Facility managers must overcome many challenges, not the least of which is stretching their limited resources yearly. And they can expect to operate on limited budgets throughout 2023. As shown in a 2022 CBRE report, facilities management professionals are facing a near future fraught with rising energy and logistics costs. They must use all available tools, including artificial intelligence (AI) driven systems with machine learning (ML) capabilities.
What makes cash flow forecasting using machine learning a natural asset for facility managers? ML programs are known for their predictive capacities. An ML-fueled software solution can make recommendations based on large swaths of historical data. The solution parses the data and tests it based on a pre-programmed algorithm to come to conclusions. As a result, the ML program can help facility managers lessen some of the guesswork from their financial decisions.
For instance, an ML solution could predict how much energy a structure will likely use in a given year based on a wide range of factors. These factors could include decades of weather data to occupancy rates. The facility manager could then use the findings to request annual funding more confidently based not on instincts but facts.
Tips for Facility Managers Interested in Leveraging ML to Reduce Costs
Without a doubt, ML can assist budget-crunched facilities professionals in many ways. Below are just some of the fiscal-related applications of ML in the facilities field.
1. Maintenance Predictions
Maintaining a building can be a multifaceted challenge because buildings are made up of so many parts. Case in point, all types of equipment will require maintenance, repairs, and replacement. However, juggling the maintenance and lifecycle needs of all equipment, furnishings, amenities, etc., can be difficult and expensive.
ML can make the process more streamlined by predicting potential issues before they occur. This can extend the working potential for HVAC and other equipment and avoid unnecessarily high emergency and replacement bills. Plus, ML may show how a building can reduce bills in the long run by investing in energy-efficient machines or renewables now.
2. Integrated Technologies
Many facilities managers are leaning into ML to assist in integrating all their technologies. It’s no secret that the Internet of Things (IoT) is fostering the interoperability of devices. Facility managers can make their buildings “smarter” by connecting once-siloed systems.
Through this interconnectivity augmented by ML, they can stay on top of building needs and optimize each system’s functionality. As data flows between devices and systems, it can be used to run those devices and systems more effectively and efficiently.
3. Cybersecurity Considerations
Because facility managers now handle data and data-packed solutions, they must consider cybersecurity measures. Data breaches can be costly for any facility. ML can identify areas of weakness and alert human users to potential threats.
Once identified by a trustworthy ML system, any security gaps can be closed before nefarious actors can attack and steal data or infiltrate the system. Fewer cyber attacks in the here and now mean fewer expensive problems later. Additionally, they take away the financial sting that can accompany bad press.
4. Tenant Communications
Keeping building tenants up to date can eat up time that facilities managers don’t have in their busy schedules. As the saying goes, time is money. Therefore, using ML-focused systems to automate communications can reduce engagement costs.
For instance, facilities managers may give tenants limited access to monthly reports. These reports can be generated and automatically deployed monthly or quarterly without human intervention. If tenants request additional data, the ML system can “learn” to anticipate these requests and adjust reports accordingly.
5. Labor Needs
The labor market continues to be extremely tight, making it more difficult to keep teams in place by the day. Many organizations and hiring managers — including those in the facilities management field — are having trouble holding onto good employees and filling open positions. Consequently, this can leave a building without the personnel needed during various times of the day.
With ML, facilities managers can better adjust to labor shortages by knowing exactly how many personnel are needed, where, and when they are needed. Though not a perfect science, this enables facilities managers to maximize their staff's advantages.
Facilities management has evolved out of necessity since the pandemic. Inflation is higher. The cost of doing business is higher. And talent can be difficult to source. Nevertheless, ML solutions like software programs, centralized systems, and cloud-based products can help any facility manager spend less without sacrificing quality or performance. The key is to find out what’s working for others in the facilities management industry and try those novel applications.