The Internet of Things (IoT) is radically changing the way we interact with the world around us. The ability to electronically manage and monitor physical objects, and optimise procedures and system functions puts data-driven decision making front and centre.
With the help of these tools, businesses and individuals can save time and enhance their quality of life.
In the context of building systems, IoT predictive maintenance is becoming mainstream. Using real-time information to forecast and fend off breakdowns can diminish downtime by a whopping 50%.
In the long term, being proactive and using data analytics is beneficial for you, your tenants, and your property.
In the face of variables and circumstances beyond your control, there’s a best practice to follow when it comes to asset management. To better understand the pros and cons of each maintenance approach, let’s break them down (pun intended).
This kind of upkeep reacts to equipment breakdowns or malfunction to return the asset to optimum operation after the incident.
Yes, it has lower upfront costs, but this type of maintenance work costs more in the long run. Without weekly and monthly inspections, it requires less staff, but conversely – when problems do arise, you won’t have the work orders or relevant staff on hand to react quickly. And when they do, you’ll probably have to pay overtime.
There’s also no need to spend time planning, which means no scheduled downtime. However, when equipment fails, you’ll be dealing with downtime anyway. Without a basic preventive maintenance program, shorter equipment life expectancy is also a reality.
Having a purely reactive maintenance strategy isn’t the way to go. Budgeting is unpredictable because when things do go wrong, you’ll need to fix it fast – often paying more for this fast turnaround as well. If equipment goes completely kaput, then there’s no time to shop around and, again, you’ll be paying more.
Lastly, equipment that’s not thoroughly maintained will use more energy. Even slight faults cause electrical surges.
This approach to servicing is the middle ground. It aims to anticipate equipment issues before they happen by taking steps to stop them from occurring. This method requires scheduled regular maintenance and checks by a team of specialists, rather than reacting to breakdowns when they occur.
The schedule isn’t determined by data, but rather by a general maintenance plan.
Yes, checking assets in regular periods can increase lifespan and reduce downtime, but the monitoring and analysis demands a significant investment of time, along with the added inconvenience of prep and delegation.
While it’s a cut above a solely reactive approach, there are plenty of pitfalls. It works similarly to car maintenance: manufacturer-recommended service intervals based on assumptions around part deterioration under standard driving conditions.
Compared to reactive maintenance, the main benefits are extending asset life which can help avoid downtime, as well as better workplace safety compliance. However, assets will still fail, and you’ll still waste time and resources. The risk of damage to machine components during unnecessary maintenance activities is also problematic.
This method is pretty much data-driven proactive maintenance, using recurring maintenance to stop equipment failures, and in turn, downtime.
Predictive maintenance is smarter and more advanced than proactive maintenance because it’s based on cumulative data. It focuses on identifying trends and isolating the root causes of failure, ensuring the working order of equipment over an extended life span. For example, say historical data determines that machine Y’s bushings expire after an average of X days. A technician will then replace the bushings on machine Y every X days. This is done whether or not the machine specifically needs attention at that time.
This method also integrates HVAC, fire, electrical and other systems with IoT devices, so data is captured on all equipment and systems running your building.
Regular inspections protect people and property. As the opposite of a run-to-failure approach, predictive maintenance allows you to detect issues before they become a problem. You can then correct them and learn from your collective data so you don’t make the same mistakes twice.
This extends the life of your assets as they don’t depreciate as quickly. Predicting issues is cost-effective as it allows you to lessen energy costs while upping efficiency. And because you’re lessening large-scale unplanned repairs, there aren’t as many breakdowns. Planned work only happens in slow periods, which means less disturbance to schedules and production.
Budgeting is better too – with tighter control, this approach gives you plenty of time to plan, source and buy parts, and line up labour. It also makes sure you comply with health and safety and grows customer service by providing efficient operations, on time, all the time.
The Internet of Things is transformative, from optimising operations to gathering better insights for data-driven decision making. The implications of IoT for building maintenance are titanic, with connected devices and sensors able to track control machines and key performance indicators (KPIs).
Building IoT to create a smart building accelerates data collection and yields in-depth analysis and pattern recognition leading to tech improvement and cost reductions. It can also increase customer experience and safety, and reduce risks. No matter the outcome, property owners, occupiers and managers agree ‘PropTech’ will change the world.
Grosvenor Engineering Group (GEG) has created distinctive technology, data, software platforms and end-to-end solutions in this space.
Switching to prediction maintenance takes time. As you collect more info from your equipment, you’re left with precise, real-time data. This allows you to develop smarter maintenance strategies and to start phasing out scheduled proactive maintenance. Eventually, your predictive model will prioritise maintenance where it’s most needed.
To successfully move from a reactive to a proactive approach, you need to understand the three areas of proactive facilities management.
GEG has applied data-driven maintenance (DDM) for many years via our Actionable Insights infrastructure – InsightsAI™. We find options for optimisation and can trial DDM on key areas like HVAC, fire and electrical, then scale to include plumbing, BMS, elevators, computer hardware and software, and internet services.
Extend the life of your assets, reduce the unforeseen costs of expensive breakdowns and improve building performance Speak with our talented Engineering Team to learn how we can help you proactively manage your operational assets.