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DRPS : Course Catalogue : Business School : Common Courses (Management School)

Postgraduate Course: Performance Analytics with DEA (CMSE11653)

Course Outline
SchoolBusiness School CollegeCollege of Arts, Humanities and Social Sciences
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryThis course provides students with the theory of Data Envelopment Analysis (DEA) and its applications in performance evaluation and benchmarking.
Course description Data Envelopment Analysis (DEA) is a performance evaluation and benchmarking methodology. DEA is a non-parametric and frontier-based methodology that benchmarks against the best or the worst practice frontier. Nowadays, DEA is commonly used for the relative performance evaluation and risk assessment of entities such as banks, bank branches, firms listed on stock markets, investment vehicles including projects, production technologies, suppliers, etc. This course aims at training students in the field of performance evaluation and management using DEA as the main methodology. The course shall cover the key concepts in performance management along with a general classification of performance evaluation methodologies; DEA concepts and generic methodology; static black-box, dynamic black-box, network, and dynamic-network DEA models and their use in business applications; and practical issues in DEA and how to address them.

Outline content

1. Key concepts in performance management along with a general classification of performance evaluation methodologies.

2. DEA concepts and generic methodology.

3. Static black-box, dynamic black-box, network, and dynamic-network DEA models and their use in business applications.

4. Practical issues in DEA and how to address them.

Student Learning Experience

Weekly lectures and hands-on applications with MaxDEA software.

Teaching will take the form of in class and/or recorded lectures and in person and/or online supervised discussions and lab sessions, as judged appropriate. The live hours may be supplemented by pre-recorded lecture material for students to engage with asynchronously. The supervised discussions and lab sessions aim at putting into practice the concepts and methods presented in the lectures and learned from personalised readings. In addition, these sessions also serve as advice/support sessions so that students can seek assistance and formative feedback on their term projects work-in-progress. Some of the material covered in lectures and discussion sessions will be research-led and based on recent publications from the academic literature. Besides attending lectures and supervised discussion and lab sessions, students will work in groups on realistic projects and present their work in class to an audience of practitioners when the term projects are provided by industry.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites Students MUST also take: Prescriptive Analytics with Mathematical Programming (CMSE11431)
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2024/25, Not available to visiting students (SS1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 20, Seminar/Tutorial Hours 10, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 166 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 60% Class test (Individual) - Assesses all course Learning Outcomes
40% Project report includes 20% peer review (group) - Assesses all course Learning Outcomes
Feedback Formative: Feedback will be provided throughout the course.

Summative: Feedback will be provided on assessments within agreed deadlines.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Discuss the concept and methods of performance measurement, evaluation, and management using the proper terminology.
  2. Identify and properly state performance problems in different business settings,
  3. Address performance problems with both basic and advanced conceptual DEA frameworks and choose the right DEA models to devise solutions,
  4. Formulate managerial guidelines in the area of performance management and make recommendations based on basic DEA analyses.
  5. Communicate performance problems and solutions effectively and efficiently to a critical audience.
Reading List
Core text

Cooper WW, Seiford LM and Tone K. (2007) Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software. Second Edition. Springer
Additional Information
Graduate Attributes and Skills Communication, ICT, and Numeracy Skills

After completing this course, students should be able to:

Critically evaluate and present digital and other sources, research methods, data and information; discern their limitations, accuracy, validity, reliability and suitability; and apply responsibly in a wide variety of organisational contexts.

Cognitive Skills

After completing this course, students should be able to:

Be self-motivated; curious; show initiative; set, achieve and surpass goals; as well as demonstrating adaptability, capable of handling complexity and ambiguity, with a willingness to learn; as well as being able to demonstrate the use digital and other tools to carry out tasks effectively, productively, and with attention to quality.

Knowledge and Understanding

After completing this course, students should be able to:

Demonstrate a thorough knowledge and understanding of contemporary organisational disciplines; comprehend the role of business within the contemporary world; and critically evaluate and synthesise primary and secondary research and sources of evidence in order to make, and present, well informed and transparent organisation-related decisions, which have a positive global impact.

Identify, define and analyse theoretical and applied business and management problems, and develop approaches, informed by an understanding of appropriate quantitative and/or qualitative techniques, to explore and solve them responsibly.
KeywordsDEA,Data Envelopment Analysis,Performance Analytics,Benchmarking,Efficiency Measurement
Course organiserProf Jamal Ouenniche
Tel: (0131 6)50 3792
Course secretary
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