Purchase Orders Over €20,000 Q1 2022

Spending records

Payment date^*	Supplier	Description	Kind	Amount
31 Mar 2022	Fitzgerald Kavanagh	Professional fees	Purchase Order	€140,327.70
31 Mar 2022	TU Dublin	Services	Purchase Order	€24,155.24
31 Mar 2022	OHLA Townlink JV	Construction	Purchase Order	€73,896,403.88
31 Mar 2022	O'Donnell Tuomey Architects	Professional fees	Purchase Order	€221,400.00
11 Mar 2022	RPS Consulting Engineers	Professional fees	Purchase Order	€43,886.40
11 Mar 2022	Duke McCaffrey Consulting	Professional fees	Purchase Order	€34,440.00
11 Mar 2022	Heneghan Peng Architects	Professional fees	Purchase Order	€243,172.41
11 Mar 2022	Tolmac Construction	Construction	Purchase Order	€1,635,467.41
11 Mar 2022	Kroll	Professional fees	Purchase Order	€36,900.00
11 Mar 2022	Carr Cotter & Naessens	Professional fees	Purchase Order	€153,504.00
03 Mar 2022	Nicholas O'Dwyer	Professional fees	Purchase Order	€31,566.72
01 Mar 2022	Fitzgerald Kavanagh	Professional fees	Purchase Order	€84,264.66
02 Feb 2022	HCS Business Solutions	Services	Purchase Order	€24,924.60
02 Feb 2022	HCS Business Solutions	Services	Purchase Order	€59,704.50
02 Feb 2022	Max Fordham LLP	Professional fees	Purchase Order	€44,280.00
20 Jan 2022	CPL Business Solutions	Professional fees	Purchase Order	€27,759.87
13 Jan 2022	TU Dublin	Services	Purchase Order	€34,578.82

^* Payment date may be the end of the reporting quarter when the entity does not publish an exact date.

How to read these figures

These are reported values from published purchase order and payment records — not verified cash paid, and not total spending by this organisation. A row may be a purchase order, a payment, or another reported transaction; the source files often do not say which.

Coverage and quality vary. Some fields are blank, supplier names are free text (the same firm can appear under many labels), and descriptions are inconsistent rather than a controlled taxonomy. An amount alone says nothing about value for money and it may or may not include VAT.

Records are retrieved from each organisation’s published files such as spreadsheets, PDFs, and other formats of uneven quality. Extraction techniques (including automated parsing) can introduce gaps, misreads, or incomplete rows when source layout or quality is poor. Treat the data as a starting point for investigation, not a definitive ledger.

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