In 1970, Rank Xerox realized that they were selling their products at a price higher
than those of other Japanese manufacturers. However, their margins were very small
as compared to other Japanese manufacturers. The main purpose was to analyze what
could be done in order to stay in business. An in-depth study was conducted which
concluded that there was a need to overhaul the company. Companies started comparing
and evaluating themselves through a process which came to be known as
competitive benchmarking. This is essential for various reasons. In the present scenario,
benchmarking is taken as a process improvement factor. According to Zairi (1994), benchmarking is
used at the strategic level to determine the standard of performance against four
corporate prioritiescustomer satisfaction,
employee motivation and satisfaction, market share
and return on assets. Generally, benchmarking is done with respect to the best in the
industry. For benchmarking, efficiency measurement is necessary.
Efficiency can be described as the "degree to which the observed use of resources
produce outputs of a given quality which matches the optimal use of resources to produce
outputs of a given quality". In economics, efficiency (or more specifically, technical
efficiency) is measured by the ratio of outputs to inputs (Färe
et al., 1994; and Cooper et al.,
1999). Efficiency measurement is a commonly used tool to measure the performance of
any unit, generally, the more the efficiency, the better the performance in terms of
output. These more efficient units are generally referred for
improvement and are termed as benchmarking units.
Weidong (2005) reveals that domestic and international scholars have brought up
many kinds of evaluation methods, the main and most frequently used being
Analytic Hierarchy Processing (AHP), operations research [Data Envelopment Analysis
(DEA)], statistic methods [Cluster Analysis (CA), Principal Component Analysis (PCA)
and Factor Analysis (FA)], fuzzy evaluation methods [Fuzzy Comprehensive
Evaluation (FCE), fuzzy cluster and fuzzy AHP]. Other traditional statistical approaches
for measuring efficiency such as measures of central tendency
that are characterized by comparison of efficiency of a unit with an average efficiency of similar
units.
In statistical models, the regression equation represents a combination of data
points. This is because the regression equation represents the central tendency of the
dataset. Statistical regression models also make certain assumptions about the distribution
of error terms. |
