Productivity diagnosis via fuzzy clustering and classification: An application to machinery industry

Abstract

Business units are always faced with intensifying pressure in a competitive economy. Increasing productivity is an effective solution for a firm to survive and prosper. The relative productivity in an industry has evolved into a significant determinant of the competitive position for a firm. This paper proposes a productivity diagnosis process for a firm on the basis of the productivity characters of an industry to gain an insight into the firm's relative productivity and to find the shortcomings in its management of resources. Firstly, productivity structure is determined. Pattern recognition technologies, namely fuzzy clustering and fuzzy classification, are then employed. After fuzzily clustering a training set according to three feature spaces, the productivity characters of the industry can be determined. A business unit can be diagnosed through fuzzily classifying its productivity features in a particular feature space and productivity indications can be furnished based on the associated productivity characters. As an illustration, data from 23 machinery firms in Taiwan are collected as a training set to analyze the productivity characters in each space, and two hypothetical firms are diagnosed. Copyright © 1996 Elsevier Science Ltd.