NHS Hospital

The first stage was to extract all of the data from the Maximo DB2 instance, totalling millions of data points. Each location was cleansed and added to a location hierarchy which automatically fixed the issues identified by the Platform. All correctly located assets were then loaded into the Platform along with associated work orders, preventive maintenance plans and tasks. As part of this process, each asset was automatically allocated an SFG20 (a widely used industry maintenance standard) classification based on the name of the asset. SFG20 tasks were automatically compared with Maximo DB2 work orders to ascertain if the maintenance had been completed in accordance with the SFG20 tasks, HSE approved code of practices and statutory regulations (at the time of the work order). We marked all assets which had missed maintenance windows as assets of concern, allocating each asset a condition rating based on the maintenance which had been completed and any related service requests and remedial works.

Using this information, and pairing it with the RICS asset replacements costs database, we predicted when each asset will need replacement and how much it is likely to cost. We also estimated the maintenance costs for each asset based on the SFG20 task time and skill required based on average industry pay rates. We added the cost of consumables to provide an overall prediction for asset costs.

We then presented our findings back to Project Co directors, before also presenting to the Facilities Management company and the Management Services Provider (MSP). Following this, each presented their response to the findings back, and what changes they planned to make in light of the review.