← Use cases
Delivery and turnaround

Two stores, the same orders. Why is one far slower?

Bicycle brings warehouse, picking and staffing data into one live view for each store, region and the central team, and names why productivity drifts.

Book a demo Start for free

No credit card required

Before

Productivity data is split across the warehouse system, the picking app and the rosters. A slow store looks like a busy store until someone pulls it all together.

With Bicycle

Every store has one live view, from the associate to the network, with flags when order-to-ready time or picker utilization drifts and the reason why.

What happens when a KPI moves.

  1. 01

    Detect

    Order-to-ready time or picker utilization drifts in one store.

  2. 02

    Explain

    The reason is named: an order spike, an attendance gap, slow picking, idle time or rejected picking jobs.

  3. 03

    Act

    Bicycle recommends the next step: store managers reassign jobs or rebalance shifts, and regional managers take patterns that repeat.

  4. 04

    Learn

    Associate-level history backs coaching, training and staffing, and trends across stores inform store design.

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

Runs on
  • Warehouse jobs
  • Picking system
  • Orders
  • Staff rosters
Used by
  • Store managers
  • Regional operations
  • Workforce planning

Common questions

How do you find out why one fulfillment store is less productive than another?
Bicycle does not just flag a slow store, Bicycle explains it. Bicycle separates an order spike, where orders per picker diverge from plan, from an attendance gap, where fewer pickers log in than planned, and from efficiency, utilization and offline-time issues. Each carries a different intervention, and capacity-planning views show whether the underlying issue is the staffing model, rostering accuracy or associate-level efficiency.
What data do you need to track warehouse productivity in real time?
Bicycle brings together associate status, the picking system, the order database, put-away and bin-audit locations, and the rostering system. Bicycle already models job types such as picking, put-away and bin audit, associate states such as busy, offline and logged out, and the rhythm of peak and non-peak hours. Your team does not set alert thresholds by hand; they are tuned per store and cluster.
Who can see productivity data by store and associate?
Bicycle gives central, regional and store users the same data with different scopes: the full network for leadership, hierarchy-filtered views for regional managers, single-store hourly views for store managers, and associate-level views for one-on-ones. Store managers act on their own store, regional managers escalate systemic patterns, and workforce planning adjusts the rostering model from the peak shapes it observes.

More on delivery and turnaround

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.

Book a demo