RSA Q1 2020 Purchase Orders Over €20K

Spending records

Payment date^*	Supplier	Description	Kind	Amount
10 Jan 2020	Regus CME Ireland Limited	Facility Management	Purchase Order	€48,000.00
10 Jan 2020	Packside ltd	Facility Management	Purchase Order	€67,648.00
09 Jan 2020	Unit 4 Business Software Ireland Ltd	Finance	Purchase Order	€22,000.00
09 Jan 2020	Gemalto Credit Card Systems Ireland Ltd	Contract Management Process, Tachograph and Communication	Purchase Order	€22,267.38
09 Jan 2020	Osprey Hotel & Spa	Facility Management	Purchase Order	€39,120.00
09 Jan 2020	RSM Ireland	National Car Testing Service	Purchase Order	€62,187.50
09 Jan 2020	Abtran Limited	NDLS Service Delivery & Process Improvement	Purchase Order	€406,221.80
08 Jan 2020	Annual Rental	Facility Management	Purchase Order	€26,400.00
08 Jan 2020	Farrell Brothers (Ardee) Ltd	Facility Management	Purchase Order	€27,809.00
08 Jan 2020	Gemalto Credit Card Systems Ireland Ltd	NDLS Service Delivery & Process Improvement	Purchase Order	€162,285.99
07 Jan 2020	Bearing Point	Strategy	Purchase Order	€32,271.19
07 Jan 2020	Bearing Point	Strategy	Purchase Order	€42,733.33
07 Jan 2020	Bearing Point	Vehicle Standards	Purchase Order	€54,931.99
07 Jan 2020	SGS Ireland Limited	Road Safety	Purchase Order	€58,536.58
06 Jan 2020	NCT Consultancy	National Car Testing Service	Purchase Order	€37,102.50

^* 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