The Supervisor's Associate: An Intelligent Shop Management System for Production Supervision

Abstract

Conventional software approaches, such as Management Information Systems or Decision Support Systems, are seldom adequate to support the production supervisor. This insufficiency derives from the algorithmic methods that form the foundation of these systems. In practice, the majority of supervisory decision-making utilizes heuristic and intuitive techniques, as opposed to algorithmic methods. In view of the concern for the productivity of the manufacturing area, there is a definite need for the development of a software system to more fully support the manufacturing supervisor function. The application of artificial intelligence was proposed for this management support system. The approach of this research was to first formulate a generalized model, and from it develop a knowledge-based system. The model focuses on the supervisor's planning and control responsibilities, from both the perspective of the information support needs and those underlying heuristics used by the supervisor to generate solutions. It provides a linkage of facts available from the factory information system and rules obtained from analysis of the supervisor's function. As proof of the concept, a prototype expert system was developed based upon the supervisory model. This prototype system is referred to as the Supervisor's Associate. It is designed to work within the goals and with the information supplied by the factory information systems, access their output, and focus it in support of the production supervisor's needs. The Supervisor's Associate is intended to provide first-line management with a software tool which will improve the productivity, efficiency and effectivity of the manufacturing area.

Notes

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Graduation Date

1990

Semester

Spring

Advisor

Brooks, George H.

Degree

Doctor of Philosophy (Ph.D.)

College

College of Engineering

Department

Industrial Engineering and Management Systems

Degree Program

Industrial Engineering

Format

PDF

Pages

366 p.

Language

English

Length of Campus-only Access

None

Access Status

Doctoral Dissertation (Open Access)

Identifier

DP0027330

Subjects

Dissertations, Academic -- Engineering; Engineering -- Dissertations, Academic

Accessibility Status

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