With Bicycle, this vehicle transport marketplace detects operational anomalies in real time, protects margin, and improves SLA adherence before orders slip.
Our supply chain and logistics technology relies on real-time data from a variety of sources. Bicycle supplements our data science efforts by providing proactive, event-driven insights to help deliver a superior customer experience.
An end-to-end vehicle transport and logistics marketplace.
Every order balanced customer pricing, carrier bids, mileage bands, and SLA rules. With monitoring done by hand, negative margins and at-risk orders were typically caught after delivery, when the cost was already booked.
Large negative margins and overpayments to carriers frequently slipped through unnoticed, quietly eroding profit on individual orders.
Orders left unassigned or unprocessed risked violating service-level agreements, affecting customer satisfaction and contractual obligations.
Manual monitoring meant problems were usually identified after delivery, leaving no window for proactive intervention and leaving revenue exposed.
Weighing pricing, carrier bids, mileage bands, and SLA rules on every order created heavy cognitive load and repeated openings for error.
Bicycle replaced manual, post-facto monitoring with real-time operational intelligence, detecting anomalies as they happen, monitoring SLA compliance, and putting oversight in the hands of the business team.
Automated alerts fire on high carrier pay, large negative margins, zero-day margin violations, and unprocessed orders as soon as they occur.
Daily analysis flags orders at risk of an SLA breach and recommends mitigation actions before the deadline passes.
Suggests carrier pay adjustments and surfaces nearby carriers for timely pickup, keeping fulfillment efficient and cost under control.
Alerts route to Slack or email so financial operations and business teams can act immediately, cutting response time from days to near-instant.
Business users configure their own rules, track specific routes or lanes, and monitor margins independently of the data team.
Post-facto monitoring, turned into real-time protection of margin and SLA.
The team now manages orders proactively instead of reconstructing what went wrong.
Negative margins and carrier overpayments are caught as they emerge, so avoidable losses are prevented instead of discovered later.
At-risk orders surface before deadlines, giving the team time to intervene and improve on-time delivery.
Anomalies that once surfaced only after delivery are now flagged in near real time, opening a window to act.
Business users configure route alerts and monitor margins themselves, freeing analysts from repetitive ad-hoc requests.
Bring one revenue-critical KPI. We'll show how Bicycle detects the movement, explains the cause, and recommends the next step.