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Cross-benchmarking for performance evaluation: looking across best practices of different peer groups using DEA
Optimization and Control · Submitted 3 Dec 2019
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim Thus, cross-benchmarking is a flexible tool that can support a process of future planning while considering different managerial implications.
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The abstract, sentence by sentence
In benchmarking, organizations look outward to examine others' performance in their industry or sector. Often, they can learn from the best practices of some of them and improve. In order to develop this idea within the framework of Data Envelopment Analysis (DEA), this paper extends the common benchmarking framework proposed in Ruiz and Sirvent (2016) to an approach based on the benchmarking of decision making units (DMUs) against several reference sets. We refer to this approach as cross-benchmarking. First, we design a procedure aimed at making a selection of reference sets (as defined in DEA), which establish the common framework for the benchmarking. Next, benchmarking models are formulated which allow us to set the closest targets relative to the reference sets selected. The availability of a wider spectrum of targets may offer managers the possibility of choosing among alternative ways for improvements, taking into account what can be learned from the best practices of different peer groups. Thus, cross-benchmarking is a flexible tool that can support a process of future planning while considering different managerial implications.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | In benchmarking, organizations look outward to examine others' performance in their industry or sector. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Often, they can learn from the best practices of some of them and improve. | background 1.00 | 0.04 | 0.03 | hedged possibility 0.89 |
| s2 | In order to develop this idea within the framework of Data Envelopment Analysis (DEA), this paper extends the common benchmarking framework proposed in Ruiz and Sirvent (2016) to an approach based on the benchmarking of decision making units (DMUs) against several reference sets. | contribution 1.00 | 0.81 | 0.10 | asserted 0.99 |
| s3 | We refer to this approach as cross-benchmarking. | contribution 0.89 | 0.71 | 0.05 | asserted 0.98 |
| s4 | First, we design a procedure aimed at making a selection of reference sets (as defined in DEA), which establish the common framework for the benchmarking. | method 0.98 | 0.90 | 0.03 | asserted 0.99 |
| s5 | Next, benchmarking models are formulated which allow us to set the closest targets relative to the reference sets selected. | method 0.99 | 0.86 | 0.05 | asserted 0.96 |
| s6 | The availability of a wider spectrum of targets may offer managers the possibility of choosing among alternative ways for improvements, taking into account what can be learned from the best practices of different peer groups. | interpretation 0.98 | 0.21 | 0.29 | hedged possibility 1.00 |
| s7 | Thus, cross-benchmarking is a flexible tool that can support a process of future planning while considering different managerial implications. | interpretation 0.98 | 0.31 | 0.50 | asserted 0.85 |
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The paper
Original abstract
In benchmarking, organizations look outward to examine others' performance in their industry or sector. Often, they can learn from the best practices of some of them and improve. In order to develop this idea within the framework of Data Envelopment Analysis (DEA), this paper extends the common benchmarking framework proposed in Ruiz and Sirvent (2016) to an approach based on the benchmarking of decision making units (DMUs) against several reference sets. We refer to this approach as cross-benchmarking. First, we design a procedure aimed at making a selection of reference sets (as defined in DEA), which establish the common framework for the benchmarking. Next, benchmarking models are formulated which allow us to set the closest targets relative to the reference sets selected. The availability of a wider spectrum of targets may offer managers the possibility of choosing among alternative ways for improvements, taking into account what can be learned from the best practices of different peer groups. Thus, cross-benchmarking is a flexible tool that can support a process of future planning while considering different managerial implications.
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