Design and development of inventory knowledge discovery system. Issue 8 (14th September 2017)

• Record Type: Journal Article
• Authors: Lee, CKM; Mitrea, Catalin; Ip, W.H.; Choy, KL; Ho, GTS
• Is Part Of: Enterprise information systems. Volume 11:Issue 8(2017)
• Page Start: 1262
• Page End: 1282
• Publication Date: 2017-09-14
• Subjects: Artificial neural network -- forecasting -- inventory management -- knowledge discovery systems -- TREPAN algorithm; Information storage and retrieval systems -- Periodicals; Management information systems -- Periodicals; Electronic commerce -- Periodicals; 658.4038011
• DOI: 10.1080/17517575.2016.1221143
• Languages: English
• ISSNs: 1751-7575

Abstract

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.