Decisions, data and risk

​This is a website for our MBA participants at Prince Mohammed Bin Salman College (MBSC) of Business & Entrepreneurship who follow the course module "Data, Decisions and Risk". This course module examines how business decisions can be made more effectively. While some decisions depend on human judgment, complex challenges often require advanced data analysis and optimisation models. Routine and repetitive tasks, however, are best managed through automated robotic processes. Participants will learn how to build models using optimization techniques and apply these models in software tools such as MS Solver.

This web page contains all videos you need to watch and case studies you have to download. The links to the videos and case studies will become available as the course module progresses. Hint: If your download doesn't work, right-click with your mouse and select "Save As."

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^{Download this book from www.or-as.be/books/tsbd and share on LinkedIn if you like!}

Useful hint: Most links work by simply clicking, but if a download doesn't work, just right click on your mouse and choose "save as..."

INTRODUCTION

Prepare in advance: Make sure you have watched the introduction video before you start this course module!
Sneak preview: Intercom announcement: Your captain is speaking before takeoff (Live from Lisbon, Portugal)

 

LECTURES

Not all the links to sneak preview videos, case studies, and solutions are available yet, but they will be updated regularly (just refresh the page every evening).

Part 1: Deterministic Optimisation – Foundations and Theoretical Insights

The first part of this course consists of four lectures (January 1-4, 2025), during which the following three module topics will be covered:

Module 1: Data-Driven Decision Making – The Art of Doing Better
Sneak preview: Data-Driven Decision Making

Module 2: Deterministic Models – The Theory of Constraints
Sneak preview: Deterministic Models (linear programming)
Solution: The brick production example (tutorial and excel file)^{(right click on mouse and choose "save as" if normal download doesn't work)}

Module 3. Human Behaviour and Algorithmic Power – From Data Analysis to Smarter Solutions
Sneak preview: Human Behaviour and Algorithmic Power
Prepare (case study): From Farm to Can: Red Brand Canners' Approach to Tomato Production Challenges
Summary video: Watch the summary video of the lecture before you start at the assignment below (5% class participation)
Solution: The red brand canners case study solution (slides and excel file)

Part 2: Stochastic Modelling – From Accurate Data to Uncertainty

We'll see each other again later this year. This second part of this course consists of another four lectures (January 29-February 1, 2025), during which the following three module topics will be covered:

Module 4: Mastering Complexity – Trying to Leave the Milky Way and Other Challenges
Sneak preview: Mastering Complexity (integer programming)
Prepare (case study): Navigating the Skies: The SpaceY Drone Project
Solution: The SpaceY Drone Project solution (slides and excel file)^{(right click on mouse and choose "save as" if normal download doesn't work)}

Module 5: Embracing Uncertainty – Unlocking the Power of Probability
Sneak preview: Embracing Uncertainty (decision tree analysis)
Prepare (case study): Foo Fighters Live in London: Sunshine or Thunder Tour?
Solution: The Sunshine or Thunder Tour solution (slides and excel file)^{(right click on mouse and choose "save as" if normal download doesn't work)}

Module 6: Wrap Up – Summary, Conclusions and Farewell
Sneak preview: Last goodbye
Song: Last goodbye
 

COURSE EVALUTION

There is no formal exam for this course, but there is a mix of class participation and a written assignment. 

Class participation: Red brand canners (5%, deadline January 15): You have received a sheet with several questions about how to build the model for the Red Brand Canners case (if you lost it, here is a PDF version). Fill in the different questions as best as you can, write your name clearly on the sheet, take a photo (front and back), and upload it as a single PDF file to Blackboard. I will review everything and provide feedback via email. Don’t worry if you make mistakes: The 5% class participation will be counted as long as you submit the sheet on time, with or without errors. Based on your input, I will classify you into one of the levels (from beginner to expert) and refer you to some optional exercises suited to your personal knowledge level.

Class participation: SpaceY project (5%, deadline mentioned in class): Use the solution sheet and construct your model. You can work in groups but you must upload one PDF file per person. Take a photo and submit is as one single PDF file on blackboard.

Class participation: Rock concert (5%, deadline mentioned in class): Use the solution sheet and construct your model. You can work in groups but you must upload one PDF file per person. Take a photo and submit is as one single PDF file on blackboard.

Final written course work assignment (85%, deadline is February 28, 2025): The details will be discussed at the end of the course module. Please download the requirements and upload your report to Blackboard (one per person). Good luck!

 

OPTIONAL DOWNLOAD LINKS (ignore if you are too busy)

Linear programming: To become an effective decision-maker and develop models for large-scale problems, it is best to start small. Below are several exercises from various sectors (with solutions in MS Solver) to spark your inspiration. Practice makes perfect, right? If you are unsure where to begin, select exercises tailored to your profile—Explorer, Navigator, or Strategist—which you will discover after completing the case study in Module 3.

• Explorer (beginner): Hayes company case study (watch the lecture cast)
• Navigator (intermediate): Three illustrative easy examples (watch Powerco video)
• Strategist (expert): Four additional linear programming exercises

Integer programming: In module 4, we will solve integer programming models to demonstrate that, from a modeler's perspective, they are not fundamentally different from linear programming models. However, from a computational perspective, integer programming models are often significantly more complex. Keep practicing and become a Maestro in data-driven decision-making with the following examples:

• Maestro (MBA): Four additional integer programming exercises

Modelling software: More info how to add Solver in MS Excel can be found here. If you would like to build your models in Python (very popular these days), you need to install PuLP for Python (requires a bit technical expertise).

 

PICTURE

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