Purchase Orders Over €20,000 Q3 2019

Spending records

Payment date^*	Supplier	Description	Kind	Amount
30 Sep 2019	APLEONA HSG LIMITED	Facilities Management	Purchase Order	€42,176.30
30 Sep 2019	APLEONA HSG LIMITED	Facilities Management	Purchase Order	€108,177.06
30 Sep 2019	APLEONA HSG LIMITED	Facilities Management	Purchase Order	€91,997.52
30 Sep 2019	BORD GAIS ENERGY	Utilities	Purchase Order	€87,144.17
30 Sep 2019	ENERGIA	Utilities	Purchase Order	€105,129.92
30 Sep 2019	MITIE FACILITIES MGT LTD	Contract Cleaning	Purchase Order	€57,733.77
30 Sep 2019	OFFICE OF PUBLIC WORKS	Repair and Maintenance Services	Purchase Order	€88,000.00
30 Sep 2019	ENERGIA	Utilities	Purchase Order	€174,571.97
30 Sep 2019	TEAGASC	Office Rental and Service Charges	Purchase Order	€25,000.00
30 Sep 2019	OFFICE OF PUBLIC WORKS	Office Rental and Service Charges	Purchase Order	€112,977.44
30 Sep 2019	RECORDS STORAGE SERVICES	File Storage and Management Services	Purchase Order	€102,960.48
30 Sep 2019	ARAMARK	Facilities Management	Purchase Order	€26,096.52
30 Sep 2019	MOLLOY SHERRY EIRFREEZE LTD	Consignment Handling	Purchase Order	€44,452.20
30 Sep 2019	NOONAN SERVICES GROUP LTD	Contract Cleaning	Purchase Order	€139,590.58
30 Sep 2019	OFFICE OF PUBLIC WORKS	Repair and Maintenance Services	Purchase Order	€28,861.92

^* 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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