Payments over €20,000 Q3 2017

Spending records

Payment date^*	Supplier	Description	Kind	Amount
30 Sep 2017	FLYNN JF CONSTRUCTION	Construction	Purchase Order	€42,139.71
30 Sep 2017	FLYNN JF CONSTRUCTION	Construction	Purchase Order	€37,974.68
30 Sep 2017	FLYNN JF CONSTRUCTION	Construction	Purchase Order	€21,567.21
30 Sep 2017	FLYNN JF CONSTRUCTION	Construction	Purchase Order	€51,961.55
30 Sep 2017	FLYNN JF CONSTRUCTION	Construction	Purchase Order	€33,843.99
30 Sep 2017	FLYNN JF CONSTRUCTION	Construction	Purchase Order	€40,869.58
30 Sep 2017	FREEMAN ELECTRICAL	Construction	Purchase Order	€231,855.02
30 Sep 2017	PC PERIPHERALS	ICT EQUIPMENT	Purchase Order	€20,696.97
30 Sep 2017	ADVANCE DRIVE TRAINING CENTRE LTD	Contracted Training	Purchase Order	€46,438.00
30 Sep 2017	ACCESS SKILLS IRELAND LTD	Contracted Training	Purchase Order	€25,442.08
30 Sep 2017	CAREER TRAINING AND PLANNING	Contracted Training	Purchase Order	€20,400.00
30 Sep 2017	KLANGLEY INVESTMENTS (CORK) LTD	Contracted Training	Purchase Order	€20,304.06
30 Sep 2017	TLI GROUP LTD	Contracted Training	Purchase Order	€43,999.98
30 Sep 2017	CPL LEARNING & DEVELOPMENT LTD	Contracted Training	Purchase Order	€23,757.84
30 Sep 2017	FODHLA BIA TEORANTA	Contracted Training	Purchase Order	€22,531.14

^* 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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