There are several challenges facing the FM industry and it is great to see them being addressed, although more needs to be done. The term “We have always done it this way” is slowly fading and it is fueling innovative solutions that attempt to solve key issues such as maintenance inefficiency and operational energy wastage.
One such innovative solution is data-driven maintenance or condition-based maintenance. This approach utilises live asset performance data from various sources across a building which, when analysed, can be used to make more informed maintenance decisions.
Exploring traditional methods first
Before we assess the importance of condition-based maintenance, we must first explore traditional methods. Planned Preventative Maintenance (PPM) adheres to widely recognised industry standards, such as SFG20, to determine how assets should be maintained. SFG20 has served the industry well for many years and works by assigning a series of tasks for each asset type that, when carried out, guarantee’s compliance and reduces the risk of breakdown. Each maintenance task has an estimated time to complete and frequency in which they should be carried out (Weekly, Monthly, Quarterly, Biannual and Annual). The maintenance tasks are then planned throughout the year in a PPM schedule for the engineer to carry out.
Traditional Methods vs Data-Driven Approaches
The main issue with traditional maintenance methods is the inability to consider the actual condition of an asset when not on site, resulting in inefficiencies from carrying out non-compliance inspection tasks on an asset that is working well. Furthermore, with the periodic nature of PPM there is an overreliance on being in the right place at the right time and all too often the first sign of a problem is a breakdown and an emergency call out.
When there were no alternatives to this method it was a perfectly reasonable approach as risks associated with asset downtime can be costly. However, thanks to the advances
in technology, there are now readily available, cost-effective solutions that not only creates maintenance efficiencies, but also reduces operational energy wastage.
Accessing asset data is now possible either by tapping into the data that already exists within a building or implementing specialist IoT sensors. Once you have the data, it can be analysed to give users live asset performance analytics that can not only evidence when an asset is working correctly, but also highlight assets that are showing signs of inefficiency and potential breakdown. By concentrating on the problematic assets and not those that are evidently working well, you will create maintenance efficiencies and eliminate most unexpected asset failures. Not only will this save cost, but it will also reduce the workload of stretched engineering teams due to the limited availability of engineering resources.
First Time Fix
Adopting a condition-based maintenance strategy does not mean that assets will not break down, asset breakdown is inevitable, however, the callout process and overall management will be more efficient by using data for offsite diagnosis. As the old adage goes, “you can’t manage what you can’t measure” so by looking at the data you can identify likely causes which enables you to assign the right engineer, with the right tools and materials for the first time. This not only reduces the building downtime, but it also reduces the inflated costs associated with call outs.
Anticipating end-of-life for costly plant items
Predicting asset end-of-life can be a minefield and maximising the value of an asset is incredibly challenging. Trying to understand the most cost-effective time to replace an asset is near impossible using current methods. Guides like CIBSE Guide M will give you an estimated lifespan of an asset; however, the guide uses asset usage assumptions in its end-of-life calculation which may not correlate to your assets. By using data, you can take into consideration the actual rate of usage and apply it to existing guidelines to make it more accurate.
