For companies deploying workers for hands-on tasks, SAP Field Service Management has become an essential solution for scheduling services, managing parts, and providing better service to customers. Now, with SAP’s Joule AI embedded in the platform, those services can be further optimized.
Friederike Mundt and Ryan Jones, Product Marketing Managers at SAP, recently spoke at an ASUG webcast, "The Road Ahead: SAP Field Service Management, SAP Business AI, and Beyond," to share how the platform’s AI-powered tools function and to showcase demonstrations of the technology in action.
Inside SAP Field Service Management
SAP Field Service Management is closely integrated with the SAP Cloud ERP and encompasses both low-code and no-code solutions that help users with scheduling and dispatching, mobile workforce enablement, analytics, and more.
While some SAP customers are still working from paper-based systems, others have gone digital and are embracing AI, uncovering in the process that data-driven planning brings substantial ROI, according to Mundt and Jones.
In fact, automations have already been found to improve dispatcher productivity by about 50% and reduce errors by about 8%, they explained. One wholesale distribution company saved 40 metric tons of carbon emissions per year due to better routing and fuel use, as well as 13 minutes of unbilled travel time per hour, and 2 to 5 minutes while scheduling each job.
The potential value for companies — and their customers — is huge, Jones said. Coming up, fully autonomous scheduling will help companies build custom rules with company policies to make complex schedules come together even more quickly.
Joule for SAP Field Service Management
Today, there are AI features already available in SAP Field Service Management via Joule. These include:
- Joule for Dispatchers, which boosts dispatcher efficiency and provides easy access to help documentation.
- Intelligent filtering, which uses natural language processing for intuitive and accurate job search.
- Equipment insights, which provide information on past equipment performance to help with proactive maintenance and avoid downtime.
- Activity summaries, which help technicians quickly see how similar issues were resolved in the past, helping with repair planning and efficiency.
- Predictive routing, which assists in mapping the best routes to reduce travel times and save on carbon emissions.
- Job prediction duration, which uses machine learning to help plan schedules.
- AI policy designer and auto-scheduling, which helps design and simulate complex company policies for scheduling.
- Planning simulations, which enhance transparency for dispatchers.
Dispatchers using Joule for scheduling service can use 50 available out-of-the-box rules, or they can create customized rules, like requiring specific skills for certain job types or set times for service windows.
They can also view each job’s score, via a rating system that helps to quantify the quality of a schedule. The scores are helpful in testing out schedules; without publishing anything, dispatchers can evaluate how a small change to their automation’s inputs can affect the desired objective.
Customers have been asking for more capabilities in Joule, Jones said. SAP has responded by embedding natural language processing in the platform; this is intended to support dispatchers with scheduling and supply technicians with the information they need on the job. For example, if a dispatcher asks how to create a custom policy for schedules, Joule will respond with the instructions for doing so, along with SAP Help documentation, linking to where this information lives online for further context and validation.
Technicians can query Joule based on various factors like priority of job, geography, and equipment type, prompting an option for scheduling, along with alternatives.
These recent improvements to AI capabilities in SAP Field Service Management have been based on customer feedback, the presenters explained. “We are still listening to our customers and gathering every month their feedback to know which direction to go,” Mundt said.
Companies using SAP Field Service Management requested generative AI summaries for overviews of data and activity summaries for specific jobs, such as how the technician fixed the problem and the overall status of equipment on site. Technicians now have access to those summaries, so they can get more information about the history of an upcoming project.
SAP is additionally planning a release of Dispatcher Agent via Joule in Q1 2025. This update aims to autonomously schedule all jobs fitting set criteria, such as a schedule for a week that considers company policies around skills and distance. A dispatcher can view what Joule shares, accept and discard as needed, then release the schedule.
Jones emphasized that after an AI-assisted schedule is created, there still needs to be human approval and tweaks made.
Getting Started
To begin taking advantage of these AI features, Mundt advised starting with use cases that have the biggest, most easily measured business impact, and to focus on repetitive or time-critical processes. Then, Mundt recommended leveraging existing data and choosing pilot programs with measurable outcomes.
Mundt also noted it’s important to engage with end users to build trust. This is the most challenging part of implementation, she said, as there are many skeptical of AI, from people concerned AI will ultimately reduce headcount to those worried it will risk the security of enterprise data. “Here, it’s really all about communicating transparently to all the stakeholders early and sharing what your company is planning," Mundt said. Everybody should understand what the technology is intended to achieve and how to use it toward those specific aims.
Additionally, companies should keep in mind that any new technology must be aligned with human oversight to achieve the desired results. AI is designed to improve productivity, not to eliminate positions, she said. Moreover, employee satisfaction can improve through leveraging this technology, eliminating highly manual and repetitive processes such as report creation and data entry.
Ultimately, working with AI now is going to benefit companies through what Mundt referred to as “return on future,” or ROF. AI is here to stay, and many companies are already using it and preparing for whatever comes next. “The earlier you start, the more ahead you will be as a company,” she said.
For more, watch the webcast replay.