Thesis: Developing a Smart Multi-Moment Study Framework for Continuous Improvement (Kaizen 4.0)

Bachelor or Master Thesis

 

The Division of Production Management, headed by Prof. Dr. Thomas Friedli, is looking for a Bachelor or Master Thesis Student (m/f/d). The thesis will be conducted in close collaboration with an industry partner, providing practical insights and opportunities to apply theoretical concepts in a real-world context. The Institute for Technology Management conducts international research projects in close cooperation with renowned industrial companies and consulting firms. As part of your thesis, you will support the Operational Excellence team.

 

Introduction to the partner and the thesis topic

me2any AG is a Swiss-based company specializing in data-driven process optimization and operational excellence. The company focuses on analyzing and improving workflows through work sampling (Multi-Moment Analysis) — a proven method for identifying inefficiencies, quantifying task distributions, and uncovering optimization potential across diverse industries. By combining traditional work sampling methodologies with modern digital tools and AI-supported data analysis, me2any AG enables organizations to gain transparent insights into how time and resources are utilized in daily operations. These insights form the foundation for targeted process improvements, lean transformations, and evidence-based management decisions, driving measurable increases in efficiency and productivity.

Objective: This thesis aims to develop a generic framework that integrates AI-supported Multi-Moment Study analysis into continuous improvement processes.

Content: The project will focus on automated data collection and AI-driven analysis, embedding these capabilities into established PDCA or Six Sigma cycles. The framework will be tested and refined through a pilot implementation in a lab or production environment.

Value: By linking Lean methodologies with modern AI-based data analytics, this research builds a bridge between traditional continuous improvement and the digital era – supporting smarter, data-driven decision-making in operations.

 

Your Profile

  • At the end of the bachelor’s or master’s program

  • Business administration, economics, industrial engineering, healthcare management, computer science or a related field

  • A strong passion and interest in the thesis topic

  • Willing to sign a confidentiality agreement with Q-Alizer

 

Are you interested?

We are looking forward to receiving your message. Please send us your application (short motivation, curriculum vitae). Feel free to contact us if you have any questions regarding the advertised thesis.

Jessica Rebecca Helbling

Research Associate

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