Postgraduate Course: Performance Analytics with DEA: Advanced Concepts and Methods (CMSE11425)
||College||College of Arts, Humanities and Social Sciences
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Not available to visiting students
|Summary||This course builds on the concepts and methods taught in 'Performance Analytics with DEA: Basic Concepts and Method' to cover more advanced concepts and methods; namely, dynamic, network, and dynamic-network DEA models and their use in business applications.
This is an option course for the MSc in Business Analytics programme. It builds on the concepts and methods taught in 'Performance Analytics with DEA: Basic Concepts and Method' to cover more advanced concepts and methods; namely, dynamic, network, and dynamic-network DEA models and their use in business applications. These models and corresponding solution methods complement the static black-box analyses to provide the modelling frameworks and solution methods which are more appropriate for dynamic analyses, network analyses, and dynamic-network analyses to allow the analyst to take the time dynamics into account and to open the black-box of DEA to account for differences in performance of processes.
1. Dynamic DEA models and their use in business applications
2. Network DEA models and their use in business applications
3. Dynamic-Network DEA models and their use in business applications
4. Practical issues and how to address them
Student Learning Experience
Weekly lectures and hands-on programming exercises in Matlab and DEA solvers which enables students to implement the methodologies covered in class.
Entry Requirements (not applicable to Visiting Students)
|| It is RECOMMENDED that students have passed
Performance Analytics with DEA: Basic Concepts and Methods (CMSE11424)
||Other requirements|| For MSc Business Analytics students, or by permission of course organiser. Please contact the course secretary.
Course Delivery Information
|Academic year 2019/20, Not available to visiting students (SS1)
||Block 4 (Sem 2)
|Learning and Teaching activities (Further Info)
Lecture Hours 10,
Seminar/Tutorial Hours 10,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Assessment (Further Info)
|Additional Information (Assessment)
||Individual Report (100% weighting)
Assesses Learning Outcomes 1 to 5.
Students will have to undertake a performance evaluation and/or risk assessment exercise including problem statement, model building, solution design, report on findings, formulation of recommendations and managerial guidelines. The report should demonstrate effective communication of performance problems and viable solutions to demonstrate students' ability to address real world performance problems and to convince their line managers or sponsors to implement the proposed solution.
||Feedback on formative assessed work will be provided in line the Taught Assessment Regulation turnaround period, or in time to be of use in subsequent assessments within the course, whichever is sooner. Summative marks will be returned on a published timetable, which will be communicated to students during semester. All assessments will be marked according to the University Common Marking Scheme.
|No Exam Information
On completion of this course, the student will be able to:
- Discuss the advanced concept and methods of performance measurement, evaluation, and management using the proper terminology
- Identify and properly state performance problems in different business settings
- Address performance problems within a DEA framework and choose the right advanced DEA models to devise solutions
- Formulate managerial guidelines in the area of performance management and make recommendations based on advanced DEA analyses
- Communicate performance problems and advanced solutions effectively and efficiently to a critical audience
|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|
|Graduate Attributes and Skills
||Numeracy and Big data
Knowledge integration and application
|Course organiser||Prof Jamal Ouenniche
Tel: (0131 6)50 3792
|Course secretary||Miss Lauren Millson
Tel: (0131 6)51 3013