Two Expert Diagnosis Systems for SMEs: From Database- only Technologies to the Unavoidable Addition of AI Techniques

Sylvain Delisle and Josée St-Pierre ()
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Sylvain Delisle: INRPME - Institut de recherche sur les PME - UQTR - Université du Québec à Trois-Rivières, UQTR - Université du Québec à Trois-Rivières
Josée St-Pierre: INRPME - Institut de recherche sur les PME - UQTR - Université du Québec à Trois-Rivières, UQTR - Université du Québec à Trois-Rivières

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Abstract: In this application-oriented paper, we describe two expert diagnosis systems we have developed for SMEs. Both systems are fully implemented and operational, and both have been put to use on data from actual SMEs. Although both systems are packed with knowledge and expertise on SMEs, neither has been implemented with AI techniques. We explain why and how both systems relate to knowledge-based and expert systems. We identify aspects of both systems that will benefit from the addition of AI techniques in future developments. 1. Expertise for Small and Medium-sized Enterprises (SMEs) The work we describe here takes place within the context of the Research Institute for SMEs 1. The specific lab in which we have conducted the research projects we refer to in this paper is the LaRePE 2 (LAboratoire de REcherche sur la Performance des Entreprises). This lab is mainly concerned with the development of scientific expertise on the study and modeling of SMEs' performance, including a variety of interrelated subjects such as finance, management , information systems, production, technology, etc. The vast majority of research projects carried out at the LaRePE involves both theoretical and practical aspects, often necessitating in-field studies with SMEs. As a result, our research projects always attempt to provide practical solutions to real problems confronting SMEs. In this application-oriented paper we briefly describe two expert diagnosis systems we have developed for SMEs. Both can be considered as decision support systems (Turban and Aronson, 2001). The first is the PDG system: a benchmarking software that evaluates production and management activities, and the results of these activities in terms of productivity, profitability, vulnerability and efficiency. The second is the eRisC system: a software that helps identify, measure and manage the main risk factors that could compromise the success of SME development projects. Both systems are fully implemented and operational. Moreover, both have been put to use on data from actual SMEs. What is of particular interest here, especially from a knowledge-based systems perspective, is the fact that although both the PDG and the eRisC systems are packed with knowledge and 1 The core mission of the Institute (http://www.uqtr.ca/inrpme/anglais/index.html), which supports fundamental and applied research, is to foster the advancement of knowledge on SMEs to contribute to their development. 2

Date: 2003-09
Note: View the original document on HAL open archive server: https://hal.science/hal-01704979
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Citations: View citations in EconPapers (1)

Published in VIIth International Conference on Knowledge- Based Intelligent Information and Engineering Systems, Sep 2003, Oxford, United Kingdom

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-01704979

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