Business Challenge

Dark store operations generated huge amounts of data across order systems, warehouse systems, and staffing tools, but most of this data surfaced as disconnected Excel reports. This made it hard for store managers, regional leaders, and central teams to share a consistent view of performance or act quickly when in‑store times or productivity started to drift. “Data democratization” in this context means that front‑line managers and central teams should be able to access the same, trusted metrics without relying on ad hoc, analyst-built sheets.

• Disparate reports at store, zone, and region level created visibility gaps and conflicting versions of basic metrics.
• Manual Excel-based reporting slowed response to issues in the Order‑to‑Ready (OTR) chain—Open, In‑Process, Packed, Binned—and often surfaced problems only after the day was over.
• In‑store time and associate productivity varied widely across dark stores, but there was no consistent way to see which combination of staffing, planning, or execution was driving the gaps.
• Root causes such as order spikes, attendance shortfalls, or inefficient picking were difficult to isolate because signals from Hermes, WMS, rostering, and stacking/bin‑audit systems lived in silos.
• The scale of 100+ dark stores required a system that could standardize performance measurement and RCA across the network, not just provide static reports.

Traditional BI could visualize parts of this picture, but the business needed a single, operations‑ready system that connected warehouse events, associate behavior, and OTR timings into explainable, store‑level and region‑level performance stories.

Solution and Implementation

Bicycle AI’s agentic analytics platform unified order, warehouse, and workforce data into a layered dark store productivity dashboard, combining warehouse KPIs, associate behavior, and SLA tracking with built‑in root cause detection.

• Integrated Hermes (user state and jobs), Picking Platform, Order DB, dynamic locations (stacking/bin audit), and rostering into a common model at order, job, and associate level.
• Provided tiered dashboards: near real‑time store view for the in‑store OTR chain, associate view for utilization and job mix, and management view for region and network‑wide performance.
• Tracked the in‑store OTR stages—Open→In‑Process→Packed→Binned—against explicit SLAs (for example, 30 seconds, 2 minutes, 20 seconds, and <3 minutes total), highlighting stores and time windows that drifted.
• Ran a rule‑based RCA engine that automatically flagged order spikes, attendance gaps, efficiency issues (seconds per SKU), under‑utilization, high offline time, and job rejections as likely drivers of poor performance.
• Exposed these insights through color‑coded dashboards and exports that store managers, regional SPOCs, and central teams could all access directly, replacing fragmented Excel reporting with a shared, self‑serve view.

Business Impact and Outcomes

With a unified productivity platform, the retailer moved from spreadsheet‑driven reviews to continuous, shared visibility on how each dark store is performing and why.

• Faster identification of stores, shifts, or clusters where in‑store time and OTR performance were drifting away from the 3‑minute target, especially during peak hours.
• Clearer understanding of whether issues were driven by order spikes, staffing gaps, low utilization, or inefficient picking, enabling more surgical interventions rather than generic “do better” escalations.
• Reduced reliance on manual Excel reporting, as store managers, regional leaders, and central teams accessed a common, self‑serve view of dark store KPIs and RCA signals.
• Stronger foundation for network‑wide initiatives such as staffing optimization, shift planning, and training for underperforming fulfillment centers, supported by consistent associate‑level metrics.
• A scalable analytics layer under the Warehouse Management System (WMS) domain that can grow with new services, store formats, and performance metrics without re‑creating reporting from scratch.