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Data Envelopment Analysis is a "balanced benchmarking"
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article
(Vol.
54,
No.
4,
Summer 2013, 37-42)
translated and published in Harvard Deusto business review, No. 228 (2013), 6-15.
According to Cook, Tone and Zhu (2014), Data envelopment analysis: Prior to choosing a model, OMEGA, Vol. 44, 1-4.
From
the title, Data Envelopment Analysis
(DEA)
is a methodology for analysizing data. Specifically, DEA is used to
identify best-practices when multiple performance metrics or measures
are present for organizations. Based upon Cook, Tone and Zhu (2003),
although
DEA has a strong link to production theory in economics, the tool is
also used
for benchmarking in operations management, where a set of measures is
selected
to benchmark the performance of manufacturing and service operations.
In the
circumstance of benchmarking, the efficient DMUs, as defined by DEA,
may not
necessarily form a "production frontier", but rather lead to a
"best-practice
frontier". For example, if one benchmarks the performance of computers,
it is
natural to consider different features (screen size and resolution,
memory
size, process speed, hard disk size, and others). One would then have
to
classify these features into “inputs” and “outputs” in order to apply a
proper
DEA analysis. However, these features may not actually represent inputs
and
outputs at all, in the standard notion of production. In fact, if one
examines
the benchmarking literature, other terms, such as “indicators”,
“outcomes”, and
“metrics”, are used. The issue now becomes one of how to classify these
performance measures into inputs and outputs, for use in DEA.
In
general, DEA minimizes “inputs” and maximizes “outputs”; in other
words,
smaller levels of the former and larger levels of the latter represent
better
performance or efficiency. This can then be a rule for classifying
factors
under these two headings. There are, however, exceptions to this; for
example,
pollutants from a production process are outputs, yet higher levels of
these
indicate worse performance. There are DEA models that deal with such
undesirable outputs. In
certain circumstances, a factor can play a dual role of input and
output
simultaneously. For example, when
evaluating the efficiencies of a set of universities, if one considers
the numbers
of Ph.D. students trained as outcomes from the education process, then
this
factor can rightly be viewed as an output. At the same time, however,
Ph.D.
students assist in carrying out research, and can therefore be viewed
as a
resource, hence an input to the process. In such cases, the user must
clearly define
the purpose of benchmarking so that such performance measures can be
classified
as inputs or outputs. In some situations, the DMUs may have internal
structures, e.g., a two-stage process. For example, banks generate
deposits as
an output in the first stage, and then the deposits are used as an
input to
generate profit in the second stage. In this case, “deposits” is
treated as
both output (from the first stage) and input (to the second stage).
DEA can be viewed as a
multiple-criteria
evaluation methodology where DMUs are alternatives, and DEA inputs and
outputs
are two sets of performance criteria where one set (inputs) is to be
minimized
and the other (outputs) is to be maximized.
Under general benchmarking,
the DEA score may no
longer be referred to as “production efficiency”. In this case, we may
wish to
refer to the DEA score as a form of “overall performance” of an
organization.
Such “overall performance” can appear in the form of composite measure
that
aggregates individual indicators (inputs and outputs) via a DEA model.
For
example, composite measures (DEA scores) of quality indicators allow
senior
leaders to better benchmark their organization’s performance against
other
high-performing organizations
DEA
is not a form of regression model, but rather it is a frontier-based
linear
programming-based optimization technique. It is meaningless to apply a
sample
size requirement to DEA, which should be viewed as a benchmarking tool
focusing
on individual performance. It is likely that a significant portion of
DMUs will
be deemed as efficient, if there are “too many” inputs and outputs
given the
number of DMUs. If the goal is to obtain fewer efficient DMUs, then one
can use
weight restrictions or other DEA approaches to reduce the number of
efficient
DMUs.
According to Cooper, L.M. Seiford and J. Zhu (2011), "Data Envelopement Analysis: Models and Interpretations", Chapter 1, 1-39, in W.W. Cooper, L.M. Seiford and J. Zhu, eds, Handbook on Data Envelopment Analysis, 2nd edition, Springer, New York, 2011.
Data Envelopment Analysis (DEA) is a “data oriented” approach for evaluating the performance of a set of peer entities called Decision Making Units (DMUs) which convert
multiple inputs into multiple outputs. The definition of a DMU is generic and flexible. Recent years have seen a great variety of applications of DEA for use in
evaluating the performances of many different kinds of entities engaged in many different activities in many different contexts in many different countries.
These DEA applications have used DMUs of various forms to evaluate the performance of entities, such as hospitals, US Air Force wings, universities, cities,
courts, business firms, and others, including the performance of countries, regions, etc. Because it requires very few assumptions,
DEA has also opened up possibilities for use in cases which have been resistant to other approaches because of the complex (often unknown) nature of the relations
between the multiple inputs and multiple outputs involved in DMUs.
As pointed out in Cooper, Seiford and Tone (2007), DEA has also been used to supply new insights into activities (and entities) that have previously been
evaluated by other methods. For instance, studies of benchmarking practices with DEA have identified numerous sources of inefficiency in some of the most
profitable firms firms that had served as benchmarks by reference to this (profitability) criterion – but DEA has provided a vehicle for identifying better
benchmarks in many applied studies. Because of these possibilities, DEA studies of the efficiency of different legal organization forms such as "stock" vs. "mutual" insurance
companies, have shown that previous studies have fallen short in their attempts to evaluate the potentials of these different forms of organizations. Similarly, a use of
DEA has suggested reconsideration of previous studies of the efficiency with which pre- and post-merger activities have been conducted in banks that were studied by DEA.
Since DEA was first introduced in 1978 in its present form, researchers in a number of fields have quickly recognized that it is an excellent and easily used methodology
for modeling operational processes for performance evaluations. This has been accompanied by other developments. For instance, Zhu (2003, 2009) provides a number of
DEA spreadsheet models that can be used in performance evaluation and benchmarking. DEA’s empirical orientation and the absence of a need for the numerous a priori
assumptions that accompany other approaches (such as standard forms of statistical regression analysis) have resulted in its use in a number of studies involving
efficient frontier estimation in the governmental and nonprofit sector, in the regulated sector, and in the private sector. See, for instance, the use
of DEA to guide removal of the Diet and other government agencies from Tokyo as described in Takamura and Tone (2003).
In their originating article, Charnes, Cooper, and Rhodes (1978) described DEA as a ‘mathematical programming model applied to observational data [that] provides
a new way of obtaining empirical estimates of relations - such as the production functions and/or efficient production possibility surfaces – that are cornerstones
of modern economics’.
Formally, DEA is a methodology directed to frontiers rather than central tendencies. Instead of trying to fit a regression plane through the center of the data
as in statistical regressions, for example, one ‘floats’ a piecewise linear surface to rest on top of the observations. Because of this perspective, DEA
proves particularly adept at uncovering relationships that would remain hidden from other methodologies. For instance, consider what one wants to mean by “efficiency”,
or more generally, what one wants to mean by saying that one DMU is more efficient than another DMU. This is accomplished in a straightforward manner by DEA without
requiring explicitly formulated assumptions and variations with various types of models such as in linear and nonlinear regression models.
References:
1. Charnes, A., W.W. Cooper, and E. Rhodes, 1978, Measuring the efficiency of decision making units, European Journal of Operational Research 2, 429-444.
2. Cooper, W.W., Seiford, L.M. and Tone, K., 2nd ed. 2007, Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and
DEA-Solver Software, Kluwer Academic Publishers, Boston.
3. Takamura, T. and K. Tone, 2003, “A Comparative Site Evaluation Study for Relocating Japanese Government
Agencies Out of Tokyo,” Socio-Economic Planning Sciences 37, 85-102.
4. Zhu, J. 2003, Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets and DEA Excel Solver,
Kluwer Academic Publishers, Boston.
5. Zhu, J. 2009, Quantitative Models for Performance Evaluation and Benchmarking: Data Envelopment Analysis with Spreadsheets, 2nd Edition, Springer, New York.