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Table 1: Public Procurement Evaluated Analytics.
Analytic Description Result
Late deliveries (%) Percentage of over-due time deliveries 32.80 %
Late payments (%) Percentage of over-due time payments 17.47 %
Small purchase order (%) Percentage of Purchase Orders below 100C 65.75 %
Late deliveries impact (%) Impact of late deliveries penalties on suppliers’ total budget spent 0.36 %
Late payments impact (%) Impact of late payments penalties on city’s total budget spent 19.45%
uation of analytics over encrypted data. However, we
significantly enhance the adherence to regulations and
contractual security obligations. Our prototype en-
ables city of Antibes to decrease by 5% their late pay-
ment penalties.
This work paves the way to further ML-based pro-
cessing for prescriptive and predictive analytics. We
foresee the deployment of analytics capabilities for
both recommendations on the best course of actions
at run-time and predictions on the future of evolution
of those cross-organization collaborations.
ACKNOWLEDGEMENTS
This research has been conducted in the scope of the
PROPOLIS(Propolis, 2023) project, jointly funded by
ANR and BMBF.
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