| 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) |
| Examination |
|
Benchmarking:
|
| 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 |
| Duration |
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.
|
| Research-based teaching |
|
CBS’ programmes and teaching are research-based. The following
types of research-based knowledge and research-like activities are
included in this course:
Research-based knowledge
- Classic and basic theory
- New theory
- Teacher’s own research
- Methodology
- Models
Research-like activities
- Development of research questions
- Data collection
- Analysis
- Discussion, critical reflection, modelling
- Students conduct independent research-like activities under
supervision
|
| 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.
|
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