Applied Solutions

Case Studies

Real-world 3PL challenges solved with orchestrated, enterprise-grade machine learning pipelines.

CS1 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. Associates execute tasks tracked to the scan — or so it seems.

02

The Challenge

"Shadow work" — re-boxing damaged goods, fixing client labeling errors — goes untracked in the WMS. Labor isn't billed, and margin leakage is discovered 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 real-time dashboard alert: "Log VAS charge for relabeling."

Outcome

Real-Time VAS Recovery

Leakage Plugged

~100%

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CS2 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

Predicts with 92% confidence that a specific client will have overflow by a target time, based on 3 years of seasonal trends.

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.

Trailer Utilization

70% → 92%

Cost per Pallet

↓ 18%

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Coming Soon

In Development

CS3 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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