MSc Business Analytics and Artificial Intelligence

Fees for entry in 2027 have not yet been set. For reference, the fees for the academic year beginning September 2026 were as follows:

• MSc (full-time)
  UK students (per annum): £20,000
  International, including EU, students (per annum): £35,700

The fees quoted above are fully inclusive of tuition, administration and computational costs.

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Decision making is the most critical and value-added constituent part of artificial intelligence and is among the most important activities of any business. Just as Nobel Laureate Herbert Simon said, management is decision making. This course is designed to teach the advanced topics of business decision analytics. It aims to introduce important concepts, methods, models and software tools for risk analysis for business, business decision analysis under risk, multiple criteria assessment and decision analysis under various types of uncertainty, data envelopment analysis for performance measurement and evaluation, and multiple objective optimisation for performance improvement and planning. The emphasis is on how to use data and human judgments to enable these analyses in robust, explainable and practical manners. Various numerical examples, case studies and real life applications will be analysed using software tools including Excel with Solver, TreePlan and IDS Multicriteria Assessor.  
 

BMAN60092 Programme Req: BMAN60092 is only available as a core unit to students on MSc Business Analytics, and as an elective to students on MSc Quantitative Finance and MSc Data Science (Business & Management pathway)

This course unit covers risk, performance and decision modelling and analysis, including risk modelling and assessment, both single and multiple criteria decision modelling and analysis, data envelopment analysis and multiple objective optimisation. Emphasis will be placed on the integrated applications of these methods and tools to performance and efficiency analysis and planning. The aim is to familiarise students with the applications of decision modelling and performance analysis methodologies. 
 

At the end of the course unit students should be familiar with concepts, methods and tools for decision tree analysis, multiple criteria decision analysis, data envelopment analysis and multiple objective optimisation, which they can apply to support decision making and deal with performance assessment and efficiency analysis problems. They should also be able to use appropriate software tools such as Excel and IDS Multicriteria Assessor.

Topic 1: Risk, Decision Tree and Utility – Models, Methods, and Concepts 
Reading: Hillier & Lieberman, Chapter 16

Topic 2: Utility Theory and Bayesian Decision Theory – Concepts and Methods 
Reading: Hillier & Lieberman, Chapter 16

Topic 3: Performance Assessment and Multiple Criteria Decision Analysis (MCDA) 
Reading: Keeney & Raiffa, IDS, Excel, etc., Chapters 4–6

Topic 4: MCDA – Models and Preference Modelling 
Reading: Keeney & Raiffa, Chapters 4–6

Topic 5: MCDA – Process and Aggregation Methods 
Reading: Belton and Stewart, Chapters 3–4

Topic 6: MCDA – Aggregation Methods, Tools, and Applications 
Reading: Sen & Yang, Chapter 3

Topic 7: Data Envelopment Analysis – Concepts and Basic Models 
Reading: Cooper, Seiford and Tone

Topic 8: Data Envelopment Analysis – Models, Methods, Tools, and Applications 
Reading: Cooper, Seiford and Tone

Topic 9: Multiple Objective Linear Programming – Concepts and Models 
Reading: Liu, Yang and Whidborne

Topic 10: Multiple Objective Linear Programming – Methods and Applications 
Reading: Liu, Yang and Whidborne 

Lectures 33 hours 

KU1 
Demonstrate familiarity with concepts, methods, and tools for decision analysis under risk.

KU2 
Demonstrate familiarity with concepts, methods, and tools for multiple criteria decision analysis (MCDA) under different types of uncertainty.

KU3
Demonstrate familiarity with concepts, methods, and tools for performance measurement, modelling, analysis, and improvement, particularly using data envelopment analysis (DEA) and multiple objective optimisation techniques such as multiple objective linear programming (MOLP). 
 

IS1 
Able to use decision analysis models and methods as well as software platforms, such as decision tree approach and TreePlan software

IS2 
Able to use multiple criteria decision analysis models and methods, such as the multiple criteria value (or utility) function (MCVF or MCUF) approach, the evidential reasoning (ER) approach and the analytical hierarchy process (AHP) approach, and such software platforms as VISA based on the MCVF approach, IDS (Intelligent Decision System) based on the ER approach, and Expert Choice based on the AHP approach.

IS3 
Able to use performance measurement and analysis models and methods, such as various DEA models and methods and MOLP models and methods, and such software platforms as Excel Solver and Open Solver embedded in Excel and Python programming for optimisation  

PS1 
Able to use data and human judgements to analyse various business decision problems under risk, such as financial or investment decision making, new product development, new project development, etc., where risky business environments, ever changing economic and geopolitical situations and uncertain natural environment like climate change have significant influence on business decision making.  

PS2 
Able to use data and human judgements to analyse various business decision problems having multiple criteria (either tangible and intangible and either financial or non-financial) and under various types of uncertainty (not only uncertain business environments, economic and geopolitical situations and natural environment but also human behaviours, decision maker’s preferences and personalities), such as multi-dimensional assessment of new products, services, technologies, suppliers, business strategies, etc.

PS3 
Able to use data and human judgements to measure and analyse business performances and improvement strategies  
 

TS1 
Able to understand business decision problems under risk in various sectors, measure them using explainable models, analyse them using robust methods and processes, and communicate the analysis results to stakeholders.

TS2 
Able to understand and structure business decision problems of multiple criteria and under different types of uncertainty, choose correct methods and processes to analyse them, and communicate the analysis results to stakeholders and wider public.

TS3 
Able to understand business performances, collect data to structure performance assessment models, build correct methods and processes to analyse business performances from different perspectives, and communicate analysis results to business leaders. 

70% Exam

30% Group project

 

 

Hillier, F.S. & Lieberman, G.J. (2015) Introduction to Operations Research 10th Edition, McGraw-Hill, Precinct. Earlier or later editions are fine. Search topics in Index of the book to find the right pages to read about the relevant topics.

Belton, V., Stewart, T. J. (2002), Multiple Criteria Decision Analysis: An Integrated Approach. Kluwer Academic Publishers: Dordrecht.

Keeney, R.L. and Raiffa, H. (1993), Decision with Multiple Objectives: Preference and Value Tradeoffs. Cambridge University Press.

Cooper, W. W, Seiford, L. M. and Tone, K. (2007), Data Envelopment analysis: a comprehensive text with models, applications, references and DEA Solver software. 2nd edition, Springer.

Liu G. P., Yang J. B. and Whidborne, J. F. (2002), Multiobjective Optimisation and Control. Engineering Systems Modelling and Control Series, Research Studies Press Limited, Baldock, Hertfordshire, England.

Saaty, T. L. (1988), The Analytic Hierarchy Process. University of Pittsburgh, 1988.

Sen, P. and Yang, J. B. (1998), Multiple Criteria Decision Support in Engineering Design, Springer. London, ISBN 3540199322.

Yang, J. B. (2001), Rule and utility based evidential reasoning approach for multiple attribute decision analysis under uncertainty, European Journal of Operational Research, Vol. 131, No.1, pp.31-61.

Yang, J. B. and Xu, D. L. (2002), On the evidential reasoning algorithm for multi-attribute decision analysis under uncertainty, IEEE Transactions on Systems, Man, and Cybernetics Part A: Systems and Humans, Vol.32, No.3, pp.289-304.

Xu, D. L. and Yang, J. B. (2003), Intelligent decision system for self-assessment, Journal of Multiple Criteria Decision Analysis, Vol.12, 43-60.

Xu, D. L., McCarthy, G. and Yang, J. B., (2006) Intelligent decision system and its application in business innovative capability assessment, Decision Support Systems, Vol.42, pp.664-673.

Yang, J. B., Wang, Y. M., Xu, D. L. and Chin, K. S. (2006), The evidential reasoning approach for MCDA under both probabilistic and fuzzy uncertainties, European Journal of Operational Research, Vol. 171, No.1, pp.309-343.

Yang, J. B., Wong, Y. H., Xu, D. L. and Stewart, T. J. (2009), Integrating DEA-oriented performance assessment and target setting using interactive MCDA methods, European Journal of Operational Research, Vol.195, pp.205–222.

Yang, J. B., Xu, D. L., Xie, X. L. and Maddulapalli, A.K. (2011), Multicriteria evidential reasoning decision modelling and analysis – prioritising voices of customer, Journal of the Operational Research Society, Vol.62, pp.1638–1654.

Yang, J. B. and Xu, D. L. (2013), Evidential reasoning rule for evidence combination, Artificial Intelligence, Vol.205, pp.1-29, 2013.

 

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