Purchase Orders Over €20,000 Q1 2018

Spending records

Payment date^*	Supplier	Description	Kind	Amount
31 Mar 2018	WESTERN FACILITY MANAGEMENT	Facilities Management	Purchase Order	€28,683.36
31 Mar 2018	CA PARSONS	Office Furniture	Purchase Order	€23,474.55
31 Mar 2018	GROSVENOR CLEANING SRVCS LTD	Contract Cleaning	Purchase Order	€35,508.00
31 Mar 2018	TOPAZ ENERGY LTD	Gas/Oil	Purchase Order	€45,580.60
31 Mar 2018	ENERGIA	Utilities	Purchase Order	€143,734.88
31 Mar 2018	EMERALD FACILITY SERVICES	Contract Cleaning	Purchase Order	€21,385.02
31 Mar 2018	RECORDS STORAGE SERVICES	File Storage and Management Services	Purchase Order	€49,389.63
31 Mar 2018	ENERGIA	Utilities	Purchase Order	€93,652.63
31 Mar 2018	OFFICE OF PUBLIC WORKS	Office Rental and Service Charges	Purchase Order	€183,586.16
31 Mar 2018	AN POST	Postage	Purchase Order	€761,740.72
31 Mar 2018	MITIE FACILITIES MGT LTD	Contract Cleaning	Purchase Order	€55,486.48
31 Mar 2018	MID FREIGHT SHIPPING LTD	Records Storage	Purchase Order	€21,696.59
31 Mar 2018	APLEONA HSG LIMITED	Facilities Management	Purchase Order	€106,927.83
31 Mar 2018	AN POST	Postage	Purchase Order	€273,761.32

^* 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.

Here is a project that encountered similar issues