Facilities managers are constantly seeking the silver bullet that will make their lives and jobs better. One of their key responsibilities is overseeing the maintenance of their buildings and keeping costs under control and within budget. This involves seeking new and innovative ways to gain the most value from assets, minimising breakdowns, planning for the unexpected and maximising building availability.
Current buzzwords and trends include IoT, AI, machine learning, big data and data mining, to name a few. These are hot topics not just in facilities and asset management, but also in healthcare, logistics, agriculture, retail and manufacturing. It’s a lot to take in. A good place to start, however, is developing a digital road map in relation to the asset maintenance strategy. This involves relatively simple actions that can have a very real impact on everyday building and estate performance. While machine learning and AI may seem distant and unrelatable, the journey to get there can start now.
I was recently involved with a manufacturing client. They have a global footprint, but two of their manufacturing plants are in close proximity, one on either side of a busy road. One plant had a very different idea of what an asset was than the other, even in the context of building services maintenance. Organisations need to define what they mean by an asset, and what warrants identification on an asset list. This may form part of a broader asset management strategy, but it is still an important step. If we don’t know what an asset is, how can we plan an asset management strategy? Where there is more than one site or location, consistency is important so that comparisons can be made.
The next stage is to compile an asset list or inventory. This is more than a list of ‘stuff’. It will include how the assets are structured, how they are classified, and how they relate to the engineering systems. This should be done in the context of the wider business strategy: what is critical, what legislative requirements must be met, how much do we need to know about a particular asset? Which assets affect interfaces and touch points with our customers, consumers and suppliers, and what are the risks corresponding to each?
The final step is to define the structure of each data requirement and its syntax. This document will form an asset hierarchy – a tacit guidance document on what data is required and how it is to be structured, collated and stored. The result is a clear, consistent and concise guidance document for facilities and engineering stakeholders.
Ultimately, this exercise enables the FM to answer plain language questions: how many of a given asset do I have? Where are they? What are they made of? How old are they? What condition are they in? Once populated, the asset hierarchy provides a clear view of the organisation’s assets along with the ability to query the data. The hierarchy will define reporting levels and how the data can be aggregated and interpreted.
This is an important step towards improving the performance and value of engineering systems. Deployment of technology, maintenance and management systems relies on the accuracy and structure of the asset database.