Staffing Domain

Overview#

A dispatch centre supervisor starts her Monday morning shift and checks the staffing forecast for the week. The Staffing domain has already run overnight: it shows that Thursday evening is predicted to be 23% above average call volume, with a projected gap of two agents during the 18:00 to 21:00 window. She arranges overtime now, four days out, rather than scrambling when the gap materialises mid-shift. The Staffing domain makes this possible by combining historical call patterns, time-of-day and day-of-week factors, and real-time call volume data into hourly staffing recommendations with shortage detection and severity scoring.

Key Features#

Call Volume Prediction: Forecast expected call volumes based on historical patterns, time of day, day of week, and seasonal trends to anticipate staffing demands before they arise.
Agent Level Recommendations: Generate recommended staffing levels with hourly breakdowns, ensuring adequate coverage throughout each shift and identifying periods that require additional resources.
Shortage Detection: Automatically identify upcoming staffing shortages by comparing predicted call volumes against scheduled agent availability, with severity classifications to prioritise response.
Contributing Factor Analysis: Understand what drives staffing predictions by reviewing the contributing factors behind each forecast, including historical trends, special events, and weather conditions.
Severity Assessment: Classify predicted staffing issues by severity level so that managers can focus attention on the most impactful gaps first.
Real-Time Adjustment: Update predictions in real time as actual call volumes and agent availability change throughout the day.

Use Cases#

Predictive staffing is most valuable in high-volume, time-sensitive environments where under-staffing has direct operational consequences. Key industries include public safety and emergency services, healthcare, and financial services operations.

Shift Planning: Use staffing predictions to plan shift schedules that align with expected call volumes, reducing both understaffing and overstaffing.
Overtime Management: Identify upcoming shortages early enough to arrange overtime coverage rather than scrambling during peak periods.
Performance Optimisation: Maintain target service levels by ensuring the right number of agents are available during each hour of operation.
Budget Planning: Use historical staffing data and predictions to inform staffing budgets and hiring plans.

Integration#

The Staffing domain connects with operational management across the platform:

Workforce Management: Staffing predictions inform schedule creation and adjustment
Alert System: Shortage detections trigger notifications to supervisors
Analytics: Staffing metrics contribute to operational performance dashboards
Reporting: Historical staffing data supports management reporting

Open Standards#

GraphQL (June 2018 specification): All staffing prediction queries and responses are exposed through a typed GraphQL schema, enabling clients to request precisely the forecast fields they need.
OAuth 2.0 (RFC 6749) and Bearer Token Usage (RFC 6750): Access to staffing predictions is gated behind JWT bearer tokens validated against a JWKS endpoint, following the OAuth 2.0 authorisation framework.
JSON Web Token (RFC 7519): Session identity and permission claims are carried in signed JWTs, which the staffing schema layer verifies before allowing any forecast query to proceed.
ISO 8601 / RFC 3339 timestamps: All prediction timestamps and hourly-breakdown keys are expressed as UTC-anchored ISO 8601 datetime strings, ensuring unambiguous time representation across time zones.
ITU-T E.501 / Erlang-C teletraffic model: Recommended agent counts are derived using the classical Erlang-C formula: traffic intensity is computed from average handle time, and a target agent occupancy of 80 % is applied to size the workforce for acceptable queue performance.
JSON (RFC 8259): Forecast payloads, hourly volume breakdowns, and contributing-factor lists are serialised as standard JSON for interoperability with workforce management and reporting systems.

Last Reviewed: 2026-02-09 Last Updated: 2026-04-14