Design and development of a knowledge discovery system in inventory management

C. A. Mitrea, Ka Man Lee

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review


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 languageEnglish
Title of host publicationIEEM 2009 - IEEE International Conference on Industrial Engineering and Engineering Management
Number of pages5
Publication statusPublished - 1 Dec 2009
Externally publishedYes
EventIEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2009 - Hong Kong, Hong Kong
Duration: 8 Dec 200911 Dec 2009


ConferenceIEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2009
Country/TerritoryHong Kong
CityHong Kong


  • 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

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