Managing the unknown in scientific experiments: A case study from the biomedical industry

Maxime Thomas (), Pascal Le Masson (), Benoit Weil () and Sophie Hooge ()
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Maxime Thomas: CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique
Pascal Le Masson: CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique
Benoit Weil: CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique
Sophie Hooge: CGS i3 - Centre de Gestion Scientifique i3 - Mines Paris - PSL (École nationale supérieure des mines de Paris) - PSL - Université Paris Sciences et Lettres - I3 - Institut interdisciplinaire de l’innovation - CNRS - Centre National de la Recherche Scientifique

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Abstract: Managing the unknown is of paramount importance in New Product Development (NPD), particularly in research-based projects relying on intensive experimentation. While scientific experiments are widely used to reduce uncertainty, there is little research about the unknowns that scientists themselves struggle to manage when experimenting during NPD projects. To close this gap, we present an in-depth single case study of an NPD project aiming at developing a biomedical healthcare product. We focus on the fuzzy front end of the project during which the company implemented two Design of Experiments (DoE). Drawing on data from an action research protocol, we develop an original inductive methodology to identify unknowns that hinder experimentation. The study identifies six types of difficult-to-manage unknowns, four of which show strong empirical support. These findings reveal new unknown management needs for scientists beyond classical control of the experiment and contribute to the experimentation and innovation management literatures.

Keywords: Biomedical Product; Design of Experiments; Healthcare; Experimentation; Unknown (search for similar items in EconPapers)
Date: 2026-06-16
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Published in EURAM 2026 Navigating High Waters - Managing in an age of disruption, Jun 2026, Kristiansand, Norway

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05744009

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