Mathematical Optimization Aligns Every Shift to Forecasted Patient Demand
The Client:
A multi-department healthcare system where staffing decisions were made manually by department managers, often in conflict with each other and with patient volume realities.
The Engagement:
SSI deployed an OR-based scheduling optimization engine as part of a broader workforce platform taking MLP demand forecasts as input and generating shift plans that minimize staffing variance while respecting credentials, compliance, and operational constraints.
The Challenge:
Even when demand forecasts were available, translating them into actual shift plans required a skilled scheduler with deep knowledge of each department’s rules, staff credentials, and compliance requirements. This translation was slow and inconsistent.
Cross-department coordination created additional complexity: decisions made in one department would create constraints or conflicts for others. There was no unified model that could optimize across the system simultaneously.
Manual scheduling also made it difficult to test ‘what-if’ scenarios evaluating the impact of a new policy or an unexpected demand surge required rebuilding the entire roster from scratch.
The Solution:
SSI’s optimization engine ingests demand forecasts from the MLP model and applies OR-based mathematical optimization to generate shift plans. The model enforces hard constraints credentials, compliance rules, role requirements while minimizing the soft-cost of staffing variance.
The system operates across departments simultaneously, ensuring that cross-department trade-offs are resolved at the model level rather than through manual negotiation between managers.
Scenario modeling is built in: operations teams can adjust demand assumptions, add constraints, or model the impact of policy changes and see the optimized schedule output in real time.
The Impact:
Scheduling administrative time dropped by 50% as the system automated the translation from forecast to roster.
Staffing variance; the gap between scheduled staff and actual demand fell meaningfully, reducing both under-staffing risk and unnecessary labor spend.
