With SAP continuously investing in AI-based scheduling, SAP Field Service Management customers can leverage a rule engine that allows for fully automated planning of service activities. The advantages of AI-based scheduling are numerous, but it is important to also keep the consequences of optimization in mind.
AI-based scheduling ensures meticulous planning of service activities to technicians, allowing service coordinators to spend their time on higher value decisions. With the 2302 Release, SAP made the Policy Designer generally available, enabling service organizations to create their own planning policies based on their unique processes. With a roadmap full of interesting features, SAP Field Service Management is continuously developing their AI-based scheduling, but what are the advantages and disadvantages?
The advantages of AI-based scheduling are somewhat obvious. Service Coordinators should no longer spend time planning in detail. Instead service activities are planned automatically according to a planning policy designed to meet specific requirements. For example, service activities could be planned automatically to the best matching technician with the shortest route to the service site. The business case is crystal clear. Shorter driving time equals lower transportation costs and CO2 emissions and ensuring that the technician has the required skills minimizes mistakes and need for rebooking.
SAP Field Service Management offers many more rules and objectives that can affect how your service activities is automatically planned. However, despite the benefits of automated planning being indisputable, it is important to acknowledge that automated planning is cynical and follows the configured rules. For example, if a planning policy is configured to secure shortest driving distance it results in lower transportation costs and CO2 emissions. However, as the service activity is planned when it fits the route best, it might be at the expense of average repair time. If a planning policy is configured to plan jobs as fast as possible, technicians might experience longer driving time and consequently less realized work time.
If you fully commit to automatic planning and focus solely on increased efficiency and lower cost in the service organization, customer time preferences might be sidelined which could result in lower NPS scores. Nevertheless, it could allow schedulers to spend more time on customer service which potentially could result in higher scores overall.
To fully benefit from AI-based scheduling, it is important to reflect on how it initially might affect field service KPIs both positively and negatively as well as to plan how KPI goals should be met in the future.
