Telecom asset management

There’s a major shift underway in field service in the communications industry – a move away from traditional, reactive models toward proactive and predictive. The way telecom asset management has traditionally been handled – equipment would break, and the service team would respond – is inefficient, costly, and leads to significant downtime. In telecommunications, where assets can include vast and dispersed networks of field service and hardware, from cell towers to fiber facilities, ensuring it’s operational and maintained effectively is a huge part of a company’s ability to provide reliable service and be more profitable.

Now, thanks to the rise of AI, data analytics, and agents, companies are able to anticipate and then prevent problems from happening in the first place. That shift is reshaping how companies manage their assets – equipment as small as internet routers to as large as cell tower networks – focusing less on fixing problems when they arise and more on maximizing uptime and increasing customer satisfaction and loyalty. It also allows companies to optimize their operations and improve the overall performance of their assets. 

What you’ll learn

The Challenges of Telecom Asset Management
Transforming Asset Management For Proactive Solutions
Without Asset Management, Telecom Field Service Is Inefficient
Asset Management and AI, the Perfect Combination

Telecoms are responsible for an extensive array of assets spread over wide geographic regions and managed via a disparate set of typically siloed systems and tools. These assets are critical to maintaining service uptime, but they’re also highly susceptible to damage, wear, or failure. Traditional field service relies heavily on reactive repairs, and time-consuming coordination, leading to service interruptions and high operational costs. 

Think about it. You’re dealing with the manual scheduling and dispatching of technicians, inefficient asset tracking and monitoring, the costs associated with equipment failure and service interruption, and inconsistent data collection from the field. Overall, coordinating all of those moving parts can be a time-consuming proposition for field service representatives. (back to top)

Enter Data, AI, and Agents: Transforming Asset Management For Proactive Solutions

AI offers transformative solutions to these challenges. It can handle many of the traditionally manual tasks to optimize operations and ensure you’re getting the most life out of your assets with proactive management. Agents, assistive and autonomous software systems that can act like intelligent digital assistants, take that efficiency a step further.

Predictive maintenance starts with your data

One of the most significant advantages of AI is its ability to predict when an asset might fail. The ability to do that relies on unifying field service with customer  and operations support systems (OSS) data. Your customers’ behavior and sensors monitoring hardware becomes the predictive data that AI can flag to triangulate on an asset’s poor performance or potential failure. For example, a customer experiencing connectivity issues in a specific area may signal a problem with water ingress into your copper network. Or reports of poor signals may be signs of a problem with a cell tower. Agents are able to take service calls and autonomously flag an asset for predictive maintenance. 

Customer sentiment analysis can direct asset management strategies

Beyond identifying a potential problem with a specific asset, analyzing customer sentiment from various channels, such as service calls, reviews, and even social media, can detect emotions and usage patterns. This gauges your customers’ overall sentiment in ways that can’t be captured by technical data alone, an important reminder that there are customers on the end of a service call. This can include frustrations with overall asset performance, allowing you to take proactive steps to address the assets causing the most frustration. In addition to fielding calls, agents can analyze overall sentiment and correlate it to potential poor asset performance. 

Inventory and asset lifecycle management preventing downtime

Managing all of the assets that goes into telecom field service is a complex undertaking. There are the parts, materials, and tools that go into maintaining vast systems. You are also dealing with the coordination of an oftentimes large team of technicians, with varying availability, skill sets, and locations. AI can keep track of inventory levels, predict when parts and/or materials will be needed, and automate the ordering process to ensure that the right pieces to the puzzle are always on hand when technicians need them. Agents can also help extend the life of assets by scheduling maintenance actions based on historical data and usage patterns. When a technician is needed on short notice, agents can ensure the best person with the right tools and parts for the job is dispatched to fix the problem as quickly as possible. (back to top)

Let’s look at how this would all play out in real life. Say, for example, a telecommunications provider is responsible for maintaining and servicing 2,000 cell towers across the Midwest. Their network supports millions of users, and maintaining the uptime of their towers is critical to customer satisfaction. Cell towers require repair and maintenance often. That means downtime. And the manual process of assigning technicians to locations results in inefficient routing that leads to increased travel times and costs. Also, the cell tower infrastructure is only monitored during scheduled inspections, making it difficult to detect issues before they happen. 

To combat all these inefficiencies, this company invests in technology that gives them a complete view into all their assets in real-time on an AI-powered platform that also includes agents. Sensors on their towers begin feeding performance data to AI algorithms that are monitored around the clock. When early signs of potential issues are detected, agents automatically flag the tower for maintenance. 

Once that occurs, AI triggers an automated dispatch process that determines the kind of maintenance needed. If it requires a technician, agents will analyze the best mix of skill set, availability, and location, and assign the best person for the job. They can also send the technician a summary of the work needed and what tools and material will be required to fix the problem on the first visit. 

In addition, with the platform’s inventory management system, AI tracks the usage of parts and recommends orders based on predictive models, ensuring technicians always have the right equipment on hand when needed. Additionally, it tracks the lifecycle of each tower component, alerting teams when critical parts are nearing failure. Ultimately, with its modernized field service asset management system, the company reduces its technician travel time, manual inspections, emergency part orders, and – most importantly – unplanned outages and overall downtime. (back to top)

Asset Management and AI, the Perfect Combination

The telecommunications industry is rapidly evolving, and companies that rely on old, manual systems are being quickly left behind. Time-consuming and complex processes must be modernized, especially when it comes to costly operations such as field service and telecom asset management. By leveraging real-time data for predictive insights, AI and agents can transform how providers manage their field operations. The result? Getting the most out of your equipment, infrastructure, team, and ultimately, the best way to deliver on today’s growing demands of connectivity. (back to top)