Evaluating the information content of a measure of plant output: An application to high-technology manufacturing

Evaluating the information content of a measure of plant output: An application to high-technology manufacturing

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Article ID: iaor19972045
Country: Netherlands
Volume: 68
Issue: 1
Start Page Number: 329
End Page Number: 360
Publication Date: Nov 1996
Journal: Annals of Operations Research
Authors: , ,
Keywords: statistics: data envelopment analysis
Abstract:

Commonly used measures of plant output have been criticized for their inability to provide information required to manage the dynamic operations of high-technology manufacturing plants. In this paper, the authors propose tests to evaluate the information content of a measure of plant output that is specifically directed at these issues. These tests are based on recent developments in Data Envelopment Analysis (DEA), namely the Cone Ratio Envelopments. In this new application of DEA models, the authors shift the focus from Decision Making Units (DMUs) being evaluated to the DMUs that are being used to effect evaluations. The proposed tests are applied to evaluate the information content of a complexity adjusted measure of plant output, which the authors refer to as Total Net Die Equivalent (TNDE). Developed recently in the context of a high-technology manufacturing plant-a wafer fabrication plant of a merchant semiconductor manufacturing company-TNDE reflects the ongoing changes in product and process technologies, process flow characteristics, and volume of production. Evaluating the information content on joint criteria of ‘recency’ and ‘efficiency’, the results of the present tests, conducted over a 28-month period in the wafer fabrication plant, show that TNDE as a single aggregate (scalar) measure of plant output outperforms the two outputs from which it is synthesized. Thus, TNDE as a single measure of output can be used to provide an improved basis for planning a plant’s future operations. En route to the development and application of the proposed tests, the authors illustrate how DEA concepts and models provide a rigorous and systematic basis for conducting ex post technology evaluation to guide continuous improvements in a plant’s current operations.

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