Applied Solutions

Reference Patterns

Practical starting points for logistics teams with messy systems, thin reporting layers, and real operating pressure. These are patterns we adapt to the floor, the dock, and the finance close, not canned products.

RP1 Logistics & Supply Chain

Detecting Logistics Anomalies That Lead to Preventable Leakage

Systems: BlueYonder WMS Focus: Margin Retention
01

The Context

A 3PL warehouse operator relies on BlueYonder WMS and RF scanners generating high volumes of transactional telemetry. On paper, the scan history captures the work.

02

The Challenge

"Shadow work" like re-boxing damaged goods and fixing client labeling errors goes untracked in the WMS. Labor is not billed, and margin leakage is found only during 30-day retrospective audits.

Margin Leakage
03

The ML Solution

Autoencoders / Isolation Forest

Flags pick tasks taking 300% longer than baseline as high-confidence outliers.

Large Language Models

Cross-references flagged timestamps against unstructured data to extract context like "re-boxing."

XGBoost / Decision Trees

Routes a reviewable alert when the work pattern suggests billable value-added services were missed.

What this protects

Billable exception capture

Review signal

Same-day visibility

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RP2 Transportation & Logistics

Predictive Capacity Hedging for LTL Carriers

Systems: ELD Telemetry, TMS, CRM Focus: Asset Utilization & Revenue Density
01

The Context

A mid-sized LTL carrier operates a hub-and-spoke network. Trucks depart terminals daily at 70% utilization, leaving 30% dead space that represents pure overhead. Dispatchers only book what's confirmed in the TMS.

02

The Challenge

The "empty space" gamble: send a truck half-empty to meet a window, or wait for on-demand orders that may never arrive. Deadhead miles and under-utilized trailers represent pure lost margin.

Deadhead Revenue Loss
03

The ML Solution

LSTM / Prophet

Forecasts likely overflow windows by lane, customer, and seasonality so planners can shape demand earlier.

Graph Neural Networks

Cross-references predicted loads against live GPS/ELD fleet locations to calculate real-time deviation cost.

Reinforcement Learning

Automatically injects a soft stop into the driver's route based on prediction confidence and hours of service.

Capacity target

Fewer underfilled moves

Planning posture

Earlier load shaping

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Coming Soon

In Development

RP3 Finance & Legal

Predicting Cash Flow Impacts from Unstructured Contractual Clauses

Systems: ERP, Contract Repository Focus: Revenue Predictability
01

The Context

02

The Challenge

03

The ML Solution

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