AI-Based Scheduling in SAP Field Service Management
Throughout the remainder of 2023, I will continue my blog series that discusses the core features of SAP Field Service Management. In this blog, I will discuss the hot topic that is AI-Based Scheduling and some of the exciting features the solution offers. These blogs/videos reflect the updated features we have published on the Field Service Management website. See the existing blog series below:
- Digitized Field Service Management – Deliver Maintenance & Service Precision
- Crowd Services – Expand Your Talent Pool with SAP Field Service Management
AI-Based Schedule Optimization
AI-based scheduling optimization is a topic that many maintenance and service organizations depend on to optimize their operations, increase their productivity, and customer satisfaction. So, how exactly do we make use AI-Based Scheduling and what are the benefits? SAP Field Service Management (FSM) optimizes schedules by using advanced algorithms to analyze vast amounts of data and variables, such as job priority, technician skill sets, location, availability, and customer preferences. This technology enables automated decision-making, ensuring the right technician is dispatched to the right job at the right time, and prioritizing urgent tasks to minimize downtime and deliver maintenance/service precision. By automating the scheduling process, AI reduces the risk of human error, enhances operational efficiency, and increases productivity by allowing field service teams to complete more jobs in less time. Moreover, AI-based scheduling can also make use of predictive traffic routing functionality, to ensure that resources are utilized effectively, reducing unnecessary travel time and fuel emissions/costs. In the remainder of this blog, we will discuss some of the key features that bring our AI-based scheduling solution to life.
Fully automated scheduling in SAP Field Service Management is a game changer for businesses looking to streamline their field service operations. With the ability to define triggers for automated scheduling on predefined periods, companies can ensure that their resources are being utilized to their fullest potential. This means that tasks can be automatically assigned to the most appropriate field technician based on factors such as skill level, location, and availability. Additionally, the system can take into account customer preferences and SLAs, ensuring that service appointments are scheduled at the optimal time for both the customer and the technician. With automated scheduling, businesses can quickly adapt to changes in demand and respond to urgent requests with ease. By reducing the need for manual intervention, errors and delays are minimized, allowing for faster response times and increased customer satisfaction. Customers can also define specifics to how the work is scheduled – i.e. it automatically released, how is the technician or customer notififed, are certain checklists automatically linked, and so on. For more information on business rules, see here.
Best technician matching
Many customers also leverage “assisted planning” using our best matching technician algorithms. The best matching technician algorithm uses advanced analytics and artificial intelligence to optimize technician dispatch and scheduling. This algorithm considers factors such as technician skills, availability, job proximity, and service level agreements (SLAs) to determine the best match for a service request. In addition, the algorithm is highly adaptable and can be customized to suit the specific needs of different industries and maintenance/service types. This scenario is particularly useful when a dispatcher needs to make a quick decision, and can use the system to assist in the decision making. I will discuss the extensibility of the algorithm further in the next section of the blog – the Policy Designer.
The Policy Designer was introduced in the 2202 release of Field Service Management and allows customers to view, modify and create new policies for assisted or automated scheduling in a no-code designer to define company-specific requirements. With this intuitive and user-friendly tool, companies can easily create and manage their own custom policies, with little to no technical background. The policies created by the customer can be used in assisted planning scenarios i.e., the best matching technician algorithm as discussed above, or in fully-automated scheduling scenarios alike. Furthermore, the policies can be used in business rules, the planning widget, appointment booking API, and so on. The additional functionality mentioned in this section (for example, editing policies, creating new policies, etc.) requires the purchase of an additional license (SAP Field Service Management, Supplemental Services). An SAP Field Service Management license only entitles you to view policies.
There are two standard appointment booking options in SAP Field Service Management. The first uses the Customer Self-service Portal, which allows customers to schedule their own appointments using an intuitive self-service portal guided by a chatbot. The customer can also view service history, progress, as well as their equipment and history. On the other hand, many customers need to be able to schedule field service appointments from external solutions such as customer or commerce platforms using an open API. The appointment booking API from SAP Field Service Management is an interface that allows external applications to interact with the SAP Field Service Management system to schedule and manage appointments. Furthermore, using the API, customers have the option to leverage the AI-based scheduling policies created using the policy designer to ensure the best matching technician for the job will be selected.
In conclusion, AI-based scheduling in SAP Field Service Management is a game-changer for businesses looking to optimize their scheduling processes. By utilizing business rules, the Policy Designer, and appointment booking tools, companies can benefit from a tailored scheduling system that meets their unique needs. The best matching technician feature ensures that the most qualified employee is assigned to each job, allowing you to deliver service precision and improve equipment performance. Lastly, with fully automated scheduling, companies can save time and reduce the risk of human error, freeing up the schedulers to make higher value decisions where human intervention is required. Stay tuned as we continue to add more features and advanced AI-based policies and business rules to our SAP Field Service Management platform. We are also investing heavily in our AI-based optimiztion, see some of the exciting roadmap items below:
- Predictive traffic routing: support for more partners in predictive routing funtionality
- Draft optimization runs (simulations)
- Fully automated planning on the service map
- Prediction of activity duration using machine learning
Any thoughts or questions? Please share any feedback or thoughts in a comment. For related posts and more information, please join our SAP Field Service Management Community page as well as our SAP S/4HANA Cloud for Asset Management pages/communities and AI-Based Scheduling page. For more information, please also check out our SAP Field Service Management Website.