ARTIFICIAL NEURAL NETWORK (ANN) APPLIED TO THE SRC DIAGNOSTIC TOOL

This study determined the feasibility of applying artificial neural networks to a business diagnostic tool. Artificial neural networks (ANN) are a component of intelligent systems technologies and have been applied to wide range of disciplines ranging from engineering to business. In this study, an artificial neural network was to be used to model the analysis process that was manually performed after a set of diagnostic surveys were completed. The specific diagnostic survey, called the Diagnostic Tool, was developed by the Saskatchewan Research Council and the College of Commerce. The Diagnostic Tool was used to gather information from small and medium sized enterprises and, after analysis, to provide the companies with feedback on how they could improve operations. The manual analysis process was tedious and suffered from inconsistencies thus creating an interest in finding alternate methods of analysis. During the course of this study, three artificial neural network models were created. These models differed primarily by the number of inputs and type of input data. The first model used all the inputs from a section (Large model) and the second (Trimmed model) used a reduced set of inputs, determined from contribution weightings. The third model (Optimized model) used a reduced set of inputs but information from all inputs were combined into the reduced input data set. Because of insufficient actual company data, a simulation data set of 300 companies was created. Manual analysis on the 300 simulation data sets was performed and this information was provided to the artificial neural network models. For comparison purposes, a multiple regression model was created with each ANN to determine, in general, how each methodology compared. Two sections of the Diagnostic Tool were used in the study, the Human Resources and Manufacturing Management sections. In addition, a survey of business literature was conducted for the purpose of determining what practices benefit a small and medium sized enterprise. The information derived from this study was to be used in updating the Diagnostic Tool. Of the three ANN models, the Optimized model performed best. This model showed the lowest mean error for both the Human Resources and Manufacturing Management sections. The regression equations developed along with each ANN model showed similar performance to the ANN but in almost all cases the ANN models outperformed the regression equations. The ANN models were easier to implement and modify compared to the regression equations. It was observed that the results for Manufacturing Management were better (by almost 7%) than for Human Resources. This was attributed to the fact that because the Manufacturing Management section was rated by the analyst after the Human Resources section, the analyst, improved his/her rating consistency. Out of the sample mix of companies, those with average performance showed the highest standard deviation, compared to companies with very good or poor performance. This was due to the wider range of values the analyst could give to an average company.

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