Workforce Planning & Staffing Optimization
“How many service hours will each location need over the next 7 days?”
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A real-world example
How many service hours will each location need over the next 7 days?
Overstaffing wastes 15–20% of labor budgets; understaffing leads to 2–3x overtime costs and degraded service quality. Most workforce planners rely on simple averages that miss event-driven spikes, seasonal patterns, and cross-location dependencies. When a convention comes to town or flu season peaks, the model that only sees last week's hours is already behind.
How KumoRFM solves this
Relational intelligence for every forecast
Kumo connects locations to appointments, staff rosters, local event calendars, and seasonal patterns in a unified relational graph. Instead of treating each location as an independent time series, Kumo learns that Location L-05 shares a region with L-12 and both spike during trade-show weeks, that staff role mix affects appointment duration, and that holiday periods shift demand predictably. These cross-entity signals produce staffing forecasts that anticipate spikes before they hit.
From data to predictions
See the full pipeline in action
Connect your tables, write a PQL query, and get predictions with built-in explainability — all in minutes, not months.
Your data
The relational tables Kumo learns from
LOCATIONS
| location_id | location_name | region | type |
|---|---|---|---|
| L-05 | Downtown Clinic | Metro | primary |
| L-12 | Westside Branch | Metro | satellite |
| L-28 | Harbor Office | Coastal | primary |
APPOINTMENTS
| appt_id | location_id | staff_id | duration_hours | timestamp |
|---|---|---|---|---|
| APT-4001 | L-05 | EMP-110 | 1.5 | 2025-09-15 |
| APT-4002 | L-05 | EMP-115 | 2.0 | 2025-09-15 |
| APT-4003 | L-12 | EMP-220 | 1.0 | 2025-09-16 |
STAFF
| staff_id | name | role | location_id |
|---|---|---|---|
| EMP-110 | Sarah Chen | Senior Clinician | L-05 |
| EMP-115 | Marcus Rivera | Clinician | L-05 |
| EMP-220 | Priya Sharma | Clinician | L-12 |
Write your PQL query
Describe what to predict in 2–3 lines — Kumo handles the rest
PREDICT SUM(APPOINTMENTS.DURATION_HOURS, 0, 7, days) FOR EACH LOCATIONS.LOCATION_ID
Prediction output
Every entity gets a score, updated continuously
| LOCATION_ID | TIMESTAMP | TARGET_PRED |
|---|---|---|
| L-05 | 2025-09-22 | 342 |
| L-12 | 2025-09-22 | 128 |
| L-28 | 2025-09-22 | 510 |
Understand why
Every prediction includes feature attributions — no black boxes
Location L-05 (Downtown Clinic)
Predicted: 342 service hours needed in next 7 days
Top contributing features
Historical booking trend (4w)
+12%
28% attribution
Local events (trade show)
Active
24% attribution
Seasonal pattern (fall intake)
Peak
21% attribution
Staff utilization rate
87%
15% attribution
Appointment complexity mix
High
12% attribution
Feature attributions are computed automatically for every prediction. No separate tooling required. Learn more about Kumo explainability
PQL Documentation
Learn the Predictive Query Language — SQL-like syntax for defining any prediction task in 2–3 lines.
Python SDK
Integrate Kumo predictions into your pipelines. Train, evaluate, and deploy models programmatically.
Explainability Docs
Understand feature attributions, model evaluation metrics, and how to build trust with stakeholders.
Bottom line: Reduce overtime costs by 30% and improve service quality by matching staffing levels to actual predicted demand — not last month's average.
Related use cases
Explore more forecasting use cases
Topics covered
One Platform. One Model. Predict Instantly.
KumoRFM
Relational Foundation Model
Turn structured relational data into predictions in seconds. KumoRFM delivers zero-shot predictions that rival months of traditional data science. No training, feature engineering, or infrastructure required. Just connect your data and start predicting.
For critical use cases, fine-tune KumoRFM on your data using the Kumo platform and Data Science Agent for 30%+ higher accuracy than traditional models.
Book a demo and get a free trial of the full platform: data science agent, fine-tune capabilities, and forward-deployed engineer support.
