Design and development of inventory knowledge discovery system

Ka Man Lee, Catalin Mitrea, W. H. Ip, King Lun Tommy Choy, G. Ho

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)

Abstract

Inventory management (IM) performance is affected by the forecasting accuracy of both demand and supply. In this paper, an inventory knowledge discovery system (IKDS) is designed and developed to forecast and acquire knowledge among variables for demand forecasting. In IKDS, the TREes PArroting Networks (TREPAN) algorithm is used to extract knowledge from trained networks in the form of decision trees which can be used to understand previously unknown relationships between the input variables so as to improve the forecasting performance for IM. The experimental results show that the forecasting accuracy using TREPAN is superior to traditional methods like moving average and autoregressive integrated moving average. In addition, the knowledge extracted from IKDS is represented in a comprehensible way and can be used to facilitate human decision-making.
Original languageEnglish
Pages (from-to)1262-1282
Number of pages21
JournalEnterprise Information Systems
Volume11
Issue number8
DOIs
Publication statusPublished - 14 Sep 2017

Keywords

  • Artificial neural network
  • forecasting
  • inventory management
  • knowledge discovery systems
  • TREPAN algorithm

ASJC Scopus subject areas

  • Computer Science Applications
  • Information Systems and Management

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