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2023/2024  KAN-CMECV2001U  Benchmarking

English Title

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Quarter
Start time of the course Fourth Quarter
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 80
Study board
Study Board for HA/cand.merc. i erhvervsøkonomi og matematik, MSc
Course coordinator
  • Mette Asmild - Department of Finance (FI)
Main academic disciplines
  • Mathematics
  • Statistics and quantitative methods
  • Economics
Teaching methods
  • Face-to-face teaching
Last updated on 13-02-2023

Relevant links

Learning objectives
Through this course the students should be able to:
  • Explain the differences between different benchmarking model specifications and justify the choices of specific models for a given problem context.
  • Calculate efficiency scores and identify the corresponding benchmarks, peers & weights in simple examples.
  • Interpret the results from large empirical studies and discuss their implications for management.
  • Explain the differences between the envelopment and multiplier formulations of the so-called DEA models and their respective uses.
  • Argue for the relevance of different model extensions for specific scenarios, including but not limited to the use of weight restrictions and different projections onto the efficiency frontier.
Course prerequisites
Quantitative skills & interests, knowledge of linear programming (and basic understanding of LP duality)
Exam ECTS 7,5
Examination form Oral exam based on written product

In order to participate in the oral exam, the written product must be handed in before the oral exam; by the set deadline. The grade is based on an overall assessment of the written product and the individual oral performance, see also the rules about examination forms in the programme regulations.
Individual or group exam Individual oral exam based on written group product
Number of people in the group 2-3
Size of written product Max. 10 pages
Assignment type Essay
Release of assignment Subject chosen by students themselves, see guidelines if any
Written product to be submitted on specified date and time.
20 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Grading scale 7-point grading scale
Examiner(s) Internal examiner and second internal examiner
Exam period Summer
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content, structure and pedagogical approach

Being able to quantify the efficiency of resource utilisation is important, both in the private sector, due to the intensity of competition, and in the public sector, where budget cuts and demands for efficiency improvments are common.

This course covers specific benchmarking methods that are very useful in practice for assessing the performance of different organisational units, particularly the socalled Data Envelopment Analysis (DEA) approach. As well as providing measures for the extent of efficiency, DEA also identifies role model units which less efficient units can emulate and performance targets at which inefficient units might aim. Benchmarking analysis through the use of DEA thus supports the identification and adoption of operating practices conducive to the efficient utilisation of resources. The approaches are flexible enough to be able to incorporate e.g. ESG indicators in the analysis, and are therefore applicable for efficiency assessments in many different practical settings. They are also used in practice for regulation of natural monopolies within e.g. the utillity sectors (electricity distribution, water and wastewater companies) in Denmark and many other (especially European) countries.

Besides covering both theoretical and practical aspects of the use of DEA, the course also looks at some real life applications, amongst which are studies within e.g. the financial services industries undertaken by the lecturer on the course.

The course will involve combinations of lectures, small examples and exercises done in class and potentially some more realistic problems for students to practice on at home.


The approaches covered in the course are data driven, so the course is highly  quantitative - but utilising linear programming based approaches, so it is mathematical rather than statistical in nature.

Description of the teaching methods
Combination of lectures and exercises
Feedback during the teaching period
Oral feedback after examination.
Discussions in class.
Collective oral feedback during lectures based on student answers in live quizzes (polling)
Help with and discussions of projects along the way (e.g. feedback on proposed models).
Student workload
Attending lectures 32 hours
Exam, including preparation 55 hours
Preparation for lectures 120 hours
Expected literature

Bogetoft and Otto: Benchmarking with DEA, SFA and R. Forlag: Springer-Verlag New York Inc.


Thanassoulis: Introduction to the Theory and Application of Data Envelopment Analysis: A foundation text with integrated software. Forlag: Springer-Verlag New York Inc.



Last updated on 13-02-2023