Problem Statement
The national postal and parcel network faced rapidly shifting demand patterns driven by e-commerce growth, promotional spikes, and regional seasonality. Capacity planning decisions—adding sortation centres, rebalancing fleet assets, adjusting workforce rosters, redesigning routes, or changing transport modes—were made with static heuristics. Without a system-level simulation, planners could not quantify how infrastructure or operational changes would influence delivery KPIs (SLA adherence, throughput, cost per parcel), resulting in reactive interventions and inefficient capital allocation.
Solution
We delivered a hierarchical “twin of twins” network simulation that models demand, hub processing, route performance, and parcel mix as interoperable digital twins. The platform evaluates long-term planning scenarios and optimises recommendations for SLA reliability, cost efficiency, and utilisation under varying demand forecasts.
Model composition
- Regional demand twin: Stochastic forecast combining historical parcel volumes, marketing calendars, demographic trends, and macroeconomic indicators to anticipate inbound/outbound flows.
- Hub processing twin: Discrete-event model of sortation centres capturing queue dynamics, workforce schedules, shift windows, and equipment constraints.
- Route performance twin: Transit variability model incorporating traffic, weather, intermodal transfers, and vehicle capacity to predict travel times and reliability.
- Parcel mix twin: Product-level distribution (priority, economy, bulk) with handling rules that influence routing and capacity consumption.
An orchestration layer synchronises state changes across twins and enforces physical/operational constraints. Scenario simulations test interventions such as opening new regional hubs, reallocating trucks, expanding last-mile workforce, or shifting load to rail vs. road.
Planning workflow
- Planners configured strategic scenarios with budget envelopes and timeline constraints.
- Simulation runs generated KPI projections (SLA adherence, utilisation, capex/opex) over multi-year horizons.
- Decision dashboards ranked intervention bundles by marginal impact per unit cost while highlighting risk factors (fleet availability, labour saturation).
Outcomes
Running interventions through the network twin of twins gave planners the evidence needed to move from reactive fixes to orchestrated execution. Pilot deployments delivered a 9.4% uplift in SLA adherence during peak season, while automated scenario ranking cut route rebalancing cycles by 63% and lifted forecast accuracy to 92% across the twenty busiest corridors. The board adopted the model’s targeted capex recommendations, redirecting investment toward the hubs with the highest marginal impact and establishing a governance playbook for future expansions.