Logistics Plus Cuts Late Shipments with Qlik Predict in Just Three Weeks

Built and deployed an On-Time-Delivery (OTD) predictor with Qlik Predict’s CatBoost model inside Qlik Cloud for Logistics Plus. 
The model now refreshes daily, flagging at-risk loads in the client’s live tracking dashboard so planners can re-route shipments before delays hit. 

Logistics Plus , a leading worldwide provider of transportation, warehousing, fulfillment, global logistics, and supply chain solutions, moves thousands of loads a week. However, its teams could only react after a truck missed its promised delivery date. They lacked a predictive KPI that warned them which in-transit shipments were likely to be late, and manual root-cause analysis was too slow to keep customers happy.  

Data prep & feature engineering

• Highlights: Accessing thousands of unique datapoints. Cleaned five years of shipment history, balanced late/on-time records 50/50, and engineered distance-based & carrier-score features. 
• Outcome: Increased raw-data accuracy from ~45 % to 83–84%.

Qlik Predict experimentation 

• Highlights: Predicting delay risk in minutes. Ran 22 Qlik Predict experiments across 9 algorithms; CatBoost delivered best trade-off between speed (≈ 244 k preds/sec) and F1 score (0.82-0.84). 
• Outcome: Model trains in minutes and scales to millions of rows without performance hits.

Production-ready deployment 

• Highlights: Embedded predictions, confidence bands and top-driver insights in the client’s flagship tracking app; set pipeline to refresh every 24h.
• Outcome: Zero-touch upkeep and ever-improving accuracy as new data lands.

Business impact  

Real insights and real solutions across the network. In a matter of weeks, Logistics Plus drove its error rate down from 20% to below 1%, trimmed reporting cycles from hours to seconds, and is on track to push on-time delivery toward 85%. With the time saved, planners can now balance warehouse capacity and redirect high-risk loads days ahead: capabilities more often associated with much larger players. Immediate visibility into which loads are at risk this week.

• Expected OTD uplift from 80% ► 83–85% within six months
• Faster response loop: planners act days earlier, not after a customer complaint
• Higher customer-satisfaction scores and fewer penalty fees.

1. Single-consultant sprint – One B EYE data engineer delivered the MVP in roughly 60 consulting hours spread over three weeks.
2. Best-practice data science – Balanced classes, iterative feature pruning, and model-speed benchmarks ensured the predictor was both accurate and fast.
3. Front-end UX – A clean Qlik Sense sheet surfaces risk scores, key drivers (e.g., lead-time swings), and quick filters so users focus on the worst offenders first.