Artificial Intelligence Is The Future Of Field Service Management

Source: KloudGin

Introduction

Artificial intelligence (AI) and machine learning (ML) are changing the way businesses operate through streamlining tasks and optimizing resource utilization. The technologies do so by rapidly analyzing disparate data sets and presenting the best possible solutions to end users in order to help them make faster, better decisions.

The use of AI and ML is increasingly important in field service operations as well, as manual processes cannot keep up with these accelerating performance demands. Field service organizations face many challenges, including their need to service increasingly complex, connected equipment; increased pressure to improve service levels and meet strict performance guidelines; and an acute staffing shortage as firms struggle to recruit new technicians while large numbers of older staff retire, taking institutional knowledge with them.

AI and ML can support these organizations by automating and optimizing routing and scheduling operations, enhancing maintenance processes, streamlining customer interactions, and improving predictive maintenance initiatives. In addition, solutions that combine enterprise asset management and field service management applications and data can make it easier for these organizations to more quickly identify service issues, assign qualified technicians, and provide the information those technicians need to perform their jobs more quickly, accurately, and efficiently.

Artificial intelligence is the future of field service management, and forward-looking organizations will need to invest in the technology in order to remain competitive in an increasingly challenging environment.

How AI Improves Field Service

Field service organizations manage dozens or hundreds of technicians with varying skill levels, experience, and expertise. In many cases, these technicians have to manually access different applications for work order management, timekeeping, or location services. They may even have to manage their work using paper-based forms or processes. In the back office, schedulers and dispatchers have to respond in real-time to new calls and scheduling changes that create chaos in the field, increase inefficiencies, and degrade customer service.

AI-based solutions incorporate a wide variety of data points – location information, traffic data, customer history, technician experience, maintenance data, workflow models, etc. – to automate and optimize many of these manual tasks. This has the potential to improve operations in a number of ways.

Simplify Tasks: AI-based systems can automatically match work orders, technicians, and routes to maximize efficiency in the field and reduce manual processes for the back office.

Reduce Errors: Given the volume and complexity of work, manual processes inevitably introduce errors into the system. AI can reduce or eliminate human errors across many administrative tasks, from scheduling right down to technicians forgetting to start their shift in the FSM application. AI-based solutions are able to base scheduling decisions on job priority, technician availability, inventory, and service-level agreement requirements quickly and accurately. AI-based guidance during the service delivery process also helps staff to better record customer requirements, preferences and maintenance information, and communicate them across the organization.

Improve the Customer Experience: With automation in place, organizations can provide enhanced services such as online scheduling, automatic work order updates, and automated customer assistance tools like chatbots that leverage maintenance data and customer histories. AI enables 24/7 help desk services and can help manage self-service portals that provide greater customer control and satisfaction.

Enable Intelligent Field Service Delivery: Knowledge sharing is an ongoing challenge for many companies. With AI-based solutions, technicians can be provided with intelligent work order management and service planning based on previous service records, data from connected equipment, and customer preferences. Businesses will increasingly leverage machine learning to assimilate data from their daily operations and interactions to optimize future actions and outcomes.

Improve Predictive/Preventive Maintenance: Existing predictive maintenance initiatives are often based on technician input and recommended maintenance schedules from manufacturers. AI-based solutions can more accurately predict future maintenance requirements and recommend tasks based on actual equipment histories, without manual processes.

Improve Productivity: AI-based field service management can increase efficiency and productivity across the organization by streamlining tasks, improving resource and staff utilization, improving communication with both technicians and customers (via AI-powered remote assistance), and automating routing activities, while enabling scheduling adjustments in real time.

These benefits not only save time and reduce operating costs, but improve service outcomes and customer satisfaction.

Don’t Fear the AI: Addressing Common Concerns

For companies that have not yet had any experience with AI or ML-based solutions, getting started can be a daunting proposition. There are costs and challenges associated with deploying these solutions, and staff may be wary of turning over critical tasks to an automated solution. However, tried-and-true AI solutions can quickly prove their worth. Taking an open and well-planned approach to deployment can ensure successful adoption.

These AI solutions need data in order to successfully improve operations. Gathering and preparing that data may sound like an enormous challenge, there are a number of ways this can be simplified. For example, solutions like KloudGin can easily integrate with existing customer data sources, and also offer some standard data sets based on industry and roles so that companies do not have to start from ground zero when they initially deploy the solution. And the system performance improves quickly over time as company-specific data and work orders are incorporated. AI-based solutions continuously improve as they are used.

Trust is another common hurdle to deployment – experienced technicians, schedulers, and dispatchers may be dubious of system performance, or worry that their jobs will be rendered redundant. Communication is key here, as managers should explain that AI-based solutions are meant to provide best-case recommendations – employees are still required to evaluate those recommendations based on their own knowledge and experience. AI simply automates repetitive, manual tasks and gives staff more time to do their jobs more effectively. Organizations should also involve staff in testing the solution, so they can see how and why the AI solution made the recommendations that it did, and how it can provide the benefits listed above. Trust is critical.

Real World AI Utilization

How are field service organizations taking advantage of AI and ML in their operations today? Here are a few examples of how the technology is supporting improvements in the field and accelerating service delivery:

Job Scheduling and Dispatch: AI-based field service software can automatically guide dispatchers and customers through the questions needed to help determine what service is required, which technician is the best fit for the assignment, and what inventory and tools will be necessary to complete the job. From there, automated scheduling tools can handle easier and repetitive tasks associated with building the schedule, which frees up staff to focus on more challenging work assignments.

In addition, leveraging the type of analysis made possible with artificial intelligence can help companies predict the likely success of a given service job based on a variety of factors – this can help teams achieve higher first-time fix rates.

Predictive Maintenance and Management: AI-based solutions can enable the more proactive approach to service that many organizations aim for, but struggle to achieve. Using historical equipment performance and usage and repair data, companies can make more accurate predictions about when service will be required, before there is a failure that results in costly downtime. This ultimately makes scheduling easier and more predictable as well, and can reduce the need for expensive truck rolls.

Knowledge Base Management: As large numbers of older technicians retire, companies are faced with the challenge of training and managing a much younger, less experienced service team. AI tools have a unique ability to analyze and manage both visual and unstructured data in ways that can help support these technicians with enhanced troubleshooting and problem-solving. Technology such as Natural Language Processing can help technicians with intelligent searches of disparate service records. Computer vision functionality allows technicians to take photos of parts and equipment to visually search for repair or installation guides.

The KloudGin platform has helped companies in a variety of industries automate and optimize their field service operations using these approaches. In the following case studies, you can see how leveraging an AI-based solution has provided quantifiable benefits when it comes to efficiency and cost savings.

Case Study Examples

Cal Water Modernizes Field Operations

California Water Services (Cal Water) used to rely on paper-based processes for their field technicians to fulfill work orders, combined with multiple applications that required manual data entry and different logins and interfaces. The company wanted to streamline these processes while providing a better online experience for customer scheduling, and reduce hardware and software costs.

Using KloudGin’s integrated Field Service and Asset Management Suite (which incorporates artificial intelligence and machine learning), the company now has a single work order management system. The KloudGin Connected Customer solution enabled customers to receive real-time updates about service appointments, and enabled compliance reporting and analytics in a cloud-based platform. The systems integrate directly with Cal Water’s Oracle CC&B system and ESRI GIS system, along with Microsoft Active Directory for single sign-on.

As a result, the company increased workforce productivity and confirmed customer appointments by 25%, was able to handle a 25% increase in work order volume, and saved nearly $650,000 annually.

Hawaiian Telcom Increases Field Productivity

Hawaiian Telcom manages field service orders and large projects, including fiber optic cable installation, and employs a large field technician workforce. Manual management of paper-based and electronic work orders created a number of challenges, including technicians having to call dispatch for new assignments or schedule changes; multiple workflows to manage work orders; and a lack of automation around financial operations and timekeeping.

With the KloudGin Mobile Field Service & Management Suite, which leverages machine learning to automate scheduling and dispatch operations, the company now has an integrated approach for creating work orders and dispatching them to technician mobile devices while automatically tracking completion, labor hours, and inventory.

The solution has eliminated the need for phone calls to dispatchers, and increased efficiency in the field and in the office. Repair time has improved by 10-15% and completion rates have increased from 80% to 87.3%. At the same time, average hours-per-install fell from 4.2 hours to 3.96, and routing inefficiencies have been eliminated.

Conclusion

Field service management is rapidly evolving away from manual processes to advanced, automated, and technology-enabled operations. AI and ML technologies can make field service activities smarter, more automated, and intelligent by providing increased field visibility, and offering both technicians and clients live information when and where they need it.

Because field service organizations will continue to face mounting workloads, more complex repair processes, and a tight labor market, the automating and intelligence enabled by AI-based field service management solutions can provide the efficiency, accuracy, and reliability needed to maintain customer service levels, increase retention, and improve competitiveness and profitability.

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About TimeLinx
TimeLinx delivers innovative project & service management software as a complete solution that perfects the sell-track-manage-support-bill cycle that services organizations must have to delight their customers; TimeLinx brings the cycle together in a single application that offers less frustration, better project management, complete reporting, and improved profitability – all specially designed for Infor and Sage.

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