The Power of Big Data for Improved Asset Management
December 17, 2018
In the facilities management sector ‘Big Data’ seems to be a high priority, but as a recent RICS report highlighted that mass adoption of the technology to capture and manage data appears to be sometime away.
The reasons highlighted within the report are time, knowledge and investment as the key barriers to mass adoption of big data management with only 37% of respondents in the pre-adoption stage of implementing big data management while 35% are in the early adoption stage.
In this blog post, we would like to share our practical experience of big data management with a focus on asset management and how you can harness the data from each asset to improve performance, reduce spending and plan more efficiently with real-time data.
To start, it would be best to outline exactly how we capture the data, how we use it and what the outputs are. Here is a brief overview of our process:
• In the first instance, we conduct full asset surveys, and all data is captured by our team to guarantee the integrity of the data
• All assets are individually tagged and barcoded, and then the data is uploaded to our CAFM system
• PPM tasks are assigned and managed against individual assets and our helpdesk team log and manage all reactive maintenance works against the individual assets
• Contractors scan the assets barcodes on site to log all works and access previous maintenance history
• We then monitor and report on all assets in real-time through our business Intelligence systems providing bespoke reporting features for clients
• This provides us with the most accurate life-cycle cost analysis available
• We then provide Annual CapEx Spend Projections based on real client data
• This leads to improved asset procurement and spending reductions based on smarter procurement using the data
Having a clearly defined process for asset management, utilising the emerging technology that is available and funnelling all the information through one system is key to successfully managing big data.
The next step is extracting the value form the data; we use our Business Intelligence (BI) technology to do this in several ways:
• Benchmarking – We can benchmark maintenance costs for specific work disciplines. This can be done on a direct cost basis against the marketplace, against SFG20 protocols, across different countries, and against contractors tendered costs.
• KPI & SLA Monitoring – We can build contract terms into our BI and measure performance against the contract in real-time to determine an accurate reflection of the agreed KPI’s & SLA’s, removing self-reporting from contractors.
• Asset Performance – Through BI we monitor maintenance costs on assets against replacement costs. Once the costs to maintain reaches the value to replace we our BI flags this up to our team and changes the status of the asset on capital projections.
• Invoice Monitoring – Our BI monitors all invoices submitted cross-referencing them with actual time spent working on assets to guarantee that clients never overspend on works. This can include measuring contracted time on a contract against one-off reactive tasks to ensure that all work is delivered to the contractual terms.
In our experience, implementing the correct asset management plan is crucial to big data management, if you can’t measure it, how can you manage it?
At Autonomous, we have the knowledge, experience and solution to deliver at least 20% budget savings through improved big data interpretation and asset management. If you would like our expert help and advice, please contact Mark Taylor on email@example.com or 07518 444 838 for an informal discussion of your requirements.
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