The development of an intelligent inventory management system

Liang, Y

Abstract

This thesis is concerned with the development of an intelligent inventory management
system. The aim of the system is to bridge the substantial gap between the theory and
the practice of inventory management and to help industrial inventory managers to
achieve an effective and successful inventory management. The proposed system
attempts to achieve this by providing automatic pattern identification and model
selection facilities. Such a hybrid knowledge-based inventory system consists of a
collection of techniques (or pattern identifier) for identifying demand and lead time
patterns and a knowledge base (or rule base) for subsequent selection of a suitable
inventory model taking into consideration aspects of the practical situation. There are no
previous attempts in the inventory literature to develop such a system to guide model
selection.
In order to integrate the system into the established computer-based intelligent
inventory management system and facilitate the function of the pattern identifier, a
data manager has been developed to manipulate the history data required for statistical
analysis and to load the data into the system from other applications. In order to
establish the system's model base, the study of the modelling of inventory and the
features and evolution of expert systems are reviewed. The published models which
deal with similar inventory problems have been compared based on its applicability,
clarity, and being suitable to be computerised. It was necessary to further develop and
amend published models to fill gaps in the model base.
The overall structure and salient features of the proposed system and the development of
the system using Visual Basic have been described. The system has been tested using
real life data supplied by the co-operating companies. Finally, achievements and
shortcomings of the system are discussed and some suggestions for further research are
outlined.

Citation

Liang, Y. The development of an intelligent inventory management system. (Thesis). University of Salford

• Thesis Type: Thesis
• Deposit Date: Aug 17, 2011
• Publicly Available Date: Aug 17, 2011
• Award Date: Jan 1, 1997