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MSc Business Analytics: Operational Research and Risk Analysis
MSc Business Analytics: Operational Research and Risk Analysis

MSc Business Analytics: Operational Research and Risk Analysis / Course details

Year of entry: 2019

Course unit details:
Decision Behaviour, Analysis and Support

Unit code BMAN73272
Credit rating 15
Unit level FHEQ level 7 – master's degree or fourth year of an integrated master's degree
Teaching period(s) Semester 2
Offered by Alliance Manchester Business School
Available as a free choice unit? No


Taking decisions and enhancing decision making ability are important skills to have. Operational, tactical and strategic decisions however, are often complex in organisational and policy making contexts. This course provides a better understanding of soundly-based approaches for structuring and analysing decisions in the face of uncertainty and conflicting objectives (Decision Analysis). It explains how decisions are taken (Decision Behaviour) and how we can improve decision making capabilities at individual, group, organisational and societal levels (Decision Support).


BMAN73272 Programme Req: BMAN73272 is only available as an elective to students on MSc Business Analytics and MSc Advanced Computer Science and IT Management (SoCS)


The aim of this module is to provide a state-of-the-art overview on decision making in a variety of organisational settings (e.g. private, public and not-for-profit sectors). It explores how decision analysis and decision aiding technologies can help individuals, groups and organisations make better decisions. Drawing from decision theory, behavioural and psychological studies, information systems, artificial intelligence, operational research and organisational studies, the course highlights the multi-faceted challenges of decision making. The main emphasis is on prescriptive theories of decision making.

In summary, participants will gain an understanding of the capabilities and types of decision frameworks and decision aiding technologies used in businesses and their impact on business performance and competiveness

Learning outcomes

By the end of the course participants will:

  • Become aware of behavioural, normative and prescriptive models of decision making
  • Develop content and process skills for modelling and analysing critical decisions in prescriptive decision support
  • Understand a range of modelling frameworks, methods and tools for designing prescriptive decision processes and facilitating business decisions
  • Become aware of emerging trends in decision support technology

Assessment methods

Project (may involve group activities): 30%

Examination: 70% (2 hours)

Feedback methods

  • Informal advice and discussion during a lecture, seminar, workshop or lab.

  • Specific course related feedback sessions.

  • Written and/or verbal comments on assessed or non-assessed coursework.

  • Written and/or verbal comments after students have given a group or individual presentation.

  • Generic feedback posted on Blackboard regarding overall examination performance.

Recommended reading

The main course text is:

Simon French, Nadia Papamichail, John Maule.  ‘Decision Making: Behaviour, Analysis and Support’  Cambridge: Cambridge University Press, 2009

Another suitable text is:

R. Sharda, D. Delen. and E. Turban. ‘Business Intelligence and Analytics – Systems for Decision Support’ Upper Saddle River, New Jersey: Prentice Hall, 2014.

Study hours

Scheduled activity hours
Assessment written exam 2
Lectures 30
Independent study hours
Independent study 118

Teaching staff

Staff member Role
Konstantinia Papamichail Unit coordinator

Additional notes

Informal Contact Method

Office Hours

Drop in Surgeries (extra help sessions for students on material they may be struggling with)

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