Abstract
Comparing analytical approaches is crucial when important policy decisions of corporations or government agencies may be influenced by results that depend on the methodologies certain disciplines customarily use. Technical efficiency can be measured by a full-frontier production function model or by linear programming specifications. By using these modeling approaches observations pertaining to three linerboard manufacturing facilities are classified as efficient, inefficient, scale inefficient, and other. However, observations may or may not be consistently classified into these four groups when employing the two modeling approaches. In order to validate the efficiency designations of the two modeling approaches and to determine the uniqueness of observations, a fuzzy K-means clustering approach that uses a modified hat matrix H * as a similarity or information matrix is employed. This approach permits observations to be allocated to clusters in a fuzzy way by defining a membership function from 0 to 1. As the degree of fuzziness increases, a sensitivity analysis with respect to individual observations belonging to some cluster can be evaluated. At the same time, this fuzzy approach assists the analyst to assess the inconsistencies that can arise when using the mathematical programming and full-frontier modeling approaches of technical efficiency.
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The refereeing process of this paper was handled through Rolf Färe. The majority of this research work was completed when Bill Seaver was at the Department of Management Information Resources, College of Business Administration, Western Illinois University, Macomb, IL 61455.
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Seaver, B.L., Triantis, K.P. A fuzzy clustering approach used in evaluating technical efficiency measures in manufacturing. J Prod Anal 3, 337–363 (1992). https://doi.org/10.1007/BF00163432
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DOI: https://doi.org/10.1007/BF00163432