BUSI4AV15: Decision Science and Analytics in Energy Business Management

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• To provide an overview of common decision-making techniques in energy management sectors.
• To provide an appreciation of predictive and prescriptive business-analytics techniques and implement these models using appropriate software.
• To show how business analytics can help companies to make better decisions in the energy management context.
• To provide knowledge of, and ability to apply, storytelling with data to promote data-driven decision-making with the energy businesses.

• Fundamental data analytics tools such as descriptive analysis.
• Predictive techniques such as linear regression, machine learning.
• Prescriptive techniques such as linear optimisation, queueing theory.
• Decision making tools such as utility theory, game theory, decision tree.
• Project management methods such as PERT, Critical Path method.
• Computational technology such as simulations.

Subject-specific Knowledge:

• By the end of the module students should be able to:
• Demonstrate the ability to use operations research and decision theory principles to tackle complex problems in energy business management.
• Apply prescriptive analytics to make data-driven decisions in both certain and uncertain business contexts.
• Assess the value of information and make informed bets on future trends and reactions in the energy sector.
• Integrate Net-Zero objectives into traditional business analytics and decision science frameworks.
• Evaluate the impact of business decisions on the environment and recommend solutions that align with global Net-Zero ambitions.

Subject-specific Skills:

• By the end of the module students should be able to:
• Conduct basic analysis with data provided in energy business.
• Model the common optimization and decision-making problem related to Net-Zero target.
• Implement predictive and prescriptive business-analytics models using appropriate software packages.
• Interpret the results of business analytics models and their relevance for companies.
• Choose appropriate business-analytics techniques for some key management problems.

Key Skills:

• Effective written communication skills
• Oral presentation
• Planning, organising and time-management skills
• Problem solving and analytical skills
• Sourcing appropriate data and evaluating evidence
• Interpreting and using data
• Selecting appropriate modes of communication
• Making effective use of communication and information technology
• Storytelling with data
• Coding skills (preferred but not necessary)
• Group work

• This module will be delivered jointly by the Business School.
• Learning outcomes are met through lectures and computer workshops, supported by online resources. Online resources provide preparatory material for the lectures and computer workshops - typically consisting of directed reading and video content.
• The formative assessment consists of classroom-based exercises involving individual and group analyses and presentations on specific business situations/problems relevant to the learning outcomes of the module.
• The summative assessments are an individual video presentation and an individual business analytics project. Both assessments are designed to test the ability to formulate a problem, apply appropriate business-analytics techniques to analyse it, and critically interpret the results obtained.

The formative assessment consists of classroom-based exercises involving individual and group analyses and presentations on specific business situations / problems relevant to the learning outcomes of the module. Oral and written feedback will be given on a group and / or individual basis, as appropriate.

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