Purchase Orders Over €20,000 Q1 2016

Spending records

Payment date^*	Supplier	Description	Kind	Amount
31 Mar 2016	Laird Grass Machinery	Vehicle Purchase	Purchase Order	€23,500.00
31 Mar 2016	Citroen Motors Ireland	Vehicle Purchase	Purchase Order	€37,771.54
31 Mar 2016	N.C. Plant Hire	Plant Hire	Purchase Order	€28,502.48
31 Mar 2016	N.C. Plant Hire	Plant Hire	Purchase Order	€31,679.66
31 Mar 2016	Marsh Financial Services	Insurance	Purchase Order	€47,790.96
31 Mar 2016	PH7 Design	Exhibition Design	Purchase Order	€21,402.00
31 Mar 2016	DANCOR CIVIL ENGINEERING LIMITED	Sewerage Main	Purchase Order	€32,328.00
31 Mar 2016	Brendan Sammon Aughnasheelin Ltd.	Housing Construction	Purchase Order	€45,050.98
31 Mar 2016	Brendan Sammon Aughnasheelin Ltd.	Housing Construction	Purchase Order	€81,983.72
31 Mar 2016	Solar Direct Limited	Building Refurbishment	Purchase Order	€26,770.00
31 Mar 2016	Solar Direct Limited	Building Refurbishment	Purchase Order	€22,155.00
31 Mar 2016	Solar Direct Limited	Building Refurbishment	Purchase Order	€20,105.00
31 Mar 2016	SSE Airtricity Solutions Ltd	Public Lighting	Purchase Order	€34,992.09
31 Mar 2016	N.C. Plant Hire Ltd	Building Refurbishment	Purchase Order	€49,041.70
31 Mar 2016	Francis McGuigan	Building Refurbishment	Purchase Order	€49,956.30
31 Mar 2016	Francis McGuigan	Building Refurbishment	Purchase Order	€36,691.20

^* 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.

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