LogisticsStreamingData Platform3 months

A real-time data platform for 2,400 vehicles

Moving from hourly position updates to a streaming pipeline with sub-30-second latency — fast enough for dispatch to intervene before a delivery runs late.

30 sec

vehicle tracking latency

2,400

vehicles tracked continuously

+11%

on-time delivery rate

3

months to deliver

Context

A transport operator running 2,400 vehicles across six provinces, serving enterprise customers with contractual delivery windows.

The challenge

GPS data was batch-loaded every 30 to 60 minutes. Dispatchers found out a delivery was late when the customer called to complain.

What BigAI delivered

A streaming pipeline on Kafka and Flink processing position and order-status events in real time, landing in ClickHouse for analytics and Redis for instant lookup.

On top of that, the system computes estimated arrival time and raises an early warning when a delivery is trending late against its commitment — with enough lead time for dispatch to actually do something about it.

Results

On-time delivery rose from 84% to 95% within three months, driven mostly by early detection rather than faster driving.

Results

Swipe to see the full table

Results
MetricBeforeAfter
Position data latency30–60 minutesunder 30 seconds
On-time delivery rate84%95%
Detection of at-risk deliveriesAfter the fact2–4 hours ahead

Technology used

Apache KafkaApache FlinkClickHouseRedisGrafanaKubernetes

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