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A Knowledge Discovery Method to Predict the Economical Sustainability of a Company

Institute of Mobility and Transportation/Data Analysis and Modelling, Hasselt University Wetenschapspark 5 Bus 6, B-3500 Diepenbeek, Belgium; daniella.devos{at}uhasselt.be

H. Van Landeghem

Department of Industrial Management, Ghent University, Technologiepark 903, B-9052 Gent, Belgium

K. Van Hoof

Institute of Mobility and Transportation/Data Analysis and Modelling, Hasselt University Wetenschapspark 5 Bus 6, B-3500 Diepenbeek, Belgium

In this study, we are building a prototype of a machine-learning system using an inductive supervised approach to predict the logistical performance of a company. Focus lies on the learning phase, the handling of different types of data, the creation of new concepts in order to provide better measurable information. In this system, numeric financial data are combined with categorical data creating symbolic data, distinguishing the phase of model generation from examples, and the phase of model classification and interpretation. The system has been implemented in vector spaces. Our data are benchmarking surveys on concurrent engineering (CE), measuring the usage of in total 302 best practices in Belgian manufacturing companies. The general purpose for implementing a best practice is the statement that the company will improve its product processing, and that in this way the company will establish its economical existence on the market. Our model processes a limited number of predefined steps, generating value factors for the 302 best practices. The best practices are grouped into 30 subjects, the value factors combined in linear combinations. These value factors and their linear combinations are then subject to pattern interpretation relating CE performance to the past financial state of the company and, also to the economical well-doing of the company in the longer term i.e., we also refer to the sustainability of the company in the market.

Key Words: machine learning • knowledge discovery in databases • symbolic data • logistical performance • decision tool

Concurrent Engineering, Vol. 14, No. 4, 293-303 (2006)
DOI: 10.1177/1063293X06072616


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