EDSIM: expert database system for inventory management

Abstract

Purpose

Expert database system (EDS), which is still in its infancy, is developed and demonstrated for a business function by coupling expert systems (ES) and database systems.

Design/methodology/approach

An EDS for inventory management (EDSIM) using the fifth generation artificial intelligence (AI) language, Prolog, is developed. The convergence of logic programming and database techniques is focused in achieving the objective. EDSIM development is based on modular design and integration of several intelligent modules, with own knowledge base and shared dynamic database.

Findings

EDSIM developed demonstrates the peculiarities of such an EDS over a system developed using conventional procedural language. It shows that development of such systems has great deployment potential in the next generation enterprise systems where embedded intelligence is inevitable.

Research limitations/implications

Though no attempt is made to demonstrate distributed processing and interaction among various intelligent autonomous modules, an initiative in that direction definitely can kindle reengineering of today's enterprise system.

Practical implications

The language chosen for the development of EDSIM is Turbo Prolog™ and can be easily converted to a program in Visual Prolog™ which is the newer version of Turbo Prolog™. The advantage of such a conversion is that it will enable web readiness of the EDS there by facilitating e‐business in the web environment.

Originality/value

The benefits of the application of EDS, by the integration of ES and database systems in a fifth generation AI language, Prolog, paves way for capturing developments in AI and databases, simultaneously. This technology demonstrator has the potential to bring in an optimal mix of the above technologies in achieving major milestones while reengineering enterprise systems.

Citation

Subramoniam, S. and Krishnankutty, K.V. (2005), "EDSIM: expert database system for inventory management", Kybernetes, Vol. 34 No. 5, pp. 721-733. https://doi.org/10.1108/03684920510595409