PhD in Innovation & Operations Management with a focus on Knowledge Productivity

Research / Academic

Eindhoven University of Technology is looking for a PhD candidate with a background in Econometrics, Operations Research, Data Science, Innovation Management, Industrial Engineering or Management Sciences, interested in quantitative and qualitative empirical research on the interface of Innovation and Operations Management.

Eindhoven University of Technology is one of the world's leading research universities (ranked by the Times Higher Education Supplement) and is particularly well known for its joint research with industry (ranked number one worldwide by the Centre for Science and Technology Studies). The Department of Industrial Engineering & Innovation Sciences (IE&IS) of Eindhoven University of Technology is one of the longest-established engineering schools in Europe, with a strong presence in the international research and education community, especially in the fields of Operations Management and Innovation Management, which are at the core of the undergraduate BSc program. The graduate programs (MSc and PhD) in Operations Management & Logistics and Innovation Management attract top-level students from all over the world.

The PhD project is conducted in close collaboration with ASML. The PhD student works for 50% of his/her time on-site at the ASML campus and 50% within the Innovation, Technology Entrepreneurship & Marketing (ITEM) group on the TU/e campus. The ITEM group is part of the School of Industrial Engineering of the department IE&IS. The ITEM group focusses on managing innovation processes and new product development, including marketing of new products and marketing analytics.

Short description of the PhD Project
'Improving the Productivity of Knowledge Flows through Tacit and Explicit Knowledge Repositories'

Over the past decades, ASML's customers increasingly demand greater lithography machine accuracy that enables them to deliver highly advanced integrated circuits (IC) to the market. ASML has the objective to be first-to-market to give customers first-mover advantages. This requires ASML to introduce new machines in rapid succession. Therefore, every new product introduction (NPI) must go through a steep learning curve to reduce development, manufacturing, and service cycle times.

This ability to be first-to-market and reduce cycle times by mastering the learning curve explains most of ASML's success in the past 30 years. The current learning curve progress that is driven by cumulative experience and the build-up and utilization of knowledge repositories embedded in employees, tasks, and technology. However, as experience and knowledge become more distributed, complex, and heterogeneous, and because the volume of experience per system (both hours and numbers) is lower, knowledge management becomes one of the key areas of attention to be able to quickly descend the learning curve of the next-generation machines.

The aim of this PhD project is (1) to better understand the productivity of current tacit and explicit knowledge flows, (2) to optimize the creation, coordination, and utilization of explicit and tacit knowledge flows, and (3) to develop policy recommendations on how to improve the management of tacit and explicit knowledge flows.

The project builds on our two previous projects conducted together with ASML on cycle time reduction and organizational learning respectively.

Job description
You, as a successful applicant, will perform the research project outlined above in an international team. The research will be concluded with a PhD thesis. A small teaching load is part of the job.

Work Hours:

38 hours per week


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