The rise in customer expectations is continuously increasing the complexities in service delivery. Effective planning of resources is the key to optimizing field services and increasing productivity.
Forecasting service issues and proactively planning the allocation of field technicians is a daunting task for most providers. Their most common obstacles were inadequate resource management, poor visibility into field service activities, and a reactive approach to dispatching.
There is a dire need for service providers to move towards intelligent capacity planning, which helps with productive dispatches and effective usage of resources.
20%reduction in resource wastage
72%reduction in issue resolution time
Our client, a leading service provider in North America, faced a similar challenge. Inefficient management of field service operations and field technicians led to resource wastage and overheads.
A lack of end-to-end visibility into field service requests and factors like weather, holidays, and maintenance impacted their capacity planning. Beyond all, capacity planning and forecasting were challenging as they depended on legacy systems that relied on human intelligence.
The challenges mentioned above led to missed SLAs, order fallouts, high OpEx, and customer dissatisfaction.
Hence, the client was looking for a partner who could address their problems.
Prediction of demands in real-time resulted in a huge competitive advantage.
Our client lacked the appropriate tools to provide centralized access to plan a technician’s schedule and forecast natural calamities. In addition, legacy systems and mergers and acquisitions over time led to data silos hindering their efforts to build a centralized prediction system. They manually estimated field technician staffing and their work hours, which led to inadvertent errors and over/under-staffing.
We recommended an AI-powered capacity planning framework to enhance their resource utilization and reduce costs based on our diagnosis.
We implemented the AI-based framework to predict field service capacity for a given planning window correctly. We further augmented the framework with an ML-model to consider the key factors that impact capacity planning (weather, holidays, seasonal, and maintenance data) and dynamically recalculate the predicted capacity.
We also provided a business intelligence dashboard that enabled the operations team to make smarter decisions and improve the capacity planning process.