Advanced forecasting improves operational planning at major field services company
In field services, the basic need of companies is to improve the monitoring of their staff. Properly utilizing technicians across all regional offices in the US is a major challenge for field service companies. To improve workforce planning, it’s necessary to generate more accurate demand and labor forecasts. Our customer wanted to improve operational planning for day-to-day activities and implement an advanced “what –if” analysis to help planning and risk management.
Neal Analytics helped the customer by developing custom advanced forecasting models. The team analyzed ten years of historic demand data across the multiple service lines. Neal Analytics also created a data pipeline that enables data movement from one system to another using automatic updates in Azure (Synapse Analytics, SQL Server, and Power BI).
By leveraging forecast models, the field service company improved its demand and labor forecast accuracy by over 5%. The solution enabled deep-dive analysis that helped the customer examine closely the service regions, job types, and time of year. Neal Analytics also implemented an interactive “what-if” analysis through the Power BI interface that helped them mitigate risk and plan operations accordingly.