Digital Transformation of Field Service: A Review

Introduction

Field Service Management (FSM) refers to the comprehensive coordination and optimization of tasks, resources, and personnel involved in providing services or maintaining assets in the field. It encompasses planning service visits, resource management, scheduling, and engineers working at customer sites. The ultimate goal of FSM is to streamline field operations, improve response times, reduce costs, and enhance overall service quality.

It requires effective coordination between assets, parts, field engineers, staff and customers for the best performance. Several studies explain the role of one integrated system in facilitating seamless data flow across the value chain to enhance decision-making. A study published by Harvard Business Review Analytic Services explains the concept of a "Digitial Thread" where the systems and data are integrated throughout the value chain to improve decision-making (1).? Today, companies use various systems such as ERPs and CRM to achieve this objective, however, emerging technologies present opportunities to further evolve their information system architecture.

With the rise of Industry 4.0(also known as the Fourth Industrial Revolution or 4IR), changes in connectivity, analytics and intelligence, data processing and human-machine interaction are revolutionizing the manufacturing sector (2). These technological strides in real-time data gathering, processing, and analysis, driven by the Internet of Things (IoT) and Artificial intelligence (AI) can transform Field Service Management as well. Additionally, the emergence of Vertical AI presents the opportunity to leverage industry-specific data and expert knowledge to further enhance and differentiate the field service (3).? Furthermore, the emergence of Augmented Reality (AR) and Virtual Reality (VR) technologies presents new opportunities for transforming the maintenance operations in the field (4).

By reviewing various studies, surveys, field service products and technologies this article explores the areas where the convergence of Artificial Intelligence, Internet of Things, Augmented Reality and Virtual Reality can be leveraged to transform Field Service Management. ?

The New Maintenance Paradigm

In traditional Field Service Management systems, the maintenance schedule is based on the manufacturer's recommendations, past experiences and data collected by field engineers during service visits. The changes in asset condition are often not monitored between service visits. Decision-making based on outdated situational data from the field usually leads to reactive maintenance and unexpected breakdowns. By leveraging IoT sensors for dynamic data collection and using the predictive power of AI, organizations can shift from a human-centric reactive or preventive maintenance model to a data-centric predictive maintenance model (5).

In the new data-centric approach, by continuously analysing data from IoT sensors, potential faults could be identified and fixed in advance. This information is also useful in choosing the appropriate technician and schedule for service visits. The predictive maintenance approach helps to reduce asset downtime, reduce cost and help in extending asset life span.

Emerging Virtual Maintenance Environment

Even though IoT/AI integration facilitates predictive maintenance, achieving operational excellence requires field technicians to use the data effectively. The AR/VR technologies help create a new collaborative virtual environment to support maintenance operations and enhance real-time data exchange to and from the field. Furthermore, the integration enables organizations to enhance operational efficiency by merging real-time IoT and AI-driven insights with observational data from skilled field workers.????

The ability of Augmented Reality to layer virtual data over actual environments helps to enhance the interactive experience, while Virtual Reality headsets and 3D displays facilitate an immersive experience in a simulated digital environment (4). The power of AI can be utilized to further enhance the effectiveness of AR/VR. By leveraging AI, the virtual environment can be tailored to provide a unique experience for each technician. Also, AI techniques such as NLP could facilitate verbal interaction with the virtual environment.? In addition, AR/VR can be combined with computer vision AI to recognise assets, and parts, identify faults and get guidance in resolving them (6).

The new virtual environment could be used to enhance collaboration, train engineers, and provide expert support for both engineers and customers. In the predictive maintenance paradigm, while the integration of IoT with AI facilitates early fault detection, the AR/VR technology contributes to technicians' efficiency and safety.?

Asset Management?

Predictive Maintenance: Traditionally asset management is based on manufacturer gaudiness, regulatory requirements and previous asset performance. Often the asset performance data is collected by field engineers during service visits. The data collected manually may become outdated before the next inspection and could lead to unexpected and expensive breakdowns. In the predictive maintenance strategy, the synergy of IoT and AI is utilized to gather and analyse real-time and historical data related to assets. Machine Learning algorithms could use the real-time data to model a Potential-Failure (P-F) curve. This curve illustrates an asset's performance over time and helps to identify the interval when failure is likely to occur (7).

The benefits of Predictive maintenance also include extending asset lifespan, reducing chances of accidents (8), reducing repair cost and increasing field technicians' productivity. Based on a Deloitte study, “On average, predictive maintenance increases productivity by 25%, reduces breakdowns by 70% and lowers maintenance costs by 25%” (9).

Automating Defect Management: Continuous analysis of the data produced by assets could help to identify patterns that could lead to asset failure. According to McKinsey , “AI-based visual inspection based on image recognition may increase defect detection rates by up to 90% as compared to human inspection” (10). In addition, with the help of AI and IoT, device commands to repair or reset equipment can be sent to the device without onsite engineer visits (11). This enables organisations to automate fault diagnostics and resolution to reduce asset downtime and operational costs.

Enhancing Safety and Compliance: Safety is one of the main considerations in field service management as technicians often work in diverse and challenging environments. Companies require appropriate systems and processes to ensure that the operations comply with regulatory and compliance requirements such as the health and safety law (12) to ensure safety.

By analyzing the real-time health, safety and environment data and historical incident data potential safety risks could be identified early. IoT and AI-based solutions could improve worker safety by tracking environmental factors such as noise levels, air pollution and malfunctioning machinery (13).? Also, some experiments and studies demonstrate the potential of AI in identifying hazards. One such study shows how AI algorithms could identify hazards from a camera feed (14) and another research shows how AI-powered mobile applications can be used to identify asbestos (15).

The AI models could also help to develop data-driven strategies to mitigate risks and optimize the incident response process. Additionally, these systems could identify deviations from regulatory requirements and help respond to changes in regulations or when new regulations emerge (1).

Inventory Management

When planning a maintenance task, having advanced knowledge of the necessary parts can aid in selecting the appropriate field technician (7) and equipping them with the right resources. This information can also be used in providing any required training and assistance to carry out the task, thereby enhancing the chances of first-time fixes and reducing risks.

The IoT sensors can be utilised in storage facilities and vans to track inventory levels and help maintain optimal inventory levels.? AI models can also help forecast demand by analyzing the maintenance plans, historical data and trends. This helps to guarantee the availability of essential parts and materials when required.

Field Workforce Management

Each task is different and the challenge is to apply the right fix during the first visit with minimum time. The technology could help in different ways to achieve this objective, including identifying the right engineer and assisting them with the right resource. This also helps the engineers to analyse the cause of failure before they go to the site. Thereby engineers can prepare with appropriate tools, training and risk assessments to ensure the task completion safely on the first visit. There are reports and case studies showing engineers' knowledge, skill and experience level could impact service delivery and how workforce effectiveness can be increased by utilizing integrated AI solutions (16). Also, AI/ML techniques such as Natural Language Processing (NLP) and Technicians' Notes clustering could help to derive useful information such as parts and labor requirements from field engineers' historical notes and feedback (17).

In field workforce management, AR/VR technologies could be used to train engineers and provide expert support. Augmented Reality/Virtual Reality bridges the gap between the physical and digital environments. These technologies help field engineers to visualize the assets and steps required to fix issues in a virtual environment.? Also, engineers could experience the potential hazards in the virtual environment and prepare with required safety measures.

The virtual environment, by facilitating technicians to diagnose and resolve faults remotely, helps to reduce travel and truck rolls whenever possible.? Also, this can be used to provide remote expert support to technicians (6) to complete the tasks effectively.

The immersive simulations of real-world scenarios can be utilised for training engineers safely. This approach helps reduce the training time and provides real-time training. The AR/VR technology can also be used by field engineers to access work instructions such as video-driven instructions and 3D illustrations (18).

Customer Service

Analysing Customer Insights:? Trying to understand customer satisfaction only through operations KPI parameters and first-time fix rate creates a customer experience gap. This indicates the gap between customer's expectations and the service the organization delivers (19). In this context, AI techniques such as Sentiment analysis could be used to analyse customer insights.? Sentimental analysis could help to analyse customer feedback to determine sentiment polarity (positive, negative, or neutral) or emotion expressed in customer feedback. This helps in prioritizing, and categorizing tasks, and selecting the right service agent, and field engineer.

Virtual Assistance: AI powered chatbots are capable of simulating human conversations with end users. A survey conducted by Blumberg Advisory Group suggests Call Centre operations are the area where AI technology can be impacted most through the usage of chatbots (20). By utilising conversational AI and Natural Language processing techniques, a chatbot can understand user questions and automate responses to them.

Also, by analyzing the streaming data from IoT devices an AI-powered chatbot could be used to monitor asset data and automate the problem-solving process by sending appropriate instructions to Assets. For example, Dynamics 365 Customer Service uses AI-powered routing and Copilot to provide context-aware assistance to agents and customers (21).

Service Personalization: According to McKinsey, 71% of consumers expect personalized interactions,? and personalisation has an impact on the company's performance and growth as well (22). Data Science techniques could be utilised to analyze customer data, service history, and preferences to personalize service and recommendations. By leveraging customer insights, organizations can tailor service experiences to meet individual needs and expectations. Service personalization enhances customer satisfaction, fosters loyalty, and strengthens relationships with customers over time.

Challenges and Considerations

Even though digital transformation offers significant benefits for field service management, there are challenges also in implementing and adopting new technologies.

Integration Complexity: To establish a connected environment capable of generating meaningful insights and predictions it is required to integrate data from various sources (23). Also, often the HVAC systems operate in different ecosystems of languages, formats and databases. Because of these differences, standardising and integrating the assets is often challenging (7). Without proper integration, organizations risk creating data silos and fragmentation. This may lead to a situation where the data cannot be easily accessed or shared across the organization.

Data Security: The effectiveness of AI models depends on the quality and volume of data used to train the models.? In the context of Field service management, the nature of the building or the asset itself may make it sensitive. While connecting the IoT devices to collect data or images related to assets or locations companies should ensure the security of the data collected and transmitted through this network.? According to a McKinsey survey, cyber security is the biggest obstacle to IoT adoption, therefore it is important to integrate appropriate cyber security systems to counter the increasing risk of IoT-related cyber attacks (24).

In addition, many industries are subject to regulations and compliance requirements governing the collection, storage, and handling of data.? Also, the ethical implications need to be considered, while selecting the datasets and training the AI models, to avoid biased AI models.

Implementing Predictive Maintenance

Implementing Predictive Maintenance requires systems and processes to collect and organise real-time and historical data. Also, developing/adapting AI models and continuously training them requires technical expertise and industry-specific knowledge. Below is an incremental approach to implementing the predictive maintenance illustrated by Deloitte (25).

Conclusion

The technological strides in connectivity, data gathering, processing, and analysis could be used in all areas of field services. The capability of IoT sensors in collecting real-time data and the predictive power of AI can be utilized to shift from a pre-planned maintenance to a predictive maintenance strategy.

The power of AR/VR technologies could help bridge the physical world to the digital world and facilitate seamless data integration with the field. By integrating with AI, these immersive technologies could be used to enhance operational efficiency, train technicians and foster customer self-service. Also, various AI-powered tools such as chatbots and virtual assistants can be utilized to assist staff, field engineers and customers. This helps organisations to understand customer priorities and offer more personalized service.

The convergence of AI, IoT, AR and VR plays a crucial role in integrating the real-time situation data from the field with enterprise data. Even though there are challenges, with appropriate technology integration and effective AI strategy, organizations could evolve a data-driven collaborative environment to differentiate their business.

References

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