Case Study: Siemens achieves higher workforce productivity and reduced travel costs with ServicePower's AI Schedule Optimization

Siemens - Customer Case Study

Siemens, a global energy leader, tasked over 225 engineers, field technicians and meter readers (managed by 40+ supervisors) with installation, repair, maintenance and onsite problem resolution for meter operations. To support continuous improvement, Siemens needed to automate job scheduling and dispatch, raise first-time fix rates, maximize workforce productivity, and reduce travel time, costs and emissions.

Siemens implemented ServicePower’s Employed Workforce Schedule Optimization, an AI-driven, real-time scheduling solution that adapts to traffic, weather and workforce availability. The system now manages about 1,500 jobs per day (≈400,000 annually), increasing daily job capacity and technician productivity, improving first-time fix rates, lowering travel costs and carbon emissions, and boosting customer satisfaction and employee engagement.

Siemens

Jack Bradshaw

Pre-Job Process