Workforce Planning
Why this use case matters
Optimizing seasonal staffing and resourcing is a crucial challenge for utilities, especially when the exact timing of high-volume activity can change based on complex factors. Augmenting the workforce too early means diverting profit to unnecessary personnel costs while waiting until high-volume demand is already underway, risking operating below capacity and decreasing customer satisfaction (e.g., if there are long wait times).
Techniques
Supervised Learning – regression, classification, time-series
Algorithms
Linear Regression, Tree-Based Models (XGBOOST, LightGBM, Random Forest), Neural Nets, ARIMAX, SARIMA, Holt-Winters, LSTM, Prophet, DeepAR, N-BEATS, Temporal Fusion Transformer
Outcome
Predictive workforce insights allow utilities to proactively plan for peak demand events well before a spike, allowing organizations to manage their workforce to meet customer expectations while maintaining necessary slack in the schedule. Happy employees are an absolute must in a tight labor economy.
A utility operator in a chilly climate came to Mosaic with a request to build a model that predicts when customers will reactivate or service their heating systems.
Mosaic has compiled our industry expertise into a Machine Learning playbook for Utilities.
