A day in the life: The AI enabled field service organization
Field service organizations have historically run under a reactive, break-fix operating model, only able to respond to device failures after customers report a problem. Modern field service organizations need new ways to make sense of available information to enable their people and take early action to address customer needs.
Intelligent, predictive systems driven by artificial intelligence (AI) can do just that. They can automate manual, time-consuming tasks like collecting data, diagnosing problems, and identifying the best solutions to problems. AI optimizes resource management, empowers field teams through mixed reality and mobilization, and improves customer service with proactive and predictive service.
This post will look at how AI-enabled solutions, like Microsoft Dynamics 365 for Field Service, can enhance every part of a field service organization’s team and transform the customer experience.
Optimized resource management
Effectively managing resources, such as inventory and technician time, is at the heart of the challenge for any field service organization, creating a powerful competitive advantage when done well.
In a traditional field service organization, a technician would be dispatched based on availability, not proximity or experience with the specific failure. She may arrive on-site with limited access to customer information and device history, reducing her ability to complete the repair on a first visit and driving up overall costs.
Let’s take a simple example of a manufacturing customer experiencing a boiler failure. When a work order is scheduled in an intelligent system, assignments are optimized using multiple factors, such as a technician’s experience in handling the specific failure, the customer’s preferred technician, or proximity to the site. Leveraging machine learning, this intelligent system could automatically assign the work order to the best available personnel.
As more requests come in, the system would optimize the technician’s schedule to create the most efficient travel path, allowing her to perform additional calls per day. Real-time inventory management ensures that the replacement part is available on the scheduled date and that the technician has access to the tools needed to complete the job. It can identify the best parts to replace, where they should be purchased, and provide more accurate lead time predictions for the organization.
Empowered field teams
To be most effective and prevent costly return visits, technicians must have full access to the information and real-time guidance they need.
In our boiler repair example above, the technician could utilize a digital twin of the device to learn about its status and operating condition, and to train on the particular problem she will fix. AI’s cognitive capabilities can even help optimize repairs before a technician arrives, taking care of routine diagnostics and testing for common or similar issues. These capabilities ensure the technician is better prepared for the work and that her time—and the customer’s—is used efficiently.
On-site, the technician’s service app can highlight the top 2-3 things that might be wrong with the asset. Mixed reality tools, like HoloLens, create 3D renderings overlaid directly onto the boiler, highlighting missing or broken parts and allowing the technician to view performance data. AI can use data from predictive analytics to make just-in-time recommendations as she works. Her headset can identify irregularities and help her focus on the right issues without having to stop and troubleshoot, ensuring work is completed correctly the first time.
Improved customer experience
Field service customers need stability and reliability in their businesses. They want visibility into their assets and want minimized downtimes when breakdowns do occur.
Prior to AI-empowered systems, the customer in the boiler example would have to actively reach out to report that the device had failed. Depending on the type of failure, work using the boiler could be slowed or blocked for days or weeks until a technician could complete the repairs. Without intelligent support, the technician may need to return for follow-up visits, unnecessarily wasting both time and money.
AI allows for automated, remote self-healing and predictive forecasting, monitoring and analyzing connected devices for potential issues. If one is identified, the system can remotely attempt to resolve problems through self-healing processes, like having the boiler restart itself during an off-time to mitigate an overheating failure. Using historical device data and predictive analytics, the system might make a recommendation to schedule a technician site visit to head off future problems, notifying the customer of the work order. The customer could then plan around the scheduled downtime and even track the technician’s arrival to the appointment in real-time.
During the visit, using recommendations from the system, the technician would be able to discuss additional products and services with the customer that meet their specific usage and operating needs. The customer gains more control over their assets, and the field service organization is empowered to provide improved service delivery capabilities.
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AI enables field service organizations to break away from the break-fix model by empowering technicians, optimizing resources, and improving customer experiences.
Microsoft offers a unique combination of world class IoT, intelligence, scalability, and end-to-end field service capabilities. Learn how Microsoft Dynamics 365 for Field Service can transform your service organization today.
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