By Sarah Nicastro

This rapid shift for organizations to digitally transform comes at a great time with recent digital advances. There are highly sophisticated tools in market today which rely on the use of data, advanced algorithms, and machine learning techniques to predict parameters that would affect service delivery, the resources needed to achieve specified performance, and accurate estimations for work completion. In turn, this ability to make smart data-driven decisions enables field service providers to prepare for the unexpected without compromising on the quality and reliance of their delivery. 

In particular, there are three key components that field service organizations can use to achieve predictive field service.


In recent years, the Internet of Things (IoT) has played a crucial role in field service management, with many organizations using IoT tools to carry out remote monitoring. However, recent developments have seen IoT bring further benefits to the sector and allow organizations to enhance their service capabilities. Now, even the smallest IoT devices and sensors have network and internet connectivity, which enable them to feed data into a field service management (FSM) system. This FSM system is then able to convert the data into actionable insights that can help service providers carry out predictive maintenance work.

Makino, a global product manufacturing company that produces metal-cutting and EDM machines, is a perfect example of how IoT and FSM can help companies achieve predictive maintenance as part of service transformation strategy. Makino relies on an IoT Business Connector to receive and operationalize device data and deliver observations on the condition of their machines. This allows the company to accurately predict any equipment failures and take action before failure occurs.

For example, when customers permit connectivity, the IoT system can feed data from the equipment directly into their FSM system so that a call can be placed, or a ticket created automatically. This helps Makino avoid significant disruption, maximize equipment uptime, and reduce the number of unnecessary dispatches of their engineers—all while cuttings costs and improving customer satisfaction.


In a similar way to IoT, AI and advanced algorithms can help bolster a new form of business automation and assist field service organizations in their transition towards predictive field service. When it comes to the accuracy of service, AI solutions can help organizations target specific business disciplines such as intelligent scheduling. Across the entire scope of field service operations, AI solutions can optimize scheduling decisions by solving large-scale problems with multiple constraints—which is especially useful for mobile workforces within field service organizations. 

With advanced capabilities, AI can analyze real-time data within seconds and consider various parameters including traffic and technician availability. Input from machine learning techniques can also help organizations balance competing priorities and find future opportunities to combine jobs and blend planned maintenance activity. This allows human workers to focus on personalized service, solving complex problems and escalations. 


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 the 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 capability behind predictive modeling software allows businesses to understand how they can align their resources to achieve specified performance KPIs against varying demand levels. This includes how many staff members would be needed, what skillsets they should have to complete certain tasks and where staff should be ideally based.

The right predictive modeling software can also provide service organizations 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 (KPIs). 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.


The sooner businesses embrace predictive service and become digitally orientated, the greater chance they have at not only delivering on ever-higher performance levels but also getting the accuracy to detect issues before they materialize, helping them exceed customer expectations and maintain their competitive edge. 

About The Author

Sarah Nicastro, field service evangelist, brings to IFS ( over a decade of experience covering the trends, technologies, and business drivers that most impact end users of field service solutions from her tenure as editor-in-chief at Field Technologies Online. During her time at FTO, Sarah’s mission has been to help field service customers tell their stories. In her new role, Sarah will apply her expertise to translate how IFS solutions can address the challenges and pain points of savvy field service companies. Connect with her on LinkedIn @sarahhowland.  

Modern Contractor Solutions, June 2021
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