Abstract
The aim of this paper is to illustrate how a mathematical programming technique called Data Envelopment Analysis (DEA) can be used to measure the efficiency of factories of a multinational company, given the existence of multiple inputs and outputs in the transformation process with no a priori weightings. Productivity measures encompass measures of technical change, technical efficiency, scale efficiency and allocation efficiency. In a competitive environment, industrial enterprises must continuously improve their productivity to sustain long-term growth and profitability. Productivity measurement and analysis can therefore play an important role in strategic planning. In contrast to conventional performance index measures, DEA assessment incorporates all the relevant factors (classified as either inputs or outputs) into a single model. In this sense, it is non-biased, as no single factor is given preference over another. Moreover, DEA measures the relative efficiency with respect to the best observed performance, as opposed to other techniques based on the observed average values or some predetermined performance. To show the DEA potentiality, an analysis on 12 factories in the paper industry is presented. Besides identifying the inefficient units, DEA yields valuable assessments by factories that can be used to determine a suitable course of action for improving the performance of the inefficient units.
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