Logistics — last-mile delivery

The problem. The last mile is where delivery promises are kept or broken. A van running behind quietly turns a two-hour window into a missed slot; a failed delivery means a costly re-attempt and a customer who stops trusting the estimate. Dispatch needs to see a route slipping while there's still time to re-sequence or reassign, not when the angry message arrives.

What we solve here is hitting delivery windows and cutting failed first attempts.

Set up demo data

New workspace? Run this once (Python) to create a van's ETA-slack feed, create the subscription before we listen, and push a behind-schedule reading. Safe to re-run.

import datahub_sdk, pandas as pd

client = datahub_sdk.DataHubClient.from_env()

client.timeseries.create([datahub_sdk.TimeSeries(external_id="van_22_eta_slack_min", name="Van 22 ETA slack", unit="min", value_type="float")])
client.subscriptions.create([datahub_sdk.Subscription(
    external_id="fleet_eta", name="Fleet ETA", timeseries=["van_22_eta_slack_min"])])
client.timeseries.insert_from_lists(timestamps=[pd.Timestamp.now(tz="UTC")], values=[-12.0], ts="van_22_eta_slack_min")

1. Catch a slipping route live

Each van streams progress against its plan — stops completed, running ETA. When the projected arrival for the next stop slips past its window, raise a delivery_at_risk event so dispatch can act. See Consume live data.

Java

import ai.intellistream.datahub.sdk.subscriptions.SubscriptionMessage;

try (var stream = client.subscriptions().listen(List.of("fleet_eta"))
        .stream((SubscriptionMessage msg) -> {   // auto-acks after each message
            if (etaPastWindow(msg.payload())) {
                EventModel risk = new EventModel();
                risk.setExternalId("delivery_at_risk_v22_" + System.currentTimeMillis());
                risk.setType("delivery_at_risk");
                risk.setStatus("open");
                risk.setMetadata(Map.of("van", "van_22", "route", "route_oslo_e", "stops_left", "9"));
                risk.setEventTime(ZonedDateTime.now());
                client.events().create(List.of(risk));
            }
        })) {
    awaitShutdown();   // your app lifecycle; closing the stream ends delivery
}

Python

with client.subscriptions.listen(["fleet_eta"]) as listener:
    for msg in listener:
        if eta_past_window(msg.payload):
            client.events.create([datahub_sdk.Event(
                external_id=f"delivery_at_risk_v22_{int(pd.Timestamp.now().timestamp())}",
                type="delivery_at_risk", status="open",
                event_time=pd.Timestamp.now(tz="UTC"),
                metadata={"van": "van_22", "route": "route_oslo_e", "stops_left": "9"})])
        listener.ack([msg.message_id])

Rust

use dataplatform_rust_sdk::events::Event;
use chrono::Utc;

let mut listener = api.subscriptions.listen(&["fleet_eta"]).await?;
while let Some(Ok(msg)) = listener.next().await {
    if eta_past_window(&msg.payload) {
        let mut risk = Event::new(format!("delivery_at_risk_v22_{}", Utc::now().timestamp()));
        risk.r#type = Some("delivery_at_risk".into());
        risk.status = Some("open".into());
        risk.add_metadata("van".into(), "van_22".into());
        risk.add_metadata("route".into(), "route_oslo_e".into());
        risk.add_metadata("stops_left".into(), "9".into());
        risk.set_event_time(Utc::now());
        api.events.create(&vec![risk]).await?;
    }
    listener.ack(&[msg.message_id.as_str()]).await?;
}

2. Improve tomorrow's routes

On-time rate, stops-per-hour and failed-delivery counts are series per route and driver. Daily roll-ups show which routes chronically run long and where first-attempt failures cluster — the input to better planning. See Query & aggregate.

See the result

The slipping route trips the loop:

delivery_at_risk_v22_…  → open   (projected arrival past the delivery window)

See also

• Consume live data — live route tracking.
• Turn readings into events — the at-risk rule.
• Query & aggregate — on-time and failure reporting.