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Understanding how labs and technical teams manage shared equipment in practice

A set of short studies exploring how teams manage shared equipment, find what they need, and deal with real operational constraints.

Part of an ETH MAS thesis on early-stage product learning through real-world experiments

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Why this research

Many technical and research environments rely on shared equipment, distributed tools, and informal coordination. These studies aim to understand how this works in practice, where current approaches create friction, and what would realistically work in real-world environments.

This research is based on early observations from engineering and research environments where shared equipment and coordination create recurring challenges.

Available studies

Quick & interactive · about 2 minutes
New · interactive

Equipment discovery — live

Step into a realistic situation where a key piece of equipment has just failed — and use a live platform to find your way out. Adapts to your field.

Start the study

Search comparison study

Two ways to find equipment, side by side. Which one do you reach for first?

Coming soon
In-depth studies · about 4–5 minutes

Equipment visibility study

How do teams keep track of what exists, where it is, and whether it is ready to use?

Open study

Equipment discovery study

How do people identify the right equipment for a task when they do not already know the exact asset?

Open study

Asset context study

What information (setup, usage, issues) do people need before they can confidently use equipment?

Coming soon

Asset tracking realism study

What level of asset tracking is actually realistic to maintain in practice, and who would be responsible for it?

Coming soon

What to expect

Research notes

About this project. Research notes will be published here as the project progresses.

View research notes

About this project

This research is part of a MAS thesis focused on early-stage product learning through real-world experiments.

The goal is to understand where meaningful value emerges in practice and how early signals can guide product decisions. This project is conducted by Stephan Caruso as part of his MAS in Management, Technology, and Economics at ETH Zurich.

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No signup required · responses are anonymous