The use of a neural factory to investigate the effect of product line width on manufacturing performance

The dual goals of this study are: (1) to develop an empirically valid neural model of US factories in a range of industries producing discrete products, and (2) to use the model to test the effect of changes in product line width on plant performance variables. Accordingly, a neural factory was developed using 59 input and 5 output/performance variables, and was trained using field data collected from 385 US manufacturing plants. The model was validated using a holdout sample before conducting sensitivity tests. The study demonstrates that, through the use of parametric sensitivity analysis, the neural factory could be used to investigate the relationship between inputs and performance of a factory. While the focused factory principle would favor a smaller product line, economies of scope theory would favor a larger product line for the good of the factory; this implies a rather complex relationship between product line width (PLW) and plant performance. The neural factory was used to study the sensitivity of output/performance variables when product line width was varied over a range extending from 10% to 200% of the average values. The sensitivity analysis of the neural factory shows that, as the product line increases, it (1) does not affect cost-of-goods-sold (COGS), (2) decreases return on investment, (3) has a negative effect on the top management’s perception of manufacturing performance, (4) increases inventory turns, and (5) increases sales per employee. The explanations for these findings show how complex and intertwined the relationships between PLW and performance variables are. They enhance our understanding of PLW and provide some new directions for future empirical research.