keywords

ASJC Scopus subject areas

UN SDGs

More information

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Cite this