OVERVIEW

This course focuses on analytical and quantitative methods aimed at supporting strategic decision-making processes in the energy sector. It presents frameworks for strategy formulation and industry analysis, with a focus on value creation and forces of competition. The course provides insights to understand how big data and artificial intelligence have changed strategic decisions and practical guidelines to manage big data challenges in decision-making.

AIMS AND CONTENT

LEARNING OUTCOMES

This fourth contribution focuses on analytical and quantitative methods to support strategic decision-making processes in the energy sector. It presents fundamental frameworks for strategy formulation and industry analysis, with a focus on value creation and forces of competition. It provides pillars to understand how big data and artificial intelligence have changed strategic decisions and practical guidelines to manage big data challenges in decision-making.

AIMS AND LEARNING OUTCOMES

1. Fundamentals of strategy

• Describe the fundamental principles of strategy and strategic decision-making
• Analyze competitive environments and industry dynamics in the energy sector
• Evaluate how firms develop competitive advantage through resources and capabilities

2. Data-driven strategic decision-making processes

• Explain the role of data and analytics in strategic decision-making
• Interpret data-driven insights to support strategic planning and business performance
• Assess the opportunities and challenges associated with data-driven approaches

3. AI-driven strategic decision-making processes

• Describe the role of artificial intelligence in strategic and managerial decisions
• Evaluate the impact of AI technologies on innovation, competitiveness, and organizational transformation
• Recognize the opportunities, risks, and ethical implications of AI-driven decision-making

4. Conceptual frameworks for strategic decisions

• Apply conceptual and analytical frameworks to support strategic analysis
• Conduct strategic evaluations of firms, markets, and competitive positioning
• Assess strategic alternatives under conditions of uncertainty and complexity

5. Quantitative methods for strategy development

• Apply quantitative methods and analytical tools to support strategy development
• Interpret quantitative evidence for forecasting, optimization, and performance assessment
• Integrate strategic reasoning with quantitative analysis in managerial decision-making

PREREQUISITES

No specific prerequisites are required. However, a basic knowledge of the fundamental concepts of strategic management is preferable.

TEACHING METHODS

The course adopts a predominantly distance-learning teaching mode. Distance-delivered teaching consists of recorded lectures covering the theoretical and conceptual content of the module. In-presence teaching consists of practical exercises, group activities and guided discussions aimed at stimulating interaction, consolidating analytical skills and encouraging the application of methods and tools to concrete cases from the energy sector.

For students with disabilities, Specific Learning Disorders (SLD), or Special Educational Needs (SEN)

Students with disabilities, Specific Learning Disorders (SLD), or Special Educational Needs (SEN) are reminded that, in order to request exam accommodations, they must first upload the relevant certification on the University online services portal at servizionline.unige.it, under the “Students” section.

The documentation will be reviewed by the University Office for Inclusion Services for Students with Disabilities and SLD.

At the beginning of the course, students are advised to contact the lecturer in order to agree on examination arrangements that, while respecting the learning objectives of the course, take into account individual learning needs.

To request compensatory tools or dispensatory measures, students with disabilities or SLD must complete the dedicated web form available at https://unige.it/disabilita-dsa at least 7 working days before the examination.

Students with SEN may instead submit their request by email to the lecturer, copying the Departmental Contact Person, Prof. Elena Lagomarsino (inclusione.economia@unige.it), and the Inclusion Office (inclusione.studenti@info.unige.it). Requests will be assessed by the lecturer and may be approved or denied.

SYLLABUS/CONTENT

The Strategic Decision-Making Process and Quantitative Methods course covers theories and techniques to support strategic decision-making processes, with a focus on the energy sector and ecological transition. The course combines a theoretical framing of strategic concepts with analytical and quantitative tools for context analysis, identification of competitive advantage, and the use of big data and artificial intelligence to support decisions.

Main contents

1. Fundamentals of strategy

• Principles and components of strategy
• Strategic decision-making processes and strategy formulation
• Industry analysis, competitive environments, and competitive advantage
• Resources, capabilities, and value creation in the energy sector

2. Data-driven strategic decision-making processes

• Role of data and analytics in strategic decision-making
• Data-driven business models and strategic planning
• Data quality, interpretation, and evidence-based decisions
• Opportunities and challenges of data-driven approaches

3. AI-driven strategic decision-making processes

• Artificial intelligence applications in strategic and managerial decisions
• AI-enabled forecasting, optimization, and strategic analysis
• Organizational implications of AI adoption
• Opportunities, risks, and ethical issues related to AI-driven decision-making

4. Conceptual frameworks for strategic decisions

• Strategic analysis and positioning frameworks
• SWOT analysis and strategic evaluation tools
• Ex-ante and ex-post strategy evaluation
• In itinere strategy evaluation and the Balanced Scorecard
• Portfolio strategy tools for energy firms
• Concentration measurement and diversification test 

5. Quantitative methods for strategy development

• Quantitative methods for strategic analysis and decision support
• Statistical reasoning and analytical tools for managerial decisions
• Business key performance indicators (KPIs) for strategic decision-making
• Integration of quantitative analysis through Excel
• Index construction for strategic decision-making

RECOMMENDED READING/BIBLIOGRAPHY

Grant, R. M. (2021). Contemporary strategy analysis. John Wiley & Sons. [Limited to the parts indicated by the lecturer]

Ahmad, T., Zhang, D., Huang, C., Zhang, H., Dai, N., Song, Y., & Chen, H. (2021). Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. Journal of Cleaner Production, 289, 125834.

Sivarajah, U., Kamal, M. M., Irani, Z., & Weerakkody, V. (2017). Critical analysis of Big Data challenges and analytical methods. Journal of Business Research, 70, 263-286.

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

Teaching activities will begin in accordance with the academic calendar of the Department of Economics.

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The final examination will consist of a written test designed to assess understanding of the theoretical concepts and application of the analytical tools covered in the course. Further guidance on assessment methods will be provided during the course. 

ASSESSMENT METHODS

The questions in the written test focus on the fundamental themes and concepts covered in the course. Case studies are designed to measure the students’ ability to apply knowledge to real-world scenarios. Assessment criteria include the degree of understanding of key concepts, decision-making scenarios and analytical ability, as well as the appropriate use of disciplinary language.

FURTHER INFORMATION

Students with certifications of DSA, disability or other special educational needs are invited to get in touch with the lecturer at the beginning of the course in order to agree on possible adaptations in the examination test that, while respecting the educational objectives of the teaching, take into account their specific learning modalities and provide for adequate compensatory tools.