  Modern manufacturing value chains require intelligent orchestration of
processes across company borders in order to maximize profits while fostering
social and environmental sustainability. However, the implementation of
integrated, systems-level approaches for data-informed decision-making along
value chains is currently hampered by privacy concerns associated with
cross-organizational data exchange and integration. We here propose
Privacy-Preserving Partial Least Squares (P3LS) regression, a novel federated
learning technique that enables cross-organizational data integration and
process modeling with privacy guarantees. P3LS involves a singular value
decomposition (SVD) based PLS algorithm and employs removable, random masks
generated by a trusted authority in order to protect the privacy of the data
contributed by each data holder. We demonstrate the capability of P3LS to
vertically integrate process data along a hypothetical value chain consisting
of three parties and to improve the prediction performance on several
process-related key performance indicators. Furthermore, we show the numerical
equivalence of P3LS and PLS model components on simulated data and provide a
thorough privacy analysis of the former. Moreover, we propose a mechanism for
determining the relevance of the contributed data to the problem being
addressed, thus creating a basis for quantifying the contribution of
participants.
