Purchase Orders Over €20,000 Q3 2019

Spending records

Payment date^*	Supplier	Description	Kind	Amount
30 Sep 2019	Ove Arup & Partners	Professional Services	Purchase Order	€56,580.00
19 Sep 2019	Purcell Construction Ltd	Construction Works	Purchase Order	€15,677,485.00
18 Sep 2019	Fitzgerald Kavanagh & Partners	Professional Services	Purchase Order	€1,277,524.06
09 Sep 2019	Knight Frank	Professional Services	Purchase Order	€931,866.45
30 Aug 2019	Gas Network Ireland	Services	Purchase Order	€32,599.47
30 Aug 2019	Dublin City Council	Professional Services	Purchase Order	€38,510.55
26 Aug 2019	Irish Water	Services	Purchase Order	€31,285.22
07 Aug 2019	Grafton Architects	Professional Services	Purchase Order	€33,296.10
01 Aug 2019	Heneghan Peng Architects	Professional Services	Purchase Order	€23,862.00
16 Jul 2019	Dublin City Council	Professional Services	Purchase Order	€264,000.00
16 Jul 2019	Department of Health	Professional Services	Purchase Order	€121,000.00
10 Jul 2019	John Paul Construction	Construction Works	Purchase Order	€1,317,508.00
04 Jul 2019	Dublin City Council	Professional Services	Purchase Order	€38,000.00
02 Jul 2019	Amelio Group Ltd	Construction Works	Purchase Order	€43,130.00
02 Jul 2019	Hegarty Demolitions Ltd	Construction Works	Purchase Order	€438,178.10
02 Jul 2019	Kennedy Security & Consultancy	Services	Purchase Order	€104,230.20

^* 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.

Here is a project that encountered similar issues