A Regional Parcel Carrier Cuts Missed Delivery Windows with Qlik Predict in Six Weeks

Built and deployed a Missed-Window Risk predictor with Qlik Predict’s model inside Qlik Cloud.
The model refreshes daily, flagging at-risk routes and stops in a dispatcher dashboard so teams can re-sequence, reassign, and proactively reset ETAs before service breaks.

Dispatchers were drowning in exceptions: late hub arrivals, missed sort cut-offs, and ETA drift across high-volume routes. Operational data lived across TMS, scan events, telematics, and customer promise windows, so teams could see problems only after a delivery was already at risk. Root-cause analysis was manual and inconsistent, leading to reactive firefighting and missed commitments. 

B EYE unified route plans, scan events, hub timelines, and telemetry into a single Qlik operational model and added a risk score per stop. Qlik Predict surfaced the top drivers behind each risk score (for example: depot backlog, dwell time, and lane volatility), enabling teams to act fast on the worst offenders first. After rollout to priority depots, missed delivery windows dropped materially and exception handling became a proactive, repeatable process.

1. Focused delivery sprint – MVP built with dispatcher feedback and iterative tuning. 
2. Best-practice data science – Class balancing, feature pruning, and speed benchmarks ensured the predictor was both accurate and fast. 
3. Front-end UX – A clean sheet surfaces risk scores, key drivers, and quick filters so users focus on the highest-impact actions. 

“We stopped guessing which routes would break the promise. Now we can see the risk early and why it is happening, so we intervene while we still have options.” 
– Operations Control Lead