The discipline of service delivery has big changes in customer expectations during the pandemic. Rapid, contactless delivery is now a prerequisite, and many organizations are turning to advanced technologies to take the next step on the journey to keeping their customers happy and their businesses competitive.

In the process, a trifecta of critical technologies — internet of things (IoT) integration, predictive analytics and machine learning — have stepped up to take service delivery to a whole new level.

Linking IoT and FSM to enable predictive maintenance. Increasingly, the IoT is helping to advance the capabilities of field service management (FSM), with many organizations using IoT tools to carry out remote monitoring. Recent developments have seen the technology bring further benefits to the sector. Now, even the smallest IoT devices and sensors have network and internet connectivity, enabling them to feed data into an FSM system. It then converts the data into insights that can help service providers carry out predictive maintenance.

Makino, a global manufacturer of metal-cutting and electrical discharge machining, is a perfect example of how IoT and FSM can help companies achieve predictive maintenance as part of a service transformation strategy. Makino relies on an IoT business connector to receive and operationalize device data and deliver observations on the condition of its machines. This allows the company to accurately predict any equipment failures, and take action before failure occurs.

For example, when customers allow connectivity, the IoT system can feed data from the equipment directly into the FSM system so that a call can be placed, or a ticket created automatically. As a result, Makino avoids significant disruption, maximizes equipment uptime, and reduces the number of unnecessary dispatches of engineers — all while cutting costs and improving customer satisfaction.

Taking service to new intelligent heights with AI. In a similar way to IoT, artificial intelligence and advanced algorithms can help bolster a new form of business automation, allowing field service organizations to further the transition to predictive service. When it comes to service accuracy, AI helps to target specific business disciplines such as intelligent scheduling. Across the entire scope of field service operations, AI can optimize scheduling decisions by solving large-scale problems with multiple constraints, which is especially useful when dealing with mobile workforces within field service organizations.

With advanced capabilities, AI can analyze real-time data within seconds, and consider various parameters such as traffic flow and individual technicians’ skills or availability. Input from machine learning techniques allows organizations to balance competing priorities and find opportunities to combine jobs and blend planned maintenance activity. This allows human workers to focus on personalized service.

Looking forward with risk-free predictive modeling. Advanced predictive analytics tools use historic and current data collected from service activities and customers to create, process, and validate models capable of providing answers to tough questions around forecasting and what-ifs. Field service providers can then use the models to test their responses to a wide range of scenarios, months or even years before certain changes take place.

This advanced capability behind predictive modeling software allows businesses to understand how they can align their resources to achieve specified key performance indicators against varying demand levels. This includes answers to questions such as how many staff members would be needed, which skillsets they should possess, and where staff should be ideally based.

The right predictive modeling software can also provide service organizations with the flexibility to focus on both operational and strategic scheduling disciplines. It should have the capacity to combine analysis of real-time data on market changes and business performance. Organizations can then review if they need to establish new territories of approximately equal commercial value, or restructure existing territories to reflect changes in the market in order to optimize business opportunities in a specific area. This enables service companies to optimize inputs and maximize profit, all while avoiding unnecessary risks.

IoT, AI and predictive modeling have a huge role to play in the future of service delivery. Each technology provides service businesses with differentiating benefits, from increased maintenance management to better workforce scheduling and the ability to react quickly to market changes. Service organizations that prioritize a technology-first approach will be the ones able to achieve truly predictive service — to future-proof their businesses and exceed increasingly demanding customer expectations.

Sarah Nicastro is field service evangelist with IFS.