Purchase Orders Over €20,000 Q2 2017

Spending records

Payment date^*	Supplier	Description	Kind	Amount
30 Jun 2017	Spectrum Print Logistics	Stationery	Purchase Order	€24,698.40
30 Jun 2017	Spectrum Print Logistics	Stationery	Purchase Order	€24,163.35
30 Jun 2017	Aluset Limited	Stationery	Purchase Order	€30,553.20
30 Jun 2017	PFH Tech Group	Computer Equipment	Purchase Order	€991,776.00
30 Jun 2017	PFH Tech Group	Computer Equipment	Purchase Order	€488,223.00
30 Jun 2017	Oracle EMEA Limited	Software Operating Leases and Maintenance	Purchase Order	€74,026.76
30 Jun 2017	CDW Ltd	Software Operating Leases and Maintenance	Purchase Order	€63,300.00
30 Jun 2017	Hibernia Evros Technology Group	IT External Services	Purchase Order	€140,712.00
30 Jun 2017	PFH Tech Group	Computer Equipment	Purchase Order	€24,317.10
30 Jun 2017	PFH Tech Group	Computer Equipment	Purchase Order	€21,365.10
30 Jun 2017	Hewlett Packard Enterprise Ireland Limited	IT External Services	Purchase Order	€26,568.00
30 Jun 2017	Digital Imaging Services	Computer Equipment	Purchase Order	€20,590.20
30 Jun 2017	CDW Ltd	Software Operating Leases and Maintenance	Purchase Order	€27,820.20
30 Jun 2017	PFH Tech Group	Computer Equipment	Purchase Order	€96,923.00
30 Jun 2017	PFH Tech Group	Computer Training	Purchase Order	€21,114.00

^* 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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