Carrier Scorecarding

The foundational metric measuring the percentage of shipments delivered by the promised appointment time. This is calculated by comparing the actual arrival timestamp against the scheduled delivery window. Advanced systems distinguish between pickup OTP and delivery OTP to isolate where delays originate.

• Strict OTP: Arrival within the exact 15-minute window
• Flexible OTP: Arrival within a 2-hour buffer
• Transit Variance: The standard deviation of actual vs. planned transit hours

The ratio of loads a carrier accepts versus the total loads tendered to them over a defined period. A low acceptance rate signals unreliable capacity and forces brokers to expend resources on re-tendering. This metric is often segmented by lane and day of week to identify pattern-based rejection behavior.

• Primary Acceptance: Acceptance within the first tender
• Fallback Acceptance: Acceptance only after a rate increase
• Ghost Capacity: Carriers who accept but then cancel pre-pickup

A composite index aggregating federal and internal safety data. This includes CSA BASIC scores from the FMCSA, insurance status, and accident history. Real-time verification of operating authority and insurance via API integration prevents onboarding carriers with lapsed credentials.

• Vehicle Maintenance: Violation rates from roadside inspections
• Unsafe Driving: Speeding, reckless driving citations
• Driver Fitness: CDL validity and medical certification status

Measures the carrier's adherence to required technology protocols, primarily real-time GPS tracking and automated status updates. This metric evaluates the percentage of loads where the carrier consistently shares telematics data via ELD or mobile app integration, enabling accurate predictive ETAs.

• Tracking Compliance: % of loads with active, unbroken GPS pings
• Check-Call Automation: % of status updates received via API vs. manual phone calls
• Geofencing Adherence: Accuracy of automated arrival/departure triggers

The frequency and severity of cargo claims filed against a carrier. This is expressed as a claims ratio—the total dollar value of claims divided by the total freight charges. High ratios indicate poor cargo handling, inadequate equipment, or security vulnerabilities.

• Shortage Claims: Missing item counts upon delivery
• Damage Claims: Goods arriving in unsellable condition
• Temperature Excursions: Violations of cold chain parameters logged by IoT sensors

Tracks the average time a carrier's equipment is held at shipper or receiver facilities beyond the free time allowance (typically 2 hours). Excessive dwell time indicates operational inefficiency at the facility and is a leading cause of driver dissatisfaction and accessorial cost disputes.

• Average Dwell: Mean minutes from check-in to departure
• Detention Frequency: % of loads incurring detention charges
• Facility-Specific Variance: Identifies problematic loading docks

The foundational metric in any scorecard, measuring the percentage of shipments delivered by the promised appointment time. OTD is calculated by dividing on-time deliveries by total deliveries. Modern systems use Predictive ETA Engines to distinguish between carrier-caused delays and exogenous factors like weather or port congestion. A standard industry benchmark for acceptable OTD is 95% or higher, with penalties often applied below 90%.

An automated aggregation of federal and internal safety data to quantify operational risk. This score ingests public FMCSA data (CSA scores, crash reports) and internal telematics.

• Harsh Event Detection: AI analysis of accelerometer data to flag hard braking or cornering.
• Hours-of-Service (HOS) Adherence: Electronic logging device (ELD) integration to detect violations.
• Insurance Verification: Automated checks to ensure active coverage and sufficient limits.

A modern scorecard dimension evaluating the carrier's technological integration quality. This tracks the consistency and accuracy of real-time GPS pings, the acceptance rate of digital load tenders, and the speed of uploading proof-of-delivery (POD) documents. Carriers with high API adherence reduce manual check-calls and enable true exception-based surveillance, making them preferred partners in autonomous brokerage systems.

A behavioral metric tracking the percentage of offered loads a carrier accepts versus rejects. A low acceptance rate or a high tender rejection pattern signals unreliability, forcing the system to execute costly fallback sourcing. Advanced scorecards weigh this against Load Acceptance Prediction models to identify carriers who only accept spot-market premiums, degrading their strategic partner score.

A quantitative measure of freight handling quality, calculated by the frequency and severity of cargo claims filed. The index normalizes claims cost against total freight revenue to provide a fair comparison across carriers of different sizes. Zero-defect carriers with high Cargo Care Index scores are algorithmically prioritized for high-value or sensitive freight, such as pharmaceuticals or electronics.

The machine learning system that consumes scorecard data to infer implicit carrier preferences. By analyzing historical booking data against performance scores, the system identifies which carriers excel on specific lane density corridors or with particular equipment types. This profile feeds back into the matching engine to increase match acceptance rates by aligning offers with proven operational strengths.