Syllabus & Schedule

Comprehensive course structure, assessment criteria, and weekly schedule for the AI & Data Transformation in Food & Beverage course.

Duration

5 sessions plus examination

Format

In-person sessions with online resources

Credits

6 ECTS credits

Learning Outcomes

Upon successful completion of this course, students will be able to:

Analyze the strategic implications of AI and data technologies for F&B organizations
Design data-driven decision-making processes aligned with business objectives
Evaluate and select appropriate machine learning algorithms for specific business problems
Apply analytics strategy frameworks through hands-on simulation exercises
Assess organizational readiness for AI transformation
Navigate ethical and governance challenges in AI deployment
Implement predictive and prescriptive analytics for operational optimization
Communicate technical concepts to non-technical stakeholders effectively

Recommended Readings

The Fourth Revolution: How the Infosphere Is Reshaping Human Reality

Floridi, L. (2016) — Oxford University Press

A foundational exploration of how the information revolution is transforming our understanding of ourselves, our relationships, and our world.

Co-Intelligence: Living and Working with AI

Mollick, E. (2024) — Portfolio

A practical and insightful guide to understanding, leveraging, and coexisting with artificial intelligence in professional and personal contexts.

Course Schedule

0

Welcome to AI & Data Transformation

An introduction to the course structure, learning approach, and the transformative journey ahead in AI and data analytics for the Food & Beverage industry.

1

AI & Data Transformation in the Food & Beverage Industry: Enabling Technologies

Monday, February 16
180 minutes

Exploring the exponential technologies reshaping F&B: Cloud, IoT, Robotics, AR, and the intelligence layer of Big Data and AI/ML.

2

The Info-Economy and the Data Value Model: Strategic Foundations for the Digital Age

Monday, February 16
180 minutes

Understanding the economics of bits vs atoms, the knowledge pyramid, analytics spectrum, and ethical frameworks for AI deployment.

3

Analytics Strategy Simulation: Data-Driven Decision Making

Monday, February 23
180 minutes

A hands-on simulation session using the Harvard Business School case study 'Data Analytics Simulation: Strategic Decision Making' by Thomas H. Davenport.

4

Machine Learning Applications in Food & Beverage

Monday, March 9
180 minutes

Deep dive into ML algorithms, feature engineering, model evaluation, and real-world F&B applications from demand forecasting to quality control.

5

Mastering the Human Dimension of Big Data & Analytics: From Data to Decision

Monday, March 23
180 minutes

Understanding the human-centric paradigm in analytics - from customer journeys to philosophical foundations of the digital age.

Assessment Structure

Individual Performance

10%

Active engagement in in-class discussions, case analyses, and interactive exercises. Quality of contributions matters more than quantity.

Group Activities

10%

Team-based activities including the Harvard Business School simulation and case study discussion. Assessed on collaboration, analytical quality, and presentation of results.

Individual Written Examination

80%

Individual written exam covering all course materials, concepts, and their practical applications. Minimum qualifying mark: 8/20.

Note: AI tools are not permitted during the examination.