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You may think that your facilities management team is performing at its absolute best. You’re maintaining critical assets, you’ve got a preventive maintenance schedule in place, work orders are being processed and approved, invoices are being paid. That’s all well and good – but you’ll never truly know how much your efforts are paying off without digging into the data.
Thankfully, facilities managers are privy to an abundance of data. In fact, it’s more data than most of us care to access or analyze. Yet the brands who are spending smart, cutting costs, and gaining a competitive edge in this changing consumer landscape are the ones that aren’t afraid to run the numbers and look for those opportunities to make even marginal improvements.
Clearly, no facility management program can hit “next level,” digitally transformed, data-driven excellence without a sophisticated analytics program. We certainly have the technology: AI and ML-driven systems are becoming more advanced and more widely accessible by the day. Why is it, then, that so many companies are still operating largely on pen and paper?
It’s because senior executives haven’t yet grasped the immense value and potential cost savings in data-driven FM operations. It doesn’t take too much work, however, to prove it to them. In this blog, we’ll take a closer look at the data analytics technology on the market today and how to get the most out of it – ultimately making a significant impact on your year-end goals and performance.
Understanding FM Data Analytics
Think about all the different types of data you’re already collecting about your facilities or retail locations. There’s the energy consumption data, the foot traffic data, the repair spend data, the asset performance data – and that’s only the tip of the iceberg. What most teams end up with is a vast, indecipherable spreadsheet of numbers and figures. Great: You have your raw materials. Now it’s time to make sense of it.
FM analytics is the process by which a series of algorithms are applied to your relevant datasets in order to identify key trends, patterns, and insights. These patterns will, in turn, help you identify areas of potential improvement in efficiency or cost effectiveness. For example, a trend that shows quarter-over-quarter increases in HVAC energy consumption across multiple geographies may indicate that these assets have begun to decline in performance and are in need of replacement.
Facilities managers that have the tools they need to embrace data analytics will be empowered to vastly improve visibility across locations and departments, make data-backed decisions, and plan strategic initiatives.
Organizations should take advantage of the following four types of data analytics:
- Descriptive Analytics: Shows what’s happening now based on incoming data
- Diagnostic Analytics: Explains why data is as it is and why events happened
- Predictive Analytics: Helps forecast what is likely to happen
- Prescriptive Analytics: Instructs on how to influence data and make impactful changes
Many organizations rely most heavily upon descriptive analytics to understand repair and maintenance activities and drive decision-making processes. Now, however, cutting edge business intelligence tools have given facilities teams the ability to meticulously dig into their data, applying predictive and even prescriptive analytics via AI- and ML-powered solutions. This new level of sophistication unlocks unprecedented insights into business operations, efficiency, and overall FM program performance.
How to Leverage Advanced Data Analytics Capabilities in Your Facilities Management Program
Clearly, it’s worth investing the time and resources into data analytics. With the right set of data insights at their fingertips, facilities professionals can implement programs that not only cut costs, but also save time and streamline overall operations. The insights allow businesses to optimize or even replace outdated processes, take preventative measures to minimize downtime, and even hold service providers more accountable – improving performance across the board.
Here are a few areas to zero in on when thinking about how to leverage FM data analytics:
On average, multi-location businesses spend over $100 billion in repair and maintenance each year, according to a ServiceChannel study. We also found that this expenditure includes approximately $20 billion in waste. These are costs that are incurred due to inefficiencies in outsourcing and a lack of automation (meaning a large number of repetitive tasks are still being performed manually).
Scheduling preventive maintenance is one such task that, by now, organizations should absolutely be automating – and analytics must be employed to determine an ideal preventive maintenance schedule that can be implemented consistently across locations.
Don’t expect that your organization will remain competitive if you’re still operating under a reactive maintenance strategy. While many FMs see equipment failures as an inevitable reality (and to an extent, they are), advanced data analytics gives us the power to predict asset breakdowns and service assets according to optimized schedules that prevent costly emergency repairs.
It’s important to have access to key data points – including location, age, condition, warranty and service requests – for each piece of equipment before setting up a preventative maintenance schedule. Invest in a service automation platform to access the analytics tools you’ll need to gather, parse, and leverage critical facilities and asset data. Then, put the predictive analytics capabilities to use: Assess historical asset maintenance and performance records to create a data-backed preventive maintenance plan.
You’ll find that your organization is quickly able to recoup some of the formerly wasted spend simply by keeping assets up and running for longer, with fewer interruptions. Asset data will also make it easier to identify when it’s more cost-effective to replace, rather than repair, a particular asset or piece of equipment.
Energy Demand Management
Energy efficiency is, especially in recent years, one of the most pressing demands that facility managers must manage. Organizations are recognizing the need to minimize energy consumption – not only in order to cut costs, but also to minimize their carbon footprint. In addition, Demand Response programs have been ramped up; businesses are now incentivized to scale back their energy use during peak usage periods in order to keep within electricity grid constraints.
With these factors in play, it’s no wonder facilities programs have increasingly shifted their focus towards predicting trends and fluctuations in energy demand. And a huge key to success is the proper implementation of data analytics: Data center infrastructure management (DCIM) systems track facility power and cooling equipment energy consumption and, leveraged strategically, can be used to locate and respond appropriately to opportunities for increased energy efficiency.
In many cases, the IT department manages an organization’s DCIM system. However, if the facilities program can work collaboratively with IT, this data can often be shared or integrated across systems so that both teams have greater access and control. Descriptive or predictive analytics can then be applied to provide insight into how current equipment is performing (for instance, looking for sudden spikes in energy consumption) and facilitate decisions about when or how often to upgrade equipment.
By applying analytics, it’s also possible to optimize uninterruptible power supply and power distribution equipment in order to reduce wasted energy consumption. DCIM systems can, for instance, power cap systems to lower energy consumption when a certain threshold is reached, or shift loads to contain costs.
While current programs are still largely employing descriptive analytics to achieve energy management goals, technology is becoming more sophisticated by the day. AI-powered, automated energy management systems that leverage predictive and prescriptive analytics are gaining traction; we’re sure to see rapid implementation of these systems in the coming years.
Historical Spend Analysis
How much did we really spend on HVAC maintenance this quarter? What are the predicted costs for kitchen equipment replacements in the coming year? Which assets that require service are still under warranty? These questions are incredibly important when it comes to managing a successful facilities maintenance program, yet they’re too often avoided until it comes time for quarterly budgetary or CapEx planning.
Don’t push your spending data under the rug. Maintaining a 360-degree view over your program’s performance is a good thing; doing so opens the door for creative problem solving and reveals opportunities to decrease overhead or better monitor service provider and technician performance. And when analytics are applied to complex datasets, you’ll quickly be able to identify the most valuable and pressing initiatives.
Implement a service automation platform in order to make spend and budgetary forecasting an ongoing, automated process. Rather than having to dig through piles of disorganized invoices, emails, and spreadsheets, take advantage of a centralized interface: ServiceChannel’s Discovery Dashboard, for instance, produces visualizations that illustrate YoY or MoM trends in spending across time, location, category, and trade.
To take your analytics program up a notch, subscribe to daily, weekly, and monthly email or reports from your Service Automation Analytics Dashboard. Make a habit of reviewing these reports to stay up to date with urgent priorities, spending trends, and outliers. You’ll find it much easier to keep budgets in check when you have an accurate picture of where money is being channeled, how often, and for what purpose.
FM organizations have access to vast quantities of data: Energy consumption, asset performance, space occupancy, repair cost, and maintenance data – this is just scratching the surface. However, despite rapid advancements in analytics technology, a huge percentage of facilities data is still underutilized. What many facilities professionals don’t realize is that there’s immense value hiding just below the surface of these “data lakes,” and all it takes is the right platform or solution with which to aggregate, parse through, and actually put it to use.
If your repair and maintenance program is still running on “gut decisions” or generalized assumptions rather than data-driven insights, you’re not operating efficiently. Invest in a robust service automation platform in order to take advantage of advanced descriptive, diagnostic, predictive, and prescriptive analytics and watch your FM program evolve and flourish like never before.
Ready to take your facilities management analytics program to the next level? Schedule a demo with the #1 facilities management platform.