Artificial Intelligence Simplifies Field Service Management

Author photo: Inderpreet Shoker
By Inderpreet Shoker

Keywords: Asset Management, Artificial Intelligence (AI), Field Service Management (FSM), Asset Performance Management (APM), Analytics, Machine Learning (ML), Large Language Models (LLMs), Generative AI (GenAI), ARC Advisory Group.


Field Service Management

Field service involves widely distributed assets and requires features like integrated maps, route optimization, in-vehicle parts inventory management, and customer billing. While FSM solutions are typically designed for use by third-party service providers, these are often used by owner/operators for self-service of assets. Owner/operators and service providers have long realized the benefits of effective FSM. The newer advanced technologies available today have helped push the limits to go beyond reactive maintenance, which often involved unacceptably long downtime. Advanced FSM with new digital tools, especially artificial intelligence (AI) help owner/operators and service providers bring in transformation advances in field service and hence overall business performance. 

Artificial Intelligence Simplifying Field Service

As industrial organizations continue their digital transformation journey, industrial AI is becoming a key enabler. Infusion of AI in the industry continues to improve business processes, simplify jobs for the workforce, and scale technology. Industrial AI is being leveraged in various areas including asset management and FSM. Industrial AI is helping users simplify and bring in improvements throughout different stages of field service. 

Automatic Scheduling

Even today, most field services are reactive, with the user calling for service after the device fails. In addition, field service usually requires at least two visits – one to inspect and diagnose the problem to identify the needed parts and skills, and another visit to implement the repair. This can result in extensive downtime and lost production, with unacceptable impact on the user’s business.

Remote monitoring and predictive maintenance (PdM) technologies are helping expedite field services by minimizing the number of trips to the field to repair assets. PdM involves monitoring real-time data and using historic data to predict when an asset is going to fail. Machine learning, one of the fundamental techniques used to build AI systems has been used to deliver PdM capabilities for many years prior to the recent hype triggered by large language models (LLMs) and Generative AI (GenAI). 

A modern FSM application can further support technician scheduling. Based on the details of asset health conditions and technician skill sets, modern FSM solutions can leverage AI to recommend the right technician to the scheduler or even match and schedule the technician itself. By adopting AI, issue identification as well the business process for triage can be effectively automated. The proactive repair and high “first time fix rate” (FTFR) by the OEM avoid lost revenues for the manufacturer. 

Route Optimization

Route planning is a key component of the FSM solution. Service providers need to ensure that their field technicians can address the problem as fast as possible. For this, when planning the route, they need to consider various other factors, such as distance, traffic, fuel consumption, weather, customer preferences, and many more. Service providers need route optimization to consider all these factors and find the most efficient and cost-effective route for their technicians. 

AI and data analytics are proving to be of immense help to optimize the route planning process. By analyzing historical and real-time technician data and location, traffic patterns, weather trends, and inventory information, technician and route recommendations can be made. AI can help generate and compare multiple route scenarios and recommend the best one based on user requirements. In addition, with the help of AI, route plans can be updated in real-time based on changing conditions and factors. Furthermore, route performance can be continuously monitored and evaluated to identify areas for improvement. Route optimization with the help of AI is proving to be of great help to reduce costs, improve customer satisfaction, and increase operational efficiency.


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