Many mid-market retailers face the same challenge: overcoming the issue of ERP systems that are rich in data but poor in agility.
This case study shows how one multinational apparel retailer utilised RabbitHawk to break that cycle - shifting from reactive, spreadsheet-driven forecasting to an adaptive, AI-powered decision engine.
The Customer
A multinational apparel retailer with revenue just under US $100 million, operating across retail, e-commerce, department-store concessions, and wholesale channels.
The company designs locally and outsources production to China and Southeast Asia, creating a distributed supply chain with long lead times and tight cashflow windows.
The business runs Oracle NetSuite as its ERP but finds the platform rigid, complex, and slow to adapt. After repeated and costly configuration attempts by consultants, the planning team has reverted to manual processes—exporting data into spreadsheets and Power BI each month.
Each monthly cycle consumes three weeks to reconcile forecasts and approve replenishment orders. Forecasts were inconsistent, store analysis inaccurate, and over- and under-stocking became chronic. Strategic planning took a back seat.
The RabbitHawk Solution
RabbitHawk implemented its AI-driven Forecasting & Optimisation Engine, connecting to NetSuite data to create an adaptive forecasting layer without disrupting core systems.
Deployed on Azure, RabbitHawk uses NetSuite data exports. Forecasting within Rabbithawk flows into our unified UI, combining performance metrics by Style × Colour × Store × Brand, plus insights into aging inventory, projected stock-outs, MOQ impacts, and margin risk. Optimised purchase orders are exported directly back to NetSuite - a closed loop from data to action.
Within weeks, the platform transformed the retailer’s planning rhythm:
- Multi-level forecasting: SKU, category, store, and region, each with cross-functional visibility.
- Probabilistic forecasting: risk-aware predictions that show not just what is likely but what’s possible.
- Goal-driven optimisation: purchasing recommendations that dynamically balance cashflow, margin, revenue, and stock constraints (MOQ, lead time, risk, and budget).
Measured impact
- 95% improvement in forecast accuracy
- 90% reduction in manual workload
- Significant reduction in over- and under-stocking
- Faster cash-flow cycle and stronger working-capital efficiency
Impact not incrementalism
In short, what once took three weeks now takes hours, and instead of reacting to data, the business is now guided by it. A 95% accuracy uplift and 90% workload reduction aren’t incremental—they redefine the cadence of retail decision-making. Forecasting moved from a back-office function to a strategic growth lever.
Extension v Replacement
RabbitHawk extends existing ERP systems rather than displacing them. For retailers wary of multi-year transformations, this “add intelligence without rebuild” model delivers immediate ROI.
Goal-aligned optimisation
Unlike tools that chase pure accuracy, RabbitHawk optimises for business objectives - cashflow, GMROI, revenue—within real-world constraints. The system learns what success means for each retailer.
Human-centred automation
By eliminating manual drudgery, RabbitHawk amplifies expertise. Teams redirect their energy toward scenario planning, cross-functional collaboration, and proactive risk management.
Demonstrable AI ROI
While many platforms promise “decision intelligence,” few quantify outcomes in both time and capital saved. RabbitHawk does - delivering practical, compounding returns over time.
“It’s the first time our meetings started with what’s possible rather than what went wrong.”
What’s Next
Phase two will expand RabbitHawk from a focus on replenishment efficiency in two different directions - more strategic and more granular.
Our top level UI will help teams focus their daily decisions on the achievement of broad company goals.
Store-specific micro analysis will identify opportunities in local demographics and buying behaviour.
In summary
This project stands as a best-in-class example of agentic, goal-aligned AI delivering measurable business value without replacing core systems.
RabbitHawk turned a data-rich but time-poor organisation into a continuously learning, self-optimising enterprise - a real shift from data management to autonomous value creation. A strategic co-pilot for commercial growth.
