Quick Commerce Platform Improves On-Time Delivery with AI

About the Company

A leading online grocery and quick commerce platform serving millions of daily orders across multiple cities. Specializing in ultra-fast delivery of fresh groceries and Fast-Moving Consumer Goods (FMCG) essentials, the company operates a network of Dark Stores (DS)—fulfillment centers optimized for high-volume online orders only—competing in the hyper-competitive quick commerce space with aggressive SLAs like 10-15 minute deliveries.

Business Challenge

The company’s delivery promise depended on many moving parts: order creation, in‑store picking, packing and binning, handover to delivery agents, and last‑mile delivery to the customer. When delays occurred, they were often detected only after customers complained or SLA reports were generated at the end of the day. Existing reports showed “what went wrong” in aggregate but not where, when, and why within the in‑store and out‑of‑store chain

Delays could occur at any segment—Open→In‑Process, In‑Process→Packed, Packed→Binned, Binned→Ready to Ship (RTS), RTS→Delivered—but there was no unified, real‑time view across this full chain.
Operations teams struggled to distinguish whether a breach was caused by order spikes, low picker attendance, picker inefficiency, delivery partner (Customer Experience Executive) shortages, or routing and traffic issues.
Multiple systems (order logs, picking systems, routing, rostering, planning data) lived in different databases and files, forcing analysts to manually piece together explanations.
By the time patterns were identified, SLA breaches had already accumulated, driving customer dissatisfaction, higher support load, and added logistics costs.

Solution and Implementation

Bicycle AI’s agentic analytics platform connected data across orders, pickers, Customer Experience Executives (CEEs), and staffing to monitor each step of the OTR/OTDchain, detect SLA risks in near real time, and explain the operational reasons behind delays.

Unified data from Order Management System logs, picking systems, CEE routing, and rostering into an order‑ and associate‑level model covering all key events (Open, In‑Process, Packed, Binned, Ready to Ship, Delivered).
Monitored segment‑level SLAs across the in‑store and out‑of‑store journey (for example, Open→In‑Process, In‑Process→Packed, Packed→Binned, Binned→RTS, RTS→Reach) against clear time thresholds defined in the OTR benchmarks (such as 30 seconds, 2 minutes, and 20 seconds where applicable).
Automatically detected emerging anomalies at store, city, or time‑block level and classified them into root cause buckets such as order spikes, picker attendance gaps, picker efficiency issues, CEE availability and efficiency issues, and high offline / under‑utilized time.
Delivered focused dashboards and alerts to city operations, dark store managers, and central teams with drill‑downs and planned vs actual views, guiding actions like adjusting shifts, reallocating pickers, rebalancing CEE deployment, or changing delivery routing.

Business Impact and Outcomes

Real-time visibility transformed reactive operations into proactive optimization, reducing Service Level Agreement breaches and accelerating issue resolution across fulfillment centers.

Faster identification of where in the chain delays were forming—whether in‑store (picking, packing, binning) or out‑of‑store (handover, travel time, last‑mile reach)—reducing blind spots for operations teams.
More targeted interventions, as teams could respond with specific actions (such as adding pickers during spikes, improving attendance, or reallocating CEEs) instead of broad, generic escalations.
Reduced SLA breaches and improved OTR adherence by catching issues closer to real time and correcting them before they affected a large number of orders.
Lower manual analysis effort for central analytics and BI teams, who now work from an always‑on anomaly and root cause layer rather than building ad hoc reports from multiple systems.

Conclusion

City and store operations managers now rely on a live view of each stage in the delivery chain and receive clear, focused signals when a store, shift, or segment starts to slip against its OTR and OTD targets. Instead of learning about problems only through customer complaints or end‑of‑day reports, they act on explainable anomalies with direct links to staffing, efficiency, or routing gaps. Bicycle AI is seen not just as an alerting system but as an operational decision layer that helps teams keep delivery promises and maintain a reliable quick commerce experience.

In one line: Bicycle AI turned on‑time delivery from a lagging KPI into a continuously managed, explainable process where every SLA risk has a visible cause and a clear path to action.

Quick Commerce Platform Improves On‑Time Delivery with End‑to‑End Anomaly Intelligence

About the Company

Business Challenge

Solution and Implementation

Business Impact and Outcomes

Conclusion

Ready to See What Bicycle AI Can Do for You?

Serving Restaurants with Real-Time Visibility

Reducing Out-of-Stock and Delivery Delays with AI