Bridging the Gap: Effective Data Collection and Utilization in Manufacturing
Project led by Alexander Albers (PhD Candidate, ETH Zurich)

This project aims to enhance data collection and utilization for continuous improvement projects through a structured framework. Key outcomes include “data maps” to visualize data flows, the identification of data gaps to identify discrepancies between recorded, demanded, and utilized data, and the development of a “data readiness score“ based on criteria like coverage, quality, and timeliness. The project will involve selected partners in in-depth case studies. Generalizable insights from these studies will be shared with all interested alliance member companies. The goal is to provide actionable guidance on data collection and exploitation, ensuring digitalization efforts are effective.

Contact: alexander.albers@mtec.ethz.ch

Adaptability of Global Production Networks
Project led by Simon Gese (PhD Candidate, University of St.Gallen)

This project explores how companies can build more adaptable production networks, focusing on the ability to move products and processes between different plants as a key factor. Product and process mobility, which is most critical during new product launches, production ramp-up, and potential relocations, is greatly affected by a company’s ability to learn and process change. Management routines and practices play an important role in creating a learning organization able to deal with change. Identification of adaptability drivers, potentials and successful practices can vary by level: plants rely on technical capabilities, learning, and management practices, while network managers are questioning traditional “copy exactly” i.e. standardization strategies, and supply chains struggle with multi-tier rollouts. The final goal is to combine plant, network, and supply chain insights into a unified approach for designing more adaptable production networks.

Contact: simon.gese@unisg.ch

Generative AI in Practice: Strategic and Operational Integration in Manufacturing
Project led by Jingqi Liu (PhD Candidate, ETH Zurich)

This project looks at how manufacturers are starting to use Generative AI in their daily operations, not just from a technical point of view, but also how it fits into broader management and strategy decisions. It covers where and how GenAI is being applied, whether in design, on the shop floor, or in supply chain work, and what makes these efforts succeed or struggle. Through case studies with selected companies, the project will explore real examples of how decisions are made, what challenges arise, and what lessons can be shared. The aim is to develop practical guidance that helps manufacturers use GenAI in ways that actually improve their performance and support their goals for innovation and resilience.

Contact: jingqi.liu@mtec.ethz.ch

Digital Twins in Practice: Value Creation and Implementation Pathways in Manufacturing
Project led by Dr. Christian Kober (Postdoctoral Researcher, ETH Zürich)

This project investigates how manufacturing companies develop and use Digital Twins to create measurable business value. It examines not only the technological foundations, including model design and system integration, but also the organisational and strategic factors that determine success. By analysing industrial cases, the research explores how companies translate the concept of Digital Twins into tangible improvements in efficiency, decision-making, and innovation. The work aims to advance the practical understanding and implementation of Digital Twins in real industrial settings, bridging the gap between conceptual potential and realised operational benefits. Key outcomes include practical guidance on how Digital Twins can be designed, implemented, and managed to maximise their impact — covering areas such as development and implementation frameworks, optimal model fidelity, effective stakeholder communication, and the integration of AI methods in production environments.

Contact: christian.kober@mtec.ethz.ch

Circular Design in Packaging
Project led by Johannes Bunse (PhD Candidate, University of St.Gallen)

This research project aims to advance sustainable transformation in packaging by developing a strategic framework to support circular design decisions within highly regulated environments. A key focus lies in the analysis and optimisation of recyclable material flows. As part of a pilot project in the DACH region, the study also evaluates the efficiency of recycling networks, particularly in reducing emissions related to pharmaceutical waste streams. To enhance environmental assessment accuracy, the project proposes a reframing of conventional Life Cycle Assessment (LCA) methodologies by integrating material density as a central input factor. Finally, the research seeks to define cross-functional decision criteria for sustainable material selection, enabling more robust and informed trade-offs between regulatory compliance, environmental impact, and operational feasibility in packaging.

Contact: johannes.bunse@unisg.ch

AI-Driven Manufacturing Process Optimization and Cyber-Physical Architecture Evaluation

Project led by Florian Lang (PhD Candidate, University of St.Gallen)

This project examines the challenges that arise when digitization is deployed and scaled across a production network, with a particular focus on processes. It develops a digital process twin to strengthen root cause analysis, using resource-enriched process traces and machine learning models to detect quality defects and manage process deviations more effectively. A second strand explores how process models can be derived directly from video recordings, digitizing the observed process into descriptive models with natural language annotations that map to as-is operations. Alongside this, the research reviews typical data collection approaches for process data and the implementation gaps they create, and identifies the enablers of digital maturity in data and software architectures, process visualization, and operational excellence tasks such as value stream mapping. Finally, it considers the biases shaping managerial decisions on adopting and scaling AI, mapping the frameworks and challenges identified to practical remedies and the traps to avoid.

Contact: florian.lang@unisg.ch

The Presence of Geopolitical Risk Units and How Manufacturing Companies Organize themselves
Project led by Dean Sammanthan (PhD Candidate, ETH Zürich)

This project involves the support of seven Manufacturing Alliance companies, as well as that of nine firms from outside the Alliance. It notes that Geopolitical risk has shifted from a background concern to a core hurdle for manufacturers. And seeks to answer the question, “how should they best organize to prepare for this new reality?” Using in-depth interviews with the various supporting firms, the project seeks to shed light on how many firms have a dedicated geopolitical risk unit or equivalent function, and if not, how firms currently manage this paradigm of risk within their existing processes. This is done with the hope of producing insights that can enable firms to better understand the benefits and costs of operating such units, potential challenges they might face, and whether such a set-up would be suitable for their firm, and if not, why it is not the case.

Contact: dean.sammanthan@mtec.ethz.ch

Impact of Make-or-buy Decisions on Operational Performance
Project led by Ilkim Güneri (PhD Candidate, ETH Zürich)

This project investigates the relationship between make-or-buy decisions and operational performance. While the drivers of make-or-buy decisions have been studied more extensively, their empirical consequences remain under-researched in both academic literature and industrial practice. By integrating firm-level datasets with targeted interviews when needed, the study identifies how sourcing strategies and supplier learning effects influence operational and strategic outcomes. The goal is to provide guidance to companies for choosing the most suitable sourcing strategy and improve supplier management practices based on the analysis of operational data.

Contact: ilkim.gueneri@mtec.ethz.ch