Matching staffing to demand across a frontline network

A large network of complex clinics undertaking critical health care work was carrying excess labour cost. Staffing levels and opening hours were often misaligned with patient demand, and unplanned closures, overtime and idle capacity were eroding margin across the frontline labour base.

The business had limited visibility of supply against demand, which made planning difficult. A rigid workforce mix, majority permanent staff, left little flexible cover for demand peaks and absences.

We modelled the marginal economics and productivity of the network using service, timecard, roster and payroll data, then sized the opportunity across multiple distinct levers, from overstaffing to unplanned closures.

We surveyed frontline staff on workload and scheduling to better align the employee value proposition, and benchmarked opening hours against rival networks. Overstaffing and misaligned opening hours emerged as the two largest levers.

We then extended the model into a supply-and-demand database and forecasting tool, and developed the code base to implement it, covering demand forecasting, an optimal staffing strategy and employee availability. We recommended a lower permanent-staff share, to balance flexibility and cost using roaming, on-call and casual collectors.