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 and decision-making processes

• Describe the basic components of a firm’s strategy
• Explain the theoretical principles of strategic analysis
• Identify and analyze the ways in which firms formulate strategies
• Recognize the distinguishing characteristics of a firm's strategy in the energy sector

1. Strategic analysis and the competitive environment

• Analyze the structural forces that determine the competitiveness and profitability of the industry
• Apply industry analysis models to assess the market attractiveness
• Define the boundaries of an industry and identify critical success factors

1. Resources, capabilities and competitive advantage

• Identify a firm's strategic resources and capabilities
• Assess the potential of resources and capabilities to generate sustainable competitive advantage

1. SWOT analysis and portfolio strategies

• Conduct a SWOT analysis to assess the alignment between internal resources and external context
• Calculate the strategic configuration of a business portfolio
• Evaluate diversification and strategic focus choices

1. Big data and artificial intelligence in decision-making processes

• Describe the role of data in energy sector decision-making processes
• Recognize the main challenges associated with the adoption of data-driven technologies
• Analyze the critical factors for big data analysis
• Explain how artificial intelligence technologies can support strategic decisions in the energy sector

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.

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 and decision-making processes

• Definition and components of a firm’s strategy
• The strategy formulation process
• The role of strategic decisions in firms

2.Strategic analysis and the competitive environment

• Models of industry analysis
• Analysis of profitability and industry attractiveness
• Key success factors and industry boundaries

3. Resources, capabilities and competitive advantage

• Resource-based view and capability concept
• Assessment of the strategic potential of resources and capabilities

4. SWOT analysis and portfolio strategies

• Integrated analysis of internal and external factors
• Diversification, focus and portfolio configuration decisions
• Strategic assessment of opportunities and threats

5. Big data and artificial intelligence in decision-making processes

• Role and impact of data in decision-making processes in the energy sector
• Guidelines for integrating big data into business strategies, challenges and opportunities of the data-driven approach
• Principles and tools of artificial intelligence applied to the energy sector, organizational impacts and challenges related to the use of artificial intelligence

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

Exam Board

ANDREA CIACCI

STEFANO BRACCO (President Substitute)

STEFANO MASSUCCO (President Substitute)

MARTA NAI RUSCONE (President Substitute)

LESSONS

LESSONS START

Classes begin in the first week of the second semester, according to the Department of Economics teaching calendar.

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.