Self-organizing neural network-based clustering and organization of production cells

Abstract

Organizing and optimizing production in small and medium enterprises with small batch production and many different products can be very difficult. This paper presents an approach to organize the production cells by means of clustering-manufactured products into groups with similar product properties. Several clustering methods are compared, including the hierarchical clustering, k-means and self-organizing map (SOM) clustering. Clustering methods are applied to production data describing 252 products from a Slovenian company KGL. The best clustering result, evaluated by an average silhouette width for a total data set, is obtained by SOM clustering. In order to make clustering results applicable to the industrial production cell planning, an interpretation method is proposed. The method is based on percentile margins that reflect the requirements of each production cell and is further improved by incorporating the economic values of each product and consequently the economic impact of each production cell. Obtained results can be considered as a recommendation to the production floor planning that will optimize the production resources and minimize the work and material flow transfer between the production cells.