Logistics Control: Metrics, Perfect Order, Risk
Measure what the customer feels
Control closes the loop: set targets, measure, compare, correct. Build a metrics hierarchy - financial (cost per order, logistics cost as % of sales), productivity (units per labor hour, dock-to-stock time), quality (order accuracy, inventory accuracy, damage rate), and customer (OTIF, fill rate, cycle time). Leading indicators predict; lagging indicators confirm.
Perfect order
The perfect order index multiplies on-time × complete × damage-free × correct documentation. Because it is multiplicative, gains and failures both compound.
Frameworks and benchmarks
SCOR organizes processes as Plan, Source, Make, Deliver, Return, and Enable, with Level 1 metrics (perfect order, fulfillment cycle time, cost) down to diagnostic levels. Benchmarking must be normalized for product mix, geography, and service level. Activity-based costing and cost-to-serve expose customers and SKUs that consume disproportionate cost - usually a Pareto pattern.
Inventory control
Cycle counting validates inventory continuously; typical tolerance is ±1–2% on A items. Root-cause codes (receiving error, mispick, damage) matter more than the count itself.
Worked example
Perfect order components: 96% on time, 98% complete, 99% damage-free, 97% documentation correct. POI = 0.96 × 0.98 × 0.99 × 0.97 ≈ 0.904, about 90%. Averaging the four (97.5%) overstates performance by roughly seven points - a classic trap.
Risk and compliance
Control also covers HazMat documentation, customs compliance, carrier safety scores, contract and insurance review, and continuity planning for single-source nodes. An internally efficient metric can hide service failure: low cost per unit shipped means nothing if fill rate is falling.