Johnson Controls Set to Advance False Alarm Reduction With SaaS Offering
Johnson Controls says its approach in commercializing the false alarm reduction service is centered on solving the industry’s false alarm dilemma.
BOCA RATON, Fla. — In an industry first, Johnson Controls (JCI) has begun marketing a false alarm reduction service that applies machine learning algorithms to intrusion alarm panel data and provides end customers with insights to prioritize actions that are said to vastly reduce preventable nuisance alarms.
To train its algorithms and confirm their accuracy, the company utilized a mass amount of closed-loop data — five years’ worth — from the 757,000 panels it has in the field.
A customer beta-testing phase, which lasted a year-and-a-half, then bore results the company claims can give end users data-driven recommendations to reduce 50% to 70% of their false alarms.
The algorithms have been proven to be 96% accurate, Christine Trainor, director of data intelligence at JCI, told SSI in an exclusive interview.
“This is our 1.0 release. We are not in the monitoring center on the back end of the receiver. We have a roadmap to get there, but our initial release is to all of the preventable alarm activity that really the customer owns and can prevent by decisions being made on the business side,” Trainor said. “It is about eliminating that alarm activity altogether.”
JCI is rolling out the solution to a limited number of businesses as a Cloud-based, software as a service (SaaS) offering. No physical deployment is needed; the software system is panel-vendor agnostic.
The only requirement is that the customer use the MASterMind integrated software platform by United Technologies Corp. JCI’s technology analyzes MASterMind data to uncover recurring patterns.
These patterns are mined to provide recommendations on how to improve policies and procedures to reduce false alarms, police dispatch fines, business disruptions and increase operational cost savings.
“On our journey we discovered an incredible amount of manual effort that is put in place to try to stop the chain of events to dispatch,” said Trainor, who invented the machine learning solution along with JCI Data Scientist Gopi Subramanian. “What we’ve done in this solution is to say, ‘Let’s first tackle the problem on the front end of the buffer to remove as much as possible out of that 98% [overall false alarm rate].’”
Door delays and schedule violations are but two examples from the raft of nonlife-threating false alarms related to intrusion detection that the solution is designed to distinguish as preventable alarm activity.
Among JCI’s beta customers was a men’s clothing chain with 219 retail sites across the United States. In all, the corporation rang up about 6,600 false alarm calls in 2017.
According to Trainor, the JCI solution could have eliminated 5,800 of those signals from ever triggering to the receiver. With an estimated $3.2 billion spent annually on false alarms fines and associated costs by organizations and municipalities, dramatic cost savings is a key attraction to the JCI solution.
“Most of these corporations don’t realize what their cost is. A lot of fine activity is hidden at a local level. They put it in where they pay taxes because they will get in trouble if it goes to corporate,” she said.
Intent to Solve an Industry Dilemma
The subscription-based service is a new topline growth revenue target born out of JCI’s recently established Data-Enabled Business.
The standalone group is an investment that JCI Chairman and CEO George Oliver and his executive team view not only as being industry-disrupting but also dynamically changing how they do business as a company.
JCI’s approach in commercializing the false alarm reduction service, including the models and the algorithms developed for it, is fundamentally centered on solving the industry’s longstanding false alarm dilemma — and not solely as a competitive tool for the company, Trainor explained.
The company engineered the SaaS offering with three capabilities:
- The solution is agnostic and can run on any Cloud computing plat-form, such as Microsoft Azure or Amazon Web Services
- It can operate as an on-premises solution to meet regulatory man-dates for customers that run their own security operation centers; for these types of applications, JCI will license the software to the customer
- A competing strategic partner can license the solution and operate it as a proprietary Cloud service
JCI is currently in talks with a competing systems integrator to license the service, a soon-to-be announced alliance that shows JCI’s genuineness to share its technology for the betterment of the entire industry, Trainor commented.
“It’s about uniting with integrators to not only partner for the benefits to the customers, to ourselves — because we become more profitable when our operations costs go down — but it is also partnering with regulatory and municipalities to really solve this issue in the monitoring ecosystem,” she said.
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About the Author
Rodney Bosch, Senior Editor
Although Bosch’s name is quite familiar to those in the security industry, his previous experience has been in daily newspaper journalism. Prior to joining SECURITY SALES & INTEGRATION in 2006, he spent 15 years with the Los Angeles Times, where he performed a wide assortment of editorial responsibilities, including feature and metro department assignments as well as content producing for latimes.com. Bosch is a graduate of California State University, Fresno with a degree in Mass Communication & Journalism. In 2007, he successfully completed the National Burglar and Fire Alarm Association’s National Training School coursework to become a Certified Level I Alarm Technician.
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Hopefully you will be coordinating with alarm industry legal specialists, like Les Gold and Ken Kirschenbaum, to remain compatible with existing contracts with millions of existing customers.
“The algorithms have been proven to be 96% accurate, Christine Trainor, director of data intelligence at JCI, told SSI in an exclusive interview.”
With almost seven thousand false alarms from one customer, it’s not surprising that using software to make an educated guess that an alarm is false is 96% accurate. What happened to the remaining 4%? Were they actual intrusions that were ignored because the software decided it was a false alarm?
Respectfully, automating the process of which signals to ignore doesn’t sound like a solution I’d want for my clients.