Leverage AI-powered capacity planning to modernize field services
Most service providers face challenges in planning and allocating field technicians based on the demand vs capacity. According to Gartner, “Balancing available resources against the demand for those resources is essential to successful initiative completion“. Inefficient capacity planning often leads to over-staffing or under-staffing of field technicians. This further results in order fallouts and dissatisfied customers. The most common sources of dysfunction are:
- Unavailability of tools to estimate capacity in real-time
- Lack of strategy to identify the key influencing factors that impact the capacity planning process
- Lack of mechanisms to assign the right technician for the right service
- No end-to-end visibility into field service capacity
According to Gartner, “Balancing available resources against the demand for those resources is essential to successful initiative completion“.
To overcome these challenges and handle the diverse field data, service providers in the connectedness industry should move towards intelligent capacity planning, which helps in the real-time mapping of dispatches and the optimal usage of resources. Leveraging an AI-powered capacity planning framework helps the service providers to reduce resource wastage by 20% and improve the effectiveness of service response and customer satisfaction. Enterprise AI can, over time, improve the prediction of field technician work hours by considering the key factors such as weather, season and maintenance data.
Fig: Leverage AI-powered capacity planning framework for real-time field tech resource management