Machine learning in facilities management is a tidal wave, and it is coming much faster than many realize.
Machine learning refers to the use of technology and systems to self-monitor and self-optimize operations. By its nature, machine learning will increase the efficiency and intelligence of systems over time. Combined with predictive analytics, machine learning technology can increase facilities management efficiency, enabling optimal decision making without the need to consult an endless dashboard.
Data relevancy is another component of machine learning in facilities management. Machine learning technology analyzes and categorizes data based on its needed action. Since machine learning technology automatically organizes data, Facilities Managers can use the data more easily.
Machine Learning Transforms Big Data Analytics With Self-Correcting Behaviors
Most Facilities Managers understand the basic principles of big data analytics, but when analytics exhibit qualities found in artificial intelligence (AI) and can self-correct and self-optimize systems, they move from being analytics to a form of machine learning technology. This inherently automates the facilities management controls, and it is the self-correcting and self-optimizing behaviors that allow the big data analytics transformation into machine learning. As the system gathers more information about performance and efficiency while self-optimizing, it can further refine its controls and algorithms to produce even greater savings.
Most Existing Facilities Lack Systems With Machine-Learning Capabilities
Facilities Managers considering implementing machine learning technology have an advantage; understanding the potential efficiency gains provided by machine learning systems, and the adaptive nature of the technology, frees them to think more strategy rather than tactics. Most Facilities Managers have not yet implemented machine learning technologies. They may have implemented smart building solutions, capable of providing information and insight about actions, but it is the lack of automation and self-optimization that still results in lost opportunities in the course of business. If your organization does implement machine learning technologies, you can develop strategies to garner greater savings and improvements in efficiency of your building assets.
Machine Learning Sits Atop the Gartner Group Hype Cycle for Emerging Technologies
Facilities Managers around the globe have taken notice of the need to implement machine learning technologies to ensure continued success and viability in an increasingly competitive market. According to the 2017 Gartner Group Hype Cycle for Emerging Technologies, machine learning sits at the top of the curve as one of the most potentially disruptive technologies. This means there is significant near-term potential, within the next 2-5 years, for the technology to propel organizations into opportunities not previously possible. Most Facilities Managers are turning their sights toward machine learning, so organizations that do not implement machine learning technologies will fall behind within the next few years.
When Is the Right Time to Invest in Machine Learning?
Machine learning technologies are an investment. They tend to have a higher upfront investment cost than traditional facilities management systems, but the long-term savings outweigh initial investment costs. Since this is a newer technology, Facilities Managers considering implementing machine learning technologies can evaluate the readiness of their organization to implement such solutions by asking these three questions.
- How many variables will affect the machine learning technology? This question simply reflects the amount of information captured by a specific aspect of machine learning technology. For instance, HVAC runtime is a simple variable, but HVAC runtime, energy usage, building occupancy ratio, average indoor temperature, average outdoor temperature, and other factors increase the variability within the machine learning technology. As a result, the technology will be more accurate and able to self-optimize, but this requires advanced algorithms and encoding that will likely incur a higher upfront investment.
- What is the likelihood of a black swan event? In addition to considering variables, Facilities Managers considering implementing machine learning technology should also consider the likelihood of black swan events. Black swan events are events that are unpredicted, seem to come out of nowhere, and dramatically affect productivity and ability of systems to self-optimize. Adverse events, such as hurricanes, man-made disasters, and sudden shifts in the economy, can be considered black swan events, and these events should be taken into consideration. The ability of a machine learning technology to adapt to changes can help prepare for the unpredictability of black swan events.
- Is there enough room in the budget to implement ML-based systems? As noted previously, the upfront cost of implementing machine-learning technology can be higher than expected. As a result, Facilities Managers need to scrutinize the budget for available funds for implementing machine learning technology before making the first step. One should also consider the savings achieved from the efficiency gains to get a true sense of the return on the investment and develop the business case for the technology. Fortunately, software-as-a-service platforms are being developed with machine learning built-in, which will help to mitigate these costs.
Start Automating and Using New Technology in Your Facility to Prepare for the Machine Learning Revolution
The advancements in machine learning will continue, and Facilities Managers need to understand how machine learning in facilities management will drive continued growth and success in the industry. If your organization needs help navigating the complex world machine learning in facilities management, or if you need help implementing smart building solutions, visit ENTOUCH online or give us a call at 1-800-820-3511 today.