Data-driven productivity improvement in machinery supply chains
by Rafael Lorenz; Torbjørn H. Netland; Philip Roh; Valentin Holzwarth; Andreas Kunz; Konrad Wegener
International Journal of Mechatronics and Manufacturing Systems (IJMMS), Vol. 12, No. 3/4, 2019

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.

Online publication date: Wed, 30-Oct-2019

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