Demand and Capacity Planning Upgrade for a UK Logistics Operator

Business Context

The organisation managed daily routing and capacity planning across multiple depots. Demand peaks were hard to predict with confidence, and planning teams often adjusted staffing and lane allocation reactively.

Core Challenges

1. Forecasting inconsistency by region and service type.
2. Over-allocation and under-allocation cycles in the same week.
3. Limited cross-team visibility into forecast confidence.

Delivery Approach

1. Demand signal fusion from order intake, seasonal factors, and route history.
2. Forecast confidence scoring surfaced in planning dashboards.
3. Decision workflows for lane-level capacity adjustments and escalation.

Implementation Timeline

• Weeks 1-2: KPI baseline and planning-process diagnostics.
• Weeks 3-7: Forecast model build and validation by region.
• Weeks 8-10: Dashboard and planning workflow integration.
• Weeks 11-13: Pilot adoption, planner training, and model tuning.

Operational Outcomes

Planning teams made earlier and more consistent adjustments, reducing last-minute interventions. Leadership gained clearer transparency on demand volatility and confidence levels by lane and depot.

Next-Phase Roadmap

Planned enhancements include dynamic pricing signals and automated staffing recommendations by shift profile.