Tying together IoT and predictive analytics
The public’s desire – indeed demand – to be connected at all times and in all aspects of work and play means that “off” is no longer an option for most businesses. For telecommunications carriers this creates a tremendous challenge: how to efficiently and cost-effectively maintain the critical infrastructure and equipment that support uninterrupted service delivery.
Fortunately the “Internet of Things” has brought technology and techniques to aid the carriers. IoT applications such as remote monitoring and control use sensor-enabled equipment to provide data on remote conditions. This data forms the building blocks of intelligent site management.
The raw data by itself is not enough, however. To become useful it must be interpreted and related to other relevant information. This is where predictive analytics – a variety of statistical techniques that analyze current and historical data to make predictions about future events – comes in. It provides actionable information that enables companies to manage sites intelligently, leveraging historical trends, real‐time data and predictive analytics to enable remote, preventive and reliable site management.
Carriers put analytics to work
Leveraging data and predictive analytics enables carriers to maximize uptime. For example, when Hurricane Sandy ripped through the Northeast in 2012, carriers operating in the metro New York City area needed to restore service as quickly as possible. Each carrier deployed portable generators, but one company had an edge because of its pre-existing plan for efficient generator deployment during power outages.
To create the plan, the carrier’s operations team considered several key factors: the priority of each location so they could sequence deployment to the highest priority sites; the predicted runtime of the backup batteries so they could gauge how long each site could stay on the air before the portable generator would be needed; and which locations had a portable generator interface or “gen plug” so the site could actually use a portable generator. In addition, to determine how long a portable generator would need to remain at each site, the team considered the equipment configuration, the power load and the generator power rating. These calculations are necessary because the portable generator must both maintain service and simultaneously recharge the batteries before being redeployed elsewhere.
Using site-specific information and predictive analytics software, the operations team prioritized the deployment sequence and computed an estimate of the time the portable generator would be needed at each location before it would be moved to another site. Thus, when the hurricane struck, this carrier was able to deploy generators efficiently, minimizing service downtime.
Not just a foul-weather friend
Carriers don’t need to wait for a hurricane to reap the benefits of remote monitoring and predictive analytics. One fair-weather example of intelligent site management is optimizing the HVAC systems critical to maintaining operating environments, thereby decreasing maintenance and operating costs, reducing energy usage and increasing site reliability.
Consider the case of the carrier whose unmanned remote site experienced HVAC performance degradation, but not actual failure. Without remote sensors to continuously monitor the HVAC intake and exhaust, the site would have appeared normal to the network operation center because the equipment would be performing within acceptable parameters and therefore would not trigger an alarm. All would appear normal until the HVAC unit failed completely and created an immediate, expensive remediation event.
However, because the carrier had implemented a remote monitoring system with predictive analytics technology, it was able to detect that the supply and return temperature differences were decreasing and that the supply-line pressure was lower than it should be. The predictive analytics software flagged the unit as degraded and it alerted the carrier to schedule a regular maintenance visit to repair the unit. The carrier maintained uptime service while saving money by avoiding potential HVAC failure that would have triggered an emergency call and expensive emergency repairs.
Managing energy usage for reduced costs and greener footprint
Predictive analytics also helps carriers understand and manage energy usage. For example, one company had an unmanned site running dual five-ton HVAC units. Prior to having the machine-to-machine communications made available in the age of IoT, the NOC might have viewed the site as normal.
Instead, the technological edge provided by real-time remote monitoring coupled with predictive analytics enabled the team to see that one unit was running nearly continuously in heating mode, while the other ran nearly continuously in cooling mode. Scheduling a normal maintenance visit to adjust the airflow and correct the faulty placement of thermostats resolved the problem and enabled the units to operate in sync. This saved many hours of wasteful HVAC runtime and a significant amount of energy, while preserving the useable life of the equipment.
In another example, a carrier had an unmanned site with two HVAC units, one of which failed in early spring. At that time of year the external temperature remained moderate, so a single unit was adequate for maintaining proper operating temperature at the site. Come summer and hot weather, one unit would no longer have been able to maintain temperature and might have failed under the increased load, resulting in the overheated site going off line and requiring an emergency call. However, because the company had implemented intelligent site management, sensors detected that only one HVAC system was operational and the second unit never cycled on. Data analytics enabled the operations team to recognize the aberrant situation and schedule a normal shift call to rectify the problem while the weather was still temperate, thus keeping the site on-line and saving an emergency truck roll and an emergency repair.
Data plus analysis produces results for carriers
Data collection alone is not enough to keep operations running smoothly and cost-effectively. It is the transformation of data into actionable information that provides true value to organizations. Predictive analytics makes intelligent site management possible. It reduces the risk of downtime, optimizes preventive maintenance, reduces on-site emergency visits, lowers costs and increases customer satisfaction. For carriers today, who operate in a world where “off” is no longer an option, intelligent site management is critical to success.
Jonathan Quint is CEO and co-founder of Ontegrity, provider of IoT-enabled applications and predictive analytics to companies that cannot afford an interruption. Ontegrity’s technology and talent give companies a more accurate picture of site conditions and present the right course of action to increase product and service availability. More information is available at www.Ontegrity.com.
Editor’s Note: The RCR Wireless News Reality Check section is where C-level executives and advisory firms from across the mobile industry share unique insights and experiences.
