← Use cases
Delivery and turnaround

This order will miss its pickup window tomorrow. Today you can still fix it.

Bicycle predicts every day which orders are at risk of missing their window, gives the reason, and recommends a fix to the dispatcher.

No credit card required

Before

An order misses its window long before the deadline: no carrier has accepted it, the pay is below what the lane needs, or nobody nearby is free. By the time it shows in on-time delivery, the customer has been let down.

With Bicycle

At-risk orders are flagged a day ahead, each with its reason and a recommended fix.

What happens when a KPI moves.

  1. 01

    Detect

    Orders at risk of missing their window are predicted daily, and unassigned orders are flagged.

  2. 02

    Explain

    Each one carries its reason: no carrier, a gap in the lane, or tight timing.

  3. 03

    Act

    Bicycle recommends raising carrier pay or offering the order to carriers in nearby lanes, and the dispatcher decides.

  4. 04

    Learn

    The prediction is tuned on what happened to the orders it flagged.

75% → 82%

on-time delivery, trending toward 85%

Seen at: an automotive logistics provider. Read the case study

Everyone can ask. Data & Analytics controls what gets trusted, published, and acted on.

Runs on
  • Orders
  • Carrier availability
  • Pickup history
Used by
  • Operations managers
  • Dispatch

Common questions

How do you predict which orders will be delivered late?
Bicycle predicts every day which orders are at risk of missing their window, and also flags orders that are unassigned or unprocessed. For each at-risk order, Bicycle checks each possible cause and attaches the reason, such as no carrier assigned, a gap in the lane or tight timing, so your dispatcher knows what to fix.
Does the late-order prediction improve over time?
Yes. Bicycle tunes the prediction on the outcomes of the orders the prediction flagged, so what happened to those orders shapes the next predictions. A vehicle logistics marketplace that uses the daily at-risk predictions and recommended carrier changes saw its on-time delivery rise as a result, from 75% to 82%.

See it on your own KPI.

Bring one revenue-critical KPI. We'll show how Bicycle detects the movement, explains the cause, and recommends the next step.