Title: Data-driven productivity improvement in machinery supply chains

Authors: Rafael Lorenz; Torbjørn H. Netland; Philip Roh; Valentin Holzwarth; Andreas Kunz; Konrad Wegener

Addresses: Chair of Production and Operations Management, Department of Management, Technology, and Economics, ETH Zürich, Zurich, Switzerland ' Chair of Production and Operations Management, Department of Management, Technology, and Economics, ETH Zürich, Zurich, Switzerland ' Institute of Machine Tools and Manufacturing (IWF), Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland ' Institute of Machine Tools and Manufacturing (IWF), Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland ' Institute of Machine Tools and Manufacturing (IWF), Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland ' Institute of Machine Tools and Manufacturing (IWF), Department of Mechanical and Process Engineering, ETH Zurich, Zurich, Switzerland

Abstract: Modern manufacturing machines are equipped with numerous sensors that collect a large amount of various data. This data can be used to improve the machines' productivity. Both the users and suppliers of machines could benefit from such opportunities. However, because machine users risk the loss of intellectual property, they are often reluctant to share their data. This represents a major inhibitor of data sharing in machinery supply chains. This paper proposes a five-step method for initialising data sharing between machine users and their machine suppliers. The method was tested and validated in a case company, and the potential benefits for machine users were quantified.

Keywords: data sharing; digital services; smart machining systems.

DOI: 10.1504/IJMMS.2019.103483

International Journal of Mechatronics and Manufacturing Systems, 2019 Vol.12 No.3/4, pp.255 - 271

Received: 04 Apr 2019
Accepted: 14 Jun 2019

Published online: 30 Oct 2019 *

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