Purchase Orders over €20,000 Q2 2017

Spending records

Payment date^*	Supplier	Description	Kind	Amount
30 Jun 2017	PLAZAMONT LTD T/A DAN MORRISSEY LTD	Road maintenance	Purchase Order	€20,580.00
30 Jun 2017	LEISURE PROJECT MANAGEMENT LTD	Feasibility Study	Purchase Order	€72,301.49
30 Jun 2017	HAYES HIGGINS CONSULTING ENGINEERS	Energy Upgrade Works Housing	Purchase Order	€25,577.85
30 Jun 2017	APCOA PARKING IRELAND LTD	Carparking Greystones MD	Purchase Order	€22,673.88
30 Jun 2017	JONS CIVIL ENGINEERING COMPANY LTD	River Dargle Flood Defence Scheme	Purchase Order	€52,778.72
30 Jun 2017	JBA CONSULTING ENGINEERS & SCIENTISTS LTD	River Dargle Flood Defence Scheme	Purchase Order	€38,762.52
30 Jun 2017	QUALITY EDUCATION DEVELOPMENT LIMITED	Creative Corridor, LEO	Purchase Order	€33,469.00
30 Jun 2017	ARKENVALE LTD	Housing Remediation Works	Purchase Order	€105,750.00
30 Jun 2017	TOM FOLEY MANAGEMENT LIMITED	River Dargle Flood Defence Scheme	Purchase Order	€69,437.35
30 Jun 2017	HAYES HIGGINS CONSULTING ENGINEERS	Energy Upgrade Works Housing	Purchase Order	€25,577.85
30 Jun 2017	INSTITUTE OF PUBLIC ADMINISTRATION	IPA Training Levy 2017	Purchase Order	€25,487.98
30 Jun 2017	QUALITY EDUCATION DEVELOPMENT LIMITED	Creative Corridor, LEO	Purchase Order	€22,784.32

^* 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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