Business Challenge

Before implementing Bicycle AI, the retailer faced significant challenges in managing out-of-stock (OOS) situations and ensuring timely order fulfillment:

• Fragmented Inventory and Order Data: SKU availability, minimum base quantity (MBQ), indent requests, and warehouse operations data were spread across multiple systems, making it difficult to track SKUs at risk of stockouts or delayed fulfillment.
  
• Reactive and Manual Processes: Store and replenishment teams had to identify OOS events manually or post occurrence, leading to delayed interventions.
  
• Revenue Leakage and Lost Sales: Frequent unavailability of top-selling SKUs led to missed orders, lower customer satisfaction, and negative impact on NPS.
  
• Operational Inefficiency: Manual root cause analysis (RCA) across multiple systems consumed significant time, slowing replenishment cycles and SLA adherence.‍
• Chronic OOS Patterns: Certain SKUs repeatedly faced stockouts due to inaccurate MBQ, skipped indents, stacking delays, or delayed goods receipt notes (GRNs).

Impact:

These challenges caused revenue loss, low fill rates for high-demand SKUs, inefficient inventory utilization, and reduced customer satisfaction. Decision makers lacked real-time, actionable insights to prioritize corrective actions or optimize replenishment workflows.

Solution and Implementation

The retailer implemented Bicycle AI to unify inventory, order, and replenishment data across all stores and fulfillment centers. The platform enabled real-time anomaly detection, automated root cause analysis, and actionable alerts for out-of-stock and order fulfillment issues.

Key Capabilities Enabled by Bicycle AI:

• Real-Time Out-of-Stock Detection: Continuous monitoring of top SKUs to identify potential stockouts before they occur.
  
• Automated Root Cause Analysis: Classification of OOS events into causes such as low MBQ, skipped indents, GRN or stacking delays, and chronic supply chain bottlenecks.
  
• Predictive and Proactive Alerts: Early warnings triggered replenishment actions to prevent lost sales.
  
• Dashboard Visibility: Centralized dashboards offering SKU- and store-level insights, including OOS trends, RCA breakdowns, and SLA metrics.
  
• Operational Workflow Optimization: Action recommendations for raising indents, correcting MBQ, and reallocating inventory routed automatically to relevant teams.
  
• Stakeholder Enablement: Supply chain, category, and regional teams gained self-serve access to analytics for faster, data-backed decision-making.

Bicycle AI replaced manual, reactive monitoring with a unified, intelligent decision system.

Business Impact and Outcomes

Bicycle AI helped the retailer boost SKU availability, reduce lost sales, and streamline fulfillment operations by enabling proactive, data-driven decision-making across stores and fulfillment centers.

Quantitative Outcomes:

• Availability Uplift: Top 1,000 SKUs improved from ~80% to ~95% availability.
  
• Reduced Revenue Leakage: Real-time detection and proactive replenishment increased order completion rates.
  
• Faster RCA Turnaround: Shifted from daily/post-facto RCA to real-time and same-day resolution.
  
• Operational Efficiency: Reduced manual monitoring, saving months of engineering and analyst effort.
  
• Unified Data Systems: Consolidated fragmented systems into a single source of truth for inventory, order, and replenishment data.
  

Qualitative Outcomes:

• Enhanced customer experience through higher product availability and reduced basket abandonment.
• Empowered regional and store teams with self-serve insights and proactive actions.
• Strengthened collaboration among category, planning, and supply chain functions.

Customer Experience

Teams now rely on Bicycle AI for real-time, self-serve insights—reducing dependency on manual reports or analytics teams. With visibility into stockouts and fulfillment metrics, managers make faster, more confident decisions that directly improve order completion and customer satisfaction.